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Sample records for automatic anatomy segmentation

  1. Anatomy-based automatic detection and segmentation of major vessels in thoracic CTA images

    International Nuclear Information System (INIS)

    Zou Xiaotao; Liang Jianming; Wolf, M.; Salganicoff, M.; Krishnan, A.; Nadich, D.P.

    2007-01-01

    Existing approaches for automated computerized detection of pulmonary embolism (PE) using computed tomography angiography (CTA) usually focus on segmental and sub-segmental emboli. The goal of our current research is to extend our existing approach to automated detection of central PE. In order to detect central emboli, the major vessels must be first identified and segmented automatically. This submission presents an anatomy-based method for automatic computerized detection and segmentation of aortas and main pulmonary arteries in CTA images. (orig.)

  2. CAnat: An algorithm for the automatic segmentation of anatomy of medical images

    International Nuclear Information System (INIS)

    Caon, M.; Gobert, L.; Mariusz, B.

    2011-01-01

    Full text: To develop a method to automatically categorise organs and tissues displayed in medical images. Dosimetry calculations using Monte Carlo methods require a mathematical representation of human anatomy e.g. a voxel phantom. For a whole body, their construction involves processing several hundred images to identify each organ and tissue-the process is very time-consuming. This project is developing a Computational Anatomy (CAnat) algorithm to automatically recognise and classify the different tissue in a tomographic image. Methods The algorithm utilizes the Statistical Region Merging technique (SRM). The SRM depends on one estimated parameter. The parameter is a measure of statistical complexity of the image and can be automatically adjusted to suit individual image features. This allows for automatic tuning of coarseness of the overall segmentation as well as object specific selection for further tasks. CAnat is tested on two CT images selected to represent different anatomical complexities. In the mid-thigh image, tissues/. regions of interest are air, fat, muscle, bone marrow and compact bone. In the pelvic image, fat, urinary bladder and anus/colon, muscle, cancellous bone, and compact bone. Segmentation results were evaluated using the Jaccard index which is a measure of set agreement. An index of one indicates perfect agreement between CAnat and manual segmentation. The Jaccard indices for the mid-thigh CT were 0.99, 0.89, 0.97, 0.63 and 0.88, respectively and for the pelvic CT were 0.99, 0.81, 0.77, 0.93, 0.53, 0.76, respectively. Conclusion The high accuracy preliminary segmentation results demonstrate the feasibility of the CAnat algorithm.

  3. Automatic segmentation of male pelvic anatomy on computed tomography images: a comparison with multiple observers in the context of a multicentre clinical trial.

    Science.gov (United States)

    Geraghty, John P; Grogan, Garry; Ebert, Martin A

    2013-04-30

    This study investigates the variation in segmentation of several pelvic anatomical structures on computed tomography (CT) between multiple observers and a commercial automatic segmentation method, in the context of quality assurance and evaluation during a multicentre clinical trial. CT scans of two prostate cancer patients ('benchmarking cases'), one high risk (HR) and one intermediate risk (IR), were sent to multiple radiotherapy centres for segmentation of prostate, rectum and bladder structures according to the TROG 03.04 "RADAR" trial protocol definitions. The same structures were automatically segmented using iPlan software for the same two patients, allowing structures defined by automatic segmentation to be quantitatively compared with those defined by multiple observers. A sample of twenty trial patient datasets were also used to automatically generate anatomical structures for quantitative comparison with structures defined by individual observers for the same datasets. There was considerable agreement amongst all observers and automatic segmentation of the benchmarking cases for bladder (mean spatial variations segmenting a prostate with considerably more volume (mean +113.3%) than that automatically segmented. Similar results were seen across the twenty sample datasets, with disagreement between iPlan and observers dominant at the prostatic apex and superior part of the rectum, which is consistent with observations made during quality assurance reviews during the trial. This study has demonstrated quantitative analysis for comparison of multi-observer segmentation studies. For automatic segmentation algorithms based on image-registration as in iPlan, it is apparent that agreement between observer and automatic segmentation will be a function of patient-specific image characteristics, particularly for anatomy with poor contrast definition. For this reason, it is suggested that automatic registration based on transformation of a single reference dataset

  4. 3D automatic anatomy segmentation based on iterative graph-cut-ASM.

    Science.gov (United States)

    Chen, Xinjian; Bagci, Ulas

    2011-08-01

    This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images. The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al. [Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine. The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 degrees and 0.03, and over all foot bones are about 3.5709 mm, 0.35 degrees and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and all foot bones for

  5. 3D automatic anatomy segmentation based on iterative graph-cut-ASM

    International Nuclear Information System (INIS)

    Chen, Xinjian; Bagci, Ulas

    2011-01-01

    Purpose: This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images. Methods: The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al.[Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine. Results: The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 deg. and 0.03, and over all foot bones are about 3.5709 mm, 0.35 deg. and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and

  6. Automatic segmentation of male pelvic anatomy on computed tomography images: a comparison with multiple observers in the context of a multicentre clinical trial

    International Nuclear Information System (INIS)

    Geraghty, John P; Grogan, Garry; Ebert, Martin A

    2013-01-01

    algorithms based on image-registration as in iPlan, it is apparent that agreement between observer and automatic segmentation will be a function of patient-specific image characteristics, particularly for anatomy with poor contrast definition. For this reason, it is suggested that automatic registration based on transformation of a single reference dataset adds a significant systematic bias to the resulting volumes and their use in the context of a multicentre trial should be carefully considered

  7. Creation of voxel-based models for paediatric dosimetry from automatic segmentation methods

    International Nuclear Information System (INIS)

    Acosta, O.; Li, R.; Ourselin, S.; Caon, M.

    2006-01-01

    Full text: The first computational models representing human anatomy were mathematical phantoms, but still far from accurate representations of human body. These models have been used with radiation transport codes (Monte Carlo) to estimate organ doses from radiological procedures. Although new medical imaging techniques have recently allowed the construction of voxel-based models based on the real anatomy, few children models from individual CT or MRI data have been reported [1,3]. For pediatric dosimetry purposes, a large range of voxel models by ages is required since scaling the anatomy from existing models is not sufficiently accurate. The small number of models available arises from the small number of CT or MRI data sets of children available and the long amount of time required to segment the data sets. The existing models have been constructed by manual segmentation slice by slice and using simple thresholding techniques. In medical image segmentation, considerable difficulties appear when applying classical techniques like thresholding or simple edge detection. Until now, any evidence of more accurate or near-automatic methods used in construction of child voxel models exists. We aim to construct a range of pediatric voxel models, integrating automatic or semi-automatic 3D segmentation techniques. In this paper we present the first stage of this work using pediatric CT data.

  8. Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model

    Energy Technology Data Exchange (ETDEWEB)

    He, Baochun; Huang, Cheng; Zhou, Shoujun; Hu, Qingmao; Jia, Fucang, E-mail: fc.jia@siat.ac.cn [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055 (China); Sharp, Gregory [Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114 (United States); Fang, Chihua; Fan, Yingfang [Department of Hepatology (I), Zhujiang Hospital, Southern Medical University, Guangzhou 510280 (China)

    2016-05-15

    Purpose: A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. Methods: The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods—3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration—are used to establish shape correspondence. Results: The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. Conclusions: The proposed automatic approach

  9. Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model.

    Science.gov (United States)

    He, Baochun; Huang, Cheng; Sharp, Gregory; Zhou, Shoujun; Hu, Qingmao; Fang, Chihua; Fan, Yingfang; Jia, Fucang

    2016-05-01

    A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods-3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration-are used to establish shape correspondence. The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. The proposed automatic approach achieves robust, accurate, and fast liver

  10. Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

    Science.gov (United States)

    Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.

    2014-03-01

    This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

  11. Automatic segmentation of rotational x-ray images for anatomic intra-procedural surface generation in atrial fibrillation ablation procedures.

    Science.gov (United States)

    Manzke, Robert; Meyer, Carsten; Ecabert, Olivier; Peters, Jochen; Noordhoek, Niels J; Thiagalingam, Aravinda; Reddy, Vivek Y; Chan, Raymond C; Weese, Jürgen

    2010-02-01

    Since the introduction of 3-D rotational X-ray imaging, protocols for 3-D rotational coronary artery imaging have become widely available in routine clinical practice. Intra-procedural cardiac imaging in a computed tomography (CT)-like fashion has been particularly compelling due to the reduction of clinical overhead and ability to characterize anatomy at the time of intervention. We previously introduced a clinically feasible approach for imaging the left atrium and pulmonary veins (LAPVs) with short contrast bolus injections and scan times of approximately 4 -10 s. The resulting data have sufficient image quality for intra-procedural use during electro-anatomic mapping (EAM) and interventional guidance in atrial fibrillation (AF) ablation procedures. In this paper, we present a novel technique to intra-procedural surface generation which integrates fully-automated segmentation of the LAPVs for guidance in AF ablation interventions. Contrast-enhanced rotational X-ray angiography (3-D RA) acquisitions in combination with filtered-back-projection-based reconstruction allows for volumetric interrogation of LAPV anatomy in near-real-time. An automatic model-based segmentation algorithm allows for fast and accurate LAPV mesh generation despite the challenges posed by image quality; relative to pre-procedural cardiac CT/MR, 3-D RA images suffer from more artifacts and reduced signal-to-noise. We validate our integrated method by comparing 1) automatic and manual segmentations of intra-procedural 3-D RA data, 2) automatic segmentations of intra-procedural 3-D RA and pre-procedural CT/MR data, and 3) intra-procedural EAM point cloud data with automatic segmentations of 3-D RA and CT/MR data. Our validation results for automatically segmented intra-procedural 3-D RA data show average segmentation errors of 1) approximately 1.3 mm compared with manual 3-D RA segmentations 2) approximately 2.3 mm compared with automatic segmentation of pre-procedural CT/MR data and 3

  12. Modeling and segmentation of intra-cochlear anatomy in conventional CT

    Science.gov (United States)

    Noble, Jack H.; Rutherford, Robert B.; Labadie, Robert F.; Majdani, Omid; Dawant, Benoit M.

    2010-03-01

    Cochlear implant surgery is a procedure performed to treat profound hearing loss. Since the cochlea is not visible in surgery, the physician uses anatomical landmarks to estimate the pose of the cochlea. Research has indicated that implanting the electrode in a particular cavity of the cochlea, the scala tympani, results in better hearing restoration. The success of the scala tympani implantation is largely dependent on the point of entry and angle of electrode insertion. Errors can occur due to the imprecise nature of landmark-based, manual navigation as well as inter-patient variations between scala tympani and the anatomical landmarks. In this work, we use point distribution models of the intra-cochlear anatomy to study the inter-patient variations between the cochlea and the typical anatomic landmarks, and we implement an active shape model technique to automatically localize intra-cochlear anatomy in conventional CT images, where intra-cochlear structures are not visible. This fully automatic segmentation could aid the surgeon to choose the point of entry and angle of approach to maximize the likelihood of scala tympani insertion, resulting in more substantial hearing restoration.

  13. Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation.

    Science.gov (United States)

    Daisne, Jean-François; Blumhofer, Andreas

    2013-06-26

    Intensity modulated radiotherapy for head and neck cancer necessitates accurate definition of organs at risk (OAR) and clinical target volumes (CTV). This crucial step is time consuming and prone to inter- and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. We aim to test a new commercial atlas algorithm for automatic segmentation of OAR and CTV in both ideal and clinical conditions. The updated Brainlab automatic head and neck atlas segmentation was tested on 20 patients: 10 cN0-stages (ideal population) and 10 unselected N-stages (clinical population). Following manual delineation of OAR and CTV, automatic segmentation of the same set of structures was performed and afterwards manually corrected. Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Maximal Surface Distance (MSD) were calculated for "manual to automatic" and "manual to corrected" volumes comparisons. In both groups, automatic segmentation saved about 40% of the corresponding manual segmentation time. This effect was more pronounced for OAR than for CTV. The edition of the automatically obtained contours significantly improved DSC, ASD and MSD. Large distortions of normal anatomy or lack of iodine contrast were the limiting factors. The updated Brainlab atlas-based automatic segmentation tool for head and neck Cancer patients is timesaving but still necessitates review and corrections by an expert.

  14. Automatic Segmentation of Optic Disc in Eye Fundus Images: A Survey

    OpenAIRE

    Allam, Ali; Youssif, Aliaa; Ghalwash, Atef

    2015-01-01

    Optic disc detection and segmentation is one of the key elements for automatic retinal disease screening systems. The aim of this survey paper is to review, categorize and compare the optic disc detection algorithms and methodologies, giving a description of each of them, highlighting their key points and performance measures. Accordingly, this survey firstly overviews the anatomy of the eye fundus showing its main structural components along with their properties and functions. Consequently,...

  15. Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation

    International Nuclear Information System (INIS)

    Daisne, Jean-François; Blumhofer, Andreas

    2013-01-01

    Intensity modulated radiotherapy for head and neck cancer necessitates accurate definition of organs at risk (OAR) and clinical target volumes (CTV). This crucial step is time consuming and prone to inter- and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. We aim to test a new commercial atlas algorithm for automatic segmentation of OAR and CTV in both ideal and clinical conditions. The updated Brainlab automatic head and neck atlas segmentation was tested on 20 patients: 10 cN0-stages (ideal population) and 10 unselected N-stages (clinical population). Following manual delineation of OAR and CTV, automatic segmentation of the same set of structures was performed and afterwards manually corrected. Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Maximal Surface Distance (MSD) were calculated for “manual to automatic” and “manual to corrected” volumes comparisons. In both groups, automatic segmentation saved about 40% of the corresponding manual segmentation time. This effect was more pronounced for OAR than for CTV. The edition of the automatically obtained contours significantly improved DSC, ASD and MSD. Large distortions of normal anatomy or lack of iodine contrast were the limiting factors. The updated Brainlab atlas-based automatic segmentation tool for head and neck Cancer patients is timesaving but still necessitates review and corrections by an expert

  16. Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms : VISCERAL Anatomy Benchmarks

    OpenAIRE

    Jimenez-del-Toro, Oscar; Muller, Henning; Krenn, Markus; Gruenberg, Katharina; Taha, Abdel Aziz; Winterstein, Marianne; Eggel, Ivan; Foncubierta-Rodriguez, Antonio; Goksel, Orcun; Jakab, Andres; Kontokotsios, Georgios; Langs, Georg; Menze, Bjoern H.; Fernandez, Tomas Salas; Schaer, Roger

    2016-01-01

    Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the ...

  17. Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma.

    Science.gov (United States)

    Ciller, Carlos; De Zanet, Sandro I; Rüegsegger, Michael B; Pica, Alessia; Sznitman, Raphael; Thiran, Jean-Philippe; Maeder, Philippe; Munier, Francis L; Kowal, Jens H; Cuadra, Meritxell Bach

    2015-07-15

    Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium-enhanced MRI: Towards objective atrial scar assessment.

    Science.gov (United States)

    Tao, Qian; Ipek, Esra Gucuk; Shahzad, Rahil; Berendsen, Floris F; Nazarian, Saman; van der Geest, Rob J

    2016-08-01

    To realize objective atrial scar assessment, this study aimed to develop a fully automatic method to segment the left atrium (LA) and pulmonary veins (PV) from late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI). The extent and distribution of atrial scar, visualized by LGE-MRI, provides important information for clinical treatment of atrial fibrillation (AF) patients. Forty-six AF patients (age 62 ± 8, 14 female) who underwent cardiac MRI prior to RF ablation were included. A contrast-enhanced MR angiography (MRA) sequence was acquired for anatomy assessment followed by an LGE sequence for LA scar assessment. A fully automatic segmentation method was proposed consisting of two stages: 1) global segmentation by multiatlas registration; and 2) local refinement by 3D level-set. These automatic segmentation results were compared with manual segmentation. The LA and PVs were automatically segmented in all subjects. Compared with manual segmentation, the method yielded a surface-to-surface distance of 1.49 ± 0.65 mm in the LA region when using both MRA and LGE, and 1.80 ± 0.93 mm when using LGE alone (P automatic and manual segmentation was comparable to the interobserver difference (P = 0.8 in LA region and P = 0.7 in PV region). We developed a fully automatic method for LA and PV segmentation from LGE-MRI, with comparable performance to a human observer. Inclusion of an MRA sequence further improves the segmentation accuracy. The method leads to automatic generation of a patient-specific model, and potentially enables objective atrial scar assessment for AF patients. J. Magn. Reson. Imaging 2016;44:346-354. © 2016 Wiley Periodicals, Inc.

  19. Automatic segmentation of vertebrae from radiographs

    DEFF Research Database (Denmark)

    Mysling, Peter; Petersen, Peter Kersten; Nielsen, Mads

    2011-01-01

    Segmentation of vertebral contours is an essential task in the design of automatic tools for vertebral fracture assessment. In this paper, we propose a novel segmentation technique which does not require operator interaction. The proposed technique solves the segmentation problem in a hierarchical...... is constrained by a conditional shape model, based on the variability of the coarse spine location estimates. The technique is evaluated on a data set of manually annotated lumbar radiographs. The results compare favorably to the previous work in automatic vertebra segmentation, in terms of both segmentation...

  20. Multiatlas segmentation of thoracic and abdominal anatomy with level set-based local search.

    Science.gov (United States)

    Schreibmann, Eduard; Marcus, David M; Fox, Tim

    2014-07-08

    Segmentation of organs at risk (OARs) remains one of the most time-consuming tasks in radiotherapy treatment planning. Atlas-based segmentation methods using single templates have emerged as a practical approach to automate the process for brain or head and neck anatomy, but pose significant challenges in regions where large interpatient variations are present. We show that significant changes are needed to autosegment thoracic and abdominal datasets by combining multi-atlas deformable registration with a level set-based local search. Segmentation is hierarchical, with a first stage detecting bulk organ location, and a second step adapting the segmentation to fine details present in the patient scan. The first stage is based on warping multiple presegmented templates to the new patient anatomy using a multimodality deformable registration algorithm able to cope with changes in scanning conditions and artifacts. These segmentations are compacted in a probabilistic map of organ shape using the STAPLE algorithm. Final segmentation is obtained by adjusting the probability map for each organ type, using customized combinations of delineation filters exploiting prior knowledge of organ characteristics. Validation is performed by comparing automated and manual segmentation using the Dice coefficient, measured at an average of 0.971 for the aorta, 0.869 for the trachea, 0.958 for the lungs, 0.788 for the heart, 0.912 for the liver, 0.884 for the kidneys, 0.888 for the vertebrae, 0.863 for the spleen, and 0.740 for the spinal cord. Accurate atlas segmentation for abdominal and thoracic regions can be achieved with the usage of a multi-atlas and perstructure refinement strategy. To improve clinical workflow and efficiency, the algorithm was embedded in a software service, applying the algorithm automatically on acquired scans without any user interaction.

  1. Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma

    Energy Technology Data Exchange (ETDEWEB)

    Ciller, Carlos, E-mail: carlos.cillerruiz@unil.ch [Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne (Switzerland); Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern (Switzerland); Centre d’Imagerie BioMédicale, University of Lausanne, Lausanne (Switzerland); De Zanet, Sandro I.; Rüegsegger, Michael B. [Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern (Switzerland); Department of Ophthalmology, Inselspital, Bern University Hospital, Bern (Switzerland); Pica, Alessia [Department of Radiation Oncology, Inselspital, Bern University Hospital, Bern (Switzerland); Sznitman, Raphael [Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern (Switzerland); Department of Ophthalmology, Inselspital, Bern University Hospital, Bern (Switzerland); Thiran, Jean-Philippe [Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne (Switzerland); Signal Processing Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne (Switzerland); Maeder, Philippe [Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne (Switzerland); Munier, Francis L. [Unit of Pediatric Ocular Oncology, Jules Gonin Eye Hospital, Lausanne (Switzerland); Kowal, Jens H. [Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern (Switzerland); Department of Ophthalmology, Inselspital, Bern University Hospital, Bern (Switzerland); and others

    2015-07-15

    Purpose: Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Methods and Materials: Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor.

  2. Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma

    International Nuclear Information System (INIS)

    Ciller, Carlos; De Zanet, Sandro I.; Rüegsegger, Michael B.; Pica, Alessia; Sznitman, Raphael; Thiran, Jean-Philippe; Maeder, Philippe; Munier, Francis L.; Kowal, Jens H.

    2015-01-01

    Purpose: Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Methods and Materials: Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor

  3. Doses to organs at cerebral risks: optimization by robotized stereotaxic radiotherapy and automatic segmentation atlas versus three dimensional conformal radiotherapy

    International Nuclear Information System (INIS)

    Bondiau, P.Y.; Thariat, J.; Benezery, K.; Herault, J.; Dalmasso, C.; Marcie, S.; Malandain, G.

    2007-01-01

    The stereotaxic radiotherapy robotized by 'Cyberknife fourth generation' allows a dosimetric optimization with a high conformity index on the tumor and radiation doses limited on organs at risk. A cerebral automatic anatomic segmentation atlas of organs at risk are used in routine in three dimensions. This study evaluated the superiority of the stereotaxic radiotherapy in comparison with the three dimensional conformal radiotherapy on the preservation of organs at risk in regard of the delivered dose to tumors justifying an accelerated hypo fractionation and a dose escalation. This automatic segmentation atlas should allow to establish correlations between anatomy and cerebral dosimetry; This atlas allows to underline the dosimetry optimization by stereotaxic radiotherapy robotized for organs at risk. (N.C.)

  4. Adapting Mask-RCNN for Automatic Nucleus Segmentation

    OpenAIRE

    Johnson, Jeremiah W.

    2018-01-01

    Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Nucleus detection is an important example of this task. Mask-RCNN is a recently proposed state-of-the-art algorithm for object detection, object localization, and object instance segmentation of natural images. In this paper we demonstrate that Mask-RCNN can be used to perform highly effective and efficient automatic segmentations of a wide range of microscopy images of cell nuclei, for ...

  5. Fully automatic algorithm for segmenting full human diaphragm in non-contrast CT Images

    Science.gov (United States)

    Karami, Elham; Gaede, Stewart; Lee, Ting-Yim; Samani, Abbas

    2015-03-01

    The diaphragm is a sheet of muscle which separates the thorax from the abdomen and it acts as the most important muscle of the respiratory system. As such, an accurate segmentation of the diaphragm, not only provides key information for functional analysis of the respiratory system, but also can be used for locating other abdominal organs such as the liver. However, diaphragm segmentation is extremely challenging in non-contrast CT images due to the diaphragm's similar appearance to other abdominal organs. In this paper, we present a fully automatic algorithm for diaphragm segmentation in non-contrast CT images. The method is mainly based on a priori knowledge about the human diaphragm anatomy. The diaphragm domes are in contact with the lungs and the heart while its circumference runs along the lumbar vertebrae of the spine as well as the inferior border of the ribs and sternum. As such, the diaphragm can be delineated by segmentation of these organs followed by connecting relevant parts of their outline properly. More specifically, the bottom surface of the lungs and heart, the spine borders and the ribs are delineated, leading to a set of scattered points which represent the diaphragm's geometry. Next, a B-spline filter is used to find the smoothest surface which pass through these points. This algorithm was tested on a noncontrast CT image of a lung cancer patient. The results indicate that there is an average Hausdorff distance of 2.96 mm between the automatic and manually segmented diaphragms which implies a favourable accuracy.

  6. Automatic ultrasound image enhancement for 2D semi-automatic breast-lesion segmentation

    Science.gov (United States)

    Lu, Kongkuo; Hall, Christopher S.

    2014-03-01

    Breast cancer is the fastest growing cancer, accounting for 29%, of new cases in 2012, and second leading cause of cancer death among women in the United States and worldwide. Ultrasound (US) has been used as an indispensable tool for breast cancer detection/diagnosis and treatment. In computer-aided assistance, lesion segmentation is a preliminary but vital step, but the task is quite challenging in US images, due to imaging artifacts that complicate detection and measurement of the suspect lesions. The lesions usually present with poor boundary features and vary significantly in size, shape, and intensity distribution between cases. Automatic methods are highly application dependent while manual tracing methods are extremely time consuming and have a great deal of intra- and inter- observer variability. Semi-automatic approaches are designed to counterbalance the advantage and drawbacks of the automatic and manual methods. However, considerable user interaction might be necessary to ensure reasonable segmentation for a wide range of lesions. This work proposes an automatic enhancement approach to improve the boundary searching ability of the live wire method to reduce necessary user interaction while keeping the segmentation performance. Based on the results of segmentation of 50 2D breast lesions in US images, less user interaction is required to achieve desired accuracy, i.e. < 80%, when auto-enhancement is applied for live-wire segmentation.

  7. AUTOMATIC MULTILEVEL IMAGE SEGMENTATION BASED ON FUZZY REASONING

    Directory of Open Access Journals (Sweden)

    Liang Tang

    2011-05-01

    Full Text Available An automatic multilevel image segmentation method based on sup-star fuzzy reasoning (SSFR is presented. Using the well-known sup-star fuzzy reasoning technique, the proposed algorithm combines the global statistical information implied in the histogram with the local information represented by the fuzzy sets of gray-levels, and aggregates all the gray-levels into several classes characterized by the local maximum values of the histogram. The presented method has the merits of determining the number of the segmentation classes automatically, and avoiding to calculating thresholds of segmentation. Emulating and real image segmentation experiments demonstrate that the SSFR is effective.

  8. Automatic Segmentation of Vessels in In-Vivo Ultrasound Scans

    DEFF Research Database (Denmark)

    Tamimi-Sarnikowski, Philip; Brink-Kjær, Andreas; Moshavegh, Ramin

    2017-01-01

    presents a fully automatic segmentation algorithm, for robustly segmenting the vessel lumen in longitudinal B-mode ultrasound images. The automatic segmentation is performed using a combination of B-mode and power Doppler images. The proposed algorithm includes a series of preprocessing steps, and performs......Ultrasound has become highly popular to monitor atherosclerosis, by scanning the carotid artery. The screening involves measuring the thickness of the vessel wall and diameter of the lumen. An automatic segmentation of the vessel lumen, can enable the determination of lumen diameter. This paper...... a vessel segmentation by use of the marker-controlled watershed transform. The ultrasound images used in the study were acquired using the bk3000 ultrasound scanner (BK Ultrasound, Herlev, Denmark) with two transducers ”8L2 Linear” and ”10L2w Wide Linear” (BK Ultrasound, Herlev, Denmark). The algorithm...

  9. Semi-automatic segmentation of myocardium at risk in T2-weighted cardiovascular magnetic resonance

    Directory of Open Access Journals (Sweden)

    Sjögren Jane

    2012-01-01

    Full Text Available Abstract Background T2-weighted cardiovascular magnetic resonance (CMR has been shown to be a promising technique for determination of ischemic myocardium, referred to as myocardium at risk (MaR, after an acute coronary event. Quantification of MaR in T2-weighted CMR has been proposed to be performed by manual delineation or the threshold methods of two standard deviations from remote (2SD, full width half maximum intensity (FWHM or Otsu. However, manual delineation is subjective and threshold methods have inherent limitations related to threshold definition and lack of a priori information about cardiac anatomy and physiology. Therefore, the aim of this study was to develop an automatic segmentation algorithm for quantification of MaR using anatomical a priori information. Methods Forty-seven patients with first-time acute ST-elevation myocardial infarction underwent T2-weighted CMR within 1 week after admission. Endocardial and epicardial borders of the left ventricle, as well as the hyper enhanced MaR regions were manually delineated by experienced observers and used as reference method. A new automatic segmentation algorithm, called Segment MaR, defines the MaR region as the continuous region most probable of being MaR, by estimating the intensities of normal myocardium and MaR with an expectation maximization algorithm and restricting the MaR region by an a priori model of the maximal extent for the user defined culprit artery. The segmentation by Segment MaR was compared against inter observer variability of manual delineation and the threshold methods of 2SD, FWHM and Otsu. Results MaR was 32.9 ± 10.9% of left ventricular mass (LVM when assessed by the reference observer and 31.0 ± 8.8% of LVM assessed by Segment MaR. The bias and correlation was, -1.9 ± 6.4% of LVM, R = 0.81 (p Conclusions There is a good agreement between automatic Segment MaR and manually assessed MaR in T2-weighted CMR. Thus, the proposed algorithm seems to be a

  10. Automatic anatomy recognition via multiobject oriented active shape models.

    Science.gov (United States)

    Chen, Xinjian; Udupa, Jayaram K; Alavi, Abass; Torigian, Drew A

    2010-12-01

    This paper studies the feasibility of developing an automatic anatomy recognition (AAR) system in clinical radiology and demonstrates its operation on clinical 2D images. The anatomy recognition method described here consists of two main components: (a) multiobject generalization of OASM and (b) object recognition strategies. The OASM algorithm is generalized to multiple objects by including a model for each object and assigning a cost structure specific to each object in the spirit of live wire. The delineation of multiobject boundaries is done in MOASM via a three level dynamic programming algorithm, wherein the first level is at pixel level which aims to find optimal oriented boundary segments between successive landmarks, the second level is at landmark level which aims to find optimal location for the landmarks, and the third level is at the object level which aims to find optimal arrangement of object boundaries over all objects. The object recognition strategy attempts to find that pose vector (consisting of translation, rotation, and scale component) for the multiobject model that yields the smallest total boundary cost for all objects. The delineation and recognition accuracies were evaluated separately utilizing routine clinical chest CT, abdominal CT, and foot MRI data sets. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF and FPVF). The recognition accuracy was assessed (1) in terms of the size of the space of the pose vectors for the model assembly that yielded high delineation accuracy, (2) as a function of the number of objects and objects' distribution and size in the model, (3) in terms of the interdependence between delineation and recognition, and (4) in terms of the closeness of the optimum recognition result to the global optimum. When multiple objects are included in the model, the delineation accuracy in terms of TPVF can be improved to 97%-98% with a low FPVF of 0.1%-0.2%. Typically, a

  11. CLG for Automatic Image Segmentation

    OpenAIRE

    Christo Ananth; S.Santhana Priya; S.Manisha; T.Ezhil Jothi; M.S.Ramasubhaeswari

    2017-01-01

    This paper proposes an automatic segmentation method which effectively combines Active Contour Model, Live Wire method and Graph Cut approach (CLG). The aim of Live wire method is to provide control to the user on segmentation process during execution. Active Contour Model provides a statistical model of object shape and appearance to a new image which are built during a training phase. In the graph cut technique, each pixel is represented as a node and the distance between those nodes is rep...

  12. Automatic lung segmentation in the presence of alveolar collapse

    Directory of Open Access Journals (Sweden)

    Noshadi Areg

    2017-09-01

    Full Text Available Lung ventilation and perfusion analyses using chest imaging methods require a correct segmentation of the lung to offer anatomical landmarks for the physiological data. An automatic segmentation approach simplifies and accelerates the analysis. However, the segmentation of the lungs has shown to be difficult if collapsed areas are present that tend to share similar gray values with surrounding non-pulmonary tissue. Our goal was to develop an automatic segmentation algorithm that is able to approximate dorsal lung boundaries even if alveolar collapse is present in the dependent lung areas adjacent to the pleura. Computed tomography data acquired in five supine pigs with injured lungs were used for this purpose. First, healthy lung tissue was segmented using a standard 3D region growing algorithm. Further, the bones in the chest wall surrounding the lungs were segmented to find the contact points of ribs and pleura. Artificial boundaries of the dorsal lung were set by spline interpolation through these contact points. Segmentation masks of the entire lung including the collapsed regions were created by combining the splines with the segmentation masks of the healthy lung tissue through multiple morphological operations. The automatically segmented images were then evaluated by comparing them to manual segmentations and determining the Dice similarity coefficients (DSC as a similarity measure. The developed method was able to accurately segment the lungs including the collapsed regions (DSCs over 0.96.

  13. Automatic Segmentation of Dermoscopic Images by Iterative Classification

    Directory of Open Access Journals (Sweden)

    Maciel Zortea

    2011-01-01

    Full Text Available Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.

  14. Automatic initialization and quality control of large-scale cardiac MRI segmentations.

    Science.gov (United States)

    Albà, Xènia; Lekadir, Karim; Pereañez, Marco; Medrano-Gracia, Pau; Young, Alistair A; Frangi, Alejandro F

    2018-01-01

    Continuous advances in imaging technologies enable ever more comprehensive phenotyping of human anatomy and physiology. Concomitant reduction of imaging costs has resulted in widespread use of imaging in large clinical trials and population imaging studies. Magnetic Resonance Imaging (MRI), in particular, offers one-stop-shop multidimensional biomarkers of cardiovascular physiology and pathology. A wide range of analysis methods offer sophisticated cardiac image assessment and quantification for clinical and research studies. However, most methods have only been evaluated on relatively small databases often not accessible for open and fair benchmarking. Consequently, published performance indices are not directly comparable across studies and their translation and scalability to large clinical trials or population imaging cohorts is uncertain. Most existing techniques still rely on considerable manual intervention for the initialization and quality control of the segmentation process, becoming prohibitive when dealing with thousands of images. The contributions of this paper are three-fold. First, we propose a fully automatic method for initializing cardiac MRI segmentation, by using image features and random forests regression to predict an initial position of the heart and key anatomical landmarks in an MRI volume. In processing a full imaging database, the technique predicts the optimal corrective displacements and positions in relation to the initial rough intersections of the long and short axis images. Second, we introduce for the first time a quality control measure capable of identifying incorrect cardiac segmentations with no visual assessment. The method uses statistical, pattern and fractal descriptors in a random forest classifier to detect failures to be corrected or removed from subsequent statistical analysis. Finally, we validate these new techniques within a full pipeline for cardiac segmentation applicable to large-scale cardiac MRI databases. The

  15. Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation

    International Nuclear Information System (INIS)

    Shekhar, Raj; Lei, Peng; Castro-Pareja, Carlos R.; Plishker, William L.; D'Souza, Warren D.

    2007-01-01

    Conventional radiotherapy is planned using free-breathing computed tomography (CT), ignoring the motion and deformation of the anatomy from respiration. New breath-hold-synchronized, gated, and four-dimensional (4D) CT acquisition strategies are enabling radiotherapy planning utilizing a set of CT scans belonging to different phases of the breathing cycle. Such 4D treatment planning relies on the availability of tumor and organ contours in all phases. The current practice of manual segmentation is impractical for 4D CT, because it is time consuming and tedious. A viable solution is registration-based segmentation, through which contours provided by an expert for a particular phase are propagated to all other phases while accounting for phase-to-phase motion and anatomical deformation. Deformable image registration is central to this task, and a free-form deformation-based nonrigid image registration algorithm will be presented. Compared with the original algorithm, this version uses novel, computationally simpler geometric constraints to preserve the topology of the dense control-point grid used to represent free-form deformation and prevent tissue fold-over. Using mean squared difference as an image similarity criterion, the inhale phase is registered to the exhale phase of lung CT scans of five patients and of characteristically low-contrast abdominal CT scans of four patients. In addition, using expert contours for the inhale phase, the corresponding contours were automatically generated for the exhale phase. The accuracy of the segmentation (and hence deformable image registration) was judged by comparing automatically segmented contours with expert contours traced directly in the exhale phase scan using three metrics: volume overlap index, root mean square distance, and Hausdorff distance. The accuracy of the segmentation (in terms of radial distance mismatch) was approximately 2 mm in the thorax and 3 mm in the abdomen, which compares favorably to the

  16. Deformable meshes for medical image segmentation accurate automatic segmentation of anatomical structures

    CERN Document Server

    Kainmueller, Dagmar

    2014-01-01

    ? Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author's core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-shaped features of anatomical structures. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatom

  17. Automatic liver volume segmentation and fibrosis classification

    Science.gov (United States)

    Bal, Evgeny; Klang, Eyal; Amitai, Michal; Greenspan, Hayit

    2018-02-01

    In this work, we present an automatic method for liver segmentation and fibrosis classification in liver computed-tomography (CT) portal phase scans. The input is a full abdomen CT scan with an unknown number of slices, and the output is a liver volume segmentation mask and a fibrosis grade. A multi-stage analysis scheme is applied to each scan, including: volume segmentation, texture features extraction and SVM based classification. Data contains portal phase CT examinations from 80 patients, taken with different scanners. Each examination has a matching Fibroscan grade. The dataset was subdivided into two groups: first group contains healthy cases and mild fibrosis, second group contains moderate fibrosis, severe fibrosis and cirrhosis. Using our automated algorithm, we achieved an average dice index of 0.93 ± 0.05 for segmentation and a sensitivity of 0.92 and specificity of 0.81for classification. To the best of our knowledge, this is a first end to end automatic framework for liver fibrosis classification; an approach that, once validated, can have a great potential value in the clinic.

  18. Automatic bladder segmentation on CBCT for multiple plan ART of bladder cancer using a patient-specific bladder model

    Energy Technology Data Exchange (ETDEWEB)

    Xiangfei, Chai; Hulshof, Maarten; Bel, Arjan [Department of Radiotherapy, Academic medical Center, University of Amsterdam, 1105 AZ, Amsterdam (Netherlands); Van Herk, Marcel; Betgen, Anja [Department of Radiotherapy, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, 1066 CX, Amsterdam (Netherlands)

    2012-06-21

    In multiple plan adaptive radiotherapy (ART) strategies of bladder cancer, a library of plans corresponding to different bladder volumes is created based on images acquired in early treatment sessions. Subsequently, the plan for the smallest PTV safely covering the bladder on cone-beam CT (CBCT) is selected as the plan of the day. The aim of this study is to develop an automatic bladder segmentation approach suitable for CBCT scans and test its ability to select the appropriate plan from the library of plans for such an ART procedure. Twenty-three bladder cancer patients with a planning CT and on average 11.6 CBCT scans were included in our study. For each patient, all CBCT scans were matched to the planning CT on bony anatomy. Bladder contours were manually delineated for each planning CT (for model building) and CBCT (for model building and validation). The automatic segmentation method consisted of two steps. A patient-specific bladder deformation model was built from the training data set of each patient (the planning CT and the first five CBCT scans). Then, the model was applied to automatically segment bladders in the validation data of the same patient (the remaining CBCT scans). Principal component analysis (PCA) was applied to the training data to model patient-specific bladder deformation patterns. The number of PCA modes for each patient was chosen such that the bladder shapes in the training set could be represented by such number of PCA modes with less than 0.1 cm mean residual error. The automatic segmentation started from the bladder shape of a reference CBCT, which was adjusted by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to fit the bladder in the validation image. A cost function was defined by the absolute difference between the directional gradient field of reference CBCT sampled on the corresponding bladder contour and the directional gradient field of validation

  19. Automatic bladder segmentation on CBCT for multiple plan ART of bladder cancer using a patient-specific bladder model

    International Nuclear Information System (INIS)

    Chai Xiangfei; Hulshof, Maarten; Bel, Arjan; Van Herk, Marcel; Betgen, Anja

    2012-01-01

    In multiple plan adaptive radiotherapy (ART) strategies of bladder cancer, a library of plans corresponding to different bladder volumes is created based on images acquired in early treatment sessions. Subsequently, the plan for the smallest PTV safely covering the bladder on cone-beam CT (CBCT) is selected as the plan of the day. The aim of this study is to develop an automatic bladder segmentation approach suitable for CBCT scans and test its ability to select the appropriate plan from the library of plans for such an ART procedure. Twenty-three bladder cancer patients with a planning CT and on average 11.6 CBCT scans were included in our study. For each patient, all CBCT scans were matched to the planning CT on bony anatomy. Bladder contours were manually delineated for each planning CT (for model building) and CBCT (for model building and validation). The automatic segmentation method consisted of two steps. A patient-specific bladder deformation model was built from the training data set of each patient (the planning CT and the first five CBCT scans). Then, the model was applied to automatically segment bladders in the validation data of the same patient (the remaining CBCT scans). Principal component analysis (PCA) was applied to the training data to model patient-specific bladder deformation patterns. The number of PCA modes for each patient was chosen such that the bladder shapes in the training set could be represented by such number of PCA modes with less than 0.1 cm mean residual error. The automatic segmentation started from the bladder shape of a reference CBCT, which was adjusted by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to fit the bladder in the validation image. A cost function was defined by the absolute difference between the directional gradient field of reference CBCT sampled on the corresponding bladder contour and the directional gradient field of validation

  20. Automatic blood vessel based-liver segmentation using the portal phase abdominal CT

    Science.gov (United States)

    Maklad, Ahmed S.; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Shimada, Mitsuo; Iinuma, Gen

    2018-02-01

    Liver segmentation is the basis for computer-based planning of hepatic surgical interventions. In diagnosis and analysis of hepatic diseases and surgery planning, automatic segmentation of liver has high importance. Blood vessel (BV) has showed high performance at liver segmentation. In our previous work, we developed a semi-automatic method that segments the liver through the portal phase abdominal CT images in two stages. First stage was interactive segmentation of abdominal blood vessels (ABVs) and subsequent classification into hepatic (HBVs) and non-hepatic (non-HBVs). This stage had 5 interactions that include selective threshold for bone segmentation, selecting two seed points for kidneys segmentation, selection of inferior vena cava (IVC) entrance for starting ABVs segmentation, identification of the portal vein (PV) entrance to the liver and the IVC-exit for classifying HBVs from other ABVs (non-HBVs). Second stage is automatic segmentation of the liver based on segmented ABVs as described in [4]. For full automation of our method we developed a method [5] that segments ABVs automatically tackling the first three interactions. In this paper, we propose full automation of classifying ABVs into HBVs and non- HBVs and consequently full automation of liver segmentation that we proposed in [4]. Results illustrate that the method is effective at segmentation of the liver through the portal abdominal CT images.

  1. Segmentation of Extrapulmonary Tuberculosis Infection Using Modified Automatic Seeded Region Growing

    Directory of Open Access Journals (Sweden)

    Nordin Abdul

    2009-01-01

    Full Text Available Abstract In the image segmentation process of positron emission tomography combined with computed tomography (PET/CT imaging, previous works used information in CT only for segmenting the image without utilizing the information that can be provided by PET. This paper proposes to utilize the hot spot values in PET to guide the segmentation in CT, in automatic image segmentation using seeded region growing (SRG technique. This automatic segmentation routine can be used as part of automatic diagnostic tools. In addition to the original initial seed selection using hot spot values in PET, this paper also introduces a new SRG growing criterion, the sliding windows. Fourteen images of patients having extrapulmonary tuberculosis have been examined using the above-mentioned method. To evaluate the performance of the modified SRG, three fidelity criteria are measured: percentage of under-segmentation area, percentage of over-segmentation area, and average time consumption. In terms of the under-segmentation percentage, SRG with average of the region growing criterion shows the least error percentage (51.85%. Meanwhile, SRG with local averaging and variance yielded the best results (2.67% for the over-segmentation percentage. In terms of the time complexity, the modified SRG with local averaging and variance growing criterion shows the best performance with 5.273 s average execution time. The results indicate that the proposed methods yield fairly good performance in terms of the over- and under-segmentation area. The results also demonstrated that the hot spot values in PET can be used to guide the automatic segmentation in CT image.

  2. Semi-automatic segmentation of myocardium at risk in T2-weighted cardiovascular magnetic resonance.

    Science.gov (United States)

    Sjögren, Jane; Ubachs, Joey F A; Engblom, Henrik; Carlsson, Marcus; Arheden, Håkan; Heiberg, Einar

    2012-01-31

    T2-weighted cardiovascular magnetic resonance (CMR) has been shown to be a promising technique for determination of ischemic myocardium, referred to as myocardium at risk (MaR), after an acute coronary event. Quantification of MaR in T2-weighted CMR has been proposed to be performed by manual delineation or the threshold methods of two standard deviations from remote (2SD), full width half maximum intensity (FWHM) or Otsu. However, manual delineation is subjective and threshold methods have inherent limitations related to threshold definition and lack of a priori information about cardiac anatomy and physiology. Therefore, the aim of this study was to develop an automatic segmentation algorithm for quantification of MaR using anatomical a priori information. Forty-seven patients with first-time acute ST-elevation myocardial infarction underwent T2-weighted CMR within 1 week after admission. Endocardial and epicardial borders of the left ventricle, as well as the hyper enhanced MaR regions were manually delineated by experienced observers and used as reference method. A new automatic segmentation algorithm, called Segment MaR, defines the MaR region as the continuous region most probable of being MaR, by estimating the intensities of normal myocardium and MaR with an expectation maximization algorithm and restricting the MaR region by an a priori model of the maximal extent for the user defined culprit artery. The segmentation by Segment MaR was compared against inter observer variability of manual delineation and the threshold methods of 2SD, FWHM and Otsu. MaR was 32.9 ± 10.9% of left ventricular mass (LVM) when assessed by the reference observer and 31.0 ± 8.8% of LVM assessed by Segment MaR. The bias and correlation was, -1.9 ± 6.4% of LVM, R = 0.81 (p Segment MaR, -2.3 ± 4.9%, R = 0.91 (p Segment MaR and manually assessed MaR in T2-weighted CMR. Thus, the proposed algorithm seems to be a promising, objective method for standardized MaR quantification in T2

  3. Automatic segmentation of MR brain images with a convolutional neural network

    NARCIS (Netherlands)

    Moeskops, P.; Viergever, M.A.; Mendrik, A.M.; de Vries, L.S.; Benders, M.J.N.L.; Išgum, I.

    2016-01-01

    Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure

  4. Segmenting articular cartilage automatically using a voxel classification approach

    DEFF Research Database (Denmark)

    Folkesson, Jenny; Dam, Erik B; Olsen, Ole F

    2007-01-01

    We present a fully automatic method for articular cartilage segmentation from magnetic resonance imaging (MRI) which we use as the foundation of a quantitative cartilage assessment. We evaluate our method by comparisons to manual segmentations by a radiologist and by examining the interscan...... reproducibility of the volume and area estimates. Training and evaluation of the method is performed on a data set consisting of 139 scans of knees with a status ranging from healthy to severely osteoarthritic. This is, to our knowledge, the only fully automatic cartilage segmentation method that has good...... agreement with manual segmentations, an interscan reproducibility as good as that of a human expert, and enables the separation between healthy and osteoarthritic populations. While high-field scanners offer high-quality imaging from which the articular cartilage have been evaluated extensively using manual...

  5. Automatic aortic root segmentation in CTA whole-body dataset

    Science.gov (United States)

    Gao, Xinpei; Kitslaar, Pieter H.; Scholte, Arthur J. H. A.; Lelieveldt, Boudewijn P. F.; Dijkstra, Jouke; Reiber, Johan H. C.

    2016-03-01

    Trans-catheter aortic valve replacement (TAVR) is an evolving technique for patients with serious aortic stenosis disease. Typically, in this application a CTA data set is obtained of the patient's arterial system from the subclavian artery to the femoral arteries, to evaluate the quality of the vascular access route and analyze the aortic root to determine if and which prosthesis should be used. In this paper, we concentrate on the automated segmentation of the aortic root. The purpose of this study was to automatically segment the aortic root in computed tomography angiography (CTA) datasets to support TAVR procedures. The method in this study includes 4 major steps. First, the patient's cardiac CTA image was resampled to reduce the computation time. Next, the cardiac CTA image was segmented using an atlas-based approach. The most similar atlas was selected from a total of 8 atlases based on its image similarity to the input CTA image. Third, the aortic root segmentation from the previous step was transferred to the patient's whole-body CTA image by affine registration and refined in the fourth step using a deformable subdivision surface model fitting procedure based on image intensity. The pipeline was applied to 20 patients. The ground truth was created by an analyst who semi-automatically corrected the contours of the automatic method, where necessary. The average Dice similarity index between the segmentations of the automatic method and the ground truth was found to be 0.965±0.024. In conclusion, the current results are very promising.

  6. Interactive vs. automatic ultrasound image segmentation methods for staging hepatic lipidosis.

    Science.gov (United States)

    Weijers, Gert; Starke, Alexander; Haudum, Alois; Thijssen, Johan M; Rehage, Jürgen; De Korte, Chris L

    2010-07-01

    The aim of this study was to test the hypothesis that automatic segmentation of vessels in ultrasound (US) images can produce similar or better results in grading fatty livers than interactive segmentation. A study was performed in postpartum dairy cows (N=151), as an animal model of human fatty liver disease, to test this hypothesis. Five transcutaneous and five intraoperative US liver images were acquired in each animal and a liverbiopsy was taken. In liver tissue samples, triacylglycerol (TAG) was measured by biochemical analysis and hepatic diseases other than hepatic lipidosis were excluded by histopathologic examination. Ultrasonic tissue characterization (UTC) parameters--Mean echo level, standard deviation (SD) of echo level, signal-to-noise ratio (SNR), residual attenuation coefficient (ResAtt) and axial and lateral speckle size--were derived using a computer-aided US (CAUS) protocol and software package. First, the liver tissue was interactively segmented by two observers. With increasing fat content, fewer hepatic vessels were visible in the ultrasound images and, therefore, a smaller proportion of the liver needed to be excluded from these images. Automatic-segmentation algorithms were implemented and it was investigated whether better results could be achieved than with the subjective and time-consuming interactive-segmentation procedure. The automatic-segmentation algorithms were based on both fixed and adaptive thresholding techniques in combination with a 'speckle'-shaped moving-window exclusion technique. All data were analyzed with and without postprocessing as contained in CAUS and with different automated-segmentation techniques. This enabled us to study the effect of the applied postprocessing steps on single and multiple linear regressions ofthe various UTC parameters with TAG. Improved correlations for all US parameters were found by using automatic-segmentation techniques. Stepwise multiple linear-regression formulas where derived and used

  7. Automatic segmentation of psoriasis lesions

    Science.gov (United States)

    Ning, Yang; Shi, Chenbo; Wang, Li; Shu, Chang

    2014-10-01

    The automatic segmentation of psoriatic lesions is widely researched these years. It is an important step in Computer-aid methods of calculating PASI for estimation of lesions. Currently those algorithms can only handle single erythema or only deal with scaling segmentation. In practice, scaling and erythema are often mixed together. In order to get the segmentation of lesions area - this paper proposes an algorithm based on Random forests with color and texture features. The algorithm has three steps. The first step, the polarized light is applied based on the skin's Tyndall-effect in the imaging to eliminate the reflection and Lab color space are used for fitting the human perception. The second step, sliding window and its sub windows are used to get textural feature and color feature. In this step, a feature of image roughness has been defined, so that scaling can be easily separated from normal skin. In the end, Random forests will be used to ensure the generalization ability of the algorithm. This algorithm can give reliable segmentation results even the image has different lighting conditions, skin types. In the data set offered by Union Hospital, more than 90% images can be segmented accurately.

  8. Fast Appearance Modeling for Automatic Primary Video Object Segmentation.

    Science.gov (United States)

    Yang, Jiong; Price, Brian; Shen, Xiaohui; Lin, Zhe; Yuan, Junsong

    2016-02-01

    Automatic segmentation of the primary object in a video clip is a challenging problem as there is no prior knowledge of the primary object. Most existing techniques thus adapt an iterative approach for foreground and background appearance modeling, i.e., fix the appearance model while optimizing the segmentation and fix the segmentation while optimizing the appearance model. However, these approaches may rely on good initialization and can be easily trapped in local optimal. In addition, they are usually time consuming for analyzing videos. To address these limitations, we propose a novel and efficient appearance modeling technique for automatic primary video object segmentation in the Markov random field (MRF) framework. It embeds the appearance constraint as auxiliary nodes and edges in the MRF structure, and can optimize both the segmentation and appearance model parameters simultaneously in one graph cut. The extensive experimental evaluations validate the superiority of the proposed approach over the state-of-the-art methods, in both efficiency and effectiveness.

  9. Morphometric Atlas Selection for Automatic Brachial Plexus Segmentation

    International Nuclear Information System (INIS)

    Van de Velde, Joris; Wouters, Johan; Vercauteren, Tom; De Gersem, Werner; Duprez, Fréderic; De Neve, Wilfried; Van Hoof, Tom

    2015-01-01

    Purpose: The purpose of this study was to determine the effects of atlas selection based on different morphometric parameters, on the accuracy of automatic brachial plexus (BP) segmentation for radiation therapy planning. The segmentation accuracy was measured by comparing all of the generated automatic segmentations with anatomically validated gold standard atlases developed using cadavers. Methods and Materials: Twelve cadaver computed tomography (CT) atlases (3 males, 9 females; mean age: 73 years) were included in the study. One atlas was selected to serve as a patient, and the other 11 atlases were registered separately onto this “patient” using deformable image registration. This procedure was repeated for every atlas as a patient. Next, the Dice and Jaccard similarity indices and inclusion index were calculated for every registered BP with the original gold standard BP. In parallel, differences in several morphometric parameters that may influence the BP segmentation accuracy were measured for the different atlases. Specific brachial plexus-related CT-visible bony points were used to define the morphometric parameters. Subsequently, correlations between the similarity indices and morphometric parameters were calculated. Results: A clear negative correlation between difference in protraction-retraction distance and the similarity indices was observed (mean Pearson correlation coefficient = −0.546). All of the other investigated Pearson correlation coefficients were weak. Conclusions: Differences in the shoulder protraction-retraction position between the atlas and the patient during planning CT influence the BP autosegmentation accuracy. A greater difference in the protraction-retraction distance between the atlas and the patient reduces the accuracy of the BP automatic segmentation result

  10. An Automatic Indirect Immunofluorescence Cell Segmentation System

    Directory of Open Access Journals (Sweden)

    Yung-Kuan Chan

    2014-01-01

    Full Text Available Indirect immunofluorescence (IIF with HEp-2 cells has been used for the detection of antinuclear autoantibodies (ANA in systemic autoimmune diseases. The ANA testing allows us to scan a broad range of autoantibody entities and to describe them by distinct fluorescence patterns. Automatic inspection for fluorescence patterns in an IIF image can assist physicians, without relevant experience, in making correct diagnosis. How to segment the cells from an IIF image is essential in developing an automatic inspection system for ANA testing. This paper focuses on the cell detection and segmentation; an efficient method is proposed for automatically detecting the cells with fluorescence pattern in an IIF image. Cell culture is a process in which cells grow under control. Cell counting technology plays an important role in measuring the cell density in a culture tank. Moreover, assessing medium suitability, determining population doubling times, and monitoring cell growth in cultures all require a means of quantifying cell population. The proposed method also can be used to count the cells from an image taken under a fluorescence microscope.

  11. Automatic 2D segmentation of airways in thorax computed tomography images

    International Nuclear Information System (INIS)

    Cavalcante, Tarique da Silveira; Cortez, Paulo Cesar; Almeida, Thomaz Maia de; Felix, John Hebert da Silva; Holanda, Marcelo Alcantara

    2013-01-01

    Introduction: much of the world population is affected by pulmonary diseases, such as the bronchial asthma, bronchitis and bronchiectasis. The bronchial diagnosis is based on the airways state. In this sense, the automatic segmentation of the airways in Computed Tomography (CT) scans is a critical step in the aid to diagnosis of these diseases. Methods: this paper evaluates algorithms for airway automatic segmentation, using Neural Network Multilayer Perceptron (MLP) and Lung Densities Analysis (LDA) for detecting airways, along with Region Growing (RG), Active Contour Method (ACM) Balloon and Topology Adaptive to segment them. Results: we obtained results in three stages: comparative analysis of the detection algorithms MLP and LDA, with a gold standard acquired by three physicians with expertise in CT imaging of the chest; comparative analysis of segmentation algorithms ACM Balloon, ACM Topology Adaptive, MLP and RG; and evaluation of possible combinations between segmentation and detection algorithms, resulting in the complete method for automatic segmentation of the airways in 2D. Conclusion: the low incidence of false negative and the significant reduction of false positive, results in similarity coefficient and sensitivity exceeding 91% and 87% respectively, for a combination of algorithms with satisfactory segmentation quality. (author)

  12. AISLE: an automatic volumetric segmentation method for the study of lung allometry.

    Science.gov (United States)

    Ren, Hongliang; Kazanzides, Peter

    2011-01-01

    We developed a fully automatic segmentation method for volumetric CT (computer tomography) datasets to support construction of a statistical atlas for the study of allometric laws of the lung. The proposed segmentation method, AISLE (Automated ITK-Snap based on Level-set), is based on the level-set implementation from an existing semi-automatic segmentation program, ITK-Snap. AISLE can segment the lung field without human interaction and provide intermediate graphical results as desired. The preliminary experimental results show that the proposed method can achieve accurate segmentation, in terms of volumetric overlap metric, by comparing with the ground-truth segmentation performed by a radiologist.

  13. Automatic segmentation and disease classification using cardiac cine MR images

    NARCIS (Netherlands)

    Wolterink, Jelmer M.; Leiner, Tim; Viergever, Max A.; Išgum, Ivana

    2018-01-01

    Segmentation of the heart in cardiac cine MR is clinically used to quantify cardiac function. We propose a fully automatic method for segmentation and disease classification using cardiac cine MR images. A convolutional neural network (CNN) was designed to simultaneously segment the left ventricle

  14. Automatic labeling and segmentation of vertebrae in CT images

    Science.gov (United States)

    Rasoulian, Abtin; Rohling, Robert N.; Abolmaesumi, Purang

    2014-03-01

    Labeling and segmentation of the spinal column from CT images is a pre-processing step for a range of image- guided interventions. State-of-the art techniques have focused either on image feature extraction or template matching for labeling of the vertebrae followed by segmentation of each vertebra. Recently, statistical multi- object models have been introduced to extract common statistical characteristics among several anatomies. In particular, we have created models for segmentation of the lumbar spine which are robust, accurate, and computationally tractable. In this paper, we reconstruct a statistical multi-vertebrae pose+shape model and utilize it in a novel framework for labeling and segmentation of the vertebra in a CT image. We validate our technique in terms of accuracy of the labeling and segmentation of CT images acquired from 56 subjects. The method correctly labels all vertebrae in 70% of patients and is only one level off for the remaining 30%. The mean distance error achieved for the segmentation is 2.1 +/- 0.7 mm.

  15. Color Image Segmentation Based on Different Color Space Models Using Automatic GrabCut

    Directory of Open Access Journals (Sweden)

    Dina Khattab

    2014-01-01

    Full Text Available This paper presents a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. GrabCut is considered as one of the semiautomatic image segmentation techniques, since it requires user interaction for the initialization of the segmentation process. The automation of the GrabCut technique is proposed as a modification of the original semiautomatic one in order to eliminate the user interaction. The automatic GrabCut utilizes the unsupervised Orchard and Bouman clustering technique for the initialization phase. Comparisons with the original GrabCut show the efficiency of the proposed automatic technique in terms of segmentation, quality, and accuracy. As no explicit color space is recommended for every segmentation problem, automatic GrabCut is applied with RGB, HSV, CMY, XYZ, and YUV color spaces. The comparative study and experimental results using different color images show that RGB color space is the best color space representation for the set of the images used.

  16. Automatic segmentation of the lateral geniculate nucleus: Application to control and glaucoma patients.

    Science.gov (United States)

    Wang, Jieqiong; Miao, Wen; Li, Jing; Li, Meng; Zhen, Zonglei; Sabel, Bernhard; Xian, Junfang; He, Huiguang

    2015-11-30

    The lateral geniculate nucleus (LGN) is a key relay center of the visual system. Because the LGN morphology is affected by different diseases, it is of interest to analyze its morphology by segmentation. However, existing LGN segmentation methods are non-automatic, inefficient and prone to experimenters' bias. To address these problems, we proposed an automatic LGN segmentation algorithm based on T1-weighted imaging. First, the prior information of LGN was used to create a prior mask. Then region growing was applied to delineate LGN. We evaluated this automatic LGN segmentation method by (1) comparison with manually segmented LGN, (2) anatomically locating LGN in the visual system via LGN-based tractography, (3) application to control and glaucoma patients. The similarity coefficients of automatic segmented LGN and manually segmented one are 0.72 (0.06) for the left LGN and 0.77 (0.07) for the right LGN. LGN-based tractography shows the subcortical pathway seeding from LGN passes the optic tract and also reaches V1 through the optic radiation, which is consistent with the LGN location in the visual system. In addition, LGN asymmetry as well as LGN atrophy along with age is observed in normal controls. The investigation of glaucoma effects on LGN volumes demonstrates that the bilateral LGN volumes shrink in patients. The automatic LGN segmentation is objective, efficient, valid and applicable. Experiment results proved the validity and applicability of the algorithm. Our method will speed up the research on visual system and greatly enhance studies of different vision-related diseases. Copyright © 2015 Elsevier B.V. All rights reserved.

  17. Soft tissue segmentation and 3D display from computerized tomography and magnetic resonance imaging

    International Nuclear Information System (INIS)

    Fan, R.T.; Trivedi, S.S.; Fellingham, L.L.; Gamboa-Aldeco, A.; Hedgcock, M.W.

    1987-01-01

    Volume calculation and 3D display of human anatomy facilitate a physician's diagnosis, treatment, and evaluation. Accurate segmentation of soft tissue structures is a prerequisite for such volume calculations and 3D displays, but segmentation by hand-outlining structures is often tedious and time-consuming. In this paper, methods based on analysis of statistics of image gray level are applied to segmentation of soft tissue in medical images, with the goal of making segmentation automatic or semi-automatic. The resulting segmented images, volume calculations, and 3D displays are analyzed and compared with results based on physician-drawn outlines as well as actual volume measurements

  18. Comparison of automatic and visual methods used for image segmentation in Endodontics: a microCT study.

    Science.gov (United States)

    Queiroz, Polyane Mazucatto; Rovaris, Karla; Santaella, Gustavo Machado; Haiter-Neto, Francisco; Freitas, Deborah Queiroz

    2017-01-01

    To calculate root canal volume and surface area in microCT images, an image segmentation by selecting threshold values is required, which can be determined by visual or automatic methods. Visual determination is influenced by the operator's visual acuity, while the automatic method is done entirely by computer algorithms. To compare between visual and automatic segmentation, and to determine the influence of the operator's visual acuity on the reproducibility of root canal volume and area measurements. Images from 31 extracted human anterior teeth were scanned with a μCT scanner. Three experienced examiners performed visual image segmentation, and threshold values were recorded. Automatic segmentation was done using the "Automatic Threshold Tool" available in the dedicated software provided by the scanner's manufacturer. Volume and area measurements were performed using the threshold values determined both visually and automatically. The paired Student's t-test showed no significant difference between visual and automatic segmentation methods regarding root canal volume measurements (p=0.93) and root canal surface (p=0.79). Although visual and automatic segmentation methods can be used to determine the threshold and calculate root canal volume and surface, the automatic method may be the most suitable for ensuring the reproducibility of threshold determination.

  19. Assessment of automatic segmentation of teeth using a watershed-based method.

    Science.gov (United States)

    Galibourg, Antoine; Dumoncel, Jean; Telmon, Norbert; Calvet, Adèle; Michetti, Jérôme; Maret, Delphine

    2018-01-01

    Tooth 3D automatic segmentation (AS) is being actively developed in research and clinical fields. Here, we assess the effect of automatic segmentation using a watershed-based method on the accuracy and reproducibility of 3D reconstructions in volumetric measurements by comparing it with a semi-automatic segmentation(SAS) method that has already been validated. The study sample comprised 52 teeth, scanned with micro-CT (41 µm voxel size) and CBCT (76; 200 and 300 µm voxel size). Each tooth was segmented by AS based on a watershed method and by SAS. For all surface reconstructions, volumetric measurements were obtained and analysed statistically. Surfaces were then aligned using the SAS surfaces as the reference. The topography of the geometric discrepancies was displayed by using a colour map allowing the maximum differences to be located. AS reconstructions showed similar tooth volumes when compared with SAS for the 41 µm voxel size. A difference in volumes was observed, and increased with the voxel size for CBCT data. The maximum differences were mainly found at the cervical margins and incisal edges but the general form was preserved. Micro-CT, a modality used in dental research, provides data that can be segmented automatically, which is timesaving. AS with CBCT data enables the general form of the region of interest to be displayed. However, our AS method can still be used for metrically reliable measurements in the field of clinical dentistry if some manual refinements are applied.

  20. Segmentation of kidney using C-V model and anatomy priors

    Science.gov (United States)

    Lu, Jinghua; Chen, Jie; Zhang, Juan; Yang, Wenjia

    2007-12-01

    This paper presents an approach for kidney segmentation on abdominal CT images as the first step of a virtual reality surgery system. Segmentation for medical images is often challenging because of the objects' complicated anatomical structures, various gray levels, and unclear edges. A coarse to fine approach has been applied in the kidney segmentation using Chan-Vese model (C-V model) and anatomy prior knowledge. In pre-processing stage, the candidate kidney regions are located. Then C-V model formulated by level set method is applied in these smaller ROI, which can reduce the calculation complexity to a certain extent. At last, after some mathematical morphology procedures, the specified kidney structures have been extracted interactively with prior knowledge. The satisfying results on abdominal CT series show that the proposed approach keeps all the advantages of C-V model and overcome its disadvantages.

  1. Automatic segmentation of mandible in panoramic x-ray

    OpenAIRE

    Abdi, Amir Hossein; Kasaei, Shohreh; Mehdizadeh, Mojdeh

    2015-01-01

    As the panoramic x-ray is the most common extraoral radiography in dentistry, segmentation of its anatomical structures facilitates diagnosis and registration of dental records. This study presents a fast and accurate method for automatic segmentation of mandible in panoramic x-rays. In the proposed four-step algorithm, a superior border is extracted through horizontal integral projections. A modified Canny edge detector accompanied by morphological operators extracts the inferior border of t...

  2. Automatic tissue image segmentation based on image processing and deep learning

    Science.gov (United States)

    Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

    2018-02-01

    Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies or other novel imaging technologies. Plus, image segmentation also provides detailed structure description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation method. Here we used image enhancement, operators, and morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in a deep learning way. We also introduced parallel computing. Such approaches greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. Our results can be used as a criteria when diagnosing diseases such as cerebral atrophy, which is caused by pathological changes in gray matter or white matter. We demonstrated the great potential of such image processing and deep leaning combined automatic tissue image segmentation in personalized medicine, especially in monitoring, and treatments.

  3. Automatic segmentation of time-lapse microscopy images depicting a live Dharma embryo.

    Science.gov (United States)

    Zacharia, Eleni; Bondesson, Maria; Riu, Anne; Ducharme, Nicole A; Gustafsson, Jan-Åke; Kakadiaris, Ioannis A

    2011-01-01

    Biological inferences about the toxicity of chemicals reached during experiments on the zebrafish Dharma embryo can be greatly affected by the analysis of the time-lapse microscopy images depicting the embryo. Among the stages of image analysis, automatic and accurate segmentation of the Dharma embryo is the most crucial and challenging. In this paper, an accurate and automatic segmentation approach for the segmentation of the Dharma embryo data obtained by fluorescent time-lapse microscopy is proposed. Experiments performed in four stacks of 3D images over time have shown promising results.

  4. Segmentation of human skull in MRI using statistical shape information from CT data.

    Science.gov (United States)

    Wang, Defeng; Shi, Lin; Chu, Winnie C W; Cheng, Jack C Y; Heng, Pheng Ann

    2009-09-01

    To automatically segment the skull from the MRI data using a model-based three-dimensional segmentation scheme. This study exploited the statistical anatomy extracted from the CT data of a group of subjects by means of constructing an active shape model of the skull surfaces. To construct a reliable shape model, a novel approach was proposed to optimize the automatic landmarking on the coupled surfaces (i.e., the skull vault) by minimizing the description length that incorporated local thickness information. This model was then used to locate the skull shape in MRI of a different group of patients. Compared with performing landmarking separately on the coupled surfaces, the proposed landmarking method constructed models that had better generalization ability and specificity. The segmentation accuracies were measured by the Dice coefficient and the set difference, and compared with the method based on mathematical morphology operations. The proposed approach using the active shape model based on the statistical skull anatomy presented in the head CT data contributes to more reliable segmentation of the skull from MRI data.

  5. Automatic segmentation of the heart in radiotherapy for breast cancer

    DEFF Research Database (Denmark)

    Laugaard Lorenzen, Ebbe; Ewertz, Marianne; Brink, Carsten

    2014-01-01

    Background. The aim of this study was to evaluate two fully automatic segmentation methods in comparison with manual delineations for their use in delineating the heart on planning computed tomography (CT) used in radiotherapy for breast cancer. Material and methods. Automatic delineation of heart...... in 15 breast cancer patients was performed by two different automatic delineation systems. Analysis of accuracy and precision of the differences between manual and automatic delineations were evaluated on volume, mean dose, maximum dose and spatial distance differences. Two sets of manual delineations...

  6. Robust automatic high resolution segmentation of SOFC anode porosity in 3D

    DEFF Research Database (Denmark)

    Jørgensen, Peter Stanley; Bowen, Jacob R.

    2008-01-01

    Routine use of 3D characterization of SOFCs by focused ion beam (FIB) serial sectioning is generally restricted by the time consuming task of manually delineating structures within each image slice. We apply advanced image analysis algorithms to automatically segment the porosity phase of an SOFC...... anode in 3D. The technique is based on numerical approximations to partial differential equations to evolve a 3D surface to the desired phase boundary. Vector fields derived from the experimentally acquired data are used as the driving force. The automatic segmentation compared to manual delineation...... reveals and good correspondence and the two approaches are quantitatively compared. It is concluded that the. automatic approach is more robust, more reproduceable and orders of magnitude quicker than manual segmentation of SOFC anode porosity for subsequent quantitative 3D analysis. Lastly...

  7. Application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint.

    Science.gov (United States)

    Liukkonen, Mimmi K; Mononen, Mika E; Tanska, Petri; Saarakkala, Simo; Nieminen, Miika T; Korhonen, Rami K

    2017-10-01

    Manual segmentation of articular cartilage from knee joint 3D magnetic resonance images (MRI) is a time consuming and laborious task. Thus, automatic methods are needed for faster and reproducible segmentations. In the present study, we developed a semi-automatic segmentation method based on radial intensity profiles to generate 3D geometries of knee joint cartilage which were then used in computational biomechanical models of the knee joint. Six healthy volunteers were imaged with a 3T MRI device and their knee cartilages were segmented both manually and semi-automatically. The values of cartilage thicknesses and volumes produced by these two methods were compared. Furthermore, the influences of possible geometrical differences on cartilage stresses and strains in the knee were evaluated with finite element modeling. The semi-automatic segmentation and 3D geometry construction of one knee joint (menisci, femoral and tibial cartilages) was approximately two times faster than with manual segmentation. Differences in cartilage thicknesses, volumes, contact pressures, stresses, and strains between segmentation methods in femoral and tibial cartilage were mostly insignificant (p > 0.05) and random, i.e. there were no systematic differences between the methods. In conclusion, the devised semi-automatic segmentation method is a quick and accurate way to determine cartilage geometries; it may become a valuable tool for biomechanical modeling applications with large patient groups.

  8. Comparison of acute and chronic traumatic brain injury using semi-automatic multimodal segmentation of MR volumes.

    Science.gov (United States)

    Irimia, Andrei; Chambers, Micah C; Alger, Jeffry R; Filippou, Maria; Prastawa, Marcel W; Wang, Bo; Hovda, David A; Gerig, Guido; Toga, Arthur W; Kikinis, Ron; Vespa, Paul M; Van Horn, John D

    2011-11-01

    Although neuroimaging is essential for prompt and proper management of traumatic brain injury (TBI), there is a regrettable and acute lack of robust methods for the visualization and assessment of TBI pathophysiology, especially for of the purpose of improving clinical outcome metrics. Until now, the application of automatic segmentation algorithms to TBI in a clinical setting has remained an elusive goal because existing methods have, for the most part, been insufficiently robust to faithfully capture TBI-related changes in brain anatomy. This article introduces and illustrates the combined use of multimodal TBI segmentation and time point comparison using 3D Slicer, a widely-used software environment whose TBI data processing solutions are openly available. For three representative TBI cases, semi-automatic tissue classification and 3D model generation are performed to perform intra-patient time point comparison of TBI using multimodal volumetrics and clinical atrophy measures. Identification and quantitative assessment of extra- and intra-cortical bleeding, lesions, edema, and diffuse axonal injury are demonstrated. The proposed tools allow cross-correlation of multimodal metrics from structural imaging (e.g., structural volume, atrophy measurements) with clinical outcome variables and other potential factors predictive of recovery. In addition, the workflows described are suitable for TBI clinical practice and patient monitoring, particularly for assessing damage extent and for the measurement of neuroanatomical change over time. With knowledge of general location, extent, and degree of change, such metrics can be associated with clinical measures and subsequently used to suggest viable treatment options.

  9. Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images.

    Science.gov (United States)

    Chiu, Stephanie J; Izatt, Joseph A; O'Connell, Rachelle V; Winter, Katrina P; Toth, Cynthia A; Farsiu, Sina

    2012-01-05

    To automatically segment retinal spectral domain optical coherence tomography (SD-OCT) images of eyes with age-related macular degeneration (AMD) and various levels of image quality to advance the study of retinal pigment epithelium (RPE)+drusen complex (RPEDC) volume changes indicative of AMD progression. A general segmentation framework based on graph theory and dynamic programming was used to segment three retinal boundaries in SD-OCT images of eyes with drusen and geographic atrophy (GA). A validation study for eyes with nonneovascular AMD was conducted, forming subgroups based on scan quality and presence of GA. To test for accuracy, the layer thickness results from two certified graders were compared against automatic segmentation results for 220 B-scans across 20 patients. For reproducibility, automatic layer volumes were compared that were generated from 0° versus 90° scans in five volumes with drusen. The mean differences in the measured thicknesses of the total retina and RPEDC layers were 4.2 ± 2.8 and 3.2 ± 2.6 μm for automatic versus manual segmentation. When the 0° and 90° datasets were compared, the mean differences in the calculated total retina and RPEDC volumes were 0.28% ± 0.28% and 1.60% ± 1.57%, respectively. The average segmentation time per image was 1.7 seconds automatically versus 3.5 minutes manually. The automatic algorithm accurately and reproducibly segmented three retinal boundaries in images containing drusen and GA. This automatic approach can reduce time and labor costs and yield objective measurements that potentially reveal quantitative RPE changes in longitudinal clinical AMD studies. (ClinicalTrials.gov number, NCT00734487.).

  10. Automatic segmentation and 3-dimensional display based on the knowledge of head MRI images

    International Nuclear Information System (INIS)

    Suzuki, Hidetomo; Toriwaki, Jun-ichiro.

    1987-01-01

    In this paper we present a procedure which automatically extracts soft tissues, such as subcutaneous fat, brain, and cerebral ventricle, from the multislice MRI images of head region, and displays their 3-dimensional images. Segmentation of soft tissues is done by use of an iterative thresholding. In order to select the optimum threshold value automatically, we introduce a measure to evaluate the goodness of segmentation into this procedure. When the measure satisfies given conditions, iteration of thresholding terminates, and the final result of segmentation is extracted by using the current threshold value. Since this procedure can execute segmentation and calculation of the goodness measure in each slice automatically, it remarkably decreases efforts of users. Moreover, the 3-dimensional display of the segmented tissues shows that this procedure can extract the shape of each soft tissue with reasonable precision for clinical use. (author)

  11. Automatic short axis orientation of the left ventricle in 3D ultrasound recordings

    Science.gov (United States)

    Pedrosa, João.; Heyde, Brecht; Heeren, Laurens; Engvall, Jan; Zamorano, Jose; Papachristidis, Alexandros; Edvardsen, Thor; Claus, Piet; D'hooge, Jan

    2016-04-01

    The recent advent of three-dimensional echocardiography has led to an increased interest from the scientific community in left ventricle segmentation frameworks for cardiac volume and function assessment. An automatic orientation of the segmented left ventricular mesh is an important step to obtain a point-to-point correspondence between the mesh and the cardiac anatomy. Furthermore, this would allow for an automatic division of the left ventricle into the standard 17 segments and, thus, fully automatic per-segment analysis, e.g. regional strain assessment. In this work, a method for fully automatic short axis orientation of the segmented left ventricle is presented. The proposed framework aims at detecting the inferior right ventricular insertion point. 211 three-dimensional echocardiographic images were used to validate this framework by comparison to manual annotation of the inferior right ventricular insertion point. A mean unsigned error of 8, 05° +/- 18, 50° was found, whereas the mean signed error was 1, 09°. Large deviations between the manual and automatic annotations (> 30°) only occurred in 3, 79% of cases. The average computation time was 666ms in a non-optimized MATLAB environment, which potentiates real-time application. In conclusion, a successful automatic real-time method for orientation of the segmented left ventricle is proposed.

  12. Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks.

    Science.gov (United States)

    Ma, Jinlian; Wu, Fa; Jiang, Tian'an; Zhao, Qiyu; Kong, Dexing

    2017-11-01

    Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background. In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images. Our CNN-based method formulates a thyroid nodule segmentation problem as a patch classification task, where the relationship among patches is ignored. Specifically, the CNN used image patches from images of normal thyroids and thyroid nodules as inputs and then generated the segmentation probability maps as outputs. A multi-view strategy is used to improve the performance of the CNN-based model. Additionally, we compared the performance of our approach with that of the commonly used segmentation methods on the same dataset. The experimental results suggest that our proposed method outperforms prior methods on thyroid nodule segmentation. Moreover, the results show that the CNN-based model is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. In detail, our CNN-based model can achieve an average of the overlap metric, dice ratio, true positive rate, false positive rate, and modified Hausdorff distance as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] on overall folds, respectively. Our proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time-consuming and tedious manual segmentation approach, demonstrating the potential clinical applications.

  13. Automatic identification of inertial sensor placement on human body segments during walking

    NARCIS (Netherlands)

    Weenk, D.; van Beijnum, Bernhard J.F.; Baten, Christian T.M.; Hermens, Hermanus J.; Veltink, Petrus H.

    2013-01-01

    We present a novel method for the automatic identification of inertial sensors on human body segments during walking. This method allows the user to place (wireless) inertial sensors on arbitrary body segments. Next, the user walks for just a few seconds and the segment to which each sensor is

  14. Automatic Cell Segmentation in Fluorescence Images of Confluent Cell Monolayers Using Multi-object Geometric Deformable Model.

    Science.gov (United States)

    Yang, Zhen; Bogovic, John A; Carass, Aaron; Ye, Mao; Searson, Peter C; Prince, Jerry L

    2013-03-13

    With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in fluorescence images of confluent cell monolayers. This method addresses several challenges through a combination of ideas. 1) It realizes a fully automatic segmentation process by first detecting the cell nuclei as initial seeds and then using a multi-object geometric deformable model (MGDM) for final segmentation. 2) To deal with different defects in the fluorescence images, the cell junctions are enhanced by applying an order-statistic filter and principal curvature based image operator. 3) The final segmentation using MGDM promotes robust and accurate segmentation results, and guarantees no overlaps and gaps between neighboring cells. The automatic segmentation results are compared with manually delineated cells, and the average Dice coefficient over all distinguishable cells is 0.88.

  15. Automatic and manual segmentation of healthy retinas using high-definition optical coherence tomography.

    Science.gov (United States)

    Golbaz, Isabelle; Ahlers, Christian; Goesseringer, Nina; Stock, Geraldine; Geitzenauer, Wolfgang; Prünte, Christian; Schmidt-Erfurth, Ursula Margarethe

    2011-03-01

    This study compared automatic- and manual segmentation modalities in the retina of healthy eyes using high-definition optical coherence tomography (HD-OCT). Twenty retinas in 20 healthy individuals were examined using an HD-OCT system (Carl Zeiss Meditec, Inc.). Three-dimensional imaging was performed with an axial resolution of 6 μm at a maximum scanning speed of 25,000 A-scans/second. Volumes of 6 × 6 × 2 mm were scanned. Scans were analysed using a matlab-based algorithm and a manual segmentation software system (3D-Doctor). The volume values calculated by the two methods were compared. Statistical analysis revealed a high correlation between automatic and manual modes of segmentation. The automatic mode of measuring retinal volume and the corresponding three-dimensional images provided similar results to the manual segmentation procedure. Both methods were able to visualize retinal and subretinal features accurately. This study compared two methods of assessing retinal volume using HD-OCT scans in healthy retinas. Both methods were able to provide realistic volumetric data when applied to raster scan sets. Manual segmentation methods represent an adequate tool with which to control automated processes and to identify clinically relevant structures, whereas automatic procedures will be needed to obtain data in larger patient populations. © 2009 The Authors. Journal compilation © 2009 Acta Ophthalmol.

  16. Evaluating automatic laughter segmentation in meetings using acoustic and acoustic-phonetic features

    NARCIS (Netherlands)

    Truong, K.P.; Leeuwen, D.A. van

    2007-01-01

    In this study, we investigated automatic laughter segmentation in meetings. We first performed laughterspeech discrimination experiments with traditional spectral features and subsequently used acousticphonetic features. In segmentation, we used Gaussian Mixture Models that were trained with

  17. Automatic segmentation of myocardium at risk from contrast enhanced SSFP CMR: validation against expert readers and SPECT

    International Nuclear Information System (INIS)

    Tufvesson, Jane; Carlsson, Marcus; Aletras, Anthony H.; Engblom, Henrik; Deux, Jean-François; Koul, Sasha; Sörensson, Peder; Pernow, John; Atar, Dan; Erlinge, David; Arheden, Håkan; Heiberg, Einar

    2016-01-01

    Efficacy of reperfusion therapy can be assessed as myocardial salvage index (MSI) by determining the size of myocardium at risk (MaR) and myocardial infarction (MI), (MSI = 1-MI/MaR). Cardiovascular magnetic resonance (CMR) can be used to assess MI by late gadolinium enhancement (LGE) and MaR by either T2-weighted imaging or contrast enhanced SSFP (CE-SSFP). Automatic segmentation algorithms have been developed and validated for MI by LGE as well as for MaR by T2-weighted imaging. There are, however, no algorithms available for CE-SSFP. Therefore, the aim of this study was to develop and validate automatic segmentation of MaR in CE-SSFP. The automatic algorithm applies surface coil intensity correction and classifies myocardial intensities by Expectation Maximization to define a MaR region based on a priori regional criteria, and infarct region from LGE. Automatic segmentation was validated against manual delineation by expert readers in 183 patients with reperfused acute MI from two multi-center randomized clinical trials (RCT) (CHILL-MI and MITOCARE) and against myocardial perfusion SPECT in an additional set (n = 16). Endocardial and epicardial borders were manually delineated at end-diastole and end-systole. Manual delineation of MaR was used as reference and inter-observer variability was assessed for both manual delineation and automatic segmentation of MaR in a subset of patients (n = 15). MaR was expressed as percent of left ventricular mass (%LVM) and analyzed by bias (mean ± standard deviation). Regional agreement was analyzed by Dice Similarity Coefficient (DSC) (mean ± standard deviation). MaR assessed by manual and automatic segmentation were 36 ± 10 % and 37 ± 11 %LVM respectively with bias 1 ± 6 %LVM and regional agreement DSC 0.85 ± 0.08 (n = 183). MaR assessed by SPECT and CE-SSFP automatic segmentation were 27 ± 10 %LVM and 29 ± 7 %LVM respectively with bias 2 ± 7 %LVM. Inter-observer variability was 0 ± 3 %LVM for manual delineation and

  18. Interactive vs. automatic ultrasound image segmentation methods for staging hepatic lipidosis.

    NARCIS (Netherlands)

    Weijers, G.; Starke, A.; Haudum, A.; Thijssen, J.M.; Rehage, J.; Korte, C.L. de

    2010-01-01

    The aim of this study was to test the hypothesis that automatic segmentation of vessels in ultrasound (US) images can produce similar or better results in grading fatty livers than interactive segmentation. A study was performed in postpartum dairy cows (N=151), as an animal model of human fatty

  19. A framework for automatic segmentation in three dimensions of microstructural tomography data

    DEFF Research Database (Denmark)

    Jørgensen, Peter Stanley; Hansen, Karin Vels; Larsen, Rasmus

    2010-01-01

    Routine use of quantitative three dimensional analysis of material microstructure by in particular, focused ion beam (FIB) serial sectioning is generally restricted by the time consuming task of manually delineating structures within each image slice or the quality of manual and automatic...... segmentation schemes. We present here a framework for performing automatic segmentation of complex microstructures using a level set method. The technique is based on numerical approximations to partial differential equations to evolve a 3D surface to capture the phase boundaries. Vector fields derived from...

  20. Fully automatic registration and segmentation of first-pass myocardial perfusion MR image sequences.

    Science.gov (United States)

    Gupta, Vikas; Hendriks, Emile A; Milles, Julien; van der Geest, Rob J; Jerosch-Herold, Michael; Reiber, Johan H C; Lelieveldt, Boudewijn P F

    2010-11-01

    Derivation of diagnostically relevant parameters from first-pass myocardial perfusion magnetic resonance images involves the tedious and time-consuming manual segmentation of the myocardium in a large number of images. To reduce the manual interaction and expedite the perfusion analysis, we propose an automatic registration and segmentation method for the derivation of perfusion linked parameters. A complete automation was accomplished by first registering misaligned images using a method based on independent component analysis, and then using the registered data to automatically segment the myocardium with active appearance models. We used 18 perfusion studies (100 images per study) for validation in which the automatically obtained (AO) contours were compared with expert drawn contours on the basis of point-to-curve error, Dice index, and relative perfusion upslope in the myocardium. Visual inspection revealed successful segmentation in 15 out of 18 studies. Comparison of the AO contours with expert drawn contours yielded 2.23 ± 0.53 mm and 0.91 ± 0.02 as point-to-curve error and Dice index, respectively. The average difference between manually and automatically obtained relative upslope parameters was found to be statistically insignificant (P = .37). Moreover, the analysis time per slice was reduced from 20 minutes (manual) to 1.5 minutes (automatic). We proposed an automatic method that significantly reduced the time required for analysis of first-pass cardiac magnetic resonance perfusion images. The robustness and accuracy of the proposed method were demonstrated by the high spatial correspondence and statistically insignificant difference in perfusion parameters, when AO contours were compared with expert drawn contours. Copyright © 2010 AUR. Published by Elsevier Inc. All rights reserved.

  1. Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

    Science.gov (United States)

    Wang, Jun Yi; Ngo, Michael M; Hessl, David; Hagerman, Randi J; Rivera, Susan M

    2016-01-01

    Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as

  2. Automatic anatomy recognition on CT images with pathology

    Science.gov (United States)

    Huang, Lidong; Udupa, Jayaram K.; Tong, Yubing; Odhner, Dewey; Torigian, Drew A.

    2016-03-01

    Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.

  3. Semi-automatic watershed medical image segmentation methods for customized cancer radiation treatment planning simulation

    International Nuclear Information System (INIS)

    Kum Oyeon; Kim Hye Kyung; Max, N.

    2007-01-01

    A cancer radiation treatment planning simulation requires image segmentation to define the gross tumor volume, clinical target volume, and planning target volume. Manual segmentation, which is usual in clinical settings, depends on the operator's experience and may, in addition, change for every trial by the same operator. To overcome this difficulty, we developed semi-automatic watershed medical image segmentation tools using both the top-down watershed algorithm in the insight segmentation and registration toolkit (ITK) and Vincent-Soille's bottom-up watershed algorithm with region merging. We applied our algorithms to segment two- and three-dimensional head phantom CT data and to find pixel (or voxel) numbers for each segmented area, which are needed for radiation treatment optimization. A semi-automatic method is useful to avoid errors incurred by both human and machine sources, and provide clear and visible information for pedagogical purpose. (orig.)

  4. Automatic lung segmentation using control feedback system: morphology and texture paradigm.

    Science.gov (United States)

    Noor, Norliza M; Than, Joel C M; Rijal, Omar M; Kassim, Rosminah M; Yunus, Ashari; Zeki, Amir A; Anzidei, Michele; Saba, Luca; Suri, Jasjit S

    2015-03-01

    Interstitial Lung Disease (ILD) encompasses a wide array of diseases that share some common radiologic characteristics. When diagnosing such diseases, radiologists can be affected by heavy workload and fatigue thus decreasing diagnostic accuracy. Automatic segmentation is the first step in implementing a Computer Aided Diagnosis (CAD) that will help radiologists to improve diagnostic accuracy thereby reducing manual interpretation. Automatic segmentation proposed uses an initial thresholding and morphology based segmentation coupled with feedback that detects large deviations with a corrective segmentation. This feedback is analogous to a control system which allows detection of abnormal or severe lung disease and provides a feedback to an online segmentation improving the overall performance of the system. This feedback system encompasses a texture paradigm. In this study we studied 48 males and 48 female patients consisting of 15 normal and 81 abnormal patients. A senior radiologist chose the five levels needed for ILD diagnosis. The results of segmentation were displayed by showing the comparison of the automated and ground truth boundaries (courtesy of ImgTracer™ 1.0, AtheroPoint™ LLC, Roseville, CA, USA). The left lung's performance of segmentation was 96.52% for Jaccard Index and 98.21% for Dice Similarity, 0.61 mm for Polyline Distance Metric (PDM), -1.15% for Relative Area Error and 4.09% Area Overlap Error. The right lung's performance of segmentation was 97.24% for Jaccard Index, 98.58% for Dice Similarity, 0.61 mm for PDM, -0.03% for Relative Area Error and 3.53% for Area Overlap Error. The segmentation overall has an overall similarity of 98.4%. The segmentation proposed is an accurate and fully automated system.

  5. Automatic speech signal segmentation based on the innovation adaptive filter

    Directory of Open Access Journals (Sweden)

    Makowski Ryszard

    2014-06-01

    Full Text Available Speech segmentation is an essential stage in designing automatic speech recognition systems and one can find several algorithms proposed in the literature. It is a difficult problem, as speech is immensely variable. The aim of the authors’ studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi et al., (2006, and it is a modified version of Brandt’s algorithm. The article presents a new algorithm for unsupervised automatic speech signal segmentation. It performs segmentation without access to information about the phonetic content of the utterances, relying exclusively on second-order statistics of a speech signal. The starting point for the proposed method is time-varying Schur coefficients of an innovation adaptive filter. The Schur algorithm is known to be fast, precise, stable and capable of rapidly tracking changes in second order signal statistics. A transfer from one phoneme to another in the speech signal always indicates a change in signal statistics caused by vocal track changes. In order to allow for the properties of human hearing, detection of inter-phoneme boundaries is performed based on statistics defined on the mel spectrum determined from the reflection coefficients. The paper presents the structure of the algorithm, defines its properties, lists parameter values, describes detection efficiency results, and compares them with those for another algorithm. The obtained segmentation results, are satisfactory.

  6. Dosimetric Evaluation of Automatic Segmentation for Adaptive IMRT for Head-and-Neck Cancer

    International Nuclear Information System (INIS)

    Tsuji, Stuart Y.; Hwang, Andrew; Weinberg, Vivian; Yom, Sue S.; Quivey, Jeanne M.; Xia Ping

    2010-01-01

    Purpose: Adaptive planning to accommodate anatomic changes during treatment requires repeat segmentation. This study uses dosimetric endpoints to assess automatically deformed contours. Methods and Materials: Sixteen patients with head-and-neck cancer had adaptive plans because of anatomic change during radiotherapy. Contours from the initial planning computed tomography (CT) were deformed to the mid-treatment CT using an intensity-based free-form registration algorithm then compared with the manually drawn contours for the same CT using the Dice similarity coefficient and an overlap index. The automatic contours were used to create new adaptive plans. The original and automatic adaptive plans were compared based on dosimetric outcomes of the manual contours and on plan conformality. Results: Volumes from the manual and automatic segmentation were similar; only the gross tumor volume (GTV) was significantly different. Automatic plans achieved lower mean coverage for the GTV: V95: 98.6 ± 1.9% vs. 89.9 ± 10.1% (p = 0.004) and clinical target volume: V95: 98.4 ± 0.8% vs. 89.8 ± 6.2% (p 3 of the spinal cord 39.9 ± 3.7 Gy vs. 42.8 ± 5.4 Gy (p = 0.034), but no difference for the remaining structures. Conclusions: Automatic segmentation is not robust enough to substitute for physician-drawn volumes, particularly for the GTV. However, it generates normal structure contours of sufficient accuracy when assessed by dosimetric end points.

  7. Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles

    Science.gov (United States)

    Li, Ke; Ye, Chuyang; Yang, Zhen; Carass, Aaron; Ying, Sarah H.; Prince, Jerry L.

    2016-03-01

    Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method—supervised classification—was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers—linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)—were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.

  8. Do Three-dimensional Visualization and Three-dimensional Printing Improve Hepatic Segment Anatomy Teaching? A Randomized Controlled Study.

    Science.gov (United States)

    Kong, Xiangxue; Nie, Lanying; Zhang, Huijian; Wang, Zhanglin; Ye, Qiang; Tang, Lei; Li, Jianyi; Huang, Wenhua

    2016-01-01

    Hepatic segment anatomy is difficult for medical students to learn. Three-dimensional visualization (3DV) is a useful tool in anatomy teaching, but current models do not capture haptic qualities. However, three-dimensional printing (3DP) can produce highly accurate complex physical models. Therefore, in this study we aimed to develop a novel 3DP hepatic segment model and compare the teaching effectiveness of a 3DV model, a 3DP model, and a traditional anatomical atlas. A healthy candidate (female, 50-years old) was recruited and scanned with computed tomography. After three-dimensional (3D) reconstruction, the computed 3D images of the hepatic structures were obtained. The parenchyma model was divided into 8 hepatic segments to produce the 3DV hepatic segment model. The computed 3DP model was designed by removing the surrounding parenchyma and leaving the segmental partitions. Then, 6 experts evaluated the 3DV and 3DP models using a 5-point Likert scale. A randomized controlled trial was conducted to evaluate the educational effectiveness of these models compared with that of the traditional anatomical atlas. The 3DP model successfully displayed the hepatic segment structures with partitions. All experts agreed or strongly agreed that the 3D models provided good realism for anatomical instruction, with no significant differences between the 3DV and 3DP models in each index (p > 0.05). Additionally, the teaching effects show that the 3DV and 3DP models were significantly better than traditional anatomical atlas in the first and second examinations (p < 0.05). Between the first and second examinations, only the traditional method group had significant declines (p < 0.05). A novel 3DP hepatic segment model was successfully developed. Both the 3DV and 3DP models could improve anatomy teaching significantly. Copyright © 2015 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

  9. Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution

    Directory of Open Access Journals (Sweden)

    I. Cruz-Aceves

    2013-01-01

    Full Text Available This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.

  10. Automatic segmentation of liver structure in CT images

    International Nuclear Information System (INIS)

    Bae, K.T.; Giger, M.L.; Chen, C.; Kahn, C.E. Jr.

    1993-01-01

    The segmentation and three-dimensional representation of the liver from a computed tomography (CT) scan is an important step in many medical applications, such as in the surgical planning for a living-donor liver transplant and in the automatic detection and documentation of pathological states. A method is being developed to automatically extract liver structure from abdominal CT scans using a priori information about liver morphology and digital image-processing techniques. Segmentation is performed sequentially image-by-image (slice-by-slice), starting with a reference image in which the liver occupies almost the entire right half of the abdomen cross section. Image processing techniques include gray-level thresholding, Gaussian smoothing, and eight-point connectivity tracking. For each case, the shape, size, and pixel density distribution of the liver are recorded for each CT image and used in the processing of other CT images. Extracted boundaries of the liver are smoothed using mathematical morphology techniques and B-splines. Computer-determined boundaries were compared with those drawn by a radiologist. The boundary descriptions from the two methods were in agreement, and the calculated areas were within 10%

  11. Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

    Directory of Open Access Journals (Sweden)

    Jun Yi Wang

    Full Text Available Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation to 0.978 (for SegAdapter-corrected segmentation for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large

  12. User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy.

    Science.gov (United States)

    Ramkumar, Anjana; Dolz, Jose; Kirisli, Hortense A; Adebahr, Sonja; Schimek-Jasch, Tanja; Nestle, Ursula; Massoptier, Laurent; Varga, Edit; Stappers, Pieter Jan; Niessen, Wiro J; Song, Yu

    2016-04-01

    Accurate segmentation of organs at risk is an important step in radiotherapy planning. Manual segmentation being a tedious procedure and prone to inter- and intra-observer variability, there is a growing interest in automated segmentation methods. However, automatic methods frequently fail to provide satisfactory result, and post-processing corrections are often needed. Semi-automatic segmentation methods are designed to overcome these problems by combining physicians' expertise and computers' potential. This study evaluates two semi-automatic segmentation methods with different types of user interactions, named the "strokes" and the "contour", to provide insights into the role and impact of human-computer interaction. Two physicians participated in the experiment. In total, 42 case studies were carried out on five different types of organs at risk. For each case study, both the human-computer interaction process and quality of the segmentation results were measured subjectively and objectively. Furthermore, different measures of the process and the results were correlated. A total of 36 quantifiable and ten non-quantifiable correlations were identified for each type of interaction. Among those pairs of measures, 20 of the contour method and 22 of the strokes method were strongly or moderately correlated, either directly or inversely. Based on those correlated measures, it is concluded that: (1) in the design of semi-automatic segmentation methods, user interactions need to be less cognitively challenging; (2) based on the observed workflows and preferences of physicians, there is a need for flexibility in the interface design; (3) the correlated measures provide insights that can be used in improving user interaction design.

  13. Another Method of Building 2D Entropy to Realize Automatic Segmentation

    International Nuclear Information System (INIS)

    Zhang, Y F; Zhang, Y

    2006-01-01

    2D entropy formed during the process of building 2D histogram can realize automatic segmentation. Traditional method utilizes central pixel grey value and the others or all of pixels grey mean value in 4-neighbor to build 2D histogram. In fact, the change of the greyscale value between two ''invariable position vectors'' cannot represent the total characteristics among neighbour pixels very well. A new method is proposed which makes use of minimum grey value in the 4-neighbor and of maximum grey value in the 3x3 neighbour except pixels of the 4-neighbor. New method and traditional one are used in contrast to realize image automatic segmentation. The experimental results of the classical image prove the new method is effective

  14. Comparison of human and automatic segmentations of kidneys from CT images

    International Nuclear Information System (INIS)

    Rao, Manjori; Stough, Joshua; Chi, Y.-Y.; Muller, Keith; Tracton, Gregg; Pizer, Stephen M.; Chaney, Edward L.

    2005-01-01

    Purpose: A controlled observer study was conducted to compare a method for automatic image segmentation with conventional user-guided segmentation of right and left kidneys from planning computerized tomographic (CT) images. Methods and materials: Deformable shape models called m-reps were used to automatically segment right and left kidneys from 12 target CT images, and the results were compared with careful manual segmentations performed by two human experts. M-rep models were trained based on manual segmentations from a collection of images that did not include the targets. Segmentation using m-reps began with interactive initialization to position the kidney model over the target kidney in the image data. Fully automatic segmentation proceeded through two stages at successively smaller spatial scales. At the first stage, a global similarity transformation of the kidney model was computed to position the model closer to the target kidney. The similarity transformation was followed by large-scale deformations based on principal geodesic analysis (PGA). During the second stage, the medial atoms comprising the m-rep model were deformed one by one. This procedure was iterated until no changes were observed. The transformations and deformations at both stages were driven by optimizing an objective function with two terms. One term penalized the currently deformed m-rep by an amount proportional to its deviation from the mean m-rep derived from PGA of the training segmentations. The second term computed a model-to-image match term based on the goodness of match of the trained intensity template for the currently deformed m-rep with the corresponding intensity data in the target image. Human and m-rep segmentations were compared using quantitative metrics provided in a toolset called Valmet. Metrics reported in this article include (1) percent volume overlap; (2) mean surface distance between two segmentations; and (3) maximum surface separation (Hausdorff distance

  15. Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach.

    Science.gov (United States)

    Avendi, Michael R; Kheradvar, Arash; Jafarkhani, Hamid

    2017-12-01

    This study aims to accurately segment the right ventricle (RV) from cardiac MRI using a fully automatic learning-based method. The proposed method uses deep learning algorithms, i.e., convolutional neural networks and stacked autoencoders, for automatic detection and initial segmentation of the RV chamber. The initial segmentation is then combined with the deformable models to improve the accuracy and robustness of the process. We trained our algorithm using 16 cardiac MRI datasets of the MICCAI 2012 RV Segmentation Challenge database and validated our technique using the rest of the dataset (32 subjects). An average Dice metric of 82.5% along with an average Hausdorff distance of 7.85 mm were achieved for all the studied subjects. Furthermore, a high correlation and level of agreement with the ground truth contours for end-diastolic volume (0.98), end-systolic volume (0.99), and ejection fraction (0.93) were observed. Our results show that deep learning algorithms can be effectively used for automatic segmentation of the RV. Computed quantitative metrics of our method outperformed that of the existing techniques participated in the MICCAI 2012 challenge, as reported by the challenge organizers. Magn Reson Med 78:2439-2448, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  16. Automatic segmentation of thoracic and pelvic CT images for radiotherapy planning using implicit anatomic knowledge and organ-specific segmentation strategies

    International Nuclear Information System (INIS)

    Haas, B; Coradi, T; Scholz, M; Kunz, P; Huber, M; Oppitz, U; Andre, L; Lengkeek, V; Huyskens, D; Esch, A van; Reddick, R

    2008-01-01

    Automatic segmentation of anatomical structures in medical images is a valuable tool for efficient computer-aided radiotherapy and surgery planning and an enabling technology for dynamic adaptive radiotherapy. This paper presents the design, algorithms and validation of new software for the automatic segmentation of CT images used for radiotherapy treatment planning. A coarse to fine approach is followed that consists of presegmentation, anatomic orientation and structure segmentation. No user input or a priori information about the image content is required. In presegmentation, the body outline, the bones and lung equivalent tissue are detected. Anatomic orientation recognizes the patient's position, orientation and gender and creates an elastic mapping of the slice positions to a reference scale. Structure segmentation is divided into localization, outlining and refinement, performed by procedures with implicit anatomic knowledge using standard image processing operations. The presented version of algorithms automatically segments the body outline and bones in any gender and patient position, the prostate, bladder and femoral heads for male pelvis in supine position, and the spinal canal, lungs, heart and trachea in supine position. The software was developed and tested on a collection of over 600 clinical radiotherapy planning CT stacks. In a qualitative validation on this test collection, anatomic orientation correctly detected gender, patient position and body region in 98% of the cases, a correct mapping was produced for 89% of thorax and 94% of pelvis cases. The average processing time for the entire segmentation of a CT stack was less than 1 min on a standard personal computer. Two independent retrospective studies were carried out for clinical validation. Study I was performed on 66 cases (30 pelvis, 36 thorax) with dosimetrists, study II on 52 cases (39 pelvis, 13 thorax) with radio-oncologists as experts. The experts rated the automatically produced

  17. Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images.

    Science.gov (United States)

    Tian, Jing; Marziliano, Pina; Baskaran, Mani; Tun, Tin Aung; Aung, Tin

    2013-03-01

    Enhanced Depth Imaging (EDI) optical coherence tomography (OCT) provides high-definition cross-sectional images of the choroid in vivo, and hence is used in many clinical studies. However, the quantification of the choroid depends on the manual labelings of two boundaries, Bruch's membrane and the choroidal-scleral interface. This labeling process is tedious and subjective of inter-observer differences, hence, automatic segmentation of the choroid layer is highly desirable. In this paper, we present a fast and accurate algorithm that could segment the choroid automatically. Bruch's membrane is detected by searching the pixel with the biggest gradient value above the retinal pigment epithelium (RPE) and the choroidal-scleral interface is delineated by finding the shortest path of the graph formed by valley pixels using Dijkstra's algorithm. The experiments comparing automatic segmentation results with the manual labelings are conducted on 45 EDI-OCT images and the average of Dice's Coefficient is 90.5%, which shows good consistency of the algorithm with the manual labelings. The processing time for each image is about 1.25 seconds.

  18. An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM).

    Science.gov (United States)

    Feng, Xiang; Deistung, Andreas; Dwyer, Michael G; Hagemeier, Jesper; Polak, Paul; Lebenberg, Jessica; Frouin, Frédérique; Zivadinov, Robert; Reichenbach, Jürgen R; Schweser, Ferdinand

    2017-06-01

    Accurate and robust segmentation of subcortical gray matter (SGM) nuclei is required in many neuroimaging applications. FMRIB's Integrated Registration and Segmentation Tool (FIRST) is one of the most popular software tools for automated subcortical segmentation based on T 1 -weighted (T1w) images. In this work, we demonstrate that FIRST tends to produce inaccurate SGM segmentation results in the case of abnormal brain anatomy, such as present in atrophied brains, due to a poor spatial match of the subcortical structures with the training data in the MNI space as well as due to insufficient contrast of SGM structures on T1w images. Consequently, such deviations from the average brain anatomy may introduce analysis bias in clinical studies, which may not always be obvious and potentially remain unidentified. To improve the segmentation of subcortical nuclei, we propose to use FIRST in combination with a special Hybrid image Contrast (HC) and Non-Linear (nl) registration module (HC-nlFIRST), where the hybrid image contrast is derived from T1w images and magnetic susceptibility maps to create subcortical contrast that is similar to that in the Montreal Neurological Institute (MNI) template. In our approach, a nonlinear registration replaces FIRST's default linear registration, yielding a more accurate alignment of the input data to the MNI template. We evaluated our method on 82 subjects with particularly abnormal brain anatomy, selected from a database of >2000 clinical cases. Qualitative and quantitative analyses revealed that HC-nlFIRST provides improved segmentation compared to the default FIRST method. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy

    NARCIS (Netherlands)

    A. Ramkumar (Anjana); J. Dolz (Jose); H.A. Kirisli (Hortense); S. Adebahr (Sonja); T. Schimek-Jasch (Tanja); U. Nestle (Ursula); L. Massoptier (Laurent); E. Varga (Edit); P.J. Stappers (P.); W.J. Niessen (Wiro); Y. Song (Yu)

    2016-01-01

    textabstractAccurate segmentation of organs at risk is an important step in radiotherapy planning. Manual segmentation being a tedious procedure and prone to inter- and intra-observer variability, there is a growing interest in automated segmentation methods. However, automatic methods frequently

  20. Automatic segmentation of mandible in panoramic x-ray.

    Science.gov (United States)

    Abdi, Amir Hossein; Kasaei, Shohreh; Mehdizadeh, Mojdeh

    2015-10-01

    As the panoramic x-ray is the most common extraoral radiography in dentistry, segmentation of its anatomical structures facilitates diagnosis and registration of dental records. This study presents a fast and accurate method for automatic segmentation of mandible in panoramic x-rays. In the proposed four-step algorithm, a superior border is extracted through horizontal integral projections. A modified Canny edge detector accompanied by morphological operators extracts the inferior border of the mandible body. The exterior borders of ramuses are extracted through a contour tracing method based on the average model of mandible. The best-matched template is fetched from the atlas of mandibles to complete the contour of left and right processes. The algorithm was tested on a set of 95 panoramic x-rays. Evaluating the results against manual segmentations of three expert dentists showed that the method is robust. It achieved an average performance of [Formula: see text] in Dice similarity, specificity, and sensitivity.

  1. User Interaction in Semi-Automatic Segmentation of Organs at Risk : A Case Study in Radiotherapy

    NARCIS (Netherlands)

    Ramkumar, A.; Dolz, J.; Kirisli, H.A.; Adebahr, S.; Schimek-Jasch, T.; Nestle, U.; Massoptier, L.; Varga, E.; Stappers, P.J.; Niessen, W.J.; Song, Y.

    2015-01-01

    Accurate segmentation of organs at risk is an important step in radiotherapy planning. Manual segmentation being a tedious procedure and prone to inter- and intra-observer variability, there is a growing interest in automated segmentation methods. However, automatic methods frequently fail to

  2. Automatic segmentation of brain MRIs and mapping neuroanatomy across the human lifespan

    Science.gov (United States)

    Keihaninejad, Shiva; Heckemann, Rolf A.; Gousias, Ioannis S.; Rueckert, Daniel; Aljabar, Paul; Hajnal, Joseph V.; Hammers, Alexander

    2009-02-01

    A robust model for the automatic segmentation of human brain images into anatomically defined regions across the human lifespan would be highly desirable, but such structural segmentations of brain MRI are challenging due to age-related changes. We have developed a new method, based on established algorithms for automatic segmentation of young adults' brains. We used prior information from 30 anatomical atlases, which had been manually segmented into 83 anatomical structures. Target MRIs came from 80 subjects (~12 individuals/decade) from 20 to 90 years, with equal numbers of men, women; data from two different scanners (1.5T, 3T), using the IXI database. Each of the adult atlases was registered to each target MR image. By using additional information from segmentation into tissue classes (GM, WM and CSF) to initialise the warping based on label consistency similarity before feeding this into the previous normalised mutual information non-rigid registration, the registration became robust enough to accommodate atrophy and ventricular enlargement with age. The final segmentation was obtained by combination of the 30 propagated atlases using decision fusion. Kernel smoothing was used for modelling the structural volume changes with aging. Example linear correlation coefficients with age were, for lateral ventricular volume, rmale=0.76, rfemale=0.58 and, for hippocampal volume, rmale=-0.6, rfemale=-0.4 (allρ<0.01).

  3. Multi-atlas-based automatic 3D segmentation for prostate brachytherapy in transrectal ultrasound images

    Science.gov (United States)

    Nouranian, Saman; Mahdavi, S. Sara; Spadinger, Ingrid; Morris, William J.; Salcudean, S. E.; Abolmaesumi, P.

    2013-03-01

    One of the commonly used treatment methods for early-stage prostate cancer is brachytherapy. The standard of care for planning this procedure is segmentation of contours from transrectal ultrasound (TRUS) images, which closely follow the prostate boundary. This process is currently performed either manually or using semi-automatic techniques. This paper introduces a fully automatic segmentation algorithm which uses a priori knowledge of contours in a reference data set of TRUS volumes. A non-parametric deformable registration method is employed to transform the atlas prostate contours to a target image coordinates. All atlas images are sorted based on their registration results and the highest ranked registration results are selected for decision fusion. A Simultaneous Truth and Performance Level Estimation algorithm is utilized to fuse labels from registered atlases and produce a segmented target volume. In this experiment, 50 patient TRUS volumes are obtained and a leave-one-out study on TRUS volumes is reported. We also compare our results with a state-of-the-art semi-automatic prostate segmentation method that has been clinically used for planning prostate brachytherapy procedures and we show comparable accuracy and precision within clinically acceptable runtime.

  4. Automatic coronary artery segmentation based on multi-domains remapping and quantile regression in angiographies.

    Science.gov (United States)

    Li, Zhixun; Zhang, Yingtao; Gong, Huiling; Li, Weimin; Tang, Xianglong

    2016-12-01

    Coronary artery disease has become the most dangerous diseases to human life. And coronary artery segmentation is the basis of computer aided diagnosis and analysis. Existing segmentation methods are difficult to handle the complex vascular texture due to the projective nature in conventional coronary angiography. Due to large amount of data and complex vascular shapes, any manual annotation has become increasingly unrealistic. A fully automatic segmentation method is necessary in clinic practice. In this work, we study a method based on reliable boundaries via multi-domains remapping and robust discrepancy correction via distance balance and quantile regression for automatic coronary artery segmentation of angiography images. The proposed method can not only segment overlapping vascular structures robustly, but also achieve good performance in low contrast regions. The effectiveness of our approach is demonstrated on a variety of coronary blood vessels compared with the existing methods. The overall segmentation performances si, fnvf, fvpf and tpvf were 95.135%, 3.733%, 6.113%, 96.268%, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Automatic Segmenting Structures in MRI's Based on Texture Analysis and Fuzzy Logic

    Science.gov (United States)

    Kaur, Mandeep; Rattan, Munish; Singh, Pushpinder

    2017-12-01

    The purpose of this paper is to present the variational method for geometric contours which helps the level set function remain close to the sign distance function, therefor it remove the need of expensive re-initialization procedure and thus, level set method is applied on magnetic resonance images (MRI) to track the irregularities in them as medical imaging plays a substantial part in the treatment, therapy and diagnosis of various organs, tumors and various abnormalities. It favors the patient with more speedy and decisive disease controlling with lesser side effects. The geometrical shape, the tumor's size and tissue's abnormal growth can be calculated by the segmentation of that particular image. It is still a great challenge for the researchers to tackle with an automatic segmentation in the medical imaging. Based on the texture analysis, different images are processed by optimization of level set segmentation. Traditionally, optimization was manual for every image where each parameter is selected one after another. By applying fuzzy logic, the segmentation of image is correlated based on texture features, to make it automatic and more effective. There is no initialization of parameters and it works like an intelligent system. It segments the different MRI images without tuning the level set parameters and give optimized results for all MRI's.

  6. A contextual image segmentation system using a priori information for automatic data classification in nuclear physics

    International Nuclear Information System (INIS)

    Benkirane, A.; Auger, G.; Chbihi, A.; Bloyet, D.; Plagnol, E.

    1994-01-01

    This paper presents an original approach to solve an automatic data classification problem by means of image processing techniques. The classification is achieved using image segmentation techniques for extracting the meaningful classes. Two types of information are merged for this purpose: the information contained in experimental images and a priori information derived from underlying physics (and adapted to image segmentation problem). This data fusion is widely used at different stages of the segmentation process. This approach yields interesting results in terms of segmentation performances, even in very noisy cases. Satisfactory classification results are obtained in cases where more ''classical'' automatic data classification methods fail. (authors). 25 refs., 14 figs., 1 append

  7. A contextual image segmentation system using a priori information for automatic data classification in nuclear physics

    Energy Technology Data Exchange (ETDEWEB)

    Benkirane, A; Auger, G; Chbihi, A [Grand Accelerateur National d` Ions Lourds (GANIL), 14 - Caen (France); Bloyet, D [Caen Univ., 14 (France); Plagnol, E [Paris-11 Univ., 91 - Orsay (France). Inst. de Physique Nucleaire

    1994-12-31

    This paper presents an original approach to solve an automatic data classification problem by means of image processing techniques. The classification is achieved using image segmentation techniques for extracting the meaningful classes. Two types of information are merged for this purpose: the information contained in experimental images and a priori information derived from underlying physics (and adapted to image segmentation problem). This data fusion is widely used at different stages of the segmentation process. This approach yields interesting results in terms of segmentation performances, even in very noisy cases. Satisfactory classification results are obtained in cases where more ``classical`` automatic data classification methods fail. (authors). 25 refs., 14 figs., 1 append.

  8. Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT.

    Science.gov (United States)

    Cheimariotis, Grigorios-Aris; Al-Mashat, Mariam; Haris, Kostas; Aletras, Anthony H; Jögi, Jonas; Bajc, Marika; Maglaveras, Nicolaos; Heiberg, Einar

    2018-02-01

    Image segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes. A total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images. The Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83 ± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p automatic quantification of wide range of measurements.

  9. A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT

    Science.gov (United States)

    Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J.; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa

    2016-01-01

    On‐board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real‐time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image‐guided radiotherapy (MR‐IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k‐means (FKM), k‐harmonic means (KHM), and reaction‐diffusion level set evolution (RD‐LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR‐TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR‐TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD‐LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP‐TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high‐contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR‐TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and

  10. A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

    Science.gov (United States)

    Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

    2016-03-01

    On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different

  11. Efficient Semi-Automatic 3D Segmentation for Neuron Tracing in Electron Microscopy Images

    Science.gov (United States)

    Jones, Cory; Liu, Ting; Cohan, Nathaniel Wood; Ellisman, Mark; Tasdizen, Tolga

    2015-01-01

    0.1. Background In the area of connectomics, there is a significant gap between the time required for data acquisition and dense reconstruction of the neural processes contained in the same dataset. Automatic methods are able to eliminate this timing gap, but the state-of-the-art accuracy so far is insufficient for use without user corrections. If completed naively, this process of correction can be tedious and time consuming. 0.2. New Method We present a new semi-automatic method that can be used to perform 3D segmentation of neurites in EM image stacks. It utilizes an automatic method that creates a hierarchical structure for recommended merges of superpixels. The user is then guided through each predicted region to quickly identify errors and establish correct links. 0.3. Results We tested our method on three datasets with both novice and expert users. Accuracy and timing were compared with published automatic, semi-automatic, and manual results. 0.4. Comparison with Existing Methods Post-automatic correction methods have also been used in [1] and [2]. These methods do not provide navigation or suggestions in the manner we present. Other semi-automatic methods require user input prior to the automatic segmentation such as [3] and [4] and are inherently different than our method. 0.5. Conclusion Using this method on the three datasets, novice users achieved accuracy exceeding state-of-the-art automatic results, and expert users achieved accuracy on par with full manual labeling but with a 70% time improvement when compared with other examples in publication. PMID:25769273

  12. Automatic segmentation of the glenohumeral cartilages from magnetic resonance images

    International Nuclear Information System (INIS)

    Neubert, A.; Yang, Z.; Engstrom, C.; Xia, Y.; Strudwick, M. W.; Chandra, S. S.; Crozier, S.; Fripp, J.

    2016-01-01

    Purpose: Magnetic resonance (MR) imaging plays a key role in investigating early degenerative disorders and traumatic injuries of the glenohumeral cartilages. Subtle morphometric and biochemical changes of potential relevance to clinical diagnosis, treatment planning, and evaluation can be assessed from measurements derived from in vivo MR segmentation of the cartilages. However, segmentation of the glenohumeral cartilages, using approaches spanning manual to automated methods, is technically challenging, due to their thin, curved structure and overlapping intensities of surrounding tissues. Automatic segmentation of the glenohumeral cartilages from MR imaging is not at the same level compared to the weight-bearing knee and hip joint cartilages despite the potential applications with respect to clinical investigation of shoulder disorders. In this work, the authors present a fully automated segmentation method for the glenohumeral cartilages using MR images of healthy shoulders. Methods: The method involves automated segmentation of the humerus and scapula bones using 3D active shape models, the extraction of the expected bone–cartilage interface, and cartilage segmentation using a graph-based method. The cartilage segmentation uses localization, patient specific tissue estimation, and a model of the cartilage thickness variation. The accuracy of this method was experimentally validated using a leave-one-out scheme on a database of MR images acquired from 44 asymptomatic subjects with a true fast imaging with steady state precession sequence on a 3 T scanner (Siemens Trio) using a dedicated shoulder coil. The automated results were compared to manual segmentations from two experts (an experienced radiographer and an experienced musculoskeletal anatomist) using the Dice similarity coefficient (DSC) and mean absolute surface distance (MASD) metrics. Results: Accurate and precise bone segmentations were achieved with mean DSC of 0.98 and 0.93 for the humeral head

  13. Automatic segmentation of the glenohumeral cartilages from magnetic resonance images

    Energy Technology Data Exchange (ETDEWEB)

    Neubert, A., E-mail: ales.neubert@csiro.au [School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072, Australia and The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Brisbane 4029 (Australia); Yang, Z. [School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072, Australia and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 (China); Engstrom, C. [School of Human Movement Studies, University of Queensland, Brisbane 4072 (Australia); Xia, Y.; Strudwick, M. W.; Chandra, S. S.; Crozier, S. [School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072 (Australia); Fripp, J. [The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, 4029 (Australia)

    2016-10-15

    Purpose: Magnetic resonance (MR) imaging plays a key role in investigating early degenerative disorders and traumatic injuries of the glenohumeral cartilages. Subtle morphometric and biochemical changes of potential relevance to clinical diagnosis, treatment planning, and evaluation can be assessed from measurements derived from in vivo MR segmentation of the cartilages. However, segmentation of the glenohumeral cartilages, using approaches spanning manual to automated methods, is technically challenging, due to their thin, curved structure and overlapping intensities of surrounding tissues. Automatic segmentation of the glenohumeral cartilages from MR imaging is not at the same level compared to the weight-bearing knee and hip joint cartilages despite the potential applications with respect to clinical investigation of shoulder disorders. In this work, the authors present a fully automated segmentation method for the glenohumeral cartilages using MR images of healthy shoulders. Methods: The method involves automated segmentation of the humerus and scapula bones using 3D active shape models, the extraction of the expected bone–cartilage interface, and cartilage segmentation using a graph-based method. The cartilage segmentation uses localization, patient specific tissue estimation, and a model of the cartilage thickness variation. The accuracy of this method was experimentally validated using a leave-one-out scheme on a database of MR images acquired from 44 asymptomatic subjects with a true fast imaging with steady state precession sequence on a 3 T scanner (Siemens Trio) using a dedicated shoulder coil. The automated results were compared to manual segmentations from two experts (an experienced radiographer and an experienced musculoskeletal anatomist) using the Dice similarity coefficient (DSC) and mean absolute surface distance (MASD) metrics. Results: Accurate and precise bone segmentations were achieved with mean DSC of 0.98 and 0.93 for the humeral head

  14. CT-based patient modeling for head and neck hyperthermia treatment planning: manual versus automatic normal-tissue-segmentation.

    Science.gov (United States)

    Verhaart, René F; Fortunati, Valerio; Verduijn, Gerda M; van Walsum, Theo; Veenland, Jifke F; Paulides, Margarethus M

    2014-04-01

    Clinical trials have shown that hyperthermia, as adjuvant to radiotherapy and/or chemotherapy, improves treatment of patients with locally advanced or recurrent head and neck (H&N) carcinoma. Hyperthermia treatment planning (HTP) guided H&N hyperthermia is being investigated, which requires patient specific 3D patient models derived from Computed Tomography (CT)-images. To decide whether a recently developed automatic-segmentation algorithm can be introduced in the clinic, we compared the impact of manual- and automatic normal-tissue-segmentation variations on HTP quality. CT images of seven patients were segmented automatically and manually by four observers, to study inter-observer and intra-observer geometrical variation. To determine the impact of this variation on HTP quality, HTP was performed using the automatic and manual segmentation of each observer, for each patient. This impact was compared to other sources of patient model uncertainties, i.e. varying gridsizes and dielectric tissue properties. Despite geometrical variations, manual and automatic generated 3D patient models resulted in an equal, i.e. 1%, variation in HTP quality. This variation was minor with respect to the total of other sources of patient model uncertainties, i.e. 11.7%. Automatically generated 3D patient models can be introduced in the clinic for H&N HTP. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  15. CT-based patient modeling for head and neck hyperthermia treatment planning: Manual versus automatic normal-tissue-segmentation

    International Nuclear Information System (INIS)

    Verhaart, René F.; Fortunati, Valerio; Verduijn, Gerda M.; Walsum, Theo van; Veenland, Jifke F.; Paulides, Margarethus M.

    2014-01-01

    Background and purpose: Clinical trials have shown that hyperthermia, as adjuvant to radiotherapy and/or chemotherapy, improves treatment of patients with locally advanced or recurrent head and neck (H and N) carcinoma. Hyperthermia treatment planning (HTP) guided H and N hyperthermia is being investigated, which requires patient specific 3D patient models derived from Computed Tomography (CT)-images. To decide whether a recently developed automatic-segmentation algorithm can be introduced in the clinic, we compared the impact of manual- and automatic normal-tissue-segmentation variations on HTP quality. Material and methods: CT images of seven patients were segmented automatically and manually by four observers, to study inter-observer and intra-observer geometrical variation. To determine the impact of this variation on HTP quality, HTP was performed using the automatic and manual segmentation of each observer, for each patient. This impact was compared to other sources of patient model uncertainties, i.e. varying gridsizes and dielectric tissue properties. Results: Despite geometrical variations, manual and automatic generated 3D patient models resulted in an equal, i.e. 1%, variation in HTP quality. This variation was minor with respect to the total of other sources of patient model uncertainties, i.e. 11.7%. Conclusions: Automatically generated 3D patient models can be introduced in the clinic for H and N HTP

  16. Automatic segmentation of coronary vessels from digital subtracted angiograms: a knowledge-based approach

    International Nuclear Information System (INIS)

    Stansfield, S.A.

    1986-01-01

    This paper presents a rule-based expert system for identifying and isolating coronary vessels in digital angiograms. The system is written in OPS5 and LISP and uses low level processors written in C. The system embodies both stages of the vision hierarchy: The low level image processing stage works concurrently with edges (or lines) and regions to segment the input image. Its knowledge is that of segmentation, grouping, and shape analysis. The high level stage then uses its knowledge of cardiac anatomy and physiology to interpret the result and to eliminate those structures not desired in the output. (Auth.)

  17. Automatic airline baggage counting using 3D image segmentation

    Science.gov (United States)

    Yin, Deyu; Gao, Qingji; Luo, Qijun

    2017-06-01

    The baggage number needs to be checked automatically during baggage self-check-in. A fast airline baggage counting method is proposed in this paper using image segmentation based on height map which is projected by scanned baggage 3D point cloud. There is height drop in actual edge of baggage so that it can be detected by the edge detection operator. And then closed edge chains are formed from edge lines that is linked by morphological processing. Finally, the number of connected regions segmented by closed chains is taken as the baggage number. Multi-bag experiment that is performed on the condition of different placement modes proves the validity of the method.

  18. Automated fetal brain segmentation from 2D MRI slices for motion correction.

    Science.gov (United States)

    Keraudren, K; Kuklisova-Murgasova, M; Kyriakopoulou, V; Malamateniou, C; Rutherford, M A; Kainz, B; Hajnal, J V; Rueckert, D

    2014-11-01

    Motion correction is a key element for imaging the fetal brain in-utero using Magnetic Resonance Imaging (MRI). Maternal breathing can introduce motion, but a larger effect is frequently due to fetal movement within the womb. Consequently, imaging is frequently performed slice-by-slice using single shot techniques, which are then combined into volumetric images using slice-to-volume reconstruction methods (SVR). For successful SVR, a key preprocessing step is to isolate fetal brain tissues from maternal anatomy before correcting for the motion of the fetal head. This has hitherto been a manual or semi-automatic procedure. We propose an automatic method to localize and segment the brain of the fetus when the image data is acquired as stacks of 2D slices with anatomy misaligned due to fetal motion. We combine this segmentation process with a robust motion correction method, enabling the segmentation to be refined as the reconstruction proceeds. The fetal brain localization process uses Maximally Stable Extremal Regions (MSER), which are classified using a Bag-of-Words model with Scale-Invariant Feature Transform (SIFT) features. The segmentation process is a patch-based propagation of the MSER regions selected during detection, combined with a Conditional Random Field (CRF). The gestational age (GA) is used to incorporate prior knowledge about the size and volume of the fetal brain into the detection and segmentation process. The method was tested in a ten-fold cross-validation experiment on 66 datasets of healthy fetuses whose GA ranged from 22 to 39 weeks. In 85% of the tested cases, our proposed method produced a motion corrected volume of a relevant quality for clinical diagnosis, thus removing the need for manually delineating the contours of the brain before motion correction. Our method automatically generated as a side-product a segmentation of the reconstructed fetal brain with a mean Dice score of 93%, which can be used for further processing. Copyright

  19. Automatic Cell Segmentation in Fluorescence Images of Confluent Cell Monolayers Using Multi-object Geometric Deformable Model

    OpenAIRE

    Yang, Zhen; Bogovic, John A.; Carass, Aaron; Ye, Mao; Searson, Peter C.; Prince, Jerry L.

    2013-01-01

    With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in fluorescence images of confluent cell monolayers. T...

  20. Automatic anatomy recognition in whole-body PET/CT images

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Huiqian [College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China and Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Udupa, Jayaram K., E-mail: jay@mail.med.upenn.edu; Odhner, Dewey; Tong, Yubing; Torigian, Drew A. [Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Zhao, Liming [Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 and Research Center of Intelligent System and Robotics, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China)

    2016-01-15

    Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process

  1. Automatic anatomy recognition in whole-body PET/CT images

    International Nuclear Information System (INIS)

    Wang, Huiqian; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.; Zhao, Liming

    2016-01-01

    Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process

  2. Automatic comic page image understanding based on edge segment analysis

    Science.gov (United States)

    Liu, Dong; Wang, Yongtao; Tang, Zhi; Li, Luyuan; Gao, Liangcai

    2013-12-01

    Comic page image understanding aims to analyse the layout of the comic page images by detecting the storyboards and identifying the reading order automatically. It is the key technique to produce the digital comic documents suitable for reading on mobile devices. In this paper, we propose a novel comic page image understanding method based on edge segment analysis. First, we propose an efficient edge point chaining method to extract Canny edge segments (i.e., contiguous chains of Canny edge points) from the input comic page image; second, we propose a top-down scheme to detect line segments within each obtained edge segment; third, we develop a novel method to detect the storyboards by selecting the border lines and further identify the reading order of these storyboards. The proposed method is performed on a data set consisting of 2000 comic page images from ten printed comic series. The experimental results demonstrate that the proposed method achieves satisfactory results on different comics and outperforms the existing methods.

  3. Facilitating coronary artery evaluation in MDCT using a 3D automatic vessel segmentation tool

    International Nuclear Information System (INIS)

    Fawad Khan, M.; Gurung, Jessen; Maataoui, Adel; Brehmer, Boris; Herzog, Christopher; Vogl, Thomas J.; Wesarg, Stefan; Dogan, Selami; Ackermann, Hanns; Assmus, Birgit

    2006-01-01

    The purpose of this study was to investigate a 3D coronary artery segmentation algorithm using 16-row MDCT data sets. Fifty patients underwent cardiac CT (Sensation 16, Siemens) and coronary angiography. Automatic and manual detection of coronary artery stenosis was performed. A 3D coronary artery segmentation algorithm (Fraunhofer Institute for Computer Graphics, Darmstadt) was used for automatic evaluation. All significant stenoses (>50%) in vessels >1.5 mm in diameter were protocoled. Each detection tool was used by one reader who was blinded to the results of the other detection method and the results of coronary angiography. Sensitivity and specificity were determined for automatic and manual detection as well as was the time for both CT-based evaluation methods. The overall sensitivity and specificity of the automatic and manual approach were 93.1 vs. 95.83% and 86.1 vs. 81.9%. The time required for automatic evaluation was significantly shorter than with the manual approach, i.e., 246.04±43.17 s for the automatic approach and 526.88±45.71 s for the manual approach (P<0.0001). In 94% of the coronary artery branches, automatic detection required less time than the manual approach. Automatic coronary vessel evaluation is feasible. It reduces the time required for cardiac CT evaluation with similar sensitivity and specificity as well as facilitates the evaluation of MDCT coronary angiography in a standardized fashion. (orig.)

  4. Automatic Melody Segmentation

    NARCIS (Netherlands)

    Rodríguez López, Marcelo

    2016-01-01

    The work presented in this dissertation investigates music segmentation. In the field of Musicology, segmentation refers to a score analysis technique, whereby notated pieces or passages of these pieces are divided into “units” referred to as sections, periods, phrases, and so on. Segmentation

  5. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.

    Science.gov (United States)

    Hu, Peijun; Wu, Fa; Peng, Jialin; Bao, Yuanyuan; Chen, Feng; Kong, Dexing

    2017-03-01

    Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images. The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model. Our method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency. A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.

  6. Automatic segmentation of the left ventricle in a cardiac MR short axis image using blind morphological operation

    Science.gov (United States)

    Irshad, Mehreen; Muhammad, Nazeer; Sharif, Muhammad; Yasmeen, Mussarat

    2018-04-01

    Conventionally, cardiac MR image analysis is done manually. Automatic examination for analyzing images can replace the monotonous tasks of massive amounts of data to analyze the global and regional functions of the cardiac left ventricle (LV). This task is performed using MR images to calculate the analytic cardiac parameter like end-systolic volume, end-diastolic volume, ejection fraction, and myocardial mass, respectively. These analytic parameters depend upon genuine delineation of epicardial, endocardial, papillary muscle, and trabeculations contours. In this paper, we propose an automatic segmentation method using the sum of absolute differences technique to localize the left ventricle. Blind morphological operations are proposed to segment and detect the LV contours of the epicardium and endocardium, automatically. We test the benchmark Sunny Brook dataset for evaluation of the proposed work. Contours of epicardium and endocardium are compared quantitatively to determine contour's accuracy and observe high matching values. Similarity or overlapping of an automatic examination to the given ground truth analysis by an expert are observed with high accuracy as with an index value of 91.30% . The proposed method for automatic segmentation gives better performance relative to existing techniques in terms of accuracy.

  7. Fully automatic segmentation of femurs with medullary canal definition in high and in low resolution CT scans.

    Science.gov (United States)

    Almeida, Diogo F; Ruben, Rui B; Folgado, João; Fernandes, Paulo R; Audenaert, Emmanuel; Verhegghe, Benedict; De Beule, Matthieu

    2016-12-01

    Femur segmentation can be an important tool in orthopedic surgical planning. However, in order to overcome the need of an experienced user with extensive knowledge on the techniques, segmentation should be fully automatic. In this paper a new fully automatic femur segmentation method for CT images is presented. This method is also able to define automatically the medullary canal and performs well even in low resolution CT scans. Fully automatic femoral segmentation was performed adapting a template mesh of the femoral volume to medical images. In order to achieve this, an adaptation of the active shape model (ASM) technique based on the statistical shape model (SSM) and local appearance model (LAM) of the femur with a novel initialization method was used, to drive the template mesh deformation in order to fit the in-image femoral shape in a time effective approach. With the proposed method a 98% convergence rate was achieved. For high resolution CT images group the average error is less than 1mm. For the low resolution image group the results are also accurate and the average error is less than 1.5mm. The proposed segmentation pipeline is accurate, robust and completely user free. The method is robust to patient orientation, image artifacts and poorly defined edges. The results excelled even in CT images with a significant slice thickness, i.e., above 5mm. Medullary canal segmentation increases the geometric information that can be used in orthopedic surgical planning or in finite element analysis. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

  8. ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

    Science.gov (United States)

    Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

    2016-07-07

    Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

  9. Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics

    Directory of Open Access Journals (Sweden)

    Wei Chen

    2018-01-01

    Full Text Available Gliomas are the most common primary brain tumors, and the objective grading is of great importance for treatment. This paper presents an automatic computer-aided diagnosis of gliomas that combines automatic segmentation and radiomics, which can improve the diagnostic ability. The MRI data containing 220 high-grade gliomas and 54 low-grade gliomas are used to evaluate our system. A multiscale 3D convolutional neural network is trained to segment whole tumor regions. A wide range of radiomic features including first-order features, shape features, and texture features is extracted. By using support vector machines with recursive feature elimination for feature selection, a CAD system that has an extreme gradient boosting classifier with a 5-fold cross-validation is constructed for the grading of gliomas. Our CAD system is highly effective for the grading of gliomas with an accuracy of 91.27%, a weighted macroprecision of 91.27%, a weighted macrorecall of 91.27%, and a weighted macro-F1 score of 90.64%. This demonstrates that the proposed CAD system can assist radiologists for high accurate grading of gliomas and has the potential for clinical applications.

  10. Automatic MPST-cut for segmentation of carpal bones from MR volumes.

    Science.gov (United States)

    Gemme, Laura; Nardotto, Sonia; Dellepiane, Silvana G

    2017-08-01

    In the context of rheumatic diseases, several studies suggest that Magnetic Resonance Imaging (MRI) allows the detection of the three main signs of Rheumatoid Arthritis (RA) at higher sensitivities than available through conventional radiology. The rapid, accurate segmentation of bones is an essential preliminary step for quantitative diagnosis, erosion evaluation, and multi-temporal data fusion. In the present paper, a new, semi-automatic, 3D graph-based segmentation method to extract carpal bone data is proposed. The method is unsupervised, does not employ any a priori model or knowledge, and is adaptive to the individual variability of the acquired data. After selecting one source point inside the Region of Interest (ROI), a segmentation process is initiated, which consists of two automatic stages: a cost-labeling phase and a graph-cutting phase. The algorithm finds optimal paths based on a new cost function by creating a Minimum Path Spanning Tree (MPST). To extract the region, a cut of the obtained tree is necessary. A new criterion of the MPST-cut based on compactness shape factor was conceived and developed. The proposed approach is applied to a large database of 96 T1-weighted MR bone volumes. Performance quality is evaluated by comparing the results with gold-standard bone volumes manually defined by rheumatologists through the computation of metrics extracted from the confusion matrix. Furthermore, comparisons with the existing literature are carried out. The results show that this method is efficient and provides satisfactory performance for bone segmentation on low-field MR volumes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Multi-atlas segmentation for abdominal organs with Gaussian mixture models

    Science.gov (United States)

    Burke, Ryan P.; Xu, Zhoubing; Lee, Christopher P.; Baucom, Rebeccah B.; Poulose, Benjamin K.; Abramson, Richard G.; Landman, Bennett A.

    2015-03-01

    Abdominal organ segmentation with clinically acquired computed tomography (CT) is drawing increasing interest in the medical imaging community. Gaussian mixture models (GMM) have been extensively used through medical segmentation, most notably in the brain for cerebrospinal fluid / gray matter / white matter differentiation. Because abdominal CT exhibit strong localized intensity characteristics, GMM have recently been incorporated in multi-stage abdominal segmentation algorithms. In the context of variable abdominal anatomy and rich algorithms, it is difficult to assess the marginal contribution of GMM. Herein, we characterize the efficacy of an a posteriori framework that integrates GMM of organ-wise intensity likelihood with spatial priors from multiple target-specific registered labels. In our study, we first manually labeled 100 CT images. Then, we assigned 40 images to use as training data for constructing target-specific spatial priors and intensity likelihoods. The remaining 60 images were evaluated as test targets for segmenting 12 abdominal organs. The overlap between the true and the automatic segmentations was measured by Dice similarity coefficient (DSC). A median improvement of 145% was achieved by integrating the GMM intensity likelihood against the specific spatial prior. The proposed framework opens the opportunities for abdominal organ segmentation by efficiently using both the spatial and appearance information from the atlases, and creates a benchmark for large-scale automatic abdominal segmentation.

  12. Automatic brain caudate nuclei segmentation and classification in diagnostic of Attention-Deficit/Hyperactivity Disorder.

    Science.gov (United States)

    Igual, Laura; Soliva, Joan Carles; Escalera, Sergio; Gimeno, Roger; Vilarroya, Oscar; Radeva, Petia

    2012-12-01

    We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods. Copyright © 2012 Elsevier Ltd. All rights reserved.

  13. Automatic segmentation of equine larynx for diagnosis of laryngeal hemiplegia

    Science.gov (United States)

    Salehin, Md. Musfequs; Zheng, Lihong; Gao, Junbin

    2013-10-01

    This paper presents an automatic segmentation method for delineation of the clinically significant contours of the equine larynx from an endoscopic image. These contours are used to diagnose the most common disease of horse larynx laryngeal hemiplegia. In this study, hierarchal structured contour map is obtained by the state-of-the-art segmentation algorithm, gPb-OWT-UCM. The conic-shaped outer boundary of equine larynx is extracted based on Pascal's theorem. Lastly, Hough Transformation method is applied to detect lines related to the edges of vocal folds. The experimental results show that the proposed approach has better performance in extracting the targeted contours of equine larynx than the results of using only the gPb-OWT-UCM method.

  14. Clinical evaluation of semi-automatic open-source algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action.

    Science.gov (United States)

    Wallner, Jürgen; Hochegger, Kerstin; Chen, Xiaojun; Mischak, Irene; Reinbacher, Knut; Pau, Mauro; Zrnc, Tomislav; Schwenzer-Zimmerer, Katja; Zemann, Wolfgang; Schmalstieg, Dieter; Egger, Jan

    2018-01-01

    Computer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However-due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice. In this retrospective, randomized, controlled trail the accuracy and accordance of the open-source based segmentation algorithm GrowCut was assessed through the comparison to the manually generated ground truth of the same anatomy using 10 CT lower jaw data-sets from the clinical routine. Assessment parameters were the segmentation time, the volume, the voxel number, the Dice Score and the Hausdorff distance. Overall semi-automatic GrowCut segmentation times were about one minute. Mean Dice Score values of over 85% and Hausdorff Distances below 33.5 voxel could be achieved between the algorithmic GrowCut-based segmentations and the manual generated ground truth schemes. Statistical differences between the assessment parameters were not significant (p 0.94) for any of the comparison made between the two groups. Complete functional stable and time saving segmentations with high accuracy and high positive correlation could be performed by the presented interactive open-source based approach. In the cranio-maxillofacial complex the used method could represent an algorithmic alternative for image-based segmentation in the clinical practice for e.g. surgical treatment planning or visualization of postoperative results and offers several advantages. Due to an open-source basis the used method could be further developed by other groups or specialists. Systematic comparisons to other segmentation approaches or with a

  15. Automatic falx cerebri and tentorium cerebelli segmentation from magnetic resonance images

    Science.gov (United States)

    Glaister, Jeffrey; Carass, Aaron; Pham, Dzung L.; Butman, John A.; Prince, Jerry L.

    2017-03-01

    The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

  16. AUTOMATIC SEGMENTATION OF BROADCAST AUDIO SIGNALS USING AUTO ASSOCIATIVE NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    P. Dhanalakshmi

    2010-12-01

    Full Text Available In this paper, we describe automatic segmentation methods for audio broadcast data. Today, digital audio applications are part of our everyday lives. Since there are more and more digital audio databases in place these days, the importance of effective management for audio databases have become prominent. Broadcast audio data is recorded from the Television which comprises of various categories of audio signals. Efficient algorithms for segmenting the audio broadcast data into predefined categories are proposed. Audio features namely Linear prediction coefficients (LPC, Linear prediction cepstral coefficients, and Mel frequency cepstral coefficients (MFCC are extracted to characterize the audio data. Auto Associative Neural Networks are used to segment the audio data into predefined categories using the extracted features. Experimental results indicate that the proposed algorithms can produce satisfactory results.

  17. Semi-automatic geographic atrophy segmentation for SD-OCT images.

    Science.gov (United States)

    Chen, Qiang; de Sisternes, Luis; Leng, Theodore; Zheng, Luoluo; Kutzscher, Lauren; Rubin, Daniel L

    2013-01-01

    Geographic atrophy (GA) is a condition that is associated with retinal thinning and loss of the retinal pigment epithelium (RPE) layer. It appears in advanced stages of non-exudative age-related macular degeneration (AMD) and can lead to vision loss. We present a semi-automated GA segmentation algorithm for spectral-domain optical coherence tomography (SD-OCT) images. The method first identifies and segments a surface between the RPE and the choroid to generate retinal projection images in which the projection region is restricted to a sub-volume of the retina where the presence of GA can be identified. Subsequently, a geometric active contour model is employed to automatically detect and segment the extent of GA in the projection images. Two image data sets, consisting on 55 SD-OCT scans from twelve eyes in eight patients with GA and 56 SD-OCT scans from 56 eyes in 56 patients with GA, respectively, were utilized to qualitatively and quantitatively evaluate the proposed GA segmentation method. Experimental results suggest that the proposed algorithm can achieve high segmentation accuracy. The mean GA overlap ratios between our proposed method and outlines drawn in the SD-OCT scans, our method and outlines drawn in the fundus auto-fluorescence (FAF) images, and the commercial software (Carl Zeiss Meditec proprietary software, Cirrus version 6.0) and outlines drawn in FAF images were 72.60%, 65.88% and 59.83%, respectively.

  18. Color Segmentation Approach of Infrared Thermography Camera Image for Automatic Fault Diagnosis

    International Nuclear Information System (INIS)

    Djoko Hari Nugroho; Ari Satmoko; Budhi Cynthia Dewi

    2007-01-01

    Predictive maintenance based on fault diagnosis becomes very important in current days to assure the availability and reliability of a system. The main purpose of this research is to configure a computer software for automatic fault diagnosis based on image model acquired from infrared thermography camera using color segmentation approach. This technique detects hot spots in equipment of the plants. Image acquired from camera is first converted to RGB (Red, Green, Blue) image model and then converted to CMYK (Cyan, Magenta, Yellow, Key for Black) image model. Assume that the yellow color in the image represented the hot spot in the equipment, the CMYK image model is then diagnosed using color segmentation model to estimate the fault. The software is configured utilizing Borland Delphi 7.0 computer programming language. The performance is then tested for 10 input infrared thermography images. The experimental result shows that the software capable to detect the faulty automatically with performance value of 80 % from 10 sheets of image input. (author)

  19. A dorsolateral prefrontal cortex semi-automatic segmenter

    Science.gov (United States)

    Al-Hakim, Ramsey; Fallon, James; Nain, Delphine; Melonakos, John; Tannenbaum, Allen

    2006-03-01

    Structural, functional, and clinical studies in schizophrenia have, for several decades, consistently implicated dysfunction of the prefrontal cortex in the etiology of the disease. Functional and structural imaging studies, combined with clinical, psychometric, and genetic analyses in schizophrenia have confirmed the key roles played by the prefrontal cortex and closely linked "prefrontal system" structures such as the striatum, amygdala, mediodorsal thalamus, substantia nigra-ventral tegmental area, and anterior cingulate cortices. The nodal structure of the prefrontal system circuit is the dorsal lateral prefrontal cortex (DLPFC), or Brodmann area 46, which also appears to be the most commonly studied and cited brain area with respect to schizophrenia. 1, 2, 3, 4 In 1986, Weinberger et. al. tied cerebral blood flow in the DLPFC to schizophrenia.1 In 2001, Perlstein et. al. demonstrated that DLPFC activation is essential for working memory tasks commonly deficient in schizophrenia. 2 More recently, groups have linked morphological changes due to gene deletion and increased DLPFC glutamate concentration to schizophrenia. 3, 4 Despite the experimental and clinical focus on the DLPFC in structural and functional imaging, the variability of the location of this area, differences in opinion on exactly what constitutes DLPFC, and inherent difficulties in segmenting this highly convoluted cortical region have contributed to a lack of widely used standards for manual or semi-automated segmentation programs. Given these implications, we developed a semi-automatic tool to segment the DLPFC from brain MRI scans in a reproducible way to conduct further morphological and statistical studies. The segmenter is based on expert neuroanatomist rules (Fallon-Kindermann rules), inspired by cytoarchitectonic data and reconstructions presented by Rajkowska and Goldman-Rakic. 5 It is semi-automated to provide essential user interactivity. We present our results and provide details on

  20. Automatic Story Segmentation for TV News Video Using Multiple Modalities

    Directory of Open Access Journals (Sweden)

    Émilie Dumont

    2012-01-01

    Full Text Available While video content is often stored in rather large files or broadcasted in continuous streams, users are often interested in retrieving only a particular passage on a topic of interest to them. It is, therefore, necessary to split video documents or streams into shorter segments corresponding to appropriate retrieval units. We propose here a method for the automatic segmentation of TV news videos into stories. A-multiple-descriptor based segmentation approach is proposed. The selected multimodal features are complementary and give good insights about story boundaries. Once extracted, these features are expanded with a local temporal context and combined by an early fusion process. The story boundaries are then predicted using machine learning techniques. We investigate the system by experiments conducted using TRECVID 2003 data and protocol of the story boundary detection task, and we show that the proposed approach outperforms the state-of-the-art methods while requiring a very small amount of manual annotation.

  1. A direct morphometric comparison of five labeling protocols for multi-atlas driven automatic segmentation of the hippocampus in Alzheimer's disease.

    Science.gov (United States)

    Nestor, Sean M; Gibson, Erin; Gao, Fu-Qiang; Kiss, Alex; Black, Sandra E

    2013-02-01

    anatomy and dorsal white matter compartments furnish the best voxel-overlap accuracies (Dice Similarity Coefficient=0.87-0.89), compared to expert manual tracings, and achieve the smallest sample sizes required to power clinical trials in MCI and AD. The greatest distribution of errors was localized to the caudal hippocampus and the alveus-fimbria compartment when these regions were excluded. The definition of the medial body did not significantly alter accuracy among more comprehensive protocols. Voxel-overlap accuracies between automatic and manual labels were lower for the more pathologically heterogeneous Sunnybrook study in comparison to the ADNI-1 sample. Finally, accuracy among protocols appears to significantly differ the most in AD subjects compared to MCI and normal elders. Together, these results suggest that selection of a candidate protocol for fully automatic multi-template based segmentation in AD can influence both segmentation accuracy when compared to expert manual labels and performance as a biomarker in MCI and AD. Copyright © 2012 Elsevier Inc. All rights reserved.

  2. Hepatic vessel segmentation for 3D planning of liver surgery experimental evaluation of a new fully automatic algorithm.

    Science.gov (United States)

    Conversano, Francesco; Franchini, Roberto; Demitri, Christian; Massoptier, Laurent; Montagna, Francesco; Maffezzoli, Alfonso; Malvasi, Antonio; Casciaro, Sergio

    2011-04-01

    The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automatic segmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liver surgery. A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding to a minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographic data set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracy was quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of the automatic segmentation performed on contrast-enhanced computed tomographic phantom images with actual model features. The optimal algorithm configuration resulted in a vessel detection sensitivity of 100% for vessels > 1 mm in diameter, 50% in the range 0.5 to 1 mm, and 14% in the range 0.2 to 0.5 mm. An average area overlap of 94.9% was obtained between automatically and manually segmented vessel sections, with an average difference of 0.06 mm(2). The average values of corresponding false-positive and false-negative ratios were 7.7% and 2.3%, respectively. A robust and accurate algorithm for automatic extraction of the hepatic vessel tree from contrast-enhanced computed tomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vessel delineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperative resections. Copyright © 2011 AUR. Published by Elsevier Inc. All rights reserved.

  3. Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee

    International Nuclear Information System (INIS)

    Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K; Ourselin, Sebastien

    2007-01-01

    The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis

  4. Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee

    Energy Technology Data Exchange (ETDEWEB)

    Fripp, Jurgen [BioMedIA Lab, Autonomous Systems Laboratory, CSIRO ICT Centre, Level 20, 300 Adelaide street, Brisbane, QLD 4001 (Australia); Crozier, Stuart [School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, QLD 4072 (Australia); Warfield, Simon K [Computational Radiology Laboratory, Harvard Medical School, Children' s Hospital Boston, 300 Longwood Avenue, Boston, MA 02115 (United States); Ourselin, Sebastien [BioMedIA Lab, Autonomous Systems Laboratory, CSIRO ICT Centre, Level 20, 300 Adelaide street, Brisbane, QLD 4001 (Australia)

    2007-03-21

    The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis.

  5. Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry

    Science.gov (United States)

    Meier, Raphael; Knecht, Urspeter; Loosli, Tina; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland; Reyes, Mauricio

    2016-03-01

    Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83-0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T2-hyperintense tumor compartments (NCE-T2). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments.

  6. Study of the renal segmental arterial anatomy with contrast-enhanced multi-detector computed tomography.

    Science.gov (United States)

    Rocco, Francesco; Cozzi, Luigi Alberto; Cozzi, Gabriele

    2015-07-01

    To use triphasic multi-detector computed tomography (MDCT) to study the renal segmental arterial anatomy and its relationship with the urinary tract to plan nephron-sparing surgery (NSS). One hundred and fifty nine patients underwent abdominal contrast-enhanced MDCT. We evaluated renal arteries and parenchymal vasculature. In 61 patients, the arteries and the urinary tract were represented simultaneously. 86.60% presented a single renal artery; 13.4%, multiple arteries. All single renal arteries divided into anterior and posterior branch before the hilum. The anterior artery branched into a superior, middle, and inferior branch. In 43.14%, the inferior artery arose before the others; in 45.75%, the superior artery arose before the others; in 9.80%, the branches shared a common trunk. In 26.80%, the posterior artery supplies the entire posterior surface; in 73.20%, it ends along the inferior calyx. In 96.73%, the upper pole was vascularized by the anterior superior branch and the posterior artery: the "tuning fork". MDCT showed four vascular segments in 96.73% and five in 3.27%. MDCT showed two avascular areas: the first along the projection of the inferior calyx on the posterior aspect, the second between the branches of the "tuning fork". The arterial phase provides the arterial tree representation; the delayed phase shows arteries and urinary tract simultaneously. MDCT provides a useful representation of the renal anatomy prior to intervascular-intrarenal NSS.

  7. Quantitative right and left ventricular functional analysis during gated whole-chest MDCT: A feasibility study comparing automatic segmentation to semi-manual contouring

    International Nuclear Information System (INIS)

    Coche, Emmanuel; Walker, Matthew J.; Zech, Francis; Crombrugghe, Rodolphe de; Vlassenbroek, Alain

    2010-01-01

    Purpose: To evaluate the feasibility of an automatic, whole-heart segmentation algorithm for measuring global heart function from gated, whole-chest MDCT images. Material and methods: 15 patients with suspicion of PE underwent whole-chest contrast-enhanced MDCT with retrospective ECG synchronization. Two observers computed right and left ventricular functional indices using a semi-manual and an automatic whole-heart segmentation algorithm. The two techniques were compared using Bland-Altman analysis and paired Student's t-test. Measurement reproducibility was calculated using intraclass correlation coefficient. Results: Ventricular analysis with automatic segmentation was successful in 13/15 (86%) and in 15/15 (100%) patients for the right ventricle and left ventricle, respectively. Reproducibility of measurements for both ventricles was perfect (ICC: 1.00) and very good for automatic and semi-manual measurements, respectively. Ventricular volumes and functional indices except right ventricular ejection fraction obtained from the automatic method were significantly higher for the RV compared to the semi-manual methods. Conclusions: The automatic, whole-heart segmentation algorithm enabled highly reproducible global heart function to be rapidly obtained in patients undergoing gated whole-chest MDCT for assessment of acute chest pain with suspicion of pulmonary embolism.

  8. CT examination of segmental liver transplants from living donors. Anatomy and pathological findings

    International Nuclear Information System (INIS)

    Krupski, G.; Maas, R.; Rogiers, X.; Burdelski, M.; Broelsch, C.E.

    1994-01-01

    A lack of suitable pediatric donors and significantly better results than conventional transplantation have contributed to the steady increase in the number of segmental liver transplants from living donors throughout the world. This article describes the diagnostic impact of axial CT scans following transplantation in a retrospective evaluation of 18 CT examinations of 10 children with an average age of two years. Both spiral and conventional CT scans permit precise visualization of the postoperative anatomy of the upper abdomen that is more distinct than the images provided by ultrasonic scans. Thus, CT scans better facilitate detection of pathological findings. In 60% of the patients (67% of the examinations), the CT scan permitted a definite diagnosis; in the remaining cases, no morphological correlate to the clinical and laboratory findings was detected. In addition to traditional ultrasonic scanning, computed tomography represents a further noninvasive imaging technique for postoperative diagnostics following segmental liver transplants from living donors. (orig.) [de

  9. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation.

    Directory of Open Access Journals (Sweden)

    Thomas Samaille

    Full Text Available White matter hyperintensities (WMH on T2 or FLAIR sequences have been commonly observed on MR images of elderly people. They have been associated with various disorders and have been shown to be a strong risk factor for stroke and dementia. WMH studies usually required visual evaluation of WMH load or time-consuming manual delineation. This paper introduced WHASA (White matter Hyperintensities Automated Segmentation Algorithm, a new method for automatically segmenting WMH from FLAIR and T1 images in multicentre studies. Contrary to previous approaches that were based on intensities, this method relied on contrast: non linear diffusion filtering alternated with watershed segmentation to obtain piecewise constant images with increased contrast between WMH and surroundings tissues. WMH were then selected based on subject dependant automatically computed threshold and anatomical information. WHASA was evaluated on 67 patients from two studies, acquired on six different MRI scanners and displaying a wide range of lesion load. Accuracy of the segmentation was assessed through volume and spatial agreement measures with respect to manual segmentation; an intraclass correlation coefficient (ICC of 0.96 and a mean similarity index (SI of 0.72 were obtained. WHASA was compared to four other approaches: Freesurfer and a thresholding approach as unsupervised methods; k-nearest neighbours (kNN and support vector machines (SVM as supervised ones. For these latter, influence of the training set was also investigated. WHASA clearly outperformed both unsupervised methods, while performing at least as good as supervised approaches (ICC range: 0.87-0.91 for kNN; 0.89-0.94 for SVM. Mean SI: 0.63-0.71 for kNN, 0.67-0.72 for SVM, and did not need any training set.

  10. Automatic lung tumor segmentation on PET/CT images using fuzzy Markov random field model.

    Science.gov (United States)

    Guo, Yu; Feng, Yuanming; Sun, Jian; Zhang, Ning; Lin, Wang; Sa, Yu; Wang, Ping

    2014-01-01

    The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice's similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.

  11. Automatic Lung Tumor Segmentation on PET/CT Images Using Fuzzy Markov Random Field Model

    Directory of Open Access Journals (Sweden)

    Yu Guo

    2014-01-01

    Full Text Available The combination of positron emission tomography (PET and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice’s similarity coefficient (DSC was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.

  12. Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric MR images

    Science.gov (United States)

    Suzani, Amin; Rasoulian, Abtin; Seitel, Alexander; Fels, Sidney; Rohling, Robert N.; Abolmaesumi, Purang

    2015-03-01

    This paper proposes an automatic method for vertebra localization, labeling, and segmentation in multi-slice Magnetic Resonance (MR) images. Prior work in this area on MR images mostly requires user interaction while our method is fully automatic. Cubic intensity-based features are extracted from image voxels. A deep learning approach is used for simultaneous localization and identification of vertebrae. The localized points are refined by local thresholding in the region of the detected vertebral column. Thereafter, a statistical multi-vertebrae model is initialized on the localized vertebrae. An iterative Expectation Maximization technique is used to register the vertebral body of the model to the image edges and obtain a segmentation of the lumbar vertebral bodies. The method is evaluated by applying to nine volumetric MR images of the spine. The results demonstrate 100% vertebra identification and a mean surface error of below 2.8 mm for 3D segmentation. Computation time is less than three minutes per high-resolution volumetric image.

  13. Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation

    Directory of Open Access Journals (Sweden)

    Sandra González-Villà

    2017-01-01

    Full Text Available In recent years, many automatic brain structure segmentation methods have been proposed. However, these methods are commonly tested with non-lesioned brains and the effect of lesions on their performance has not been evaluated. Here, we analyze the effect of multiple sclerosis (MS lesions on three well-known automatic brain structure segmentation methods, namely, FreeSurfer, FIRST and multi-atlas fused by majority voting, which use learning-based, deformable and atlas-based strategies, respectively. To perform a quantitative analysis, 100 synthetic images of MS patients with a total of 2174 lesions are simulated on two public databases with available brain structure ground truth information (IBSR18 and MICCAI’12. The Dice similarity coefficient (DSC differences and the volume differences between the healthy and the simulated images are calculated for the subcortical structures and the brainstem. We observe that the three strategies are affected when lesions are present. However, the effects of the lesions do not follow the same pattern; the lesions either make the segmentation method underperform or surprisingly augment the segmentation accuracy. The obtained results show that FreeSurfer is the method most affected by the presence of lesions, with DSC differences (generated − healthy ranging from −0.11 ± 0.54 to 9.65 ± 9.87, whereas FIRST tends to be the most robust method when lesions are present (−2.40 ± 5.54 to 0.44 ± 0.94. Lesion location is not important for global strategies such as FreeSurfer or majority voting, where structure segmentation is affected wherever the lesions exist. On the other hand, FIRST is more affected when the lesions are overlaid or close to the structure of analysis. The most affected structure by the presence of lesions is the nucleus accumbens (from −1.12 ± 2.53 to 1.32 ± 4.00 for the left hemisphere and from −2.40 ± 5.54 to 9.65 ± 9.87 for the right hemisphere, whereas the

  14. Automatic segmentation of dynamic neuroreceptor single-photon emission tomography images using fuzzy clustering

    International Nuclear Information System (INIS)

    Acton, P.D.; Pilowsky, L.S.; Kung, H.F.; Ell, P.J.

    1999-01-01

    The segmentation of medical images is one of the most important steps in the analysis and quantification of imaging data. However, partial volume artefacts make accurate tissue boundary definition difficult, particularly for images with lower resolution commonly used in nuclear medicine. In single-photon emission tomography (SPET) neuroreceptor studies, areas of specific binding are usually delineated by manually drawing regions of interest (ROIs), a time-consuming and subjective process. This paper applies the technique of fuzzy c-means clustering (FCM) to automatically segment dynamic neuroreceptor SPET images. Fuzzy clustering was tested using a realistic, computer-generated, dynamic SPET phantom derived from segmenting an MR image of an anthropomorphic brain phantom. Also, the utility of applying FCM to real clinical data was assessed by comparison against conventional ROI analysis of iodine-123 iodobenzamide (IBZM) binding to dopamine D 2 /D 3 receptors in the brains of humans. In addition, a further test of the methodology was assessed by applying FCM segmentation to [ 123 I]IDAM images (5-iodo-2-[[2-2-[(dimethylamino)methyl]phenyl]thio] benzyl alcohol) of serotonin transporters in non-human primates. In the simulated dynamic SPET phantom, over a wide range of counts and ratios of specific binding to background, FCM correlated very strongly with the true counts (correlation coefficient r 2 >0.99, P 123 I]IBZM data comparable with manual ROI analysis, with the binding ratios derived from both methods significantly correlated (r 2 =0.83, P<0.0001). Fuzzy clustering is a powerful tool for the automatic, unsupervised segmentation of dynamic neuroreceptor SPET images. Where other automated techniques fail completely, and manual ROI definition would be highly subjective, FCM is capable of segmenting noisy images in a robust and repeatable manner. (orig.)

  15. Automatic Vessel Segmentation on Retinal Images

    Institute of Scientific and Technical Information of China (English)

    Chun-Yuan Yu; Chia-Jen Chang; Yen-Ju Yao; Shyr-Shen Yu

    2014-01-01

    Several features of retinal vessels can be used to monitor the progression of diseases. Changes in vascular structures, for example, vessel caliber, branching angle, and tortuosity, are portents of many diseases such as diabetic retinopathy and arterial hyper-tension. This paper proposes an automatic retinal vessel segmentation method based on morphological closing and multi-scale line detection. First, an illumination correction is performed on the green band retinal image. Next, the morphological closing and subtraction processing are applied to obtain the crude retinal vessel image. Then, the multi-scale line detection is used to fine the vessel image. Finally, the binary vasculature is extracted by the Otsu algorithm. In this paper, for improving the drawbacks of multi-scale line detection, only the line detectors at 4 scales are used. The experimental results show that the accuracy is 0.939 for DRIVE (digital retinal images for vessel extraction) retinal database, which is much better than other methods.

  16. Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks

    Science.gov (United States)

    Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun; Li, Liandong

    2017-03-01

    Constrained by the physiology, the temporal factors associated with human behavior, irrespective of facial movement or body gesture, are described by four phases: neutral, onset, apex, and offset. Although they may benefit related recognition tasks, it is not easy to accurately detect such temporal segments. An automatic temporal segment detection framework using bilateral long short-term memory recurrent neural networks (BLSTM-RNN) to learn high-level temporal-spatial features, which synthesizes the local and global temporal-spatial information more efficiently, is presented. The framework is evaluated in detail over the face and body database (FABO). The comparison shows that the proposed framework outperforms state-of-the-art methods for solving the problem of temporal segment detection.

  17. Characterization of a sequential pipeline approach to automatic tissue segmentation from brain MR Images

    International Nuclear Information System (INIS)

    Hou, Zujun; Huang, Su

    2008-01-01

    Quantitative analysis of gray matter and white matter in brain magnetic resonance imaging (MRI) is valuable for neuroradiology and clinical practice. Submission of large collections of MRI scans to pipeline processing is increasingly important. We characterized this process and suggest several improvements. To investigate tissue segmentation from brain MR images through a sequential approach, a pipeline that consecutively executes denoising, skull/scalp removal, intensity inhomogeneity correction and intensity-based classification was developed. The denoising phase employs a 3D-extension of the Bayes-Shrink method. The inhomogeneity is corrected by an improvement of the Dawant et al.'s method with automatic generation of reference points. The N3 method has also been evaluated. Subsequently the brain tissue is segmented into cerebrospinal fluid, gray matter and white matter by a generalized Otsu thresholding technique. Intensive comparisons with other sequential or iterative methods have been carried out using simulated and real images. The sequential approach with judicious selection on the algorithm selection in each stage is not only advantageous in speed, but also can attain at least as accurate segmentation as iterative methods under a variety of noise or inhomogeneity levels. A sequential approach to tissue segmentation, which consecutively executes the wavelet shrinkage denoising, scalp/skull removal, inhomogeneity correction and intensity-based classification was developed to automatically segment the brain tissue into CSF, GM and WM from brain MR images. This approach is advantageous in several common applications, compared with other pipeline methods. (orig.)

  18. SU-C-BRA-06: Automatic Brain Tumor Segmentation for Stereotactic Radiosurgery Applications

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Y; Stojadinovic, S; Jiang, S; Timmerman, R; Abdulrahman, R; Nedzi, L; Gu, X [UT Southwestern Medical Center, Dallas, TX (United States)

    2016-06-15

    Purpose: Stereotactic radiosurgery (SRS), which delivers a potent dose of highly conformal radiation to the target in a single fraction, requires accurate tumor delineation for treatment planning. We present an automatic segmentation strategy, that synergizes intensity histogram thresholding, super-voxel clustering, and level-set based contour evolving methods to efficiently and accurately delineate SRS brain tumors on contrast-enhance T1-weighted (T1c) Magnetic Resonance Images (MRI). Methods: The developed auto-segmentation strategy consists of three major steps. Firstly, tumor sites are localized through 2D slice intensity histogram scanning. Then, super voxels are obtained through clustering the corresponding voxels in 3D with reference to the similarity metrics composited from spatial distance and intensity difference. The combination of the above two could generate the initial contour surface. Finally, a localized region active contour model is utilized to evolve the surface to achieve the accurate delineation of the tumors. The developed method was evaluated on numerical phantom data, synthetic BRATS (Multimodal Brain Tumor Image Segmentation challenge) data, and clinical patients’ data. The auto-segmentation results were quantitatively evaluated by comparing to ground truths with both volume and surface similarity metrics. Results: DICE coefficient (DC) was performed as a quantitative metric to evaluate the auto-segmentation in the numerical phantom with 8 tumors. DCs are 0.999±0.001 without noise, 0.969±0.065 with Rician noise and 0.976±0.038 with Gaussian noise. DC, NMI (Normalized Mutual Information), SSIM (Structural Similarity) and Hausdorff distance (HD) were calculated as the metrics for the BRATS and patients’ data. Assessment of BRATS data across 25 tumor segmentation yield DC 0.886±0.078, NMI 0.817±0.108, SSIM 0.997±0.002, and HD 6.483±4.079mm. Evaluation on 8 patients with total 14 tumor sites yield DC 0.872±0.070, NMI 0.824±0

  19. Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis

    Directory of Open Access Journals (Sweden)

    Yehu Shen

    2014-01-01

    Full Text Available Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions and hair color likelihood distribution function are estimated from these labels efficiently. Secondly, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood. This energy function is further optimized according to graph cuts technique and initial hair region is obtained. Finally, K-means algorithm and image postprocessing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. Experimental results show that the average processing time for each image is about 280 ms and the average hair region detection accuracy is above 90%. The proposed algorithm is applied to a facial caricature synthesis system. Experiments proved that with our proposed hair segmentation algorithm the facial caricatures are vivid and satisfying.

  20. Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

    Directory of Open Access Journals (Sweden)

    Maíla de Lima Claro

    2016-08-01

    Full Text Available The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classiffication of images in glaucomatous or not. We obtained results of 93% accuracy.

  1. Automatic segmentation of coronary arteries from computed tomography angiography data cloud using optimal thresholding

    Science.gov (United States)

    Ansari, Muhammad Ahsan; Zai, Sammer; Moon, Young Shik

    2017-01-01

    Manual analysis of the bulk data generated by computed tomography angiography (CTA) is time consuming, and interpretation of such data requires previous knowledge and expertise of the radiologist. Therefore, an automatic method that can isolate the coronary arteries from a given CTA dataset is required. We present an automatic yet effective segmentation method to delineate the coronary arteries from a three-dimensional CTA data cloud. Instead of a region growing process, which is usually time consuming and prone to leakages, the method is based on the optimal thresholding, which is applied globally on the Hessian-based vesselness measure in a localized way (slice by slice) to track the coronaries carefully to their distal ends. Moreover, to make the process automatic, we detect the aorta using the Hough transform technique. The proposed segmentation method is independent of the starting point to initiate its process and is fast in the sense that coronary arteries are obtained without any preprocessing or postprocessing steps. We used 12 real clinical datasets to show the efficiency and accuracy of the presented method. Experimental results reveal that the proposed method achieves 95% average accuracy.

  2. Automatic segmentation in three-dimensional analysis of fibrovascular pigmentepithelial detachment using high-definition optical coherence tomography.

    Science.gov (United States)

    Ahlers, C; Simader, C; Geitzenauer, W; Stock, G; Stetson, P; Dastmalchi, S; Schmidt-Erfurth, U

    2008-02-01

    A limited number of scans compromise conventional optical coherence tomography (OCT) to track chorioretinal disease in its full extension. Failures in edge-detection algorithms falsify the results of retinal mapping even further. High-definition-OCT (HD-OCT) is based on raster scanning and was used to visualise the localisation and volume of intra- and sub-pigment-epithelial (RPE) changes in fibrovascular pigment epithelial detachments (fPED). Two different scanning patterns were evaluated. 22 eyes with fPED were imaged using a frequency-domain, high-speed prototype of the Cirrus HD-OCT. The axial resolution was 6 mum, and the scanning speed was 25 kA scans/s. Two different scanning patterns covering an area of 6 x 6 mm in the macular retina were compared. Three-dimensional topographic reconstructions and volume calculations were performed using MATLAB-based automatic segmentation software. Detailed information about layer-specific distribution of fluid accumulation and volumetric measurements can be obtained for retinal- and sub-RPE volumes. Both raster scans show a high correlation (p0.89) of measured values, that is PED volume/area, retinal volume and mean retinal thickness. Quality control of the automatic segmentation revealed reasonable results in over 90% of the examinations. Automatic segmentation allows for detailed quantitative and topographic analysis of the RPE and the overlying retina. In fPED, the 128 x 512 scanning-pattern shows mild advantages when compared with the 256 x 256 scan. Together with the ability for automatic segmentation, HD-OCT clearly improves the clinical monitoring of chorioretinal disease by adding relevant new parameters. HD-OCT is likely capable of enhancing the understanding of pathophysiology and benefits of treatment for current anti-CNV strategies in future.

  3. Generic method for automatic bladder segmentation on cone beam CT using a patient-specific bladder shape model

    International Nuclear Information System (INIS)

    Schoot, A. J. A. J. van de; Schooneveldt, G.; Wognum, S.; Stalpers, L. J. A.; Rasch, C. R. N.; Bel, A.; Hoogeman, M. S.; Chai, X.

    2014-01-01

    Purpose: The aim of this study is to develop and validate a generic method for automatic bladder segmentation on cone beam computed tomography (CBCT), independent of gender and treatment position (prone or supine), using only pretreatment imaging data. Methods: Data of 20 patients, treated for tumors in the pelvic region with the entire bladder visible on CT and CBCT, were divided into four equally sized groups based on gender and treatment position. The full and empty bladder contour, that can be acquired with pretreatment CT imaging, were used to generate a patient-specific bladder shape model. This model was used to guide the segmentation process on CBCT. To obtain the bladder segmentation, the reference bladder contour was deformed iteratively by maximizing the cross-correlation between directional grey value gradients over the reference and CBCT bladder edge. To overcome incorrect segmentations caused by CBCT image artifacts, automatic adaptations were implemented. Moreover, locally incorrect segmentations could be adapted manually. After each adapted segmentation, the bladder shape model was expanded and new shape patterns were calculated for following segmentations. All available CBCTs were used to validate the segmentation algorithm. The bladder segmentations were validated by comparison with the manual delineations and the segmentation performance was quantified using the Dice similarity coefficient (DSC), surface distance error (SDE) and SD of contour-to-contour distances. Also, bladder volumes obtained by manual delineations and segmentations were compared using a Bland-Altman error analysis. Results: The mean DSC, mean SDE, and mean SD of contour-to-contour distances between segmentations and manual delineations were 0.87, 0.27 cm and 0.22 cm (female, prone), 0.85, 0.28 cm and 0.22 cm (female, supine), 0.89, 0.21 cm and 0.17 cm (male, supine) and 0.88, 0.23 cm and 0.17 cm (male, prone), respectively. Manual local adaptations improved the segmentation

  4. Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks.

    Science.gov (United States)

    López-Linares, Karen; Aranjuelo, Nerea; Kabongo, Luis; Maclair, Gregory; Lete, Nerea; Ceresa, Mario; García-Familiar, Ainhoa; Macía, Iván; González Ballester, Miguel A

    2018-05-01

    Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation. The DetecNet detection network is adapted to perform region of interest extraction from a complete CTA and a new segmentation network architecture, based on Fully Convolutional Networks and a Holistically-Nested Edge Detection Network, is presented. These networks are trained, validated and tested in 13 post-operative CTA volumes of different patients using a 4-fold cross-validation approach to provide more robustness to the results. Our pipeline achieves a Dice score of more than 82% for post-operative thrombus segmentation and provides a mean relative volume difference between ground truth and automatic segmentation that lays within the experienced human observer variance without the need of human intervention in most common cases. Copyright © 2018 Elsevier B.V. All rights reserved.

  5. Automatic segmentation of left ventricle in cardiac cine MRI images based on deep learning

    Science.gov (United States)

    Zhou, Tian; Icke, Ilknur; Dogdas, Belma; Parimal, Sarayu; Sampath, Smita; Forbes, Joseph; Bagchi, Ansuman; Chin, Chih-Liang; Chen, Antong

    2017-02-01

    In developing treatment of cardiovascular diseases, short axis cine MRI has been used as a standard technique for understanding the global structural and functional characteristics of the heart, e.g. ventricle dimensions, stroke volume and ejection fraction. To conduct an accurate assessment, heart structures need to be segmented from the cine MRI images with high precision, which could be a laborious task when performed manually. Herein a fully automatic framework is proposed for the segmentation of the left ventricle from the slices of short axis cine MRI scans of porcine subjects using a deep learning approach. For training the deep learning models, which generally requires a large set of data, a public database of human cine MRI scans is used. Experiments on the 3150 cine slices of 7 porcine subjects have shown that when comparing the automatic and manual segmentations the mean slice-wise Dice coefficient is about 0.930, the point-to-curve error is 1.07 mm, and the mean slice-wise Hausdorff distance is around 3.70 mm, which demonstrates the accuracy and robustness of the proposed inter-species translational approach.

  6. Variability and reliability of the vastus lateralis muscle anatomy.

    Science.gov (United States)

    D'Arpa, Salvatore; Toia, Francesca; Brenner, Erich; Melloni, Carlo; Moschella, Francesco; Cordova, Adriana

    2016-08-01

    The aims of this study are to investigate the variability of the morphological and neurovascular anatomy of the vastus lateralis (VL) muscle and to describe the relationships among its intramuscular partitions and with the other muscles of the quadriceps femoris. Clinical implications in its reliability as a flap donor are also discussed. In 2012, the extra- and intramuscular neurovascular anatomy of the VL was investigated in 10 cadaveric lower limbs. In three specimens, the segmental arterial pedicles were injected with latex of different colors to point out their anastomotic connections. The morphological anatomy was investigated with regard to the mutual relationship of the three muscular partitions and the relation of the VL with the other muscles of the quadriceps femoris. The VL has a segmental morphological anatomy. However, the fibers of its three partitions interconnect individually and with the other bellies of the quadriceps femoris, particularly, in several variable portions with the vastus intermedius and mainly in the posterior part of the VL. The lateral circumflex femoral artery and its branches have variable origin, but demonstrate constant segmental distribution. Intramuscular dissection and colored latex injections show a rich anastomotic vascular network among the three partitions. Moderate variability exists in both the myological and the neurovascular anatomy of the VL. Despite this variability, the anatomy of the VL always has a constant segmental pattern, which makes the VL a reliable flap donor. Detailed knowledge of the VL anatomy could have useful applications in a broad clinical field.

  7. Template-based automatic breast segmentation on MRI by excluding the chest region

    International Nuclear Information System (INIS)

    Lin, Muqing; Chen, Jeon-Hor; Wang, Xiaoyong; Su, Min-Ying; Chan, Siwa; Chen, Siping

    2013-01-01

    Purpose: Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method. Methods: Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary of the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subject's image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis. Results: The breast volumes measured by the proposed algorithm were very close to the radiologist's corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1.03%. The

  8. Template-based automatic breast segmentation on MRI by excluding the chest region

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Muqing [Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697-5020 and National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Department of Biomedical Engineering, School of Medicine, Shenzhen University, 518060 China (China); Chen, Jeon-Hor [Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697-5020 and Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung 82445, Taiwan (China); Wang, Xiaoyong; Su, Min-Ying, E-mail: msu@uci.edu [Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697-5020 (United States); Chan, Siwa [Department of Radiology, Taichung Veterans General Hospital, Taichung 40407, Taiwan (China); Chen, Siping [National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Department of Biomedical Engineering, School of Medicine, Shenzhen University, 518060 China (China)

    2013-12-15

    Purpose: Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method. Methods: Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary of the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subject's image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis. Results: The breast volumes measured by the proposed algorithm were very close to the radiologist's corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1

  9. Spatially adapted augmentation of age-specific atlas-based segmentation using patch-based priors

    Science.gov (United States)

    Liu, Mengyuan; Seshamani, Sharmishtaa; Harrylock, Lisa; Kitsch, Averi; Miller, Steven; Chau, Van; Poskitt, Kenneth; Rousseau, Francois; Studholme, Colin

    2014-03-01

    One of the most common approaches to MRI brain tissue segmentation is to employ an atlas prior to initialize an Expectation- Maximization (EM) image labeling scheme using a statistical model of MRI intensities. This prior is commonly derived from a set of manually segmented training data from the population of interest. However, in cases where subject anatomy varies significantly from the prior anatomical average model (for example in the case where extreme developmental abnormalities or brain injuries occur), the prior tissue map does not provide adequate information about the observed MRI intensities to ensure the EM algorithm converges to an anatomically accurate labeling of the MRI. In this paper, we present a novel approach for automatic segmentation of such cases. This approach augments the atlas-based EM segmentation by exploring methods to build a hybrid tissue segmentation scheme that seeks to learn where an atlas prior fails (due to inadequate representation of anatomical variation in the statistical atlas) and utilize an alternative prior derived from a patch driven search of the atlas data. We describe a framework for incorporating this patch-based augmentation of EM (PBAEM) into a 4D age-specific atlas-based segmentation of developing brain anatomy. The proposed approach was evaluated on a set of MRI brain scans of premature neonates with ages ranging from 27.29 to 46.43 gestational weeks (GWs). Results indicated superior performance compared to the conventional atlas-based segmentation method, providing improved segmentation accuracy for gray matter, white matter, ventricles and sulcal CSF regions.

  10. Segmentation of radiographic images under topological constraints: application to the femur.

    Science.gov (United States)

    Gamage, Pavan; Xie, Sheng Quan; Delmas, Patrice; Xu, Wei Liang

    2010-09-01

    A framework for radiographic image segmentation under topological control based on two-dimensional (2D) image analysis was developed. The system is intended for use in common radiological tasks including fracture treatment analysis, osteoarthritis diagnostics and osteotomy management planning. The segmentation framework utilizes a generic three-dimensional (3D) model of the bone of interest to define the anatomical topology. Non-rigid registration is performed between the projected contours of the generic 3D model and extracted edges of the X-ray image to achieve the segmentation. For fractured bones, the segmentation requires an additional step where a region-based active contours curve evolution is performed with a level set Mumford-Shah method to obtain the fracture surface edge. The application of the segmentation framework to analysis of human femur radiographs was evaluated. The proposed system has two major innovations. First, definition of the topological constraints does not require a statistical learning process, so the method is generally applicable to a variety of bony anatomy segmentation problems. Second, the methodology is able to handle both intact and fractured bone segmentation. Testing on clinical X-ray images yielded an average root mean squared distance (between the automatically segmented femur contour and the manual segmented ground truth) of 1.10 mm with a standard deviation of 0.13 mm. The proposed point correspondence estimation algorithm was benchmarked against three state-of-the-art point matching algorithms, demonstrating successful non-rigid registration for the cases of interest. A topologically constrained automatic bone contour segmentation framework was developed and tested, providing robustness to noise, outliers, deformations and occlusions.

  11. Segmentation of radiographic images under topological constraints: application to the femur

    International Nuclear Information System (INIS)

    Gamage, Pavan; Xie, Sheng Quan; Delmas, Patrice; Xu, Wei Liang

    2010-01-01

    A framework for radiographic image segmentation under topological control based on two-dimensional (2D) image analysis was developed. The system is intended for use in common radiological tasks including fracture treatment analysis, osteoarthritis diagnostics and osteotomy management planning. The segmentation framework utilizes a generic three-dimensional (3D) model of the bone of interest to define the anatomical topology. Non-rigid registration is performed between the projected contours of the generic 3D model and extracted edges of the X-ray image to achieve the segmentation. For fractured bones, the segmentation requires an additional step where a region-based active contours curve evolution is performed with a level set Mumford-Shah method to obtain the fracture surface edge. The application of the segmentation framework to analysis of human femur radiographs was evaluated. The proposed system has two major innovations. First, definition of the topological constraints does not require a statistical learning process, so the method is generally applicable to a variety of bony anatomy segmentation problems. Second, the methodology is able to handle both intact and fractured bone segmentation. Testing on clinical X-ray images yielded an average root mean squared distance (between the automatically segmented femur contour and the manual segmented ground truth) of 1.10 mm with a standard deviation of 0.13 mm. The proposed point correspondence estimation algorithm was benchmarked against three state-of-the-art point matching algorithms, demonstrating successful non-rigid registration for the cases of interest. A topologically constrained automatic bone contour segmentation framework was developed and tested, providing robustness to noise, outliers, deformations and occlusions. (orig.)

  12. Segmentation of radiographic images under topological constraints: application to the femur

    Energy Technology Data Exchange (ETDEWEB)

    Gamage, Pavan; Xie, Sheng Quan [University of Auckland, Department of Mechanical Engineering (Mechatronics), Auckland (New Zealand); Delmas, Patrice [University of Auckland, Department of Computer Science, Auckland (New Zealand); Xu, Wei Liang [Massey University, School of Engineering and Advanced Technology, Auckland (New Zealand)

    2010-09-15

    A framework for radiographic image segmentation under topological control based on two-dimensional (2D) image analysis was developed. The system is intended for use in common radiological tasks including fracture treatment analysis, osteoarthritis diagnostics and osteotomy management planning. The segmentation framework utilizes a generic three-dimensional (3D) model of the bone of interest to define the anatomical topology. Non-rigid registration is performed between the projected contours of the generic 3D model and extracted edges of the X-ray image to achieve the segmentation. For fractured bones, the segmentation requires an additional step where a region-based active contours curve evolution is performed with a level set Mumford-Shah method to obtain the fracture surface edge. The application of the segmentation framework to analysis of human femur radiographs was evaluated. The proposed system has two major innovations. First, definition of the topological constraints does not require a statistical learning process, so the method is generally applicable to a variety of bony anatomy segmentation problems. Second, the methodology is able to handle both intact and fractured bone segmentation. Testing on clinical X-ray images yielded an average root mean squared distance (between the automatically segmented femur contour and the manual segmented ground truth) of 1.10 mm with a standard deviation of 0.13 mm. The proposed point correspondence estimation algorithm was benchmarked against three state-of-the-art point matching algorithms, demonstrating successful non-rigid registration for the cases of interest. A topologically constrained automatic bone contour segmentation framework was developed and tested, providing robustness to noise, outliers, deformations and occlusions. (orig.)

  13. Leveraging Automatic Speech Recognition Errors to Detect Challenging Speech Segments in TED Talks

    Science.gov (United States)

    Mirzaei, Maryam Sadat; Meshgi, Kourosh; Kawahara, Tatsuya

    2016-01-01

    This study investigates the use of Automatic Speech Recognition (ASR) systems to epitomize second language (L2) listeners' problems in perception of TED talks. ASR-generated transcripts of videos often involve recognition errors, which may indicate difficult segments for L2 listeners. This paper aims to discover the root-causes of the ASR errors…

  14. A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Jinzhong; Aristophanous, Michalis, E-mail: MAristophanous@mdanderson.org [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States); Beadle, Beth M.; Garden, Adam S. [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States); Schwartz, David L. [Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390 (United States)

    2015-09-15

    Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation–maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the “ground truth” for quantitative evaluation. Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm{sup 3} (range, 6.6–44.3 cm{sup 3}), while the PET segmented GTV was 10.2 cm{sup 3} (range, 2.8–45.1 cm{sup 3}). The median physician-defined GTV was 22.1 cm{sup 3} (range, 4.2–38.4 cm{sup 3}). The median difference between the multichannel segmented and physician-defined GTVs was −10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was −19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented

  15. A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy.

    Science.gov (United States)

    Yang, Jinzhong; Beadle, Beth M; Garden, Adam S; Schwartz, David L; Aristophanous, Michalis

    2015-09-01

    To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation-maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the "ground truth" for quantitative evaluation. The median multichannel segmented GTV of the primary tumor was 15.7 cm(3) (range, 6.6-44.3 cm(3)), while the PET segmented GTV was 10.2 cm(3) (range, 2.8-45.1 cm(3)). The median physician-defined GTV was 22.1 cm(3) (range, 4.2-38.4 cm(3)). The median difference between the multichannel segmented and physician-defined GTVs was -10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was -19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55-0.84), and the

  16. A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy

    International Nuclear Information System (INIS)

    Yang, Jinzhong; Aristophanous, Michalis; Beadle, Beth M.; Garden, Adam S.; Schwartz, David L.

    2015-01-01

    Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation–maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the “ground truth” for quantitative evaluation. Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm"3 (range, 6.6–44.3 cm"3), while the PET segmented GTV was 10.2 cm"3 (range, 2.8–45.1 cm"3). The median physician-defined GTV was 22.1 cm"3 (range, 4.2–38.4 cm"3). The median difference between the multichannel segmented and physician-defined GTVs was −10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was −19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was

  17. Automatic anatomy partitioning of the torso region on CT images by using multiple organ localizations with a group-wise calibration technique

    Science.gov (United States)

    Zhou, Xiangrong; Morita, Syoichi; Zhou, Xinxin; Chen, Huayue; Hara, Takeshi; Yokoyama, Ryujiro; Kanematsu, Masayuki; Hoshi, Hiroaki; Fujita, Hiroshi

    2015-03-01

    This paper describes an automatic approach for anatomy partitioning on three-dimensional (3D) computedtomography (CT) images that divide the human torso into several volume-of-interesting (VOI) images based on anatomical definition. The proposed approach combines several individual detections of organ-location with a groupwise organ-location calibration and correction to achieve an automatic and robust multiple-organ localization task. The essence of the proposed method is to jointly detect the 3D minimum bounding box for each type of organ shown on CT images based on intra-organ-image-textures and inter-organ-spatial-relationship in the anatomy. Machine-learning-based template matching and generalized Hough transform-based point-distribution estimation are used in the detection and calibration processes. We apply this approach to the automatic partitioning of a torso region on CT images, which are divided into 35 VOIs presenting major organ regions and tissues required by routine diagnosis in clinical medicine. A database containing 4,300 patient cases of high-resolution 3D torso CT images is used for training and performance evaluations. We confirmed that the proposed method was successful in target organ localization on more than 95% of CT cases. Only two organs (gallbladder and pancreas) showed a lower success rate: 71 and 78% respectively. In addition, we applied this approach to another database that included 287 patient cases of whole-body CT images scanned for positron emission tomography (PET) studies and used for additional performance evaluation. The experimental results showed that no significant difference between the anatomy partitioning results from those two databases except regarding the spleen. All experimental results showed that the proposed approach was efficient and useful in accomplishing localization tasks for major organs and tissues on CT images scanned using different protocols.

  18. Thoracic lymph node station recognition on CT images based on automatic anatomy recognition with an optimal parent strategy

    Science.gov (United States)

    Xu, Guoping; Udupa, Jayaram K.; Tong, Yubing; Cao, Hanqiang; Odhner, Dewey; Torigian, Drew A.; Wu, Xingyu

    2018-03-01

    Currently, there are many papers that have been published on the detection and segmentation of lymph nodes from medical images. However, it is still a challenging problem owing to low contrast with surrounding soft tissues and the variations of lymph node size and shape on computed tomography (CT) images. This is particularly very difficult on low-dose CT of PET/CT acquisitions. In this study, we utilize our previous automatic anatomy recognition (AAR) framework to recognize the thoracic-lymph node stations defined by the International Association for the Study of Lung Cancer (IASLC) lymph node map. The lymph node stations themselves are viewed as anatomic objects and are localized by using a one-shot method in the AAR framework. Two strategies have been taken in this paper for integration into AAR framework. The first is to combine some lymph node stations into composite lymph node stations according to their geometrical nearness. The other is to find the optimal parent (organ or union of organs) as an anchor for each lymph node station based on the recognition error and thereby find an overall optimal hierarchy to arrange anchor organs and lymph node stations. Based on 28 contrast-enhanced thoracic CT image data sets for model building, 12 independent data sets for testing, our results show that thoracic lymph node stations can be localized within 2-3 voxels compared to the ground truth.

  19. CT-based manual segmentation and evaluation of paranasal sinuses.

    Science.gov (United States)

    Pirner, S; Tingelhoff, K; Wagner, I; Westphal, R; Rilk, M; Wahl, F M; Bootz, F; Eichhorn, Klaus W G

    2009-04-01

    Manual segmentation of computed tomography (CT) datasets was performed for robot-assisted endoscope movement during functional endoscopic sinus surgery (FESS). Segmented 3D models are needed for the robots' workspace definition. A total of 50 preselected CT datasets were each segmented in 150-200 coronal slices with 24 landmarks being set. Three different colors for segmentation represent diverse risk areas. Extension and volumetric measurements were performed. Three-dimensional reconstruction was generated after segmentation. Manual segmentation took 8-10 h for each CT dataset. The mean volumes were: right maxillary sinus 17.4 cm(3), left side 17.9 cm(3), right frontal sinus 4.2 cm(3), left side 4.0 cm(3), total frontal sinuses 7.9 cm(3), sphenoid sinus right side 5.3 cm(3), left side 5.5 cm(3), total sphenoid sinus volume 11.2 cm(3). Our manually segmented 3D-models present the patient's individual anatomy with a special focus on structures in danger according to the diverse colored risk areas. For safe robot assistance, the high-accuracy models represent an average of the population for anatomical variations, extension and volumetric measurements. They can be used as a database for automatic model-based segmentation. None of the segmentation methods so far described provide risk segmentation. The robot's maximum distance to the segmented border can be adjusted according to the differently colored areas.

  20. Electroporation-based treatment planning for deep-seated tumors based on automatic liver segmentation of MRI images.

    Science.gov (United States)

    Pavliha, Denis; Mušič, Maja M; Serša, Gregor; Miklavčič, Damijan

    2013-01-01

    Electroporation is the phenomenon that occurs when a cell is exposed to a high electric field, which causes transient cell membrane permeabilization. A paramount electroporation-based application is electrochemotherapy, which is performed by delivering high-voltage electric pulses that enable the chemotherapeutic drug to more effectively destroy the tumor cells. Electrochemotherapy can be used for treating deep-seated metastases (e.g. in the liver, bone, brain, soft tissue) using variable-geometry long-needle electrodes. To treat deep-seated tumors, patient-specific treatment planning of the electroporation-based treatment is required. Treatment planning is based on generating a 3D model of the organ and target tissue subject to electroporation (i.e. tumor nodules). The generation of the 3D model is done by segmentation algorithms. We implemented and evaluated three automatic liver segmentation algorithms: region growing, adaptive threshold, and active contours (snakes). The algorithms were optimized using a seven-case dataset manually segmented by the radiologist as a training set, and finally validated using an additional four-case dataset that was previously not included in the optimization dataset. The presented results demonstrate that patient's medical images that were not included in the training set can be successfully segmented using our three algorithms. Besides electroporation-based treatments, these algorithms can be used in applications where automatic liver segmentation is required.

  1. A novel region-growing based semi-automatic segmentation protocol for three-dimensional condylar reconstruction using cone beam computed tomography (CBCT.

    Directory of Open Access Journals (Sweden)

    Tong Xi

    Full Text Available OBJECTIVE: To present and validate a semi-automatic segmentation protocol to enable an accurate 3D reconstruction of the mandibular condyles using cone beam computed tomography (CBCT. MATERIALS AND METHODS: Approval from the regional medical ethics review board was obtained for this study. Bilateral mandibular condyles in ten CBCT datasets of patients were segmented using the currently proposed semi-automatic segmentation protocol. This segmentation protocol combined 3D region-growing and local thresholding algorithms. The segmentation of a total of twenty condyles was performed by two observers. The Dice-coefficient and distance map calculations were used to evaluate the accuracy and reproducibility of the segmented and 3D rendered condyles. RESULTS: The mean inter-observer Dice-coefficient was 0.98 (range [0.95-0.99]. An average 90th percentile distance of 0.32 mm was found, indicating an excellent inter-observer similarity of the segmented and 3D rendered condyles. No systematic errors were observed in the currently proposed segmentation protocol. CONCLUSION: The novel semi-automated segmentation protocol is an accurate and reproducible tool to segment and render condyles in 3D. The implementation of this protocol in the clinical practice allows the CBCT to be used as an imaging modality for the quantitative analysis of condylar morphology.

  2. Semi-automatic breast ultrasound image segmentation based on mean shift and graph cuts.

    Science.gov (United States)

    Zhou, Zhuhuang; Wu, Weiwei; Wu, Shuicai; Tsui, Po-Hsiang; Lin, Chung-Chih; Zhang, Ling; Wang, Tianfu

    2014-10-01

    Computerized tumor segmentation on breast ultrasound (BUS) images remains a challenging task. In this paper, we proposed a new method for semi-automatic tumor segmentation on BUS images using Gaussian filtering, histogram equalization, mean shift, and graph cuts. The only interaction required was to select two diagonal points to determine a region of interest (ROI) on an input image. The ROI image was shrunken by a factor of 2 using bicubic interpolation to reduce computation time. The shrunken image was smoothed by a Gaussian filter and then contrast-enhanced by histogram equalization. Next, the enhanced image was filtered by pyramid mean shift to improve homogeneity. The object and background seeds for graph cuts were automatically generated on the filtered image. Using these seeds, the filtered image was then segmented by graph cuts into a binary image containing the object and background. Finally, the binary image was expanded by a factor of 2 using bicubic interpolation, and the expanded image was processed by morphological opening and closing to refine the tumor contour. The method was implemented with OpenCV 2.4.3 and Visual Studio 2010 and tested for 38 BUS images with benign tumors and 31 BUS images with malignant tumors from different ultrasound scanners. Experimental results showed that our method had a true positive rate (TP) of 91.7%, a false positive (FP) rate of 11.9%, and a similarity (SI) rate of 85.6%. The mean run time on Intel Core 2.66 GHz CPU and 4 GB RAM was 0.49 ± 0.36 s. The experimental results indicate that the proposed method may be useful in BUS image segmentation. © The Author(s) 2014.

  3. Volumetric visualization of anatomy for treatment planning

    International Nuclear Information System (INIS)

    Pelizzari, Charles A.; Grzeszczuk, Robert; Chen, George T. Y.; Heimann, Ruth; Haraf, Daniel J.; Vijayakumar, Srinivasan; Ryan, Martin J.

    1996-01-01

    Purpose: Delineation of volumes of interest for three-dimensional (3D) treatment planning is usually performed by contouring on two-dimensional sections. We explore the usage of segmentation-free volumetric rendering of the three-dimensional image data set for tumor and normal tissue visualization. Methods and Materials: Standard treatment planning computed tomography (CT) studies, with typically 5 to 10 mm slice thickness, and spiral CT studies with 3 mm slice thickness were used. The data were visualized using locally developed volume-rendering software. Similar to the method of Drebin et al., CT voxels are automatically assigned an opacity and other visual properties (e.g., color) based on a probabilistic classification into tissue types. Using volumetric compositing, a projection into the opacity-weighted volume is produced. Depth cueing, perspective, and gradient-based shading are incorporated to achieve realistic images. Unlike surface-rendered displays, no hand segmentation is required to produce detailed renditions of skin, muscle, or bony anatomy. By suitable manipulation of the opacity map, tissue classes can be made transparent, revealing muscle, vessels, or bone, for example. Manually supervised tissue masking allows irrelevant tissues overlying tumors or other structures of interest to be removed. Results: Very high-quality renditions are produced in from 5 s to 1 min on midrange computer workstations. In the pelvis, an anteroposterior (AP) volume rendered view from a typical planning CT scan clearly shows the skin and bony anatomy. A muscle opacity map permits clear visualization of the superficial thigh muscles, femoral veins, and arteries. Lymph nodes are seen in the femoral triangle. When overlying muscle and bone are cut away, the prostate, seminal vessels, bladder, and rectum are seen in 3D perspective. Similar results are obtained for thorax and for head and neck scans. Conclusion: Volumetric visualization of anatomy is useful in treatment

  4. A method for smoothing segmented lung boundary in chest CT images

    Science.gov (United States)

    Yim, Yeny; Hong, Helen

    2007-03-01

    To segment low density lung regions in chest CT images, most of methods use the difference in gray-level value of pixels. However, radiodense pulmonary vessels and pleural nodules that contact with the surrounding anatomy are often excluded from the segmentation result. To smooth lung boundary segmented by gray-level processing in chest CT images, we propose a new method using scan line search. Our method consists of three main steps. First, lung boundary is extracted by our automatic segmentation method. Second, segmented lung contour is smoothed in each axial CT slice. We propose a scan line search to track the points on lung contour and find rapidly changing curvature efficiently. Finally, to provide consistent appearance between lung contours in adjacent axial slices, 2D closing in coronal plane is applied within pre-defined subvolume. Our method has been applied for performance evaluation with the aspects of visual inspection, accuracy and processing time. The results of our method show that the smoothness of lung contour was considerably increased by compensating for pulmonary vessels and pleural nodules.

  5. Automatic nuclei segmentation in H&E stained breast cancer histopathology images.

    Directory of Open Access Journals (Sweden)

    Mitko Veta

    Full Text Available The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1 pre-processing with color unmixing and morphological operators, 2 marker-controlled watershed segmentation at multiple scales and with different markers, 3 post-processing for rejection of false regions and 4 merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A and tested on a separate validation set of 18 cases (subset B. The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value and segmentation accuracy (Dice coefficient. The mean estimated sensitivity for subset A was 0.875 (±0.092 and for subset B 0.853 (±0.077. The mean estimated positive predictive value was 0.904 (±0.075 and 0.886 (±0.069 for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8.

  6. Automatic nuclei segmentation in H&E stained breast cancer histopathology images.

    Science.gov (United States)

    Veta, Mitko; van Diest, Paul J; Kornegoor, Robert; Huisman, André; Viergever, Max A; Pluim, Josien P W

    2013-01-01

    The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E) stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1) pre-processing with color unmixing and morphological operators, 2) marker-controlled watershed segmentation at multiple scales and with different markers, 3) post-processing for rejection of false regions and 4) merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A) and tested on a separate validation set of 18 cases (subset B). The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value) and segmentation accuracy (Dice coefficient). The mean estimated sensitivity for subset A was 0.875 (±0.092) and for subset B 0.853 (±0.077). The mean estimated positive predictive value was 0.904 (±0.075) and 0.886 (±0.069) for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8.

  7. Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi-atlas (MA) approach.

    Science.gov (United States)

    Lavdas, Ioannis; Glocker, Ben; Kamnitsas, Konstantinos; Rueckert, Daniel; Mair, Henrietta; Sandhu, Amandeep; Taylor, Stuart A; Aboagye, Eric O; Rockall, Andrea G

    2017-10-01

    As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three surface distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average surface distance (ASD), root-mean-square surface distance (RMSSD), and Hausdorff distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a 'per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a 'per-organ' basis, when using different imaging combinations as input for training. All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 ± 0.18, RE = 0.73 ± 0.18, PR = 0.71 ± 0.14, CNNs: DSC = 0.81 ± 0.13, RE = 0.83 ± 0.14, PR = 0.82 ± 0.10, MA: DSC = 0.71 ± 0.22, RE = 0.70 ± 0.34, PR = 0.77 ± 0.15. Mean surface distance

  8. Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking

    Directory of Open Access Journals (Sweden)

    Shoujun Zhou

    2010-08-01

    Full Text Available Abstract Background Segmentation of the coronary angiogram is important in computer-assisted artery motion analysis or reconstruction of 3D vascular structures from a single-plan or biplane angiographic system. Developing fully automated and accurate vessel segmentation algorithms is highly challenging, especially when extracting vascular structures with large variations in image intensities and noise, as well as with variable cross-sections or vascular lesions. Methods This paper presents a novel tracking method for automatic segmentation of the coronary artery tree in X-ray angiographic images, based on probabilistic vessel tracking and fuzzy structure pattern inferring. The method is composed of two main steps: preprocessing and tracking. In preprocessing, multiscale Gabor filtering and Hessian matrix analysis were used to enhance and extract vessel features from the original angiographic image, leading to a vessel feature map as well as a vessel direction map. In tracking, a seed point was first automatically detected by analyzing the vessel feature map. Subsequently, two operators [e.g., a probabilistic tracking operator (PTO and a vessel structure pattern detector (SPD] worked together based on the detected seed point to extract vessel segments or branches one at a time. The local structure pattern was inferred by a multi-feature based fuzzy inferring function employed in the SPD. The identified structure pattern, such as crossing or bifurcation, was used to control the tracking process, for example, to keep tracking the current segment or start tracking a new one, depending on the detected pattern. Results By appropriate integration of these advanced preprocessing and tracking steps, our tracking algorithm is able to extract both vessel axis lines and edge points, as well as measure the arterial diameters in various complicated cases. For example, it can walk across gaps along the longitudinal vessel direction, manage varying vessel

  9. Automatic Segmentation of Abdominal Fat in MRI-Scans, Using Graph-Cuts and Image Derived Energies

    DEFF Research Database (Denmark)

    Christensen, Anders Nymark; Larsen, Christian Thode; Mandrup Jensen, Camilla Maria

    2017-01-01

    For many clinical studies changes in the abdominal distribution of fat is an important measure. However, the segmentation of abdominal fat in MRI scans is both difficult and time consuming using manual methods. We present here an automatic and flexible software package, that performs both bias fi...

  10. Automatic segmentation of fluorescence lifetime microscopy images of cells using multiresolution community detection--a first study.

    Science.gov (United States)

    Hu, D; Sarder, P; Ronhovde, P; Orthaus, S; Achilefu, S; Nussinov, Z

    2014-01-01

    Inspired by a multiresolution community detection based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Furthermore, using the proposed method, the mean-square error in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The multiresolution community detection method appeared to perform better than a popular spectral clustering-based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in mean-square error with increasing resolution. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

  11. Automatic lung lobe segmentation using particles, thin plate splines, and maximum a posteriori estimation.

    Science.gov (United States)

    Ross, James C; San José Estépar, Rail; Kindlmann, Gordon; Díaz, Alejandro; Westin, Carl-Fredrik; Silverman, Edwin K; Washko, George R

    2010-01-01

    We present a fully automatic lung lobe segmentation algorithm that is effective in high resolution computed tomography (CT) datasets in the presence of confounding factors such as incomplete fissures (anatomical structures indicating lobe boundaries), advanced disease states, high body mass index (BMI), and low-dose scanning protocols. In contrast to other algorithms that leverage segmentations of auxiliary structures (esp. vessels and airways), we rely only upon image features indicating fissure locations. We employ a particle system that samples the image domain and provides a set of candidate fissure locations. We follow this stage with maximum a posteriori (MAP) estimation to eliminate poor candidates and then perform a post-processing operation to remove remaining noise particles. We then fit a thin plate spline (TPS) interpolating surface to the fissure particles to form the final lung lobe segmentation. Results indicate that our algorithm performs comparably to pulmonologist-generated lung lobe segmentations on a set of challenging cases.

  12. Clinical anatomy of the subserous layer: An amalgamation of gross and clinical anatomy.

    Science.gov (United States)

    Yabuki, Yoshihiko

    2016-05-01

    The 1998 edition of Terminologia Anatomica introduced some currently used clinical anatomical terms for the pelvic connective tissue or subserous layer. These innovations persuaded the present author to consider a format in which the clinical anatomical terms could be reconciled with those of gross anatomy and incorporated into a single anatomical glossary without contradiction or ambiguity. Specific studies on the subserous layer were undertaken on 79 Japanese women who had undergone surgery for uterine cervical cancer, and on 26 female cadavers that were dissected, 17 being formalin-fixed and 9 fresh. The results were as follows: (a) the subserous layer could be segmentalized by surgical dissection in the perpendicular, horizontal and sagittal planes; (b) the segmentalized subserous layer corresponded to 12 cubes, or ligaments, of minimal dimension that enabled the pelvic organs to be extirpated; (c) each ligament had a three-dimensional (3D) structure comprising craniocaudal, mediolateral, and dorsoventral directions vis-á-vis the pelvic axis; (d) these 3D-structured ligaments were encoded morphologically in order of decreasing length; and (e) using these codes, all the surgical procedures for 19th century to present-day radical hysterectomy could be expressed symbolically. The establishment of clinical anatomical terms, represented symbolically through coding as demonstrated in this article, could provide common ground for amalgamating clinical anatomy with gross anatomy. Consequently, terms in clinical anatomy and gross anatomy could be reconciled and compiled into a single anatomical glossary. © 2015 Wiley Periodicals, Inc.

  13. An automatic system for segmentation, matching, anatomical labeling and measurement of airways from CT images

    DEFF Research Database (Denmark)

    Petersen, Jens; Feragen, Aasa; Owen, Megan

    segmental branches, and longitudinal matching of airway branches in repeated scans of the same subject. Methods and Materials: The segmentation process begins from an automatically detected seed point in the trachea. The airway centerline tree is then constructed by iteratively adding locally optimal paths...... differences. Results: The segmentation method has been used on 9711 low dose CT images from the Danish Lung Cancer Screening Trial (DLCST). Manual inspection of thumbnail images revealed gross errors in a total of 44 images. 29 were missing branches at the lobar level and only 15 had obvious false positives...... measurements to segments matched in multiple images of the same subject using image registration was observed to increase their reproducibility. The anatomical branch labeling tool was validated on a subset of 20 subjects, 5 of each category: asymptomatic, mild, moderate and severe COPD. The average inter...

  14. Automatic Segmentation and Quantification of Filamentous Structures in Electron Tomography.

    Science.gov (United States)

    Loss, Leandro A; Bebis, George; Chang, Hang; Auer, Manfred; Sarkar, Purbasha; Parvin, Bahram

    2012-10-01

    Electron tomography is a promising technology for imaging ultrastructures at nanoscale resolutions. However, image and quantitative analyses are often hindered by high levels of noise, staining heterogeneity, and material damage either as a result of the electron beam or sample preparation. We have developed and built a framework that allows for automatic segmentation and quantification of filamentous objects in 3D electron tomography. Our approach consists of three steps: (i) local enhancement of filaments by Hessian filtering; (ii) detection and completion (e.g., gap filling) of filamentous structures through tensor voting; and (iii) delineation of the filamentous networks. Our approach allows for quantification of filamentous networks in terms of their compositional and morphological features. We first validate our approach using a set of specifically designed synthetic data. We then apply our segmentation framework to tomograms of plant cell walls that have undergone different chemical treatments for polysaccharide extraction. The subsequent compositional and morphological analyses of the plant cell walls reveal their organizational characteristics and the effects of the different chemical protocols on specific polysaccharides.

  15. Straight trajectory planning for keyhole neurosurgery in sheep with automatic brain structures segmentation

    Science.gov (United States)

    Favaro, Alberto; Lad, Akash; Formenti, Davide; Zani, Davide Danilo; De Momi, Elena

    2017-03-01

    In a translational neuroscience/neurosurgery perspective, sheep are considered good candidates to study because of the similarity between their brain and the human one. Automatic planning systems for safe keyhole neurosurgery maximize the probe/catheter distance from vessels and risky structures. This work consists in the development of a trajectories planner for straight catheters placement intended to be used for investigating the drug diffusivity mechanisms in sheep brain. Automatic brain segmentation of gray matter, white matter and cerebrospinal fluid is achieved using an online available sheep atlas. Ventricles, midbrain and cerebellum segmentation have been also carried out. The veterinary surgeon is asked to select a target point within the white matter to be reached by the probe and to define an entry area on the brain cortex. To mitigate the risk of hemorrhage during the insertion process, which can prevent the success of the insertion procedure, the trajectory planner performs a curvature analysis of the brain cortex and wipes out from the poll of possible entry points the sulci, as part of brain cortex where superficial blood vessels are naturally located. A limited set of trajectories is then computed and presented to the surgeon, satisfying an optimality criteria based on a cost function which considers the distance from critical brain areas and the whole trajectory length. The planner proved to be effective in defining rectilinear trajectories accounting for the safety constraints determined by the brain morphology. It also demonstrated a short computational time and good capability in segmenting gyri and sulci surfaces.

  16. Automatic initial and final segmentation in cleft palate speech of Mandarin speakers.

    Directory of Open Access Journals (Sweden)

    Ling He

    Full Text Available The speech unit segmentation is an important pre-processing step in the analysis of cleft palate speech. In Mandarin, one syllable is composed of two parts: initial and final. In cleft palate speech, the resonance disorders occur at the finals and the voiced initials, while the articulation disorders occur at the unvoiced initials. Thus, the initials and finals are the minimum speech units, which could reflect the characteristics of cleft palate speech disorders. In this work, an automatic initial/final segmentation method is proposed. It is an important preprocessing step in cleft palate speech signal processing. The tested cleft palate speech utterances are collected from the Cleft Palate Speech Treatment Center in the Hospital of Stomatology, Sichuan University, which has the largest cleft palate patients in China. The cleft palate speech data includes 824 speech segments, and the control samples contain 228 speech segments. The syllables are extracted from the speech utterances firstly. The proposed syllable extraction method avoids the training stage, and achieves a good performance for both voiced and unvoiced speech. Then, the syllables are classified into with "quasi-unvoiced" or with "quasi-voiced" initials. Respective initial/final segmentation methods are proposed to these two types of syllables. Moreover, a two-step segmentation method is proposed. The rough locations of syllable and initial/final boundaries are refined in the second segmentation step, in order to improve the robustness of segmentation accuracy. The experiments show that the initial/final segmentation accuracies for syllables with quasi-unvoiced initials are higher than quasi-voiced initials. For the cleft palate speech, the mean time error is 4.4ms for syllables with quasi-unvoiced initials, and 25.7ms for syllables with quasi-voiced initials, and the correct segmentation accuracy P30 for all the syllables is 91.69%. For the control samples, P30 for all the

  17. Evaluation of an atlas-based automatic segmentation software for the delineation of brain organs at risk in a radiation therapy clinical context

    International Nuclear Information System (INIS)

    Isambert, Aurelie; Dhermain, Frederic; Bidault, Francois; Commowick, Olivier; Bondiau, Pierre-Yves; Malandain, Gregoire; Lefkopoulos, Dimitri

    2008-01-01

    Background and purpose: Conformal radiation therapy techniques require the delineation of volumes of interest, a time-consuming and operator-dependent task. In this work, we aimed to evaluate the potential interest of an atlas-based automatic segmentation software (ABAS) of brain organs at risk (OAR), when used under our clinical conditions. Materials and methods: Automatic and manual segmentations of the eyes, optic nerves, optic chiasm, pituitary gland, brain stem and cerebellum of 11 patients on T1-weighted magnetic resonance, 3-mm thick slice images were compared using the Dice similarity coefficient (DSC). The sensitivity and specificity of the ABAS were also computed and analysed from a radiotherapy point of view by splitting the ROC (Receiver Operating Characteristic) space into four sub-regions. Results: Automatic segmentation of OAR was achieved in 7-8 min. Excellent agreement was obtained between automatic and manual delineations for organs exceeding 7 cm 3 : the DSC was greater than 0.8. For smaller structures, the DSC was lower than 0.41. Conclusions: These tests demonstrated that this ABAS is a robust and reliable tool for automatic delineation of large structures under clinical conditions in our daily practice, even though the small structures must continue to be delineated manually by an expert

  18. 3D automatic segmentation method for retinal optical coherence tomography volume data using boundary surface enhancement

    Directory of Open Access Journals (Sweden)

    Yankui Sun

    2016-03-01

    Full Text Available With the introduction of spectral-domain optical coherence tomography (SD-OCT, much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, there is a critical need for the development of three-dimensional (3D segmentation methods for processing these data. We present here a novel 3D automatic segmentation method for retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume datasets are obtained by using a 3D smoothing filter and a 3D differential filter. Their linear combination is then calculated to generate new volume data with an enhanced boundary surface, where pixel intensity, boundary position information, and intensity changes on both sides of the boundary surface are used simultaneously. Next, preliminary discrete boundary points are detected from the A-Scans of the volume data. Finally, surface smoothness constraints and a dynamic threshold are applied to obtain a smoothed boundary surface by correcting a small number of error points. Our method can extract retinal layer boundary surfaces sequentially with a decreasing search region of volume data. We performed automatic segmentation on eight human OCT volume datasets acquired from a commercial Spectralis OCT system, where each volume of datasets contains 97 OCT B-Scan images with a resolution of 496×512 (each B-Scan comprising 512 A-Scans containing 496 pixels; experimental results show that this method can accurately segment seven layer boundary surfaces in normal as well as some abnormal eyes.

  19. Automatic 2D segmentation of airways in thorax computed tomography images; Segmentacao automatica 2D de vias aereas em imagens de tomografia computadorizada do torax

    Energy Technology Data Exchange (ETDEWEB)

    Cavalcante, Tarique da Silveira; Cortez, Paulo Cesar; Almeida, Thomaz Maia de, E-mail: tarique@lesc.ufc.br [Universidade Federal do Ceara (UFC), Fortaleza, CE (Brazil). Dept. de Engenharia de Teleinformatica; Felix, John Hebert da Silva [Universidade da Integracao Internacional da Lusofonia Afro-Brasileira (UNILAB), Redencao, CE (Brazil). Departamento de Energias; Holanda, Marcelo Alcantara [Universidade Federal do Ceara (UFC), Fortaleza, CE (Brazil). Fac. de Medicina

    2013-07-01

    Introduction: much of the world population is affected by pulmonary diseases, such as the bronchial asthma, bronchitis and bronchiectasis. The bronchial diagnosis is based on the airways state. In this sense, the automatic segmentation of the airways in Computed Tomography (CT) scans is a critical step in the aid to diagnosis of these diseases. Methods: this paper evaluates algorithms for airway automatic segmentation, using Neural Network Multilayer Perceptron (MLP) and Lung Densities Analysis (LDA) for detecting airways, along with Region Growing (RG), Active Contour Method (ACM) Balloon and Topology Adaptive to segment them. Results: we obtained results in three stages: comparative analysis of the detection algorithms MLP and LDA, with a gold standard acquired by three physicians with expertise in CT imaging of the chest; comparative analysis of segmentation algorithms ACM Balloon, ACM Topology Adaptive, MLP and RG; and evaluation of possible combinations between segmentation and detection algorithms, resulting in the complete method for automatic segmentation of the airways in 2D. Conclusion: the low incidence of false negative and the significant reduction of false positive, results in similarity coefficient and sensitivity exceeding 91% and 87% respectively, for a combination of algorithms with satisfactory segmentation quality. (author)

  20. Template-based automatic breast segmentation on MRI by excluding the chest region

    OpenAIRE

    Lin, M; Chen, JH; Wang, X; Chan, S; Chen, S; Su, MY

    2013-01-01

    Purpose: Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method. Methods: Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as th e template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the c...

  1. Patient specific anatomy: the new area of anatomy based on computer science illustrated on liver.

    Science.gov (United States)

    Soler, Luc; Mutter, Didier; Pessaux, Patrick; Marescaux, Jacques

    2015-01-01

    Over the past century, medical imaging has brought a new revolution: internal anatomy of a patient could be seen without any invasive technique. This revolution has highlighted the two main limits of current anatomy: the anatomical description is physician dependent, and the average anatomy is more and more frequently insufficient to describe anatomical variations. These drawbacks can sometimes be so important that they create mistakes but they can be overcome through the use of 3D patient-specific surgical anatomy. In this article, we propose to illustrate such improvement of standard anatomy on liver. We first propose a general scheme allowing to easily compare the four main liver anatomical descriptions by Takasaki, Goldsmith and Woodburne, Bismuth and Couinaud. From this general scheme we propose four rules to apply in order to correct these initial anatomical definitions. Application of these rules allows to correct usual vascular topological mistakes of standard anatomy. We finally validate such correction on a database of 20 clinical cases compared to the 111 clinical cases of a Couinaud article. Out of the 20 images of the database, we note a revealing difference in 14 cases (70%) on at least one important branch of the portal network. Only six cases (30%) do not present a revealing difference between both labellings. We also show that the right portal fissure location on our 20 cases defined between segment V and VI of our anatomical definition is well correlated with the real position described by Couinaud on 111 cases, knowing that the theoretical position was only found in 46 cases out of 111, i.e., 41.44% of cases with the non-corrected Couinaud definition. We have proposed a new anatomical segmentation of the liver based on four main rules to apply in order to correct topological errors of the four main standard segmentations. Our validation clearly illustrates that this new definition corrects the large amount of mistakes created by the current

  2. Texture analysis of automatic graph cuts segmentations for detection of lung cancer recurrence after stereotactic radiotherapy

    Science.gov (United States)

    Mattonen, Sarah A.; Palma, David A.; Haasbeek, Cornelis J. A.; Senan, Suresh; Ward, Aaron D.

    2015-03-01

    Stereotactic ablative radiotherapy (SABR) is a treatment for early-stage lung cancer with local control rates comparable to surgery. After SABR, benign radiation induced lung injury (RILI) results in tumour-mimicking changes on computed tomography (CT) imaging. Distinguishing recurrence from RILI is a critical clinical decision determining the need for potentially life-saving salvage therapies whose high risks in this population dictate their use only for true recurrences. Current approaches do not reliably detect recurrence within a year post-SABR. We measured the detection accuracy of texture features within automatically determined regions of interest, with the only operator input being the single line segment measuring tumour diameter, normally taken during the clinical workflow. Our leave-one-out cross validation on images taken 2-5 months post-SABR showed robustness of the entropy measure, with classification error of 26% and area under the receiver operating characteristic curve (AUC) of 0.77 using automatic segmentation; the results using manual segmentation were 24% and 0.75, respectively. AUCs for this feature increased to 0.82 and 0.93 at 8-14 months and 14-20 months post SABR, respectively, suggesting even better performance nearer to the date of clinical diagnosis of recurrence; thus this system could also be used to support and reinforce the physician's decision at that time. Based on our ongoing validation of this automatic approach on a larger sample, we aim to develop a computer-aided diagnosis system which will support the physician's decision to apply timely salvage therapies and prevent patients with RILI from undergoing invasive and risky procedures.

  3. Myocardial segmentation based on coronary anatomy using coronary computed tomography angiography: Development and validation in a pig model

    Energy Technology Data Exchange (ETDEWEB)

    Chung, Mi Sun [Chung-Ang University College of Medicine, Department of Radiology, Chung-Ang University Hospital, Seoul (Korea, Republic of); Yang, Dong Hyun; Seo, Joon Beom; Kang, Joon-Won; Lim, Tae-Hwan [Asan Medical Center, University of Ulsan College of Medicine, Department of Radiology and Research Institute of Radiology, Seoul (Korea, Republic of); Kim, Young-Hak; Kang, Soo-Jin; Jung, Joonho [Asan Medical Center, University of Ulsan College of Medicine, Heart Institute, Seoul (Korea, Republic of); Kim, Namkug [Asan Medical Center, University of Ulsan College of Medicine, Department of Convergence Medicine, Seoul (Korea, Republic of); Heo, Seung-Ho [Asan Medical Center, University of Ulsan College of Medicine, Asan institute for Life Science, Seoul (Korea, Republic of); Baek, Seunghee [Asan Medical Center, University of Ulsan College of Medicine, Department of Clinical Epidemiology and Biostatistics, Seoul (Korea, Republic of); Choi, Byoung Wook [Yonsei University, Department of Diagnostic Radiology, College of Medicine, Seoul (Korea, Republic of)

    2017-10-15

    To validate a method for performing myocardial segmentation based on coronary anatomy using coronary CT angiography (CCTA). Coronary artery-based myocardial segmentation (CAMS) was developed for use with CCTA. To validate and compare this method with the conventional American Heart Association (AHA) classification, a single coronary occlusion model was prepared and validated using six pigs. The unstained occluded coronary territories of the specimens and corresponding arterial territories from CAMS and AHA segmentations were compared using slice-by-slice matching and 100 virtual myocardial columns. CAMS more precisely predicted ischaemic area than the AHA method, as indicated by 95% versus 76% (p < 0.001) of the percentage of matched columns (defined as percentage of matched columns of segmentation method divided by number of unstained columns in the specimen). According to the subgroup analyses, CAMS demonstrated a higher percentage of matched columns than the AHA method in the left anterior descending artery (100% vs. 77%; p < 0.001) and mid- (99% vs. 83%; p = 0.046) and apical-level territories of the left ventricle (90% vs. 52%; p = 0.011). CAMS is a feasible method for identifying the corresponding myocardial territories of the coronary arteries using CCTA. (orig.)

  4. Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model.

    Science.gov (United States)

    Chen, Yunjie; Zhao, Bo; Zhang, Jianwei; Zheng, Yuhui

    2014-09-01

    Accurate segmentation of magnetic resonance (MR) images remains challenging mainly due to the intensity inhomogeneity, which is also commonly known as bias field. Recently active contour models with geometric information constraint have been applied, however, most of them deal with the bias field by using a necessary pre-processing step before segmentation of MR data. This paper presents a novel automatic variational method, which can segment brain MR images meanwhile correcting the bias field when segmenting images with high intensity inhomogeneities. We first define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. In order to reduce the effect of the noise, the local intensity variations are described by the Gaussian distributions with different means and variances. Then, the objective functions are integrated over the entire domain. In order to obtain the global optimal and make the results independent of the initialization of the algorithm, we reconstructed the energy function to be convex and calculated it by using the Split Bregman theory. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. Our method is able to estimate the bias of quite general profiles, even in 7T MR images. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. SU-F-J-113: Multi-Atlas Based Automatic Organ Segmentation for Lung Radiotherapy Planning

    International Nuclear Information System (INIS)

    Kim, J; Han, J; Ailawadi, S; Baker, J; Hsia, A; Xu, Z; Ryu, S

    2016-01-01

    Purpose: Normal organ segmentation is one time-consuming and labor-intensive step for lung radiotherapy treatment planning. The aim of this study is to evaluate the performance of a multi-atlas based segmentation approach for automatic organs at risk (OAR) delineation. Methods: Fifteen Lung stereotactic body radiation therapy patients were randomly selected. Planning CT images and OAR contours of the heart - HT, aorta - AO, vena cava - VC, pulmonary trunk - PT, and esophagus – ES were exported and used as reference and atlas sets. For automatic organ delineation for a given target CT, 1) all atlas sets were deformably warped to the target CT, 2) the deformed sets were accumulated and normalized to produce organ probability density (OPD) maps, and 3) the OPD maps were converted to contours via image thresholding. Optimal threshold for each organ was empirically determined by comparing the auto-segmented contours against their respective reference contours. The delineated results were evaluated by measuring contour similarity metrics: DICE, mean distance (MD), and true detection rate (TD), where DICE=(intersection volume/sum of two volumes) and TD = {1.0 - (false positive + false negative)/2.0}. Diffeomorphic Demons algorithm was employed for CT-CT deformable image registrations. Results: Optimal thresholds were determined to be 0.53 for HT, 0.38 for AO, 0.28 for PT, 0.43 for VC, and 0.31 for ES. The mean similarity metrics (DICE[%], MD[mm], TD[%]) were (88, 3.2, 89) for HT, (79, 3.2, 82) for AO, (75, 2.7, 77) for PT, (68, 3.4, 73) for VC, and (51,2.7, 60) for ES. Conclusion: The investigated multi-atlas based approach produced reliable segmentations for the organs with large and relatively clear boundaries (HT and AO). However, the detection of small and narrow organs with diffused boundaries (ES) were challenging. Sophisticated atlas selection and multi-atlas fusion algorithms may further improve the quality of segmentations.

  6. SU-E-J-129: Atlas Development for Cardiac Automatic Contouring Using Multi-Atlas Segmentation

    International Nuclear Information System (INIS)

    Zhou, R; Yang, J; Pan, T; Milgrom, S; Pinnix, C; Shi, A; Yang, J; Liu, Y; Nguyen, Q; Gomez, D; Dabaja, B; Balter, P; Court, L; Liao, Z

    2015-01-01

    Purpose: To develop a set of atlases for automatic contouring of cardiac structures to determine heart radiation dose and the associated toxicity. Methods: Six thoracic cancer patients with both contrast and non-contrast CT images were acquired for this study. Eight radiation oncologists manually and independently delineated cardiac contours on the non-contrast CT by referring to the fused contrast CT and following the RTOG 1106 atlas contouring guideline. Fifteen regions of interest (ROIs) were delineated, including heart, four chambers, four coronary arteries, pulmonary artery and vein, inferior and superior vena cava, and ascending and descending aorta. Individual expert contours were fused using the simultaneous truth and performance level estimation (STAPLE) algorithm for each ROI and each patient. The fused contours became atlases for an in-house multi-atlas segmentation. Using leave-one-out test, we generated auto-segmented contours for each ROI and each patient. The auto-segmented contours were compared with the fused contours using the Dice similarity coefficient (DSC) and the mean surface distance (MSD). Results: Inter-observer variability was not obvious for heart, chambers, and aorta but was large for other structures that were not clearly distinguishable on CT image. The average DSC between individual expert contours and the fused contours were less than 50% for coronary arteries and pulmonary vein, and the average MSD were greater than 4.0 mm. The largest MSD of expert contours deviating from the fused contours was 2.5 cm. The mean DSC and MSD of auto-segmented contours were within one standard deviation of expert contouring variability except the right coronary artery. The coronary arteries, vena cava, and pulmonary vein had DSC<70% and MSD>3.0 mm. Conclusion: A set of cardiac atlases was created for cardiac automatic contouring, the accuracy of which was comparable to the variability in expert contouring. However, substantial modification may need

  7. SU-F-J-113: Multi-Atlas Based Automatic Organ Segmentation for Lung Radiotherapy Planning

    Energy Technology Data Exchange (ETDEWEB)

    Kim, J; Han, J; Ailawadi, S; Baker, J; Hsia, A; Xu, Z; Ryu, S [Stony Brook University Hospital, Stony Brook, NY (United States)

    2016-06-15

    Purpose: Normal organ segmentation is one time-consuming and labor-intensive step for lung radiotherapy treatment planning. The aim of this study is to evaluate the performance of a multi-atlas based segmentation approach for automatic organs at risk (OAR) delineation. Methods: Fifteen Lung stereotactic body radiation therapy patients were randomly selected. Planning CT images and OAR contours of the heart - HT, aorta - AO, vena cava - VC, pulmonary trunk - PT, and esophagus – ES were exported and used as reference and atlas sets. For automatic organ delineation for a given target CT, 1) all atlas sets were deformably warped to the target CT, 2) the deformed sets were accumulated and normalized to produce organ probability density (OPD) maps, and 3) the OPD maps were converted to contours via image thresholding. Optimal threshold for each organ was empirically determined by comparing the auto-segmented contours against their respective reference contours. The delineated results were evaluated by measuring contour similarity metrics: DICE, mean distance (MD), and true detection rate (TD), where DICE=(intersection volume/sum of two volumes) and TD = {1.0 - (false positive + false negative)/2.0}. Diffeomorphic Demons algorithm was employed for CT-CT deformable image registrations. Results: Optimal thresholds were determined to be 0.53 for HT, 0.38 for AO, 0.28 for PT, 0.43 for VC, and 0.31 for ES. The mean similarity metrics (DICE[%], MD[mm], TD[%]) were (88, 3.2, 89) for HT, (79, 3.2, 82) for AO, (75, 2.7, 77) for PT, (68, 3.4, 73) for VC, and (51,2.7, 60) for ES. Conclusion: The investigated multi-atlas based approach produced reliable segmentations for the organs with large and relatively clear boundaries (HT and AO). However, the detection of small and narrow organs with diffused boundaries (ES) were challenging. Sophisticated atlas selection and multi-atlas fusion algorithms may further improve the quality of segmentations.

  8. Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection -A First Study

    Science.gov (United States)

    Hu, Dandan; Sarder, Pinaki; Ronhovde, Peter; Orthaus, Sandra; Achilefu, Samuel; Nussinov, Zohar

    2014-01-01

    Inspired by a multi-resolution community detection (MCD) based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Further, using the proposed method, the mean-square error (MSE) in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The MCD method appeared to perform better than a popular spectral clustering based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in MSE with increasing resolution. PMID:24251410

  9. Performance evaluation of 2D and 3D deep learning approaches for automatic segmentation of multiple organs on CT images

    Science.gov (United States)

    Zhou, Xiangrong; Yamada, Kazuma; Kojima, Takuya; Takayama, Ryosuke; Wang, Song; Zhou, Xinxin; Hara, Takeshi; Fujita, Hiroshi

    2018-02-01

    The purpose of this study is to evaluate and compare the performance of modern deep learning techniques for automatically recognizing and segmenting multiple organ regions on 3D CT images. CT image segmentation is one of the important task in medical image analysis and is still very challenging. Deep learning approaches have demonstrated the capability of scene recognition and semantic segmentation on nature images and have been used to address segmentation problems of medical images. Although several works showed promising results of CT image segmentation by using deep learning approaches, there is no comprehensive evaluation of segmentation performance of the deep learning on segmenting multiple organs on different portions of CT scans. In this paper, we evaluated and compared the segmentation performance of two different deep learning approaches that used 2D- and 3D deep convolutional neural networks (CNN) without- and with a pre-processing step. A conventional approach that presents the state-of-the-art performance of CT image segmentation without deep learning was also used for comparison. A dataset that includes 240 CT images scanned on different portions of human bodies was used for performance evaluation. The maximum number of 17 types of organ regions in each CT scan were segmented automatically and compared to the human annotations by using ratio of intersection over union (IU) as the criterion. The experimental results demonstrated the IUs of the segmentation results had a mean value of 79% and 67% by averaging 17 types of organs that segmented by a 3D- and 2D deep CNN, respectively. All the results of the deep learning approaches showed a better accuracy and robustness than the conventional segmentation method that used probabilistic atlas and graph-cut methods. The effectiveness and the usefulness of deep learning approaches were demonstrated for solving multiple organs segmentation problem on 3D CT images.

  10. Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study

    International Nuclear Information System (INIS)

    Deeley, M A; Cmelak, A J; Malcolm, A W; Moretti, L; Jaboin, J; Niermann, K; Yang, Eddy S; Yu, David S; Ding, G X; Chen, A; Datteri, R; Noble, J H; Dawant, B M; Donnelly, E F; Yei, F; Koyama, T

    2011-01-01

    The purpose of this work was to characterize expert variation in segmentation of intracranial structures pertinent to radiation therapy, and to assess a registration-driven atlas-based segmentation algorithm in that context. Eight experts were recruited to segment the brainstem, optic chiasm, optic nerves, and eyes, of 20 patients who underwent therapy for large space-occupying tumors. Performance variability was assessed through three geometric measures: volume, Dice similarity coefficient, and Euclidean distance. In addition, two simulated ground truth segmentations were calculated via the simultaneous truth and performance level estimation algorithm and a novel application of probability maps. The experts and automatic system were found to generate structures of similar volume, though the experts exhibited higher variation with respect to tubular structures. No difference was found between the mean Dice similarity coefficient (DSC) of the automatic and expert delineations as a group at a 5% significance level over all cases and organs. The larger structures of the brainstem and eyes exhibited mean DSC of approximately 0.8-0.9, whereas the tubular chiasm and nerves were lower, approximately 0.4-0.5. Similarly low DSCs have been reported previously without the context of several experts and patient volumes. This study, however, provides evidence that experts are similarly challenged. The average maximum distances (maximum inside, maximum outside) from a simulated ground truth ranged from (-4.3, +5.4) mm for the automatic system to (-3.9, +7.5) mm for the experts considered as a group. Over all the structures in a rank of true positive rates at a 2 mm threshold from the simulated ground truth, the automatic system ranked second of the nine raters. This work underscores the need for large scale studies utilizing statistically robust numbers of patients and experts in evaluating quality of automatic algorithms.

  11. Body Composition Assessment in Axial CT Images Using FEM-Based Automatic Segmentation of Skeletal Muscle.

    Science.gov (United States)

    Popuri, Karteek; Cobzas, Dana; Esfandiari, Nina; Baracos, Vickie; Jägersand, Martin

    2016-02-01

    The proportions of muscle and fat tissues in the human body, referred to as body composition is a vital measurement for cancer patients. Body composition has been recently linked to patient survival and the onset/recurrence of several types of cancers in numerous cancer research studies. This paper introduces a fully automatic framework for the segmentation of muscle and fat tissues from CT images to estimate body composition. We developed a novel finite element method (FEM) deformable model that incorporates a priori shape information via a statistical deformation model (SDM) within the template-based segmentation framework. The proposed method was validated on 1000 abdominal and 530 thoracic CT images and we obtained very good segmentation results with Jaccard scores in excess of 90% for both the muscle and fat regions.

  12. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.

    Science.gov (United States)

    Avendi, M R; Kheradvar, Arash; Jafarkhani, Hamid

    2016-05-01

    Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms combined with deformable models to develop and evaluate a fully automatic LV segmentation tool from short-axis cardiac MRI datasets. The method employs deep learning algorithms to learn the segmentation task from the ground true data. Convolutional networks are employed to automatically detect the LV chamber in MRI dataset. Stacked autoencoders are used to infer the LV shape. The inferred shape is incorporated into deformable models to improve the accuracy and robustness of the segmentation. We validated our method using 45 cardiac MR datasets from the MICCAI 2009 LV segmentation challenge and showed that it outperforms the state-of-the art methods. Excellent agreement with the ground truth was achieved. Validation metrics, percentage of good contours, Dice metric, average perpendicular distance and conformity, were computed as 96.69%, 0.94, 1.81 mm and 0.86, versus those of 79.2-95.62%, 0.87-0.9, 1.76-2.97 mm and 0.67-0.78, obtained by other methods, respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

  13. Automatic segmentation of abdominal organs and adipose tissue compartments in water-fat MRI: Application to weight-loss in obesity.

    Science.gov (United States)

    Shen, Jun; Baum, Thomas; Cordes, Christian; Ott, Beate; Skurk, Thomas; Kooijman, Hendrik; Rummeny, Ernst J; Hauner, Hans; Menze, Bjoern H; Karampinos, Dimitrios C

    2016-09-01

    To develop a fully automatic algorithm for abdominal organs and adipose tissue compartments segmentation and to assess organ and adipose tissue volume changes in longitudinal water-fat magnetic resonance imaging (MRI) data. Axial two-point Dixon images were acquired in 20 obese women (age range 24-65, BMI 34.9±3.8kg/m(2)) before and after a four-week calorie restriction. Abdominal organs, subcutaneous adipose tissue (SAT) compartments (abdominal, anterior, posterior), SAT regions along the feet-head direction and regional visceral adipose tissue (VAT) were assessed by a fully automatic algorithm using morphological operations and a multi-atlas-based segmentation method. The accuracy of organ segmentation represented by Dice coefficients ranged from 0.672±0.155 for the pancreas to 0.943±0.023 for the liver. Abdominal SAT changes were significantly greater in the posterior than the anterior SAT compartment (-11.4%±5.1% versus -9.5%±6.3%, pabdominal adipose tissue and organ segmentation, and allowed the detection of SAT and VAT subcompartments changes during weight loss. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  14. Automated segmentation of the atrial region and fossa ovalis towards computer-aided planning of inter-atrial wall interventions.

    Science.gov (United States)

    Morais, Pedro; Vilaça, João L; Queirós, Sandro; Marchi, Alberto; Bourier, Felix; Deisenhofer, Isabel; D'hooge, Jan; Tavares, João Manuel R S

    2018-07-01

    Image-fusion strategies have been applied to improve inter-atrial septal (IAS) wall minimally-invasive interventions. Hereto, several landmarks are initially identified on richly-detailed datasets throughout the planning stage and then combined with intra-operative images, enhancing the relevant structures and easing the procedure. Nevertheless, such planning is still performed manually, which is time-consuming and not necessarily reproducible, hampering its regular application. In this article, we present a novel automatic strategy to segment the atrial region (left/right atrium and aortic tract) and the fossa ovalis (FO). The method starts by initializing multiple 3D contours based on an atlas-based approach with global transforms only and refining them to the desired anatomy using a competitive segmentation strategy. The obtained contours are then applied to estimate the FO by evaluating both IAS wall thickness and the expected FO spatial location. The proposed method was evaluated in 41 computed tomography datasets, by comparing the atrial region segmentation and FO estimation results against manually delineated contours. The automatic segmentation method presented a performance similar to the state-of-the-art techniques and a high feasibility, failing only in the segmentation of one aortic tract and of one right atrium. The FO estimation method presented an acceptable result in all the patients with a performance comparable to the inter-observer variability. Moreover, it was faster and fully user-interaction free. Hence, the proposed method proved to be feasible to automatically segment the anatomical models for the planning of IAS wall interventions, making it exceptionally attractive for use in the clinical practice. Copyright © 2018 Elsevier B.V. All rights reserved.

  15. First performance evaluation of software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine at CT

    Energy Technology Data Exchange (ETDEWEB)

    Scholtz, Jan-Erik, E-mail: janerikscholtz@gmail.com; Wichmann, Julian L.; Kaup, Moritz; Fischer, Sebastian; Kerl, J. Matthias; Lehnert, Thomas; Vogl, Thomas J.; Bauer, Ralf W.

    2015-03-15

    Highlights: •Automatic segmentation and labeling of the thoracolumbar spine. •Automatically generated double-angulated and aligned axial images of spine segments. •High grade of accurateness for the symmetric depiction of anatomical structures. •Time-saving and may improve workflow in daily practice. -- Abstract: Objectives: To evaluate software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine on CT in terms of accuracy, potential for time savings and workflow improvement. Material and methods: 77 patients (28 women, 49 men, mean age 65.3 ± 14.4 years) with known or suspected spinal disorders (degenerative spine disease n = 32; disc herniation n = 36; traumatic vertebral fractures n = 9) underwent 64-slice MDCT with thin-slab reconstruction. Time for automatic labeling of the thoracolumbar spine and reconstruction of double-angulated axial images of the pathological vertebrae was compared with manually performed reconstruction of anatomical aligned axial images. Reformatted images of both reconstruction methods were assessed by two observers regarding accuracy of symmetric depiction of anatomical structures. Results: In 33 cases double-angulated axial images were created in 1 vertebra, in 28 cases in 2 vertebrae and in 16 cases in 3 vertebrae. Correct automatic labeling was achieved in 72 of 77 patients (93.5%). Errors could be manually corrected in 4 cases. Automatic labeling required 1 min in average. In cases where anatomical aligned axial images of 1 vertebra were created, reconstructions made by hand were significantly faster (p < 0.05). Automatic reconstruction was time-saving in cases of 2 and more vertebrae (p < 0.05). Both reconstruction methods revealed good image quality with excellent inter-observer agreement. Conclusion: The evaluated software for automatic labeling and anatomically aligned, double-angulated axial image reconstruction of the thoracolumbar spine on CT is time

  16. Atlas ranking and selection for automatic segmentation of the esophagus from CT scans

    Science.gov (United States)

    Yang, Jinzhong; Haas, Benjamin; Fang, Raymond; Beadle, Beth M.; Garden, Adam S.; Liao, Zhongxing; Zhang, Lifei; Balter, Peter; Court, Laurence

    2017-12-01

    In radiation treatment planning, the esophagus is an important organ-at-risk that should be spared in patients with head and neck cancer or thoracic cancer who undergo intensity-modulated radiation therapy. However, automatic segmentation of the esophagus from CT scans is extremely challenging because of the structure’s inconsistent intensity, low contrast against the surrounding tissues, complex and variable shape and location, and random air bubbles. The goal of this study is to develop an online atlas selection approach to choose a subset of optimal atlases for multi-atlas segmentation to the delineate esophagus automatically. We performed atlas selection in two phases. In the first phase, we used the correlation coefficient of the image content in a cubic region between each atlas and the new image to evaluate their similarity and to rank the atlases in an atlas pool. A subset of atlases based on this ranking was selected, and deformable image registration was performed to generate deformed contours and deformed images in the new image space. In the second phase of atlas selection, we used Kullback-Leibler divergence to measure the similarity of local-intensity histograms between the new image and each of the deformed images, and the measurements were used to rank the previously selected atlases. Deformed contours were overlapped sequentially, from the most to the least similar, and the overlap ratio was examined. We further identified a subset of optimal atlases by analyzing the variation of the overlap ratio versus the number of atlases. The deformed contours from these optimal atlases were fused together using a modified simultaneous truth and performance level estimation algorithm to produce the final segmentation. The approach was validated with promising results using both internal data sets (21 head and neck cancer patients and 15 thoracic cancer patients) and external data sets (30 thoracic patients).

  17. Automatic classification of transient ischaemic and transient non-ischaemic heart-rate related ST segment deviation episodes in ambulatory ECG records

    International Nuclear Information System (INIS)

    Faganeli, J; Jager, F

    2010-01-01

    In ambulatory ECG records, besides transient ischaemic ST segment deviation episodes, there are also transient non-ischaemic heart-rate related ST segment deviation episodes present, which appear only due to a change in heart rate and thus complicate automatic detection of true ischaemic episodes. The goal of this work was to automatically classify these two types of episodes. The tested features to classify the ST segment deviation episodes were changes of heart rate, changes of the Mahalanobis distance of the first five Karhunen–Loève transform (KLT) coefficients of the QRS complex, changes of time-domain morphologic parameters of the ST segment and changes of the Legendre orthonormal polynomial coefficients of the ST segment. We chose Legendre basis functions because they best fit typical shapes of the ST segment morphology, thus allowing direct insight into the ST segment morphology changes through the feature space. The classification was performed with the help of decision trees. We tested the classification method using all records of the Long-Term ST Database on all ischaemic and all non-ischaemic heart-rate related deviation episodes according to annotation protocol B. In order to predict the real-world performance of the classification we used second-order aggregate statistics, gross and average statistics, and the bootstrap method. We obtained the best performance when we combined the heart-rate features, the Mahalanobis distance and the Legendre orthonormal polynomial coefficient features, with average sensitivity of 98.1% and average specificity of 85.2%

  18. Automatic lung lobe segmentation of COPD patients using iterative B-spline fitting

    Science.gov (United States)

    Shamonin, D. P.; Staring, M.; Bakker, M. E.; Xiao, C.; Stolk, J.; Reiber, J. H. C.; Stoel, B. C.

    2012-02-01

    We present an automatic lung lobe segmentation algorithm for COPD patients. The method enhances fissures, removes unlikely fissure candidates, after which a B-spline is fitted iteratively through the remaining candidate objects. The iterative fitting approach circumvents the need to classify each object as being part of the fissure or being noise, and allows the fissure to be detected in multiple disconnected parts. This property is beneficial for good performance in patient data, containing incomplete and disease-affected fissures. The proposed algorithm is tested on 22 COPD patients, resulting in accurate lobe-based densitometry, and a median overlap of the fissure (defined 3 voxels wide) with an expert ground truth of 0.65, 0.54 and 0.44 for the three main fissures. This compares to complete lobe overlaps of 0.99, 0.98, 0.98, 0.97 and 0.87 for the five main lobes, showing promise for lobe segmentation on data of patients with moderate to severe COPD.

  19. Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features.

    Science.gov (United States)

    Rios Velazquez, Emmanuel; Meier, Raphael; Dunn, William D; Alexander, Brian; Wiest, Roland; Bauer, Stefan; Gutman, David A; Reyes, Mauricio; Aerts, Hugo J W L

    2015-11-18

    Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. MRI sets of 109 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA). Spearman's correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Auto-segmented sub-volumes showed moderate to high agreement with manually delineated volumes (range (r): 0.4 - 0.86). Also, the auto and manual volumes showed similar correlation with VASARI features (auto r = 0.35, 0.43 and 0.36; manual r = 0.17, 0.67, 0.41, for contrast-enhancing, necrosis and edema, respectively). The auto-segmented contrast-enhancing volume and post-contrast abnormal volume showed the highest AUC (0.66, CI: 0.55-0.77 and 0.65, CI: 0.54-0.76), comparable to manually defined volumes (0.64, CI: 0.53-0.75 and 0.63, CI: 0.52-0.74, respectively). BraTumIA and manual tumor sub-compartments showed comparable performance in terms of prognosis and correlation with VASARI features. This method can enable more reproducible definition and quantification of imaging based biomarkers and has potential in high-throughput medical imaging research.

  20. Automatic Human Movement Assessment With Switching Linear Dynamic System: Motion Segmentation and Motor Performance.

    Science.gov (United States)

    de Souza Baptista, Roberto; Bo, Antonio P L; Hayashibe, Mitsuhiro

    2017-06-01

    Performance assessment of human movement is critical in diagnosis and motor-control rehabilitation. Recent developments in portable sensor technology enable clinicians to measure spatiotemporal aspects to aid in the neurological assessment. However, the extraction of quantitative information from such measurements is usually done manually through visual inspection. This paper presents a novel framework for automatic human movement assessment that executes segmentation and motor performance parameter extraction in time-series of measurements from a sequence of human movements. We use the elements of a Switching Linear Dynamic System model as building blocks to translate formal definitions and procedures from human movement analysis. Our approach provides a method for users with no expertise in signal processing to create models for movements using labeled dataset and later use it for automatic assessment. We validated our framework on preliminary tests involving six healthy adult subjects that executed common movements in functional tests and rehabilitation exercise sessions, such as sit-to-stand and lateral elevation of the arms and five elderly subjects, two of which with limited mobility, that executed the sit-to-stand movement. The proposed method worked on random motion sequences for the dual purpose of movement segmentation (accuracy of 72%-100%) and motor performance assessment (mean error of 0%-12%).

  1. Automatic and quantitative measurement of collagen gel contraction using model-guided segmentation

    Science.gov (United States)

    Chen, Hsin-Chen; Yang, Tai-Hua; Thoreson, Andrew R.; Zhao, Chunfeng; Amadio, Peter C.; Sun, Yung-Nien; Su, Fong-Chin; An, Kai-Nan

    2013-08-01

    Quantitative measurement of collagen gel contraction plays a critical role in the field of tissue engineering because it provides spatial-temporal assessment (e.g., changes of gel area and diameter during the contraction process) reflecting the cell behavior and tissue material properties. So far the assessment of collagen gels relies on manual segmentation, which is time-consuming and suffers from serious intra- and inter-observer variability. In this study, we propose an automatic method combining various image processing techniques to resolve these problems. The proposed method first detects the maximal feasible contraction range of circular references (e.g., culture dish) and avoids the interference of irrelevant objects in the given image. Then, a three-step color conversion strategy is applied to normalize and enhance the contrast between the gel and background. We subsequently introduce a deformable circular model which utilizes regional intensity contrast and circular shape constraint to locate the gel boundary. An adaptive weighting scheme was employed to coordinate the model behavior, so that the proposed system can overcome variations of gel boundary appearances at different contraction stages. Two measurements of collagen gels (i.e., area and diameter) can readily be obtained based on the segmentation results. Experimental results, including 120 gel images for accuracy validation, showed high agreement between the proposed method and manual segmentation with an average dice similarity coefficient larger than 0.95. The results also demonstrated obvious improvement in gel contours obtained by the proposed method over two popular, generic segmentation methods.

  2. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images

    Directory of Open Access Journals (Sweden)

    Saurabh Jain

    2015-01-01

    Full Text Available The location and extent of white matter lesions on magnetic resonance imaging (MRI are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS. Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM and the appearance (hyperintense on FLAIR of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default

  3. Pancreas and cyst segmentation

    Science.gov (United States)

    Dmitriev, Konstantin; Gutenko, Ievgeniia; Nadeem, Saad; Kaufman, Arie

    2016-03-01

    Accurate segmentation of abdominal organs from medical images is an essential part of surgical planning and computer-aided disease diagnosis. Many existing algorithms are specialized for the segmentation of healthy organs. Cystic pancreas segmentation is especially challenging due to its low contrast boundaries, variability in shape, location and the stage of the pancreatic cancer. We present a semi-automatic segmentation algorithm for pancreata with cysts. In contrast to existing automatic segmentation approaches for healthy pancreas segmentation which are amenable to atlas/statistical shape approaches, a pancreas with cysts can have even higher variability with respect to the shape of the pancreas due to the size and shape of the cyst(s). Hence, fine results are better attained with semi-automatic steerable approaches. We use a novel combination of random walker and region growing approaches to delineate the boundaries of the pancreas and cysts with respective best Dice coefficients of 85.1% and 86.7%, and respective best volumetric overlap errors of 26.0% and 23.5%. Results show that the proposed algorithm for pancreas and pancreatic cyst segmentation is accurate and stable.

  4. A 3D global-to-local deformable mesh model based registration and anatomy-constrained segmentation method for image guided prostate radiotherapy

    International Nuclear Information System (INIS)

    Zhou Jinghao; Kim, Sung; Jabbour, Salma; Goyal, Sharad; Haffty, Bruce; Chen, Ting; Levinson, Lydia; Metaxas, Dimitris; Yue, Ning J.

    2010-01-01

    Purpose: In the external beam radiation treatment of prostate cancers, successful implementation of adaptive radiotherapy and conformal radiation dose delivery is highly dependent on precise and expeditious segmentation and registration of the prostate volume between the simulation and the treatment images. The purpose of this study is to develop a novel, fast, and accurate segmentation and registration method to increase the computational efficiency to meet the restricted clinical treatment time requirement in image guided radiotherapy. Methods: The method developed in this study used soft tissues to capture the transformation between the 3D planning CT (pCT) images and 3D cone-beam CT (CBCT) treatment images. The method incorporated a global-to-local deformable mesh model based registration framework as well as an automatic anatomy-constrained robust active shape model (ACRASM) based segmentation algorithm in the 3D CBCT images. The global registration was based on the mutual information method, and the local registration was to minimize the Euclidian distance of the corresponding nodal points from the global transformation of deformable mesh models, which implicitly used the information of the segmented target volume. The method was applied on six data sets of prostate cancer patients. Target volumes delineated by the same radiation oncologist on the pCT and CBCT were chosen as the benchmarks and were compared to the segmented and registered results. The distance-based and the volume-based estimators were used to quantitatively evaluate the results of segmentation and registration. Results: The ACRASM segmentation algorithm was compared to the original active shape model (ASM) algorithm by evaluating the values of the distance-based estimators. With respect to the corresponding benchmarks, the mean distance ranged from -0.85 to 0.84 mm for ACRASM and from -1.44 to 1.17 mm for ASM. The mean absolute distance ranged from 1.77 to 3.07 mm for ACRASM and from 2.45 to

  5. An anatomy-based beam segmentation tool for intensity-modulated radiation therapy and its application to head-and-neck cancer

    International Nuclear Information System (INIS)

    Gersem, Werner de; Claus, Filip; Wagter, Carlos de; Neve, Wilfried de

    2001-01-01

    Purpose: In segmental intensity-modulated radiation therapy (IMRT), the beam fluences result from superposition of unmodulated beamlets (segments). In the inverse planning approach, segments are a result of 'clipping' intensity maps. At Ghent University Hospital, segments are created by an anatomy-based segmentation tool (ABST). The objective of this report is to describe ABST. Methods and Materials: For each beam direction, ABST generates segments by a multistep procedure. During the initial steps, beam's eye view (BEV) projections of the planning target volumes (PTVs) and organs at risk (OARs) are generated. These projections are used to make a segmentation grid with negative values across the expanded OAR projections and positive values elsewhere inside the expanded PTV projections. Outside these regions, grid values are set to zero. Subsequent steps transform the positive values of the segmentation grid to increase with decreasing distance to the OAR projections and to increase with longer pathlengths measured along rays from their entrance point through the skin contours to their respective grid point. The final steps involve selection of iso-value lines of the segmentation grid as segment outlines which are transformed to leaf and jaw positions of a multileaf collimator (MLC). Segment shape approximations, if imposed by MLC constraints, are done in a way that minimizes overlap between the expanded OAR projections and the segment aperture. Results: The ABST procedure takes about 3 s/segment on a Compaq Alpha XP900 workstation. In IMRT planning problems with little complexity, such as laryngeal (example shown) or thyroid cancer, plans that are in accordance with the clinical protocol can be generated by weighting the segments generated by ABST without further optimization of their shapes. For complex IMRT plans such as paranasal sinus cancer (not shown), ABST generates a start assembly of segments from which the shapes and weights are further optimized

  6. Automatic Multi-Level Thresholding Segmentation Based on Multi-Objective Optimization

    Directory of Open Access Journals (Sweden)

    L. DJEROU,

    2012-01-01

    Full Text Available In this paper, we present a new multi-level image thresholding technique, called Automatic Threshold based on Multi-objective Optimization "ATMO" that combines the flexibility of multi-objective fitness functions with the power of a Binary Particle Swarm Optimization algorithm "BPSO", for searching the "optimum" number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare our segmentation method, based on the multi-objective optimization approach with Otsu’s, Kapur’s and Kittler’s methods. Our experimental results show that the thresholding method based on multi-objective optimization is more efficient than the classical Otsu’s, Kapur’s and Kittler’s methods.

  7. The effect of morphometric atlas selection on multi-atlas-based automatic brachial plexus segmentation

    International Nuclear Information System (INIS)

    Van de Velde, Joris; Wouters, Johan; Vercauteren, Tom; De Gersem, Werner; Achten, Eric; De Neve, Wilfried; Van Hoof, Tom

    2015-01-01

    The present study aimed to measure the effect of a morphometric atlas selection strategy on the accuracy of multi-atlas-based BP autosegmentation using the commercially available software package ADMIRE® and to determine the optimal number of selected atlases to use. Autosegmentation accuracy was measured by comparing all generated automatic BP segmentations with anatomically validated gold standard segmentations that were developed using cadavers. Twelve cadaver computed tomography (CT) atlases were included in the study. One atlas was selected as a patient in ADMIRE®, and multi-atlas-based BP autosegmentation was first performed with a group of morphometrically preselected atlases. In this group, the atlases were selected on the basis of similarity in the shoulder protraction position with the patient. The number of selected atlases used started at two and increased up to eight. Subsequently, a group of randomly chosen, non-selected atlases were taken. In this second group, every possible combination of 2 to 8 random atlases was used for multi-atlas-based BP autosegmentation. For both groups, the average Dice similarity coefficient (DSC), Jaccard index (JI) and Inclusion index (INI) were calculated, measuring the similarity of the generated automatic BP segmentations and the gold standard segmentation. Similarity indices of both groups were compared using an independent sample t-test, and the optimal number of selected atlases was investigated using an equivalence trial. For each number of atlases, average similarity indices of the morphometrically selected atlas group were significantly higher than the random group (p < 0,05). In this study, the highest similarity indices were achieved using multi-atlas autosegmentation with 6 selected atlases (average DSC = 0,598; average JI = 0,434; average INI = 0,733). Morphometric atlas selection on the basis of the protraction position of the patient significantly improves multi-atlas-based BP autosegmentation accuracy

  8. Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization.

    Science.gov (United States)

    Liao, Shu; Gao, Yaozong; Shi, Yinghuan; Yousuf, Ambereen; Karademir, Ibrahim; Oto, Aytekin; Shen, Dinggang

    2013-01-01

    Automatic prostate segmentation in MR images plays an important role in prostate cancer diagnosis. However, there are two main challenges: (1) Large inter-subject prostate shape variations; (2) Inhomogeneous prostate appearance. To address these challenges, we propose a new hierarchical prostate MR segmentation method, with the main contributions lying in the following aspects: First, the most salient features are learnt from atlases based on a subclass discriminant analysis (SDA) method, which aims to find a discriminant feature subspace by simultaneously maximizing the inter-class distance and minimizing the intra-class variations. The projected features, instead of only voxel-wise intensity, will be served as anatomical signature of each voxel. Second, based on the projected features, a new multi-atlases sparse label fusion framework is proposed to estimate the prostate likelihood of each voxel in the target image from the coarse level. Third, a domain-specific semi-supervised manifold regularization method is proposed to incorporate the most reliable patient-specific information identified by the prostate likelihood map to refine the segmentation result from the fine level. Our method is evaluated on a T2 weighted prostate MR image dataset consisting of 66 patients and compared with two state-of-the-art segmentation methods. Experimental results show that our method consistently achieves the highest segmentation accuracies than other methods under comparison.

  9. Automatic Semiconductor Wafer Image Segmentation for Defect Detection Using Multilevel Thresholding

    Directory of Open Access Journals (Sweden)

    Saad N.H.

    2016-01-01

    Full Text Available Quality control is one of important process in semiconductor manufacturing. A lot of issues trying to be solved in semiconductor manufacturing industry regarding the rate of production with respect to time. In most semiconductor assemblies, a lot of wafers from various processes in semiconductor wafer manufacturing need to be inspected manually using human experts and this process required full concentration of the operators. This human inspection procedure, however, is time consuming and highly subjective. In order to overcome this problem, implementation of machine vision will be the best solution. This paper presents automatic defect segmentation of semiconductor wafer image based on multilevel thresholding algorithm which can be further adopted in machine vision system. In this work, the defect image which is in RGB image at first is converted to the gray scale image. Median filtering then is implemented to enhance the gray scale image. Then the modified multilevel thresholding algorithm is performed to the enhanced image. The algorithm worked in three main stages which are determination of the peak location of the histogram, segmentation the histogram between the peak and determination of first global minimum of histogram that correspond to the threshold value of the image. The proposed approach is being evaluated using defected wafer images. The experimental results shown that it can be used to segment the defect correctly and outperformed other thresholding technique such as Otsu and iterative thresholding.

  10. Automatic data-driven real-time segmentation and recognition of surgical workflow.

    Science.gov (United States)

    Dergachyova, Olga; Bouget, David; Huaulmé, Arnaud; Morandi, Xavier; Jannin, Pierre

    2016-06-01

    With the intention of extending the perception and action of surgical staff inside the operating room, the medical community has expressed a growing interest towards context-aware systems. Requiring an accurate identification of the surgical workflow, such systems make use of data from a diverse set of available sensors. In this paper, we propose a fully data-driven and real-time method for segmentation and recognition of surgical phases using a combination of video data and instrument usage signals, exploiting no prior knowledge. We also introduce new validation metrics for assessment of workflow detection. The segmentation and recognition are based on a four-stage process. Firstly, during the learning time, a Surgical Process Model is automatically constructed from data annotations to guide the following process. Secondly, data samples are described using a combination of low-level visual cues and instrument information. Then, in the third stage, these descriptions are employed to train a set of AdaBoost classifiers capable of distinguishing one surgical phase from others. Finally, AdaBoost responses are used as input to a Hidden semi-Markov Model in order to obtain a final decision. On the MICCAI EndoVis challenge laparoscopic dataset we achieved a precision and a recall of 91 % in classification of 7 phases. Compared to the analysis based on one data type only, a combination of visual features and instrument signals allows better segmentation, reduction of the detection delay and discovery of the correct phase order.

  11. Automatic segmentation of MR brain images of preterm infants using supervised classification.

    Science.gov (United States)

    Moeskops, Pim; Benders, Manon J N L; Chiţ, Sabina M; Kersbergen, Karina J; Groenendaal, Floris; de Vries, Linda S; Viergever, Max A; Išgum, Ivana

    2015-09-01

    Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper presents an algorithm for the automatic segmentation of unmyelinated white matter (WM), cortical grey matter (GM), and cerebrospinal fluid in the extracerebral space (CSF). The algorithm uses supervised voxel classification in three subsequent stages. In the first stage, voxels that can easily be assigned to one of the three tissue types are labelled. In the second stage, dedicated analysis of the remaining voxels is performed. The first and the second stages both use two-class classification for each tissue type separately. Possible inconsistencies that could result from these tissue-specific segmentation stages are resolved in the third stage, which performs multi-class classification. A set of T1- and T2-weighted images was analysed, but the optimised system performs automatic segmentation using a T2-weighted image only. We have investigated the performance of the algorithm when using training data randomly selected from completely annotated images as well as when using training data from only partially annotated images. The method was evaluated on images of preterm infants acquired at 30 and 40weeks postmenstrual age (PMA). When the method was trained using random selection from the completely annotated images, the average Dice coefficients were 0.95 for WM, 0.81 for GM, and 0.89 for CSF on an independent set of images acquired at 30weeks PMA. When the method was trained using only the partially annotated images, the average Dice coefficients were 0.95 for WM, 0.78 for GM and 0.87 for CSF for the images acquired at 30weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40weeks PMA. Even though the segmentations obtained using training data

  12. Automatic intra-operative generation of geometric left atrium/pulmonary vein models from rotational X-ray angiography.

    Science.gov (United States)

    Meyer, Carsten; Manzke, Robert; Peters, Jochen; Ecabert, Olivier; Kneser, Reinhard; Reddy, Vivek Y; Chan, Raymond C; Weese, Jürgen

    2008-01-01

    Pre-procedural imaging with cardiac CT or MR has become popular for guiding complex electrophysiology procedures such as those used for atrial fibrillation ablation therapy. Electroanatomical mapping and ablation within the left atrium and pulmonary veins (LAPV) is facilitated using such data, however the pre-procedural anatomy can be quite different from that at the time of intervention. Recently, a method for intra-procedural LAPV imaging has been developed based on contrast-enhanced 3-D rotational X-ray angiography (3-D RA). These intraprocedural data now create a compelling need for rapid and automated extraction of the LAPV geometry for catheter guidance. We present a new approach to automatic intra-procedural generation of LAPV surfaces from 3-D RA volumes. Using model-based segmentation, our technique is robust to imaging noise and artifacts typical of 3-D RA imaging, strongly minimizes the user interaction time required for segmentation, and eliminates inter-subject variability. Our findings in 33 patients indicate that intra-procedural LAPV surface models accurately represent the anatomy at the time of intervention and are comparable to pre-procedural models derived from CTA or MRA.

  13. Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans

    International Nuclear Information System (INIS)

    Lassen, B C; Kuhnigk, J-M; Van Ginneken, B; Van Rikxoort, E M; Jacobs, C

    2015-01-01

    The malignancy of lung nodules is most often detected by analyzing changes of the nodule diameter in follow-up scans. A recent study showed that comparing the volume or the mass of a nodule over time is much more significant than comparing the diameter. Since the survival rate is higher when the disease is still in an early stage it is important to detect the growth rate as soon as possible. However manual segmentation of a volume is time-consuming. Whereas there are several well evaluated methods for the segmentation of solid nodules, less work is done on subsolid nodules which actually show a higher malignancy rate than solid nodules. In this work we present a fast, semi-automatic method for segmentation of subsolid nodules. As minimal user interaction the method expects a user-drawn stroke on the largest diameter of the nodule. First, a threshold-based region growing is performed based on intensity analysis of the nodule region and surrounding parenchyma. In the next step the chest wall is removed by a combination of a connected component analyses and convex hull calculation. Finally, attached vessels are detached by morphological operations. The method was evaluated on all nodules of the publicly available LIDC/IDRI database that were manually segmented and rated as non-solid or part-solid by four radiologists (Dataset 1) and three radiologists (Dataset 2). For these 59 nodules the Jaccard index for the agreement of the proposed method with the manual reference segmentations was 0.52/0.50 (Dataset 1/Dataset 2) compared to an inter-observer agreement of the manual segmentations of 0.54/0.58 (Dataset 1/Dataset 2). Furthermore, the inter-observer agreement using the proposed method (i.e. different input strokes) was analyzed and gave a Jaccard index of 0.74/0.74 (Dataset 1/Dataset 2). The presented method provides satisfactory segmentation results with minimal observer effort in minimal time and can reduce the inter-observer variability for segmentation of

  14. Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans

    Science.gov (United States)

    Lassen, B. C.; Jacobs, C.; Kuhnigk, J.-M.; van Ginneken, B.; van Rikxoort, E. M.

    2015-02-01

    The malignancy of lung nodules is most often detected by analyzing changes of the nodule diameter in follow-up scans. A recent study showed that comparing the volume or the mass of a nodule over time is much more significant than comparing the diameter. Since the survival rate is higher when the disease is still in an early stage it is important to detect the growth rate as soon as possible. However manual segmentation of a volume is time-consuming. Whereas there are several well evaluated methods for the segmentation of solid nodules, less work is done on subsolid nodules which actually show a higher malignancy rate than solid nodules. In this work we present a fast, semi-automatic method for segmentation of subsolid nodules. As minimal user interaction the method expects a user-drawn stroke on the largest diameter of the nodule. First, a threshold-based region growing is performed based on intensity analysis of the nodule region and surrounding parenchyma. In the next step the chest wall is removed by a combination of a connected component analyses and convex hull calculation. Finally, attached vessels are detached by morphological operations. The method was evaluated on all nodules of the publicly available LIDC/IDRI database that were manually segmented and rated as non-solid or part-solid by four radiologists (Dataset 1) and three radiologists (Dataset 2). For these 59 nodules the Jaccard index for the agreement of the proposed method with the manual reference segmentations was 0.52/0.50 (Dataset 1/Dataset 2) compared to an inter-observer agreement of the manual segmentations of 0.54/0.58 (Dataset 1/Dataset 2). Furthermore, the inter-observer agreement using the proposed method (i.e. different input strokes) was analyzed and gave a Jaccard index of 0.74/0.74 (Dataset 1/Dataset 2). The presented method provides satisfactory segmentation results with minimal observer effort in minimal time and can reduce the inter-observer variability for segmentation of

  15. Technical Note: PLASTIMATCH MABS, an open source tool for automatic image segmentation

    International Nuclear Information System (INIS)

    Zaffino, Paolo; Spadea, Maria Francesca; Raudaschl, Patrik; Fritscher, Karl; Sharp, Gregory C.

    2016-01-01

    Purpose: Multiatlas based segmentation is largely used in many clinical and research applications. Due to its good performances, it has recently been included in some commercial platforms for radiotherapy planning and surgery guidance. Anyway, to date, a software with no restrictions about the anatomical district and image modality is still missing. In this paper we introduce PLASTIMATCH MABS, an open source software that can be used with any image modality for automatic segmentation. Methods: PLASTIMATCH MABS workflow consists of two main parts: (1) an offline phase, where optimal registration and voting parameters are tuned and (2) an online phase, where a new patient is labeled from scratch by using the same parameters as identified in the former phase. Several registration strategies, as well as different voting criteria can be selected. A flexible atlas selection scheme is also available. To prove the effectiveness of the proposed software across anatomical districts and image modalities, it was tested on two very different scenarios: head and neck (H&N) CT segmentation for radiotherapy application, and magnetic resonance image brain labeling for neuroscience investigation. Results: For the neurological study, minimum dice was equal to 0.76 (investigated structures: left and right caudate, putamen, thalamus, and hippocampus). For head and neck case, minimum dice was 0.42 for the most challenging structures (optic nerves and submandibular glands) and 0.62 for the other ones (mandible, brainstem, and parotid glands). Time required to obtain the labels was compatible with a real clinical workflow (35 and 120 min). Conclusions: The proposed software fills a gap in the multiatlas based segmentation field, since all currently available tools (both for commercial and for research purposes) are restricted to a well specified application. Furthermore, it can be adopted as a platform for exploring MABS parameters and as a reference implementation for comparing against

  16. Technical Note: PLASTIMATCH MABS, an open source tool for automatic image segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Zaffino, Paolo; Spadea, Maria Francesca [Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro 88100 (Italy); Raudaschl, Patrik; Fritscher, Karl [Institute for Biomedical Image Analysis, Private University of Health Sciences, Medical Informatics and Technology, Hall in Tirol 6060 (Austria); Sharp, Gregory C. [Department for Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114 (United States)

    2016-09-15

    Purpose: Multiatlas based segmentation is largely used in many clinical and research applications. Due to its good performances, it has recently been included in some commercial platforms for radiotherapy planning and surgery guidance. Anyway, to date, a software with no restrictions about the anatomical district and image modality is still missing. In this paper we introduce PLASTIMATCH MABS, an open source software that can be used with any image modality for automatic segmentation. Methods: PLASTIMATCH MABS workflow consists of two main parts: (1) an offline phase, where optimal registration and voting parameters are tuned and (2) an online phase, where a new patient is labeled from scratch by using the same parameters as identified in the former phase. Several registration strategies, as well as different voting criteria can be selected. A flexible atlas selection scheme is also available. To prove the effectiveness of the proposed software across anatomical districts and image modalities, it was tested on two very different scenarios: head and neck (H&N) CT segmentation for radiotherapy application, and magnetic resonance image brain labeling for neuroscience investigation. Results: For the neurological study, minimum dice was equal to 0.76 (investigated structures: left and right caudate, putamen, thalamus, and hippocampus). For head and neck case, minimum dice was 0.42 for the most challenging structures (optic nerves and submandibular glands) and 0.62 for the other ones (mandible, brainstem, and parotid glands). Time required to obtain the labels was compatible with a real clinical workflow (35 and 120 min). Conclusions: The proposed software fills a gap in the multiatlas based segmentation field, since all currently available tools (both for commercial and for research purposes) are restricted to a well specified application. Furthermore, it can be adopted as a platform for exploring MABS parameters and as a reference implementation for comparing against

  17. A method for automatic grain segmentation of multi-angle cross-polarized microscopic images of sandstone

    Science.gov (United States)

    Jiang, Feng; Gu, Qing; Hao, Huizhen; Li, Na; Wang, Bingqian; Hu, Xiumian

    2018-06-01

    Automatic grain segmentation of sandstone is to partition mineral grains into separate regions in the thin section, which is the first step for computer aided mineral identification and sandstone classification. The sandstone microscopic images contain a large number of mixed mineral grains where differences among adjacent grains, i.e., quartz, feldspar and lithic grains, are usually ambiguous, which make grain segmentation difficult. In this paper, we take advantage of multi-angle cross-polarized microscopic images and propose a method for grain segmentation with high accuracy. The method consists of two stages, in the first stage, we enhance the SLIC (Simple Linear Iterative Clustering) algorithm, named MSLIC, to make use of multi-angle images and segment the images as boundary adherent superpixels. In the second stage, we propose the region merging technique which combines the coarse merging and fine merging algorithms. The coarse merging merges the adjacent superpixels with less evident boundaries, and the fine merging merges the ambiguous superpixels using the spatial enhanced fuzzy clustering. Experiments are designed on 9 sets of multi-angle cross-polarized images taken from the three major types of sandstones. The results demonstrate both the effectiveness and potential of the proposed method, comparing to the available segmentation methods.

  18. Brain Tumor Image Segmentation in MRI Image

    Science.gov (United States)

    Peni Agustin Tjahyaningtijas, Hapsari

    2018-04-01

    Brain tumor segmentation plays an important role in medical image processing. Treatment of patients with brain tumors is highly dependent on early detection of these tumors. Early detection of brain tumors will improve the patient’s life chances. Diagnosis of brain tumors by experts usually use a manual segmentation that is difficult and time consuming because of the necessary automatic segmentation. Nowadays automatic segmentation is very populer and can be a solution to the problem of tumor brain segmentation with better performance. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. this paper, we focus on the recent trend of automatic segmentation in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of full automatic segmentaion are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.

  19. A deformable-model approach to semi-automatic segmentation of CT images demonstrated by application to the spinal canal

    International Nuclear Information System (INIS)

    Burnett, Stuart S.C.; Starkschall, George; Stevens, Craig W.; Liao Zhongxing

    2004-01-01

    Because of the importance of accurately defining the target in radiation treatment planning, we have developed a deformable-template algorithm for the semi-automatic delineation of normal tissue structures on computed tomography (CT) images. We illustrate the method by applying it to the spinal canal. Segmentation is performed in three steps: (a) partial delineation of the anatomic structure is obtained by wavelet-based edge detection; (b) a deformable-model template is fitted to the edge set by chamfer matching; and (c) the template is relaxed away from its original shape into its final position. Appropriately chosen ranges for the model parameters limit the deformations of the template, accounting for interpatient variability. Our approach differs from those used in other deformable models in that it does not inherently require the modeling of forces. Instead, the spinal canal was modeled using Fourier descriptors derived from four sets of manually drawn contours. Segmentation was carried out, without manual intervention, on five CT data sets and the algorithm's performance was judged subjectively by two radiation oncologists. Two assessments were considered: in the first, segmentation on a random selection of 100 axial CT images was compared with the corresponding contours drawn manually by one of six dosimetrists, also chosen randomly; in the second assessment, the segmentation of each image in the five evaluable CT sets (a total of 557 axial images) was rated as either successful, unsuccessful, or requiring further editing. Contours generated by the algorithm were more likely than manually drawn contours to be considered acceptable by the oncologists. The mean proportions of acceptable contours were 93% (automatic) and 69% (manual). Automatic delineation of the spinal canal was deemed to be successful on 91% of the images, unsuccessful on 2% of the images, and requiring further editing on 7% of the images. Our deformable template algorithm thus gives a robust

  20. Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells

    Directory of Open Access Journals (Sweden)

    Carolina Wählby

    2002-01-01

    Full Text Available Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre‐processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO‐cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.

  1. Color image Segmentation using automatic thresholding techniques

    International Nuclear Information System (INIS)

    Harrabi, R.; Ben Braiek, E.

    2011-01-01

    In this paper, entropy and between-class variance based thresholding methods for color images segmentation are studied. The maximization of the between-class variance (MVI) and the entropy (ME) have been used as a criterion functions to determine an optimal threshold to segment images into nearly homogenous regions. Segmentation results from the two methods are validated and the segmentation sensitivity for the test data available is evaluated, and a comparative study between these methods in different color spaces is presented. The experimental results demonstrate the superiority of the MVI method for color image segmentation.

  2. Automatic media-adventitia IVUS image segmentation based on sparse representation framework and dynamic directional active contour model.

    Science.gov (United States)

    Zakeri, Fahimeh Sadat; Setarehdan, Seyed Kamaledin; Norouzi, Somayye

    2017-10-01

    Segmentation of the arterial wall boundaries from intravascular ultrasound images is an important image processing task in order to quantify arterial wall characteristics such as shape, area, thickness and eccentricity. Since manual segmentation of these boundaries is a laborious and time consuming procedure, many researchers attempted to develop (semi-) automatic segmentation techniques as a powerful tool for educational and clinical purposes in the past but as yet there is no any clinically approved method in the market. This paper presents a deterministic-statistical strategy for automatic media-adventitia border detection by a fourfold algorithm. First, a smoothed initial contour is extracted based on the classification in the sparse representation framework which is combined with the dynamic directional convolution vector field. Next, an active contour model is utilized for the propagation of the initial contour toward the interested borders. Finally, the extracted contour is refined in the leakage, side branch openings and calcification regions based on the image texture patterns. The performance of the proposed algorithm is evaluated by comparing the results to those manually traced borders by an expert on 312 different IVUS images obtained from four different patients. The statistical analysis of the results demonstrates the efficiency of the proposed method in the media-adventitia border detection with enough consistency in the leakage and calcification regions. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Automatic segmentation of low-visibility moving objects through energy analyis of the local 3D spectrum

    Science.gov (United States)

    Nestares, Oscar; Miravet, Carlos; Santamaria, Javier; Fonolla Navarro, Rafael

    1999-05-01

    Automatic object segmentation in highly noisy image sequences, composed by a translating object over a background having a different motion, is achieved through joint motion-texture analysis. Local motion and/or texture is characterized by the energy of the local spatio-temporal spectrum, as different textures undergoing different translational motions display distinctive features in their 3D (x,y,t) spectra. Measurements of local spectrum energy are obtained using a bank of directional 3rd order Gaussian derivative filters in a multiresolution pyramid in space- time (10 directions, 3 resolution levels). These 30 energy measurements form a feature vector describing texture-motion for every pixel in the sequence. To improve discrimination capability and reduce computational cost, we automatically select those 4 features (channels) that best discriminate object from background, under the assumptions that the object is smaller than the background and has a different velocity or texture. In this way we reject features irrelevant or dominated by noise, that could yield wrong segmentation results. This method has been successfully applied to sequences with extremely low visibility and for objects that are even invisible for the eye in absence of motion.

  4. Understanding Spatially Complex Segmental and Branch Anatomy Using 3D Printing: Liver, Lung, Prostate, Coronary Arteries, and Circle of Willis.

    Science.gov (United States)

    Javan, Ramin; Herrin, Douglas; Tangestanipoor, Ardalan

    2016-09-01

    Three-dimensional (3D) manufacturing is shaping personalized medicine, in which radiologists can play a significant role, be it as consultants to surgeons for surgical planning or by creating powerful visual aids for communicating with patients, physicians, and trainees. This report illustrates the steps in development of custom 3D models that enhance the understanding of complex anatomy. We graphically designed 3D meshes or modified imported data from cross-sectional imaging to develop physical models targeted specifically for teaching complex segmental and branch anatomy. The 3D printing itself is easily accessible through online commercial services, and the models are made of polyamide or gypsum. Anatomic models of the liver, lungs, prostate, coronary arteries, and the Circle of Willis were created. These models have advantages that include customizable detail, relative low cost, full control of design focusing on subsegments, color-coding potential, and the utilization of cross-sectional imaging combined with graphic design. Radiologists have an opportunity to serve as leaders in medical education and clinical care with 3D printed models that provide beneficial interaction with patients, clinicians, and trainees across all specialties by proactively taking on the educator's role. Complex models can be developed to show normal anatomy or common pathology for medical educational purposes. There is a need for randomized trials, which radiologists can design, to demonstrate the utility and effectiveness of 3D printed models for teaching simple and complex anatomy, simulating interventions, measuring patient satisfaction, and improving clinical care. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

  5. Embryonic Heart Morphogenesis from Confocal Microscopy Imaging and Automatic Segmentation

    Directory of Open Access Journals (Sweden)

    Hongda Mao

    2013-01-01

    Full Text Available Embryonic heart morphogenesis (EHM is a complex and dynamic process where the heart transforms from a single tube into a four-chambered pump. This process is of great biological and clinical interest but is still poorly understood for two main reasons. On the one hand, the existing imaging modalities for investigating EHM suffered from either limited penetration depth or limited spatial resolution. On the other hand, current works typically adopted manual segmentation, which was tedious, subjective, and time consuming considering the complexity of developing heart geometry and the large size of images. In this paper, we propose to utilize confocal microscopy imaging with tissue optical immersion clearing technique to image the heart at different stages of development for EHM study. The imaging method is able to produce high spatial resolution images and achieve large penetration depth at the same time. Furthermore, we propose a novel convex active contour model for automatic image segmentation. The model has the ability to deal with intensity fall-off in depth which is characterized by confocal microscopy images. We acquired the images of embryonic quail hearts from day 6 to day 14 of incubation for EHM study. The experimental results were promising and provided us with an insight view of early heart growth pattern and also paved the road for data-driven heart growth modeling.

  6. Simple Methods for Scanner Drift Normalization Validated for Automatic Segmentation of Knee Magnetic Resonance Imaging

    DEFF Research Database (Denmark)

    Dam, Erik Bjørnager

    2018-01-01

    Scanner drift is a well-known magnetic resonance imaging (MRI) artifact characterized by gradual signal degradation and scan intensity changes over time. In addition, hardware and software updates may imply abrupt changes in signal. The combined effects are particularly challenging for automatic...... image analysis methods used in longitudinal studies. The implication is increased measurement variation and a risk of bias in the estimations (e.g. in the volume change for a structure). We proposed two quite different approaches for scanner drift normalization and demonstrated the performance...... for segmentation of knee MRI using the fully automatic KneeIQ framework. The validation included a total of 1975 scans from both high-field and low-field MRI. The results demonstrated that the pre-processing method denoted Atlas Affine Normalization significantly removed scanner drift effects and ensured...

  7. Automatic segmentation of cell nuclei from confocal laser scanning microscopy images

    International Nuclear Information System (INIS)

    Kelemen, A.; Reist, H.W.

    1997-01-01

    A newly developed experimental method combines the possibility of irradiating more than a thousand cells simultaneous with an efficient colony-forming ability and with the capability of localizing a particle track through a cell nucleus together with the assessment of the energy transfer by digital superposition of the image containing the track with that of the cells. To assess the amount of energy deposition by particles traversing the cell nucleus the intersection lengths of the particle tracks have to be known. Intersection lengths can be obtained by determining the 3D surface contours of the irradiated cell nuclei. Confocal laser scanning microscopy using specific DNA fluorescent dye offers a possible way for the determination of the 3D shape of individual nuclei. Unfortunately, such experiments cannot be performed on living cells. One solution to this problem can be provided by building a statistical model of the shape of the nuclei of the exposed cells. In order to build such a statistical model, a large number of cell nuclei have to be identified and segmented from confocal laser scanning microscopy images. The present paper describes a method to perform this 3D segmentation in an automatic manner in order to create a solid basis for the statistical model. (author) 2 figs., 4 refs

  8. Automatic Craniomaxillofacial Landmark Digitization via Segmentation-guided Partially-joint Regression Forest Model and Multi-scale Statistical Features

    Science.gov (United States)

    Zhang, Jun; Gao, Yaozong; Wang, Li; Tang, Zhen; Xia, James J.; Shen, Dinggang

    2016-01-01

    Objective The goal of this paper is to automatically digitize craniomaxillofacial (CMF) landmarks efficiently and accurately from cone-beam computed tomography (CBCT) images, by addressing the challenge caused by large morphological variations across patients and image artifacts of CBCT images. Methods We propose a Segmentation-guided Partially-joint Regression Forest (S-PRF) model to automatically digitize CMF landmarks. In this model, a regression voting strategy is first adopted to localize each landmark by aggregating evidences from context locations, thus potentially relieving the problem caused by image artifacts near the landmark. Second, CBCT image segmentation is utilized to remove uninformative voxels caused by morphological variations across patients. Third, a partially-joint model is further proposed to separately localize landmarks based on the coherence of landmark positions to improve the digitization reliability. In addition, we propose a fast vector quantization (VQ) method to extract high-level multi-scale statistical features to describe a voxel's appearance, which has low dimensionality, high efficiency, and is also invariant to the local inhomogeneity caused by artifacts. Results Mean digitization errors for 15 landmarks, in comparison to the ground truth, are all less than 2mm. Conclusion Our model has addressed challenges of both inter-patient morphological variations and imaging artifacts. Experiments on a CBCT dataset show that our approach achieves clinically acceptable accuracy for landmark digitalization. Significance Our automatic landmark digitization method can be used clinically to reduce the labor cost and also improve digitalization consistency. PMID:26625402

  9. Anatomy of the Le Fort I segment: Are arterial variations a potential risk factor for avascular bone necrosis in Le Fort I osteotomies?

    Science.gov (United States)

    Bruneder, Simon; Wallner, Jürgen; Weiglein, Andreas; Kmečová, Ĺudmila; Egger, Jan; Pilsl, Ulrike; Zemann, Wolfgang

    2018-05-02

    Osteotomies of the Le Fort I segment are routine operations with low complication rates. Ischemic complications are rare, but can have severe consequences that may lead to avascular bone necrosis of the Le Fort I segment. Therefore the aim of this study was to investigate the blood supply and special arterial variants of the Le Fort I segment responsible for arterial hypoperfusion or ischemic avascular necrosis after surgery. The arterial anatomy of the Le Fort I segment's blood supply using 30 halved human cadaver head specimens was analyzed after complete dissection until the submicroscopic level. In all specimens the arterial variants of the Le Fort I segment and also the arterial diameters measured at two points were evaluated. The typical known vascularization pattern was apparent in 90% of all specimens, in which the ascending palatine (D1: 1,2 mm ± 0,34 mm; D2: 0,8 mm ± 0,34 mm) and ascending pharyngeal artery (D1: 1,3 mm ± 0,58 mm; D2: avascular segment necrosis after surgery. An individualized operation plan may prevent ischemic complications in at-risk patients. Copyright © 2018 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

  10. A workflow for the automatic segmentation of organelles in electron microscopy image stacks

    Science.gov (United States)

    Perez, Alex J.; Seyedhosseini, Mojtaba; Deerinck, Thomas J.; Bushong, Eric A.; Panda, Satchidananda; Tasdizen, Tolga; Ellisman, Mark H.

    2014-01-01

    Electron microscopy (EM) facilitates analysis of the form, distribution, and functional status of key organelle systems in various pathological processes, including those associated with neurodegenerative disease. Such EM data often provide important new insights into the underlying disease mechanisms. The development of more accurate and efficient methods to quantify changes in subcellular microanatomy has already proven key to understanding the pathogenesis of Parkinson's and Alzheimer's diseases, as well as glaucoma. While our ability to acquire large volumes of 3D EM data is progressing rapidly, more advanced analysis tools are needed to assist in measuring precise three-dimensional morphologies of organelles within data sets that can include hundreds to thousands of whole cells. Although new imaging instrument throughputs can exceed teravoxels of data per day, image segmentation and analysis remain significant bottlenecks to achieving quantitative descriptions of whole cell structural organellomes. Here, we present a novel method for the automatic segmentation of organelles in 3D EM image stacks. Segmentations are generated using only 2D image information, making the method suitable for anisotropic imaging techniques such as serial block-face scanning electron microscopy (SBEM). Additionally, no assumptions about 3D organelle morphology are made, ensuring the method can be easily expanded to any number of structurally and functionally diverse organelles. Following the presentation of our algorithm, we validate its performance by assessing the segmentation accuracy of different organelle targets in an example SBEM dataset and demonstrate that it can be efficiently parallelized on supercomputing resources, resulting in a dramatic reduction in runtime. PMID:25426032

  11. Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

    Science.gov (United States)

    Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

    2018-02-01

    Image segmentation plays an important role in medical science. One application is multimodality imaging, especially the fusion of structural imaging with functional imaging, which includes CT, MRI and new types of imaging technology such as optical imaging to obtain functional images. The fusion process require precisely extracted structural information, in order to register the image to it. Here we used image enhancement, morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in deep learning way. Such approach greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. The contours of the borders of different tissues on all images were accurately extracted and 3D visualized. This can be used in low-level light therapy and optical simulation software such as MCVM. We obtained a precise three-dimensional distribution of brain, which offered doctors and researchers quantitative volume data and detailed morphological characterization for personal precise medicine of Cerebral atrophy/expansion. We hope this technique can bring convenience to visualization medical and personalized medicine.

  12. Accuracy and reproducibility of a novel semi-automatic segmentation technique for MR volumetry of the pituitary gland

    International Nuclear Information System (INIS)

    Renz, Diane M.; Hahn, Horst K.; Rexilius, Jan; Schmidt, Peter; Lentschig, Markus; Pfeil, Alexander; Sauner, Dieter; Fitzek, Clemens; Mentzel, Hans-Joachim; Kaiser, Werner A.; Reichenbach, Juergen R.; Boettcher, Joachim

    2011-01-01

    Although several reports about volumetric determination of the pituitary gland exist, volumetries have been solely performed by indirect measurements or manual tracing on the gland's boundaries. The purpose of this study was to evaluate the accuracy and reproducibility of a novel semi-automatic MR-based segmentation technique. In an initial technical investigation, T1-weighted 3D native magnetised prepared rapid gradient echo sequences (1.5 T) with 1 mm isotropic voxel size achieved high reliability and were utilised in different in vitro and in vivo studies. The computer-assisted segmentation technique was based on an interactive watershed transform after resampling and gradient computation. Volumetry was performed by three observers with different software and neuroradiologic experiences, evaluating phantoms of known volume (0.3, 0.9 and 1.62 ml) and healthy subjects (26 to 38 years; overall 135 volumetries). High accuracy of the volumetry was shown by phantom analysis; measurement errors were 0.05). The analysed semi-automatic MR volumetry of the pituitary gland is a valid, reliable and fast technique. Possible clinical applications are hyperplasia or atrophy of the gland in pathological circumstances either by a single assessment or by monitoring in follow-up studies. (orig.)

  13. Segmentation algorithm of colon based on multi-slice CT colonography

    Science.gov (United States)

    Hu, Yizhong; Ahamed, Mohammed Shabbir; Takahashi, Eiji; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Suzuki, Masahiro; Iinuma, Gen; Moriyama, Noriyuki

    2012-02-01

    CT colonography is a radiology test that looks at people's large intestines(colon). CT colonography can screen many options of colon cancer. This test is used to detect polyps or cancers of the colon. CT colonography is safe and reliable. It can be used if people are too sick to undergo other forms of colon cancer screening. In our research, we proposed a method for automatic segmentation of the colon from abdominal computed Tomography (CT) images. Our multistage detection method extracted colon and spited colon into different parts according to the colon anatomy information. We found that among the five segmented parts of the colon, sigmoid (20%) and rectum (50%) are more sensitive toward polyps and masses than the other three parts. Our research focused on detecting the colon by the individual diagnosis of sigmoid and rectum. We think it would make the rapid and easy diagnosis of colon in its earlier stage and help doctors for analysis of correct position of each part and detect the colon rectal cancer much easier.

  14. A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning.

    Science.gov (United States)

    Rundo, Leonardo; Stefano, Alessandro; Militello, Carmelo; Russo, Giorgio; Sabini, Maria Gabriella; D'Arrigo, Corrado; Marletta, Francesco; Ippolito, Massimo; Mauri, Giancarlo; Vitabile, Salvatore; Gilardi, Maria Carla

    2017-06-01

    Nowadays, clinical practice in Gamma Knife treatments is generally based on MRI anatomical information alone. However, the joint use of MRI and PET images can be useful for considering both anatomical and metabolic information about the lesion to be treated. In this paper we present a co-segmentation method to integrate the segmented Biological Target Volume (BTV), using [ 11 C]-Methionine-PET (MET-PET) images, and the segmented Gross Target Volume (GTV), on the respective co-registered MR images. The resulting volume gives enhanced brain tumor information to be used in stereotactic neuro-radiosurgery treatment planning. GTV often does not match entirely with BTV, which provides metabolic information about brain lesions. For this reason, PET imaging is valuable and it could be used to provide complementary information useful for treatment planning. In this way, BTV can be used to modify GTV, enhancing Clinical Target Volume (CTV) delineation. A novel fully automatic multimodal PET/MRI segmentation method for Leksell Gamma Knife ® treatments is proposed. This approach improves and combines two computer-assisted and operator-independent single modality methods, previously developed and validated, to segment BTV and GTV from PET and MR images, respectively. In addition, the GTV is utilized to combine the superior contrast of PET images with the higher spatial resolution of MRI, obtaining a new BTV, called BTV MRI . A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is also presented. Overlap-based and spatial distance-based metrics were considered to quantify similarity concerning PET and MRI segmentation approaches. Statistics was also included to measure correlation among the different segmentation processes. Since it is not possible to define a gold-standard CTV according to both MRI and PET images without treatment response assessment

  15. Thigh muscle segmentation of chemical shift encoding-based water-fat magnetic resonance images: The reference database MyoSegmenTUM.

    Directory of Open Access Journals (Sweden)

    Sarah Schlaeger

    Full Text Available Magnetic resonance imaging (MRI can non-invasively assess muscle anatomy, exercise effects and pathologies with different underlying causes such as neuromuscular diseases (NMD. Quantitative MRI including fat fraction mapping using chemical shift encoding-based water-fat MRI has emerged for reliable determination of muscle volume and fat composition. The data analysis of water-fat images requires segmentation of the different muscles which has been mainly performed manually in the past and is a very time consuming process, currently limiting the clinical applicability. An automatization of the segmentation process would lead to a more time-efficient analysis. In the present work, the manually segmented thigh magnetic resonance imaging database MyoSegmenTUM is presented. It hosts water-fat MR images of both thighs of 15 healthy subjects and 4 patients with NMD with a voxel size of 3.2x2x4 mm3 with the corresponding segmentation masks for four functional muscle groups: quadriceps femoris, sartorius, gracilis, hamstrings. The database is freely accessible online at https://osf.io/svwa7/?view_only=c2c980c17b3a40fca35d088a3cdd83e2. The database is mainly meant as ground truth which can be used as training and test dataset for automatic muscle segmentation algorithms. The segmentation allows extraction of muscle cross sectional area (CSA and volume. Proton density fat fraction (PDFF of the defined muscle groups from the corresponding images and quadriceps muscle strength measurements/neurological muscle strength rating can be used for benchmarking purposes.

  16. Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform.

    Science.gov (United States)

    Sun, Shuping; Jiang, Zhongwei; Wang, Haibin; Fang, Yu

    2014-05-01

    This paper proposes a novel automatic method for the moment segmentation and peak detection analysis of heart sound (HS) pattern, with special attention to the characteristics of the envelopes of HS and considering the properties of the Hilbert transform (HT). The moment segmentation and peak location are accomplished in two steps. First, by applying the Viola integral waveform method in the time domain, the envelope (E(T)) of the HS signal is obtained with an emphasis on the first heart sound (S1) and the second heart sound (S2). Then, based on the characteristics of the E(T) and the properties of the HT of the convex and concave functions, a novel method, the short-time modified Hilbert transform (STMHT), is proposed to automatically locate the moment segmentation and peak points for the HS by the zero crossing points of the STMHT. A fast algorithm for calculating the STMHT of E(T) can be expressed by multiplying the E(T) by an equivalent window (W(E)). According to the range of heart beats and based on the numerical experiments and the important parameters of the STMHT, a moving window width of N=1s is validated for locating the moment segmentation and peak points for HS. The proposed moment segmentation and peak location procedure method is validated by sounds from Michigan HS database and sounds from clinical heart diseases, such as a ventricular septal defect (VSD), an aortic septal defect (ASD), Tetralogy of Fallot (TOF), rheumatic heart disease (RHD), and so on. As a result, for the sounds where S2 can be separated from S1, the average accuracies achieved for the peak of S1 (AP₁), the peak of S2 (AP₂), the moment segmentation points from S1 to S2 (AT₁₂) and the cardiac cycle (ACC) are 98.53%, 98.31% and 98.36% and 97.37%, respectively. For the sounds where S1 cannot be separated from S2, the average accuracies achieved for the peak of S1 and S2 (AP₁₂) and the cardiac cycle ACC are 100% and 96.69%. Copyright © 2014 Elsevier Ireland Ltd. All

  17. Automatic fuzzy inference system development for marker-based watershed segmentation

    International Nuclear Information System (INIS)

    Gonzalez, M A; Meschino, G J; Ballarin, V L

    2007-01-01

    Texture image segmentation is a constant challenge in digital image processing. The partition of an image into regions that allow the experienced observer to obtain the necessary information can be done using a Mathematical Morphology tool called the Watershed Transform. This transform is able to distinguish extremely complex objects and is easily adaptable to various kinds of images. The success of the Watershed Transform depends essentially on the existence of unequivocal markers for each of the objects of interest. The standard methods for marker detection are highly specific and complex when objects presenting great variability of shape, size and texture are processed. This paper proposes the automatic generation of a fuzzy inference system for marker detection using object selection done by the expert. This method allows applying the Watershed Transform to biomedical images with diferent kinds of texture. The results allow concluding that the method proposed is an effective tool for the application of the Watershed Transform

  18. Template-based automatic extraction of the joint space of foot bones from CT scan

    Science.gov (United States)

    Park, Eunbi; Kim, Taeho; Park, Jinah

    2016-03-01

    Clean bone segmentation is critical in studying the joint anatomy for measuring the spacing between the bones. However, separation of the coupled bones in CT images is sometimes difficult due to ambiguous gray values coming from the noise and the heterogeneity of bone materials as well as narrowing of the joint space. For fine reconstruction of the individual local boundaries, manual operation is a common practice where the segmentation remains to be a bottleneck. In this paper, we present an automatic method for extracting the joint space by applying graph cut on Markov random field model to the region of interest (ROI) which is identified by a template of 3D bone structures. The template includes encoded articular surface which identifies the tight region of the high-intensity bone boundaries together with the fuzzy joint area of interest. The localized shape information from the template model within the ROI effectively separates the bones nearby. By narrowing the ROI down to the region including two types of tissue, the object extraction problem was reduced to binary segmentation and solved via graph cut. Based on the shape of a joint space marked by the template, the hard constraint was set by the initial seeds which were automatically generated from thresholding and morphological operations. The performance and the robustness of the proposed method are evaluated on 12 volumes of ankle CT data, where each volume includes a set of 4 tarsal bones (calcaneus, talus, navicular and cuboid).

  19. Quantitative analysis of retina layer elasticity based on automatic 3D segmentation (Conference Presentation)

    Science.gov (United States)

    He, Youmin; Qu, Yueqiao; Zhang, Yi; Ma, Teng; Zhu, Jiang; Miao, Yusi; Humayun, Mark; Zhou, Qifa; Chen, Zhongping

    2017-02-01

    Age-related macular degeneration (AMD) is an eye condition that is considered to be one of the leading causes of blindness among people over 50. Recent studies suggest that the mechanical properties in retina layers are affected during the early onset of disease. Therefore, it is necessary to identify such changes in the individual layers of the retina so as to provide useful information for disease diagnosis. In this study, we propose using an acoustic radiation force optical coherence elastography (ARF-OCE) system to dynamically excite the porcine retina and detect the vibrational displacement with phase resolved Doppler optical coherence tomography. Due to the vibrational mechanism of the tissue response, the image quality is compromised during elastogram acquisition. In order to properly analyze the images, all signals, including the trigger and control signals for excitation, as well as detection and scanning signals, are synchronized within the OCE software and are kept consistent between frames, making it possible for easy phase unwrapping and elasticity analysis. In addition, a combination of segmentation algorithms is used to accommodate the compromised image quality. An automatic 3D segmentation method has been developed to isolate and measure the relative elasticity of every individual retinal layer. Two different segmentation schemes based on random walker and dynamic programming are implemented. The algorithm has been validated using a 3D region of the porcine retina, where individual layers have been isolated and analyzed using statistical methods. The errors compared to manual segmentation will be calculated.

  20. Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network.

    Science.gov (United States)

    Cui, Shaoguo; Mao, Lei; Jiang, Jingfeng; Liu, Chang; Xiong, Shuyu

    2018-01-01

    Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic resonance imaging (MRI) data with minimal human interventions remains a challenging task. In this paper, we present a novel fully automatic segmentation method from MRI data containing in vivo brain gliomas. This approach can not only localize the entire tumor region but can also accurately segment the intratumor structure. The proposed work was based on a cascaded deep learning convolutional neural network consisting of two subnetworks: (1) a tumor localization network (TLN) and (2) an intratumor classification network (ITCN). The TLN, a fully convolutional network (FCN) in conjunction with the transfer learning technology, was used to first process MRI data. The goal of the first subnetwork was to define the tumor region from an MRI slice. Then, the ITCN was used to label the defined tumor region into multiple subregions. Particularly, ITCN exploited a convolutional neural network (CNN) with deeper architecture and smaller kernel. The proposed approach was validated on multimodal brain tumor segmentation (BRATS 2015) datasets, which contain 220 high-grade glioma (HGG) and 54 low-grade glioma (LGG) cases. Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity were used as evaluation metrics. Our experimental results indicated that our method could obtain the promising segmentation results and had a faster segmentation speed. More specifically, the proposed method obtained comparable and overall better DSC values (0.89, 0.77, and 0.80) on the combined (HGG + LGG) testing set, as compared to other methods reported in the literature. Additionally, the proposed approach was able to complete a segmentation task at a rate of 1.54 seconds per slice.

  1. Hepatic Arterial Configuration in Relation to the Segmental Anatomy of the Liver; Observations on MDCT and DSA Relevant to Radioembolization Treatment

    International Nuclear Information System (INIS)

    Hoven, Andor F. van den; Leeuwen, Maarten S. van; Lam, Marnix G. E. H.; Bosch, Maurice A. A. J. van den

    2015-01-01

    PurposeCurrent anatomical classifications do not include all variants relevant for radioembolization (RE). The purpose of this study was to assess the individual hepatic arterial configuration and segmental vascularization pattern and to develop an individualized RE treatment strategy based on an extended classification.MethodsThe hepatic vascular anatomy was assessed on MDCT and DSA in patients who received a workup for RE between February 2009 and November 2012. Reconstructed MDCT studies were assessed to determine the hepatic arterial configuration (origin of every hepatic arterial branch, branching pattern and anatomical course) and the hepatic segmental vascularization territory of all branches. Aberrant hepatic arteries were defined as hepatic arterial branches that did not originate from the celiac axis/CHA/PHA. Early branching patterns were defined as hepatic arterial branches originating from the celiac axis/CHA.ResultsThe hepatic arterial configuration and segmental vascularization pattern could be assessed in 110 of 133 patients. In 59 patients (54 %), no aberrant hepatic arteries or early branching was observed. Fourteen patients without aberrant hepatic arteries (13 %) had an early branching pattern. In the 37 patients (34 %) with aberrant hepatic arteries, five also had an early branching pattern. Sixteen different hepatic arterial segmental vascularization patterns were identified and described, differing by the presence of aberrant hepatic arteries, their respective vascular territory, and origin of the artery vascularizing segment four.ConclusionsThe hepatic arterial configuration and segmental vascularization pattern show marked individual variability beyond well-known classifications of anatomical variants. We developed an individualized RE treatment strategy based on an extended anatomical classification

  2. Active Segmentation.

    Science.gov (United States)

    Mishra, Ajay; Aloimonos, Yiannis

    2009-01-01

    The human visual system observes and understands a scene/image by making a series of fixations. Every fixation point lies inside a particular region of arbitrary shape and size in the scene which can either be an object or just a part of it. We define as a basic segmentation problem the task of segmenting that region containing the fixation point. Segmenting the region containing the fixation is equivalent to finding the enclosing contour- a connected set of boundary edge fragments in the edge map of the scene - around the fixation. This enclosing contour should be a depth boundary.We present here a novel algorithm that finds this bounding contour and achieves the segmentation of one object, given the fixation. The proposed segmentation framework combines monocular cues (color/intensity/texture) with stereo and/or motion, in a cue independent manner. The semantic robots of the immediate future will be able to use this algorithm to automatically find objects in any environment. The capability of automatically segmenting objects in their visual field can bring the visual processing to the next level. Our approach is different from current approaches. While existing work attempts to segment the whole scene at once into many areas, we segment only one image region, specifically the one containing the fixation point. Experiments with real imagery collected by our active robot and from the known databases 1 demonstrate the promise of the approach.

  3. Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

    Science.gov (United States)

    Moeskops, Pim; Viergever, Max A.; Benders, Manon J. N. L.; Išgum, Ivana

    2015-03-01

    Automatic brain tissue segmentation is of clinical relevance in images acquired at all ages. The literature presents a clear distinction between methods developed for MR images of infants, and methods developed for images of adults. The aim of this work is to evaluate a method developed for neonatal images in the segmentation of adult images. The evaluated method employs supervised voxel classification in subsequent stages, exploiting spatial and intensity information. Evaluation was performed using images available within the MRBrainS13 challenge. The obtained average Dice coefficients were 85.77% for grey matter, 88.66% for white matter, 81.08% for cerebrospinal fluid, 95.65% for cerebrum, and 96.92% for intracranial cavity, currently resulting in the best overall ranking. The possibility of applying the same method to neonatal as well as adult images can be of great value in cross-sectional studies that include a wide age range.

  4. SU-E-J-132: Automated Segmentation with Post-Registration Atlas Selection Based On Mutual Information

    International Nuclear Information System (INIS)

    Ren, X; Gao, H; Sharp, G

    2015-01-01

    Purpose: The delineation of targets and organs-at-risk is a critical step during image-guided radiation therapy, for which manual contouring is the gold standard. However, it is often time-consuming and may suffer from intra- and inter-rater variability. The purpose of this work is to investigate the automated segmentation. Methods: The automatic segmentation here is based on mutual information (MI), with the atlas from Public Domain Database for Computational Anatomy (PDDCA) with manually drawn contours.Using dice coefficient (DC) as the quantitative measure of segmentation accuracy, we perform leave-one-out cross-validations for all PDDCA images sequentially, during which other images are registered to each chosen image and DC is computed between registered contour and ground truth. Meanwhile, six strategies, including MI, are selected to measure the image similarity, with MI to be the best. Then given a target image to be segmented and an atlas, automatic segmentation consists of: (a) the affine registration step for image positioning; (b) the active demons registration method to register the atlas to the target image; (c) the computation of MI values between the deformed atlas and the target image; (d) the weighted image fusion of three deformed atlas images with highest MI values to form the segmented contour. Results: MI was found to be the best among six studied strategies in the sense that it had the highest positive correlation between similarity measure (e.g., MI values) and DC. For automated segmentation, the weighted image fusion of three deformed atlas images with highest MI values provided the highest DC among four proposed strategies. Conclusion: MI has the highest correlation with DC, and therefore is an appropriate choice for post-registration atlas selection in atlas-based segmentation. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)

  5. SU-E-J-132: Automated Segmentation with Post-Registration Atlas Selection Based On Mutual Information

    Energy Technology Data Exchange (ETDEWEB)

    Ren, X; Gao, H [Shanghai Jiao Tong University, Shanghai, Shanghai (China); Sharp, G [Massachusetts General Hospital, Boston, MA (United States)

    2015-06-15

    Purpose: The delineation of targets and organs-at-risk is a critical step during image-guided radiation therapy, for which manual contouring is the gold standard. However, it is often time-consuming and may suffer from intra- and inter-rater variability. The purpose of this work is to investigate the automated segmentation. Methods: The automatic segmentation here is based on mutual information (MI), with the atlas from Public Domain Database for Computational Anatomy (PDDCA) with manually drawn contours.Using dice coefficient (DC) as the quantitative measure of segmentation accuracy, we perform leave-one-out cross-validations for all PDDCA images sequentially, during which other images are registered to each chosen image and DC is computed between registered contour and ground truth. Meanwhile, six strategies, including MI, are selected to measure the image similarity, with MI to be the best. Then given a target image to be segmented and an atlas, automatic segmentation consists of: (a) the affine registration step for image positioning; (b) the active demons registration method to register the atlas to the target image; (c) the computation of MI values between the deformed atlas and the target image; (d) the weighted image fusion of three deformed atlas images with highest MI values to form the segmented contour. Results: MI was found to be the best among six studied strategies in the sense that it had the highest positive correlation between similarity measure (e.g., MI values) and DC. For automated segmentation, the weighted image fusion of three deformed atlas images with highest MI values provided the highest DC among four proposed strategies. Conclusion: MI has the highest correlation with DC, and therefore is an appropriate choice for post-registration atlas selection in atlas-based segmentation. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)

  6. High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas

    DEFF Research Database (Denmark)

    Saygin, Z M; Kliemann, D; Iglesias, J. E.

    2017-01-01

    The amygdala is composed of multiple nuclei with unique functions and connections in the limbic system and to the rest of the brain. However, standard in vivo neuroimaging tools to automatically delineate the amygdala into its multiple nuclei are still rare. By scanning postmortem specimens at high...... resolution (100-150µm) at 7T field strength (n = 10), we were able to visualize and label nine amygdala nuclei (anterior amygdaloid, cortico-amygdaloid transition area; basal, lateral, accessory basal, central, cortical medial, paralaminar nuclei). We created an atlas from these labels using a recently...... developed atlas building algorithm based on Bayesian inference. This atlas, which will be released as part of FreeSurfer, can be used to automatically segment nine amygdala nuclei from a standard resolution structural MR image. We applied this atlas to two publicly available datasets (ADNI and ABIDE...

  7. ACM-based automatic liver segmentation from 3-D CT images by combining multiple atlases and improved mean-shift techniques.

    Science.gov (United States)

    Ji, Hongwei; He, Jiangping; Yang, Xin; Deklerck, Rudi; Cornelis, Jan

    2013-05-01

    In this paper, we present an autocontext model(ACM)-based automatic liver segmentation algorithm, which combines ACM, multiatlases, and mean-shift techniques to segment liver from 3-D CT images. Our algorithm is a learning-based method and can be divided into two stages. At the first stage, i.e., the training stage, ACM is performed to learn a sequence of classifiers in each atlas space (based on each atlas and other aligned atlases). With the use of multiple atlases, multiple sequences of ACM-based classifiers are obtained. At the second stage, i.e., the segmentation stage, the test image will be segmented in each atlas space by applying each sequence of ACM-based classifiers. The final segmentation result will be obtained by fusing segmentation results from all atlas spaces via a multiclassifier fusion technique. Specially, in order to speed up segmentation, given a test image, we first use an improved mean-shift algorithm to perform over-segmentation and then implement the region-based image labeling instead of the original inefficient pixel-based image labeling. The proposed method is evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results show that the average volume overlap error and the average surface distance achieved by our method are 8.3% and 1.5 m, respectively, which are comparable to the results reported in the existing state-of-the-art work on liver segmentation.

  8. High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas.

    Science.gov (United States)

    Saygin, Z M; Kliemann, D; Iglesias, J E; van der Kouwe, A J W; Boyd, E; Reuter, M; Stevens, A; Van Leemput, K; McKee, A; Frosch, M P; Fischl, B; Augustinack, J C

    2017-07-15

    The amygdala is composed of multiple nuclei with unique functions and connections in the limbic system and to the rest of the brain. However, standard in vivo neuroimaging tools to automatically delineate the amygdala into its multiple nuclei are still rare. By scanning postmortem specimens at high resolution (100-150µm) at 7T field strength (n = 10), we were able to visualize and label nine amygdala nuclei (anterior amygdaloid, cortico-amygdaloid transition area; basal, lateral, accessory basal, central, cortical medial, paralaminar nuclei). We created an atlas from these labels using a recently developed atlas building algorithm based on Bayesian inference. This atlas, which will be released as part of FreeSurfer, can be used to automatically segment nine amygdala nuclei from a standard resolution structural MR image. We applied this atlas to two publicly available datasets (ADNI and ABIDE) with standard resolution T1 data, used individual volumetric data of the amygdala nuclei as the measure and found that our atlas i) discriminates between Alzheimer's disease participants and age-matched control participants with 84% accuracy (AUC=0.915), and ii) discriminates between individuals with autism and age-, sex- and IQ-matched neurotypically developed control participants with 59.5% accuracy (AUC=0.59). For both datasets, the new ex vivo atlas significantly outperformed (all p amygdala derived from the segmentation in FreeSurfer 5.1 (ADNI: 75%, ABIDE: 54% accuracy), as well as classification based on whole amygdala volume (using the sum of all amygdala nuclei volumes; ADNI: 81%, ABIDE: 55% accuracy). This new atlas and the segmentation tools that utilize it will provide neuroimaging researchers with the ability to explore the function and connectivity of the human amygdala nuclei with unprecedented detail in healthy adults as well as those with neurodevelopmental and neurodegenerative disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Automatic coronary calcium scoring using noncontrast and contrast CT images

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Guanyu, E-mail: yang.list@seu.edu.cn; Chen, Yang; Shu, Huazhong [Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, No. 2, Si Pai Lou, Nanjing 210096 (China); Centre de Recherche en Information Biomédicale Sino-Français (LIA CRIBs), Nanjing 210096 (China); Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing 210096 (China); Ning, Xiufang; Sun, Qiaoyu [Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, No. 2, Si Pai Lou, Nanjing 210096 (China); Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing 210096 (China); Coatrieux, Jean-Louis [INSERM-U1099, Rennes F-35000 (France); Labotatoire Traitement du Signal et de l’Image (LTSI), Université de Rennes 1, Campus de Beaulieu, Bat. 22, Rennes 35042 Cedex (France); Centre de Recherche en Information Biomédicale Sino-Français (LIA CRIBs), Nanjing 210096 (China)

    2016-05-15

    Purpose: Calcium scoring is widely used to assess the risk of coronary heart disease (CHD). Accurate coronary artery calcification detection in noncontrast CT image is a prerequisite step for coronary calcium scoring. Currently, calcified lesions in the coronary arteries are manually identified by radiologists in clinical practice. Thus, in this paper, a fully automatic calcium scoring method was developed to alleviate the work load of the radiologists or cardiologists. Methods: The challenge of automatic coronary calcification detection is to discriminate the calcification in the coronary arteries from the calcification in the other tissues. Since the anatomy of coronary arteries is difficult to be observed in the noncontrast CT images, the contrast CT image of the same patient is used to extract the regions of the aorta, heart, and coronary arteries. Then, a patient-specific region-of-interest (ROI) is generated in the noncontrast CT image according to the segmentation results in the contrast CT image. This patient-specific ROI focuses on the regions in the neighborhood of coronary arteries for calcification detection, which can eliminate the calcifications in the surrounding tissues. A support vector machine classifier is applied finally to refine the results by removing possible image noise. Furthermore, the calcified lesions in the noncontrast images belonging to the different main coronary arteries are identified automatically using the labeling results of the extracted coronary arteries. Results: Forty datasets from four different CT machine vendors were used to evaluate their algorithm, which were provided by the MICCAI 2014 Coronary Calcium Scoring (orCaScore) Challenge. The sensitivity and positive predictive value for the volume of detected calcifications are 0.989 and 0.948. Only one patient out of 40 patients had been assigned to the wrong risk category defined according to Agatston scores (0, 1–100, 101–300, >300) by comparing with the ground

  10. Automated segmentation and reconstruction of patient-specific cardiac anatomy and pathology from in vivo MRI

    International Nuclear Information System (INIS)

    Ringenberg, Jordan; Deo, Makarand; Devabhaktuni, Vijay; Filgueiras-Rama, David; Pizarro, Gonzalo; Ibañez, Borja; Berenfeld, Omer; Boyers, Pamela; Gold, Jeffrey

    2012-01-01

    This paper presents an automated method to segment left ventricle (LV) tissues from functional and delayed-enhancement (DE) cardiac magnetic resonance imaging (MRI) scans using a sequential multi-step approach. First, a region of interest (ROI) is computed to create a subvolume around the LV using morphological operations and image arithmetic. From the subvolume, the myocardial contours are automatically delineated using difference of Gaussians (DoG) filters and GSV snakes. These contours are used as a mask to identify pathological tissues, such as fibrosis or scar, within the DE-MRI. The presented automated technique is able to accurately delineate the myocardium and identify the pathological tissue in patient sets. The results were validated by two expert cardiologists, and in one set the automated results are quantitatively and qualitatively compared with expert manual delineation. Furthermore, the method is patient-specific, performed on an entire patient MRI series. Thus, in addition to providing a quick analysis of individual MRI scans, the fully automated segmentation method is used for effectively tagging regions in order to reconstruct computerized patient-specific 3D cardiac models. These models can then be used in electrophysiological studies and surgical strategy planning. (paper)

  11. Evaluation of an automatic segmentation algorithm for definition of head and neck organs at risk.

    Science.gov (United States)

    Thomson, David; Boylan, Chris; Liptrot, Tom; Aitkenhead, Adam; Lee, Lip; Yap, Beng; Sykes, Andrew; Rowbottom, Carl; Slevin, Nicholas

    2014-08-03

    The accurate definition of organs at risk (OARs) is required to fully exploit the benefits of intensity-modulated radiotherapy (IMRT) for head and neck cancer. However, manual delineation is time-consuming and there is considerable inter-observer variability. This is pertinent as function-sparing and adaptive IMRT have increased the number and frequency of delineation of OARs. We evaluated accuracy and potential time-saving of Smart Probabilistic Image Contouring Engine (SPICE) automatic segmentation to define OARs for salivary-, swallowing- and cochlea-sparing IMRT. Five clinicians recorded the time to delineate five organs at risk (parotid glands, submandibular glands, larynx, pharyngeal constrictor muscles and cochleae) for each of 10 CT scans. SPICE was then used to define these structures. The acceptability of SPICE contours was initially determined by visual inspection and the total time to modify them recorded per scan. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm created a reference standard from all clinician contours. Clinician, SPICE and modified contours were compared against STAPLE by the Dice similarity coefficient (DSC) and mean/maximum distance to agreement (DTA). For all investigated structures, SPICE contours were less accurate than manual contours. However, for parotid/submandibular glands they were acceptable (median DSC: 0.79/0.80; mean, maximum DTA: 1.5 mm, 14.8 mm/0.6 mm, 5.7 mm). Modified SPICE contours were also less accurate than manual contours. The utilisation of SPICE did not result in time-saving/improve efficiency. Improvements in accuracy of automatic segmentation for head and neck OARs would be worthwhile and are required before its routine clinical implementation.

  12. Semi-automatic segmentation of gated blood pool emission tomographic images by watersheds: application to the determination of right and left ejection fractions

    International Nuclear Information System (INIS)

    Mariano-Goulart, D.; Collet, H.; Kotzki, P.-O.; Zanca, M.; Rossi, M.

    1998-01-01

    Tomographic multi-gated blood pool scintigraphy (TMUGA) is a widely available method which permits simultaneous assessment of right and left ventricular ejection fractions. However, the widespread clinical use of this technique is impeded by the lack of segmentation methods dedicated to an automatic analysis of ventricular activities. In this study we evaluated how a watershed algorithm succeeds in providing semi-automatic segmentation of ventricular activities in order to measure right and left ejection fractions by TMUGA. The left ejection fractions of 30 patients were evaluated both with TMUGA and with planar multi-gated blood pool scintigraphy (PMUGA). Likewise, the right ejection fractions of 25 patients were evaluated with first-pass scintigraphy (FP) and with TMUGA. The watershed algorithm was applied to the reconstructed slices in order to group together the voxels whose activity came from one specific cardiac cavity. First, the results of the watershed algorithm were compared with manual drawing around left and right ventricles. Left ejection fractions evaluated by TMUGA with the watershed procedure were not significantly different (p=0.30) from manual outlines whereas a small but significant difference was found for right ejection fractions (p=0.004). Then right and left ejection fractions evaluated by TMUGA (with the semi-automatic segmentation procedure) were compared with the results obtained by FP or PMUGA. Left ventricular ejection fractions evaluated by TMUGA showed an excellent correlation with those evaluated by PMUGA (r=0.93; SEE=5.93%; slope=0.99; intercept = 4.17%). The measurements of these ejection fractions were significantly higher with TMUGA than with PMUGA (P<0.01). The interoperator variability for the measurement of left ejection fractions by TMUGA was 4.6%. Right ventricular ejection fractions evaluated by TMUGA showed a good correlation with those evaluated by FP (r = 0.81; SEE = 6.68%; slope = 1.00; intercept = 0.85%) and were not

  13. Automatic segmentation of colon glands using object-graphs.

    Science.gov (United States)

    Gunduz-Demir, Cigdem; Kandemir, Melih; Tosun, Akif Burak; Sokmensuer, Cenk

    2010-02-01

    Gland segmentation is an important step to automate the analysis of biopsies that contain glandular structures. However, this remains a challenging problem as the variation in staining, fixation, and sectioning procedures lead to a considerable amount of artifacts and variances in tissue sections, which may result in huge variances in gland appearances. In this work, we report a new approach for gland segmentation. This approach decomposes the tissue image into a set of primitive objects and segments glands making use of the organizational properties of these objects, which are quantified with the definition of object-graphs. As opposed to the previous literature, the proposed approach employs the object-based information for the gland segmentation problem, instead of using the pixel-based information alone. Working with the images of colon tissues, our experiments demonstrate that the proposed object-graph approach yields high segmentation accuracies for the training and test sets and significantly improves the segmentation performance of its pixel-based counterparts. The experiments also show that the object-based structure of the proposed approach provides more tolerance to artifacts and variances in tissues.

  14. Three-dimensional computer-assisted dissection of pancreatic lymphatic anatomy on human fetuses: a step toward automatic image alignment.

    Science.gov (United States)

    Bardol, T; Subsol, G; Perez, M-J; Genevieve, D; Lamouroux, A; Antoine, B; Captier, G; Prudhomme, M; Bertrand, M M

    2018-05-01

    Pancreatic cancer is the fourth cause of death by cancer worldwide. Lymph node (LN) involvement is known to be the main prognostic factor. However, lymphatic anatomy is complex and only partially characterized. The aim of the study was to study the pancreatic lymphatic system using computer-assisted anatomic dissection (CAAD) technique and also to update CAAD technique by automatizing slice alignment. We dissected three human fetuses aged from 18 to 34 WA. 5-µm serial sections of duodeno-pancreas and spleen blocks were stained (hematoxylin-eosin, hematoxylin of Mayer and Masson trichrome), scanned, aligned and modeled in three dimensions. We observed a rich, diffuse but not systematized lymphatic network in the peri-pancreatic region. There was an equal distribution of LNs between the cephalic and body-tail portions. The lymphatic vascularization appeared in continuity from the celiac trunk to the distal ends of its hepatic and splenic arterial branches parallel to the nerve ramifications of the celiac plexus. We also observed a continuity between the drainage of the pancreatic head and the para-aortic region posteriorly. In view of the wealth of peri-pancreatic LNs, the number of LNs to harvest could be increased to improve nodal staging and prognostic evaluation. Pancreatic anatomy as described does not seem to be compatible with the sentinel LN procedure in pancreatic surgery. Finally, we are now able to offer an alternative to manual alignment with a semi-automated alignment.

  15. Gradient-based reliability maps for ACM-based segmentation of hippocampus.

    Science.gov (United States)

    Zarpalas, Dimitrios; Gkontra, Polyxeni; Daras, Petros; Maglaveras, Nicos

    2014-04-01

    Automatic segmentation of deep brain structures, such as the hippocampus (HC), in MR images has attracted considerable scientific attention due to the widespread use of MRI and to the principal role of some structures in various mental disorders. In this literature, there exists a substantial amount of work relying on deformable models incorporating prior knowledge about structures' anatomy and shape information. However, shape priors capture global shape characteristics and thus fail to model boundaries of varying properties; HC boundaries present rich, poor, and missing gradient regions. On top of that, shape prior knowledge is blended with image information in the evolution process, through global weighting of the two terms, again neglecting the spatially varying boundary properties, causing segmentation faults. An innovative method is hereby presented that aims to achieve highly accurate HC segmentation in MR images, based on the modeling of boundary properties at each anatomical location and the inclusion of appropriate image information for each of those, within an active contour model framework. Hence, blending of image information and prior knowledge is based on a local weighting map, which mixes gradient information, regional and whole brain statistical information with a multi-atlas-based spatial distribution map of the structure's labels. Experimental results on three different datasets demonstrate the efficacy and accuracy of the proposed method.

  16. Automatic Approach for Lung Segmentation with Juxta-Pleural Nodules from Thoracic CT Based on Contour Tracing and Correction

    Directory of Open Access Journals (Sweden)

    Jinke Wang

    2016-01-01

    Full Text Available This paper presents a fully automatic framework for lung segmentation, in which juxta-pleural nodule problem is brought into strong focus. The proposed scheme consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest skin boundary is extracted through image aligning, morphology operation, and connective region analysis. Secondly, diagonal-based border tracing is implemented for lung contour segmentation, with maximum cost path algorithm used for separating the left and right lungs. Finally, by arc-based border smoothing and concave-based border correction, the refined pulmonary parenchyma is obtained. The proposed scheme is evaluated on 45 volumes of chest scans, with volume difference (VD 11.15±69.63 cm3, volume overlap error (VOE 3.5057±1.3719%, average surface distance (ASD 0.7917±0.2741 mm, root mean square distance (RMSD 1.6957±0.6568 mm, maximum symmetric absolute surface distance (MSD 21.3430±8.1743 mm, and average time-cost 2 seconds per image. The preliminary results on accuracy and complexity prove that our scheme is a promising tool for lung segmentation with juxta-pleural nodules.

  17. Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art.

    Science.gov (United States)

    Nielsen, Jesper D; Madsen, Kristoffer H; Puonti, Oula; Siebner, Hartwig R; Bauer, Christian; Madsen, Camilla Gøbel; Saturnino, Guilherme B; Thielscher, Axel

    2018-03-12

    Anatomically realistic volume conductor models of the human head are important for accurate forward modeling of the electric field during transcranial brain stimulation (TBS), electro- (EEG) and magnetoencephalography (MEG). In particular, the skull compartment exerts a strong influence on the field distribution due to its low conductivity, suggesting the need to represent its geometry accurately. However, automatic skull reconstruction from structural magnetic resonance (MR) images is difficult, as compact bone has a very low signal in magnetic resonance imaging (MRI). Here, we evaluate three methods for skull segmentation, namely FSL BET2, the unified segmentation routine of SPM12 with extended spatial tissue priors, and the skullfinder tool of BrainSuite. To our knowledge, this study is the first to rigorously assess the accuracy of these state-of-the-art tools by comparison with CT-based skull segmentations on a group of ten subjects. We demonstrate several key factors that improve the segmentation quality, including the use of multi-contrast MRI data, the optimization of the MR sequences and the adaptation of the parameters of the segmentation methods. We conclude that FSL and SPM12 achieve better skull segmentations than BrainSuite. The former methods obtain reasonable results for the upper part of the skull when a combination of T1- and T2-weighted images is used as input. The SPM12-based results can be improved slightly further by means of simple morphological operations to fix local defects. In contrast to FSL BET2, the SPM12-based segmentation with extended spatial tissue priors and the BrainSuite-based segmentation provide coarse reconstructions of the vertebrae, enabling the construction of volume conductor models that include the neck. We exemplarily demonstrate that the extended models enable a more accurate estimation of the electric field distribution during transcranial direct current stimulation (tDCS) for montages that involve extraencephalic

  18. Cerebellar anatomy as applied to cerebellar microsurgical resections

    Directory of Open Access Journals (Sweden)

    Alejandro Ramos

    2012-06-01

    Full Text Available OBJECTIVE: To define the anatomy of dentate nucleus and cerebellar peduncles, demonstrating the surgical application of anatomic landmarks in cerebellar resections. METHODS: Twenty cerebellar hemispheres were studied. RESULTS: The majority of dentate nucleus and cerebellar peduncles had demonstrated constant relationship to other cerebellar structures, which provided landmarks for surgical approaching. The lateral border is separated from the midline by 19.5 mm in both hemispheres. The posterior border of the cortex is separated 23.3 mm from the posterior segment of the dentate nucleus; the lateral one is separated 26 mm from the lateral border of the nucleus; and the posterior segment of the dentate nucleus is separated 25.4 mm from the posterolateral angle formed by the junction of lateral and posterior borders of cerebellar hemisphere. CONCLUSIONS: Microsurgical anatomy has provided important landmarks that could be applied to cerebellar surgical resections.

  19. Validation of an enhanced knowledge-based method for segmentation and quantitative analysis of intrathoracic airway trees from three-dimensional CT images

    International Nuclear Information System (INIS)

    Sonka, M.; Park, W.; Hoffman, E.A.

    1995-01-01

    Accurate assessment of airway physiology, evaluated in terms of geometric changes, is critically dependent upon the accurate imaging and image segmentation of the three-dimensional airway tree structure. The authors have previously reported a knowledge-based method for three-dimensional airway tree segmentation from high resolution CT (HRCT) images. Here, they report a substantially improved version of the method. In the current implementation, the method consists of several stages. First, the lung borders are automatically determined in the three-dimensional set of HRCT data. The primary airway tree is semi-automatically identified. In the next stage, potential airways are determined in individual CT slices using a rule-based system that uses contextual information and a priori knowledge about pulmonary anatomy. Using three-dimensional connectivity properties of the pulmonary airway tree, the three-dimensional tree is constructed from the set of adjacent slices. The method's performance and accuracy were assessed in five 3D HRCT canine images. Computer-identified airways matched 226/258 observer-defined airways (87.6%); the computer method failed to detect the airways in the remaining 32 locations. By visual assessment of rendered airway trees, the experienced observers judged the computer-detected airway trees as highly realistic

  20. Automatic gallbladder segmentation using combined 2D and 3D shape features to perform volumetric analysis in native and secretin-enhanced MRCP sequences.

    Science.gov (United States)

    Gloger, Oliver; Bülow, Robin; Tönnies, Klaus; Völzke, Henry

    2017-11-24

    We aimed to develop the first fully automated 3D gallbladder segmentation approach to perform volumetric analysis in volume data of magnetic resonance (MR) cholangiopancreatography (MRCP) sequences. Volumetric gallbladder analysis is performed for non-contrast-enhanced and secretin-enhanced MRCP sequences. Native and secretin-enhanced MRCP volume data were produced with a 1.5-T MR system. Images of coronal maximum intensity projections (MIP) are used to automatically compute 2D characteristic shape features of the gallbladder in the MIP images. A gallbladder shape space is generated to derive 3D gallbladder shape features, which are then combined with 2D gallbladder shape features in a support vector machine approach to detect gallbladder regions in MRCP volume data. A region-based level set approach is used for fine segmentation. Volumetric analysis is performed for both sequences to calculate gallbladder volume differences between both sequences. The approach presented achieves segmentation results with mean Dice coefficients of 0.917 in non-contrast-enhanced sequences and 0.904 in secretin-enhanced sequences. This is the first approach developed to detect and segment gallbladders in MR-based volume data automatically in both sequences. It can be used to perform gallbladder volume determination in epidemiological studies and to detect abnormal gallbladder volumes or shapes. The positive volume differences between both sequences may indicate the quantity of the pancreatobiliary reflux.

  1. User-guided segmentation for volumetric retinal optical coherence tomography images

    Science.gov (United States)

    Yin, Xin; Chao, Jennifer R.; Wang, Ruikang K.

    2014-01-01

    Abstract. Despite the existence of automatic segmentation techniques, trained graders still rely on manual segmentation to provide retinal layers and features from clinical optical coherence tomography (OCT) images for accurate measurements. To bridge the gap between this time-consuming need of manual segmentation and currently available automatic segmentation techniques, this paper proposes a user-guided segmentation method to perform the segmentation of retinal layers and features in OCT images. With this method, by interactively navigating three-dimensional (3-D) OCT images, the user first manually defines user-defined (or sketched) lines at regions where the retinal layers appear very irregular for which the automatic segmentation method often fails to provide satisfactory results. The algorithm is then guided by these sketched lines to trace the entire 3-D retinal layer and anatomical features by the use of novel layer and edge detectors that are based on robust likelihood estimation. The layer and edge boundaries are finally obtained to achieve segmentation. Segmentation of retinal layers in mouse and human OCT images demonstrates the reliability and efficiency of the proposed user-guided segmentation method. PMID:25147962

  2. Fat segmentation on chest CT images via fuzzy models

    Science.gov (United States)

    Tong, Yubing; Udupa, Jayaram K.; Wu, Caiyun; Pednekar, Gargi; Subramanian, Janani Rajan; Lederer, David J.; Christie, Jason; Torigian, Drew A.

    2016-03-01

    Quantification of fat throughout the body is vital for the study of many diseases. In the thorax, it is important for lung transplant candidates since obesity and being underweight are contraindications to lung transplantation given their associations with increased mortality. Common approaches for thoracic fat segmentation are all interactive in nature, requiring significant manual effort to draw the interfaces between fat and muscle with low efficiency and questionable repeatability. The goal of this paper is to explore a practical way for the segmentation of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) components of chest fat based on a recently developed body-wide automatic anatomy recognition (AAR) methodology. The AAR approach involves 3 main steps: building a fuzzy anatomy model of the body region involving all its major representative objects, recognizing objects in any given test image, and delineating the objects. We made several modifications to these steps to develop an effective solution to delineate SAT/VAT components of fat. Two new objects representing interfaces of SAT and VAT regions with other tissues, SatIn and VatIn are defined, rather than using directly the SAT and VAT components as objects for constructing the models. A hierarchical arrangement of these new and other reference objects is built to facilitate their recognition in the hierarchical order. Subsequently, accurate delineations of the SAT/VAT components are derived from these objects. Unenhanced CT images from 40 lung transplant candidates were utilized in experimentally evaluating this new strategy. Mean object location error achieved was about 2 voxels and delineation error in terms of false positive and false negative volume fractions were, respectively, 0.07 and 0.1 for SAT and 0.04 and 0.2 for VAT.

  3. Limitations and pitfalls of Couinaud's segmentation of the liver in transaxial Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Strunk, H; Textor, J; Willinek, W [Department of Radiology, University of Bonn, Sigmund Freud-Strasse 25, 53105, Bonn (Germany); Stuckmann, G [Department of Radiology, Kantonsspital Winterthur (Switzerland)

    2003-11-01

    The segmental anatomy of the human liver has become a matter of increasing interest to the radiologist, especially in view of the need for an accurate preoperative localization of focal hepatic lesions. In this review article first an overview of the different classical concepts for delineating segmental and subsegmental anatomy on US, transaxial CT, and MR images is given. Essentially, these procedures are based on Couinaud's concept of three vertical planes that divide the liver into four segments and of a transverse scissura that further subdivides the segments into two subsegments each. In a second part, the limitations of these methods are delineated and discussed with the conclusion that if exact preoperative localization of hepatic lesions is needed, tumor must be located relative to the avascular planes between the different portal territories. (orig.)

  4. Limitations and pitfalls of Couinaud's segmentation of the liver in transaxial Imaging

    International Nuclear Information System (INIS)

    Strunk, H.; Textor, J.; Willinek, W.; Stuckmann, G.

    2003-01-01

    The segmental anatomy of the human liver has become a matter of increasing interest to the radiologist, especially in view of the need for an accurate preoperative localization of focal hepatic lesions. In this review article first an overview of the different classical concepts for delineating segmental and subsegmental anatomy on US, transaxial CT, and MR images is given. Essentially, these procedures are based on Couinaud's concept of three vertical planes that divide the liver into four segments and of a transverse scissura that further subdivides the segments into two subsegments each. In a second part, the limitations of these methods are delineated and discussed with the conclusion that if exact preoperative localization of hepatic lesions is needed, tumor must be located relative to the avascular planes between the different portal territories. (orig.)

  5. Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing

    Directory of Open Access Journals (Sweden)

    Liao Chun-Chih

    2011-08-01

    Full Text Available Abstract Background In recent years, magnetic resonance imaging (MRI has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images. This paper uses an algorithm integrating fuzzy-c-mean (FCM and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain. Methods The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT on a pixel level. Overall data were then evaluated using a quantified system. Results The quantified parameters, including the "percent match" (PM and "correlation ratio" (CR, suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain. Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related. Conclusions Results indicated

  6. Segmentation of internal brain structures in three-dimensional nuclear magnetic resonance imaging

    International Nuclear Information System (INIS)

    Geraud, Th.

    1998-01-01

    For neurological studies, the in vivo aspect of imaging systems is very attractive. Brain images are currently a classical tool used in clinical routine and research. The most appropriate system to observe brain anatomy is tridimensional magnetic resonance imaging, and a major issue of image processing is to segment automatically cerebral structures. This is the scope of our thesis. The number of applications is steadily growing: morphometric measurements, pathology detection, surgery planning, getting a reference for functional studies,a and so forth. The use of pattern recognition to classify the different cerebral tissues from the only radiometric levels of the images is limited. Even supervised, these methods can not lead to distinguish easily several classes of grey matter. When these methods are automatic, their use has to be empirical in order to ensure robust results, and has to be restricted to regions of interest in order to get reliable results. As these methods do not fully respect the spatial consistency of classes in the images, we have introduced contextual information with the help of different formalisms. With Markovian regularization, we have shown that energetic terms of localization permit the separation of two grey classes: cortex and central nuclei. With mathematical morphology, we have proposed processing chains dedicated to several cerebral objects; in particular, brain segmentation is robust and reproducible, and we have successfully obtained individual markers for lateral ventricles, caudate nuclei, putamen and thalami. We have also proposed a contextual method to estimate pure tissue characteristics from a rough segmentation. Our main contribution has been to present a recognition method which is progressive and atlas guided. The originality of this method is manifold. At first, it takes into account structural information processed as flexible spatial constraints the formalism of which relies on fuzzy set theory and information fusion

  7. GPU-based relative fuzzy connectedness image segmentation

    International Nuclear Information System (INIS)

    Zhuge Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W.

    2013-01-01

    Purpose:Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an ℓ ∞ -based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA’s Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.

  8. GPU-based relative fuzzy connectedness image segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Zhuge Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W. [Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892 (United States); Department of Mathematics, West Virginia University, Morgantown, West Virginia 26506 (United States) and Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892 (United States)

    2013-01-15

    Purpose:Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an Script-Small-L {sub {infinity}}-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA's Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8 Multiplication-Sign , 22.9 Multiplication-Sign , 20.9 Multiplication-Sign , and 17.5 Multiplication-Sign , correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.

  9. GPU-based relative fuzzy connectedness image segmentation.

    Science.gov (United States)

    Zhuge, Ying; Ciesielski, Krzysztof C; Udupa, Jayaram K; Miller, Robert W

    2013-01-01

    Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. The most common FC segmentations, optimizing an [script-l](∞)-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA's Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.

  10. GPU-based relative fuzzy connectedness image segmentation

    Science.gov (United States)

    Zhuge, Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W.

    2013-01-01

    Purpose: Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an ℓ∞-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA’s Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology. PMID:23298094

  11. Automatic segmentation of MRI head images by 3-D region growing method which utilizes edge information

    International Nuclear Information System (INIS)

    Jiang, Hao; Suzuki, Hidetomo; Toriwaki, Jun-ichiro

    1991-01-01

    This paper presents a 3-D segmentation method that automatically extracts soft tissue from multi-sliced MRI head images. MRI produces a sequence of two-dimensional (2-D) images which contains three-dimensional (3-D) information of organs. To utilize such information we need effective algorithms to treat 3-D digital images and to extract organs and tissues of interest. We developed a method to extract the brain from MRI images which uses a region growing procedure and integrates information of uniformity of gray levels and information of the presence of edge segments in the local area around the pixel of interest. First we generate a kernel region which is a part of brain tissue by simple thresholding. Then we grow the region by means of a region growing algorithm under the control of 3-D edge existence to obtain the region of the brain. Our method is rather simple because it uses basic 3-D image processing techniques like spatial difference. It is robust for variation of gray levels inside a tissue since it also refers to the edge information in the process of region growing. Therefore, the method is flexible enough to be applicable to the segmentation of other images including soft tissues which have complicated shapes and fluctuation in gray levels. (author)

  12. Automatic localization of landmark sets in head CT images with regression forests for image registration initialization

    Science.gov (United States)

    Zhang, Dongqing; Liu, Yuan; Noble, Jack H.; Dawant, Benoit M.

    2016-03-01

    Cochlear Implants (CIs) are electrode arrays that are surgically inserted into the cochlea. Individual contacts stimulate frequency-mapped nerve endings thus replacing the natural electro-mechanical transduction mechanism. CIs are programmed post-operatively by audiologists but this is currently done using behavioral tests without imaging information that permits relating electrode position to inner ear anatomy. We have recently developed a series of image processing steps that permit the segmentation of the inner ear anatomy and the localization of individual contacts. We have proposed a new programming strategy that uses this information and we have shown in a study with 68 participants that 78% of long term recipients preferred the programming parameters determined with this new strategy. A limiting factor to the large scale evaluation and deployment of our technique is the amount of user interaction still required in some of the steps used in our sequence of image processing algorithms. One such step is the rough registration of an atlas to target volumes prior to the use of automated intensity-based algorithms when the target volumes have very different fields of view and orientations. In this paper we propose a solution to this problem. It relies on a random forest-based approach to automatically localize a series of landmarks. Our results obtained from 83 images with 132 registration tasks show that automatic initialization of an intensity-based algorithm proves to be a reliable technique to replace the manual step.

  13. Automatic segmentation of 4D cardiac MR images for extraction of ventricular chambers using a spatio-temporal approach

    Science.gov (United States)

    Atehortúa, Angélica; Zuluaga, Maria A.; Ourselin, Sébastien; Giraldo, Diana; Romero, Eduardo

    2016-03-01

    An accurate ventricular function quantification is important to support evaluation, diagnosis and prognosis of several cardiac pathologies. However, expert heart delineation, specifically for the right ventricle, is a time consuming task with high inter-and-intra observer variability. A fully automatic 3D+time heart segmentation framework is herein proposed for short-axis-cardiac MRI sequences. This approach estimates the heart using exclusively information from the sequence itself without tuning any parameters. The proposed framework uses a coarse-to-fine approach, which starts by localizing the heart via spatio-temporal analysis, followed by a segmentation of the basal heart that is then propagated to the apex by using a non-rigid-registration strategy. The obtained volume is then refined by estimating the ventricular muscle by locally searching a prior endocardium- pericardium intensity pattern. The proposed framework was applied to 48 patients datasets supplied by the organizers of the MICCAI 2012 Right Ventricle segmentation challenge. Results show the robustness, efficiency and competitiveness of the proposed method both in terms of accuracy and computational load.

  14. Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images.

    Science.gov (United States)

    Abdulhay, Enas; Mohammed, Mazin Abed; Ibrahim, Dheyaa Ahmed; Arunkumar, N; Venkatraman, V

    2018-02-17

    Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size and the difficulty towards determining location of Blood Leucocytes. Physical analysis of blood tests to recognize leukocytes is tedious, time-consuming and liable to error because of the various morphological components of the cells. Segmentation of medical imagery has been considered as a difficult task because of complexity of images, and also due to the non-availability of leucocytes models which entirely captures the probable shapes in each structures and also incorporate cell overlapping, the expansive variety of the blood cells concerning their shape and size, various elements influencing the outer appearance of the blood leucocytes, and low Static Microscope Image disparity from extra issues outcoming about because of noise. We suggest a strategy towards segmentation of blood leucocytes using static microscope images which is a resultant of three prevailing systems of computer vision fiction: enhancing the image, Support vector machine for segmenting the image, and filtering out non ROI (region of interest) on the basis of Local binary patterns and texture features. Every one of these strategies are modified for blood leucocytes division issue, in this manner the subsequent techniques are very vigorous when compared with its individual segments. Eventually, we assess framework based by compare the outcome and manual division. The findings outcome from this study have shown a new approach that automatically segments the blood leucocytes and identify it from a static microscope images. Initially, the method uses a trainable segmentation procedure and trained support vector machine classifier to accurately identify the position of the ROI. After that, filtering out non ROI have proposed based on histogram analysis to avoid the non ROI and chose the right object. Finally, identify the blood leucocytes type using

  15. RFA-cut: Semi-automatic segmentation of radiofrequency ablation zones with and without needles via optimal s-t-cuts.

    Science.gov (United States)

    Egger, Jan; Busse, Harald; Brandmaier, Philipp; Seider, Daniel; Gawlitza, Matthias; Strocka, Steffen; Voglreiter, Philip; Dokter, Mark; Hofmann, Michael; Kainz, Bernhard; Chen, Xiaojun; Hann, Alexander; Boechat, Pedro; Yu, Wei; Freisleben, Bernd; Alhonnoro, Tuomas; Pollari, Mika; Moche, Michael; Schmalstieg, Dieter

    2015-01-01

    In this contribution, we present a semi-automatic segmentation algorithm for radiofrequency ablation (RFA) zones via optimal s-t-cuts. Our interactive graph-based approach builds upon a polyhedron to construct the graph and was specifically designed for computed tomography (CT) acquisitions from patients that had RFA treatments of Hepatocellular Carcinomas (HCC). For evaluation, we used twelve post-interventional CT datasets from the clinical routine and as evaluation metric we utilized the Dice Similarity Coefficient (DSC), which is commonly accepted for judging computer aided medical segmentation tasks. Compared with pure manual slice-by-slice expert segmentations from interventional radiologists, we were able to achieve a DSC of about eighty percent, which is sufficient for our clinical needs. Moreover, our approach was able to handle images containing (DSC=75.9%) and not containing (78.1%) the RFA needles still in place. Additionally, we found no statistically significant difference (p<;0.423) between the segmentation results of the subgroups for a Mann-Whitney test. Finally, to the best of our knowledge, this is the first time a segmentation approach for CT scans including the RFA needles is reported and we show why another state-of-the-art segmentation method fails for these cases. Intraoperative scans including an RFA probe are very critical in the clinical practice and need a very careful segmentation and inspection to avoid under-treatment, which may result in tumor recurrence (up to 40%). If the decision can be made during the intervention, an additional ablation can be performed without removing the entire needle. This decreases the patient stress and associated risks and costs of a separate intervention at a later date. Ultimately, the segmented ablation zone containing the RFA needle can be used for a precise ablation simulation as the real needle position is known.

  16. An Automatic Segmentation Method Combining an Active Contour Model and a Classification Technique for Detecting Polycomb-group Proteinsin High-Throughput Microscopy Images.

    Science.gov (United States)

    Gregoretti, Francesco; Cesarini, Elisa; Lanzuolo, Chiara; Oliva, Gennaro; Antonelli, Laura

    2016-01-01

    The large amount of data generated in biological experiments that rely on advanced microscopy can be handled only with automated image analysis. Most analyses require a reliable cell image segmentation eventually capable of detecting subcellular structures.We present an automatic segmentation method to detect Polycomb group (PcG) proteins areas isolated from nuclei regions in high-resolution fluorescent cell image stacks. It combines two segmentation algorithms that use an active contour model and a classification technique serving as a tool to better understand the subcellular three-dimensional distribution of PcG proteins in live cell image sequences. We obtained accurate results throughout several cell image datasets, coming from different cell types and corresponding to different fluorescent labels, without requiring elaborate adjustments to each dataset.

  17. Improving Brain Magnetic Resonance Image (MRI Segmentation via a Novel Algorithm based on Genetic and Regional Growth

    Directory of Open Access Journals (Sweden)

    Javadpour A.

    2016-06-01

    Full Text Available Background: Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective: This study describes a new method for brain Magnetic Resonance Image (MRI segmentation via a novel algorithm based on genetic and regional growth. Methods: Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results: By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion: The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases.

  18. Correlation of computed tomography, sonography, and gross anatomy of the liver

    International Nuclear Information System (INIS)

    Sexton, C.F.; Zeman, R.K.

    1983-01-01

    Although the vascular and segmental anatomy of the liver is well defined in the sonographic and computed tomographic (CT) literature, the three-dimensional relations of hepatic structures remain conceptually complex. As an aid to understanding and teaching this anatomy, axially and sagitally sectioned gross liver specimens were correlated with appropriate sonographic and CT scans. Major hepatic landmarks and vascular structures are used to identify four distinct transverse planes and four sagittal planes

  19. Automatic segmentation of blood vessels from retinal fundus images ...

    Indian Academy of Sciences (India)

    The retinal blood vessels were segmented through color space conversion and color channel .... Retinal blood vessel segmentation was also attempted through multi-scale operators. A few works in this ... fundus camera at 35 degrees field of view. The image ... vessel segmentation is available from two human observers.

  20. Segmentation-DrivenTomographic Reconstruction

    DEFF Research Database (Denmark)

    Kongskov, Rasmus Dalgas

    such that the segmentation subsequently can be carried out by use of a simple segmentation method, for instance just a thresholding method. We tested the advantages of going from a two-stage reconstruction method to a one stage segmentation-driven reconstruction method for the phase contrast tomography reconstruction......The tomographic reconstruction problem is concerned with creating a model of the interior of an object from some measured data, typically projections of the object. After reconstructing an object it is often desired to segment it, either automatically or manually. For computed tomography (CT...

  1. Nasal Anatomy

    Science.gov (United States)

    ... Caregivers Contact ARS HOME ANATOMY Nasal Anatomy Sinus Anatomy Nasal Physiology Nasal Endoscopy Skull Base Anatomy Virtual Anatomy Disclosure ... Size + - Home > ANATOMY > Nasal Anatomy Nasal Anatomy Sinus Anatomy Nasal Physiology Nasal Endoscopy Skull Base Anatomy Virtual Anatomy Disclosure ...

  2. Sinus Anatomy

    Science.gov (United States)

    ... Caregivers Contact ARS HOME ANATOMY Nasal Anatomy Sinus Anatomy Nasal Physiology Nasal Endoscopy Skull Base Anatomy Virtual Anatomy Disclosure ... Size + - Home > ANATOMY > Sinus Anatomy Nasal Anatomy Sinus Anatomy Nasal Physiology Nasal Endoscopy Skull Base Anatomy Virtual Anatomy Disclosure ...

  3. Reproducing the internal and external anatomy of fossil bones: Two new automatic digital tools.

    Science.gov (United States)

    Profico, Antonio; Schlager, Stefan; Valoriani, Veronica; Buzi, Costantino; Melchionna, Marina; Veneziano, Alessio; Raia, Pasquale; Moggi-Cecchi, Jacopo; Manzi, Giorgio

    2018-04-21

    We present two new automatic tools, developed under the R environment, to reproduce the internal and external structures of bony elements. The first method, Computer-Aided Laser Scanner Emulator (CA-LSE), provides the reconstruction of the external portions of a 3D mesh by simulating the action of a laser scanner. The second method, Automatic Segmentation Tool for 3D objects (AST-3D), performs the digital reconstruction of anatomical cavities. We present the application of CA-LSE and AST-3D methods to different anatomical remains, highly variable in terms of shape, size and structure: a modern human skull, a malleus bone, and a Neanderthal deciduous tooth. Both methods are developed in the R environment and embedded in the packages "Arothron" and "Morpho," where both the codes and the data are fully available. The application of CA-LSE and AST-3D allows the isolation and manipulation of the internal and external components of the 3D virtual representation of complex bony elements. In particular, we present the output of the four case studies: a complete modern human endocast and the right maxillary sinus, the dental pulp of the Neanderthal tooth and the inner network of blood vessels of the malleus. Both methods demonstrated to be much faster, cheaper, and more accurate than other conventional approaches. The tools we presented are available as add-ons in existing software within the R platform. Because of ease of application, and unrestrained availability of the methods proposed, these tools can be widely used by paleoanthropologists, paleontologists and anatomists. © 2018 Wiley Periodicals, Inc.

  4. A Combined Random Forests and Active Contour Model Approach for Fully Automatic Segmentation of the Left Atrium in Volumetric MRI

    Directory of Open Access Journals (Sweden)

    Chao Ma

    2017-01-01

    Full Text Available Segmentation of the left atrium (LA from cardiac magnetic resonance imaging (MRI datasets is of great importance for image guided atrial fibrillation ablation, LA fibrosis quantification, and cardiac biophysical modelling. However, automated LA segmentation from cardiac MRI is challenging due to limited image resolution, considerable variability in anatomical structures across subjects, and dynamic motion of the heart. In this work, we propose a combined random forests (RFs and active contour model (ACM approach for fully automatic segmentation of the LA from cardiac volumetric MRI. Specifically, we employ the RFs within an autocontext scheme to effectively integrate contextual and appearance information from multisource images together for LA shape inferring. The inferred shape is then incorporated into a volume-scalable ACM for further improving the segmentation accuracy. We validated the proposed method on the cardiac volumetric MRI datasets from the STACOM 2013 and HVSMR 2016 databases and showed that it outperforms other latest automated LA segmentation methods. Validation metrics, average Dice coefficient (DC and average surface-to-surface distance (S2S, were computed as 0.9227±0.0598 and 1.14±1.205 mm, versus those of 0.6222–0.878 and 1.34–8.72 mm, obtained by other methods, respectively.

  5. Automatic segmentation of myocardium from black-blood MR images using entropy and local neighborhood information.

    Directory of Open Access Journals (Sweden)

    Qian Zheng

    Full Text Available By using entropy and local neighborhood information, we present in this study a robust adaptive Gaussian regularizing Chan-Vese (CV model to segment the myocardium from magnetic resonance images with intensity inhomogeneity. By utilizing the circular Hough transformation (CHT our model is able to detect epicardial and endocardial contours of the left ventricle (LV as circles automatically, and the circles are used as the initialization. In the cost functional of our model, the interior and exterior energies are weighted by the entropy to improve the robustness of the evolving curve. Local neighborhood information is used to evolve the level set function to reduce the impact of the heterogeneity inside the regions and to improve the segmentation accuracy. An adaptive window is utilized to reduce the sensitivity to initialization. The Gaussian kernel is used to regularize the level set function, which can not only ensure the smoothness and stability of the level set function, but also eliminate the traditional Euclidean length term and re-initialization. Extensive validation of the proposed method on patient data demonstrates its superior performance over other state-of-the-art methods.

  6. Automatic, accurate, and reproducible segmentation of the brain and cerebro-spinal fluid in T1-weighted volume MRI scans and its application to serial cerebral and intracranial volumetry

    Science.gov (United States)

    Lemieux, Louis

    2001-07-01

    A new fully automatic algorithm for the segmentation of the brain and cerebro-spinal fluid (CSF) from T1-weighted volume MRI scans of the head was specifically developed in the context of serial intra-cranial volumetry. The method is an extension of a previously published brain extraction algorithm. The brain mask is used as a basis for CSF segmentation based on morphological operations, automatic histogram analysis and thresholding. Brain segmentation is then obtained by iterative tracking of the brain-CSF interface. Grey matter (GM), white matter (WM) and CSF volumes are calculated based on a model of intensity probability distribution that includes partial volume effects. Accuracy was assessed using a digital phantom scan. Reproducibility was assessed by segmenting pairs of scans from 20 normal subjects scanned 8 months apart and 11 patients with epilepsy scanned 3.5 years apart. Segmentation accuracy as measured by overlap was 98% for the brain and 96% for the intra-cranial tissues. The volume errors were: total brain (TBV): -1.0%, intra-cranial (ICV):0.1%, CSF: +4.8%. For repeated scans, matching resulted in improved reproducibility. In the controls, the coefficient of reliability (CR) was 1.5% for the TVB and 1.0% for the ICV. In the patients, the Cr for the ICV was 1.2%.

  7. Multifractal-based nuclei segmentation in fish images.

    Science.gov (United States)

    Reljin, Nikola; Slavkovic-Ilic, Marijeta; Tapia, Coya; Cihoric, Nikola; Stankovic, Srdjan

    2017-09-01

    The method for nuclei segmentation in fluorescence in-situ hybridization (FISH) images, based on the inverse multifractal analysis (IMFA) is proposed. From the blue channel of the FISH image in RGB format, the matrix of Holder exponents, with one-by-one correspondence with the image pixels, is determined first. The following semi-automatic procedure is proposed: initial nuclei segmentation is performed automatically from the matrix of Holder exponents by applying predefined hard thresholding; then the user evaluates the result and is able to refine the segmentation by changing the threshold, if necessary. After successful nuclei segmentation, the HER2 (human epidermal growth factor receptor 2) scoring can be determined in usual way: by counting red and green dots within segmented nuclei, and finding their ratio. The IMFA segmentation method is tested over 100 clinical cases, evaluated by skilled pathologist. Testing results show that the new method has advantages compared to already reported methods.

  8. Limitations and pitfalls of Couinaud's segmentation of the liver in transaxial Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Strunk, H.; Textor, J.; Willinek, W. [Department of Radiology, University of Bonn, Sigmund Freud-Strasse 25, 53105, Bonn (Germany); Stuckmann, G. [Department of Radiology, Kantonsspital Winterthur (Switzerland)

    2003-11-01

    The segmental anatomy of the human liver has become a matter of increasing interest to the radiologist, especially in view of the need for an accurate preoperative localization of focal hepatic lesions. In this review article first an overview of the different classical concepts for delineating segmental and subsegmental anatomy on US, transaxial CT, and MR images is given. Essentially, these procedures are based on Couinaud's concept of three vertical planes that divide the liver into four segments and of a transverse scissura that further subdivides the segments into two subsegments each. In a second part, the limitations of these methods are delineated and discussed with the conclusion that if exact preoperative localization of hepatic lesions is needed, tumor must be located relative to the avascular planes between the different portal territories. (orig.)

  9. TU-AB-303-07: Evaluation of Automatic Segmentation of Critical Structures for Head-And-Neck and Thoracic Radiotherapy Planning

    International Nuclear Information System (INIS)

    Yang, J; Balter, P; Court, L

    2015-01-01

    Purpose: To evaluate the performance of commercially available automatic segmentation tools built into treatment planning systems (TPS) in terms of their segmentation accuracy and flexibility in customization. Methods: Twelve head-and-neck cancer patients and twelve thoracic cancer patients were retrospectively selected to benchmark the model-based segmentation (MBS) and atlas-based segmentation (ABS) in RayStation TPS and the Smart Probabilistic Image Contouring Engine (SPICE) in Pinnacle TPS. Multi-atlas contouring service (MACS) that was developed in-house as a plug-in of Pinnacle TPS was evaluated as well. Manual contours used in clinic were reviewed and modified for consistency and served as ground truth for the evaluation. Head-and-neck evaluation included six regions of interest (ROIs): left and right parotid glands, brainstem, spinal cord, mandible, and submandibular glands. Thoracic evaluation includes seven ROIs: left and right lungs, spinal cord, heart, esophagus, and left and right brachial plexus. Auto-segmented contours were compared with the manual contours using the Dice similarity coefficient (DSC) and the mean surface distance (MSD). Results: In head- and-neck evaluation, only mandible has a high accuracy in all segmentations (DSC>85%); SPICE achieved DSC>70% for parotid glands; MACS achieved this for both parotid glands and submandibular glands; and RayStation ABS achieved this for spinal cord. In thoracic evaluation, SPICE achieved the best in lung and heart segmentation, while MACS achieved the best for all other structures. The less distinguishable structures on CT images, such as brainstem, spinal cord, parotid glands, submandibular glands, esophagus, and brachial plexus, showed great variability in different segmentation tools (mostly DSC<70% and MSD>3mm). The template for RayStation ABS can be easily customized by users, while RayStation MBS and SPICE rely on the vendors to provide the templates/models. Conclusion: Great variability was

  10. TU-AB-303-07: Evaluation of Automatic Segmentation of Critical Structures for Head-And-Neck and Thoracic Radiotherapy Planning

    Energy Technology Data Exchange (ETDEWEB)

    Yang, J; Balter, P; Court, L [MD Anderson Cancer Center, Houston, TX (United States)

    2015-06-15

    Purpose: To evaluate the performance of commercially available automatic segmentation tools built into treatment planning systems (TPS) in terms of their segmentation accuracy and flexibility in customization. Methods: Twelve head-and-neck cancer patients and twelve thoracic cancer patients were retrospectively selected to benchmark the model-based segmentation (MBS) and atlas-based segmentation (ABS) in RayStation TPS and the Smart Probabilistic Image Contouring Engine (SPICE) in Pinnacle TPS. Multi-atlas contouring service (MACS) that was developed in-house as a plug-in of Pinnacle TPS was evaluated as well. Manual contours used in clinic were reviewed and modified for consistency and served as ground truth for the evaluation. Head-and-neck evaluation included six regions of interest (ROIs): left and right parotid glands, brainstem, spinal cord, mandible, and submandibular glands. Thoracic evaluation includes seven ROIs: left and right lungs, spinal cord, heart, esophagus, and left and right brachial plexus. Auto-segmented contours were compared with the manual contours using the Dice similarity coefficient (DSC) and the mean surface distance (MSD). Results: In head- and-neck evaluation, only mandible has a high accuracy in all segmentations (DSC>85%); SPICE achieved DSC>70% for parotid glands; MACS achieved this for both parotid glands and submandibular glands; and RayStation ABS achieved this for spinal cord. In thoracic evaluation, SPICE achieved the best in lung and heart segmentation, while MACS achieved the best for all other structures. The less distinguishable structures on CT images, such as brainstem, spinal cord, parotid glands, submandibular glands, esophagus, and brachial plexus, showed great variability in different segmentation tools (mostly DSC<70% and MSD>3mm). The template for RayStation ABS can be easily customized by users, while RayStation MBS and SPICE rely on the vendors to provide the templates/models. Conclusion: Great variability was

  11. Evaluation of advanced automatic PET segmentation methods using nonspherical thin-wall inserts

    International Nuclear Information System (INIS)

    Berthon, B.; Marshall, C.; Evans, M.; Spezi, E.

    2014-01-01

    Purpose: The use of positron emission tomography (PET) within radiotherapy treatment planning requires the availability of reliable and accurate segmentation tools. PET automatic segmentation (PET-AS) methods have been recommended for the delineation of tumors, but there is still a lack of thorough validation and cross-comparison of such methods using clinically relevant data. In particular, studies validating PET segmentation tools mainly use phantoms with thick plastic walls inserts of simple spherical geometry and have not specifically investigated the effect of the target object geometry on the delineation accuracy. Our work therefore aimed at generating clinically realistic data using nonspherical thin-wall plastic inserts, for the evaluation and comparison of a set of eight promising PET-AS approaches. Methods: Sixteen nonspherical inserts were manufactured with a plastic wall of 0.18 mm and scanned within a custom plastic phantom. These included ellipsoids and toroids derived with different volumes, as well as tubes, pear- and drop-shaped inserts with different aspect ratios. A set of six spheres of volumes ranging from 0.5 to 102 ml was used for a baseline study. A selection of eight PET-AS methods, written in house, was applied to the images obtained. The methods represented promising segmentation approaches such as adaptive iterative thresholding, region-growing, clustering and gradient-based schemes. The delineation accuracy was measured in terms of overlap with the computed tomography reference contour, using the dice similarity coefficient (DSC), and error in dimensions. Results: The delineation accuracy was lower for nonspherical inserts than for spheres of the same volume in 88% cases. Slice-by-slice gradient-based methods, showed particularly lower DSC for tori (DSC 0.76 except for tori) but showed the largest errors in the recovery of pears and drops dimensions (higher than 10% and 30% of the true length, respectively). Large errors were visible

  12. Assessing hippocampal development and language in early childhood: Evidence from a new application of the Automatic Segmentation Adapter Tool.

    Science.gov (United States)

    Lee, Joshua K; Nordahl, Christine W; Amaral, David G; Lee, Aaron; Solomon, Marjorie; Ghetti, Simona

    2015-11-01

    Volumetric assessments of the hippocampus and other brain structures during childhood provide useful indices of brain development and correlates of cognitive functioning in typically and atypically developing children. Automated methods such as FreeSurfer promise efficient and replicable segmentation, but may include errors which are avoided by trained manual tracers. A recently devised automated correction tool that uses a machine learning algorithm to remove systematic errors, the Automatic Segmentation Adapter Tool (ASAT), was capable of substantially improving the accuracy of FreeSurfer segmentations in an adult sample [Wang et al., 2011], but the utility of ASAT has not been examined in pediatric samples. In Study 1, the validity of FreeSurfer and ASAT corrected hippocampal segmentations were examined in 20 typically developing children and 20 children with autism spectrum disorder aged 2 and 3 years. We showed that while neither FreeSurfer nor ASAT accuracy differed by disorder or age, the accuracy of ASAT corrected segmentations were substantially better than FreeSurfer segmentations in every case, using as few as 10 training examples. In Study 2, we applied ASAT to 89 typically developing children aged 2 to 4 years to examine relations between hippocampal volume, age, sex, and expressive language. Girls had smaller hippocampi overall, and in left hippocampus this difference was larger in older than younger girls. Expressive language ability was greater in older children, and this difference was larger in those with larger hippocampi, bilaterally. Overall, this research shows that ASAT is highly reliable and useful to examinations relating behavior to hippocampal structure. © 2015 Wiley Periodicals, Inc.

  13. Scorpion image segmentation system

    Science.gov (United States)

    Joseph, E.; Aibinu, A. M.; Sadiq, B. A.; Bello Salau, H.; Salami, M. J. E.

    2013-12-01

    Death as a result of scorpion sting has been a major public health problem in developing countries. Despite the high rate of death as a result of scorpion sting, little report exists in literature of intelligent device and system for automatic detection of scorpion. This paper proposed a digital image processing approach based on the floresencing characteristics of Scorpion under Ultra-violet (UV) light for automatic detection and identification of scorpion. The acquired UV-based images undergo pre-processing to equalize uneven illumination and colour space channel separation. The extracted channels are then segmented into two non-overlapping classes. It has been observed that simple thresholding of the green channel of the acquired RGB UV-based image is sufficient for segmenting Scorpion from other background components in the acquired image. Two approaches to image segmentation have also been proposed in this work, namely, the simple average segmentation technique and K-means image segmentation. The proposed algorithm has been tested on over 40 UV scorpion images obtained from different part of the world and results obtained show an average accuracy of 97.7% in correctly classifying the pixel into two non-overlapping clusters. The proposed 1system will eliminate the problem associated with some of the existing manual approaches presently in use for scorpion detection.

  14. Automatic Model Generation Framework for Computational Simulation of Cochlear Implantation

    DEFF Research Database (Denmark)

    Mangado Lopez, Nerea; Ceresa, Mario; Duchateau, Nicolas

    2016-01-01

    . To address such a challenge, we propose an automatic framework for the generation of patient-specific meshes for finite element modeling of the implanted cochlea. First, a statistical shape model is constructed from high-resolution anatomical μCT images. Then, by fitting the statistical model to a patient......'s CT image, an accurate model of the patient-specific cochlea anatomy is obtained. An algorithm based on the parallel transport frame is employed to perform the virtual insertion of the cochlear implant. Our automatic framework also incorporates the surrounding bone and nerve fibers and assigns......Recent developments in computational modeling of cochlear implantation are promising to study in silico the performance of the implant before surgery. However, creating a complete computational model of the patient's anatomy while including an external device geometry remains challenging...

  15. Automatic segmentation of lesions for the computer-assisted detection in fluorescence urology

    Science.gov (United States)

    Kage, Andreas; Legal, Wolfgang; Kelm, Peter; Simon, Jörg; Bergen, Tobias; Münzenmayer, Christian; Benz, Michaela

    2012-03-01

    Bladder cancer is one of the most common cancers in the western world. The diagnosis in Germany is based on the visual inspection of the bladder. This inspection performed with a cystoscope is a challenging task as some kinds of abnormal tissues do not differ much in their appearance from their surrounding healthy tissue. Fluorescence Cystoscopy has the potential to increase the detection rate. A liquid marker introduced into the bladder in advance of the inspection is concentrated in areas with high metabolism. Thus these areas appear as bright "glowing". Unfortunately, the fluorescence image contains besides the glowing of the suspicious lesions no more further visual information like for example the appearance of the blood vessels. A visual judgment of the lesion as well as a precise treatment has to be done using white light illumination. Thereby, the spatial information of the lesion provided by the fluorescence image has to be guessed by the clinical expert. This leads to a time consuming procedure due to many switches between the modalities and increases the risk of mistreatment. We introduce an automatic approach, which detects and segments any suspicious lesion in the fluorescence image automatically once the image was classified as a fluorescence image. The area of the contour of the detected lesion is transferred to the corresponding white light image and provide the clinical expert the spatial information of the lesion. The advantage of this approach is, that the clinical expert gets the spatial and the visual information of the lesion together in one image. This can save time and decrease the risk of an incomplete removal of a malign lesion.

  16. Automatic segmentation of lumbar vertebrae in CT images

    Science.gov (United States)

    Kulkarni, Amruta; Raina, Akshita; Sharifi Sarabi, Mona; Ahn, Christine S.; Babayan, Diana; Gaonkar, Bilwaj; Macyszyn, Luke; Raghavendra, Cauligi

    2017-03-01

    Lower back pain is one of the most prevalent disorders in the developed/developing world. However, its etiology is poorly understood and treatment is often determined subjectively. In order to quantitatively study the emergence and evolution of back pain, it is necessary to develop consistently measurable markers for pathology. Imaging based measures offer one solution to this problem. The development of imaging based on quantitative biomarkers for the lower back necessitates automated techniques to acquire this data. While the problem of segmenting lumbar vertebrae has been addressed repeatedly in literature, the associated problem of computing relevant biomarkers on the basis of the segmentation has not been addressed thoroughly. In this paper, we propose a Random-Forest based approach that learns to segment vertebral bodies in CT images followed by a biomarker evaluation framework that extracts vertebral heights and widths from the segmentations obtained. Our dataset consists of 15 CT sagittal scans obtained from General Electric Healthcare. Our main approach is divided into three parts: the first stage is image pre-processing which is used to correct for variations in illumination across all the images followed by preparing the foreground and background objects from images; the next stage is Machine Learning using Random-Forests, which distinguishes the interest-point vectors between foreground or background; and the last step is image post-processing, which is crucial to refine the results of classifier. The Dice coefficient was used as a statistical validation metric to evaluate the performance of our segmentations with an average value of 0.725 for our dataset.

  17. Applicability of Clearance Automatic Laser Inspection System to Clearance Measurement of Concrete Segments

    International Nuclear Information System (INIS)

    Sasaki, M.; Ogino, H.; Hattori, T.

    2009-01-01

    In the decommissioning of a nuclear power plant, large amounts of metal scrap and concrete segments require disposal when dismantling the nuclear reactor and surrounding facilities. When their activity level is negligible or sufficiently small, they can be regarded as general industrial waste. To distinguish between radioactive and nonradioactive materials, the clearance level for each radionuclide has been determined in units of activity concentration. These clearance levels are indicated in the International Atomic Energy Agency (IAEA) Safety Standards Series No. RS-G-1.7. The Japanese regulatory authority decided in 2005 to adopt the values given in RS-G-1.7 as clearance levels in legislation. Recently, a Clearance Automatic Laser Inspection System (CLALIS) has been developed utilizing gamma-ray measurement, automatic laser shape measurement and Monte Carlo calculation. CLALIS comprises four laser scanners and eight large plastic scintillation detectors surrounded by a 5-cm-thick lead shield. Using three-dimensional (3D) laser scanning, a measurement target, which is placed on the measuring tray, is represented as a dot image. The dot image is converted into voxels after noise removal, and is written in MCNP (A General Monte Carlo N-Particle Transport Code System) input files. When the gamma-ray measurement is carried out, the MCNP calculations are also carried out to obtain the calibration factor and background (BG) correction factors. For the clearance measurement of concrete segments, the effect of gamma-rays from natural radionuclides in the measurement target, such as K-40 and the radioactive decay products of Th-232 and U-238, should be taken into account to ensure adequate waste management. Since NE102A plastic scintillation detectors are used for gamma-ray measurement in CLALIS, it is impossible to distinguish between the count rates of natural radionuclides and contaminants on the basis of gamma-ray energy information. To overcome this problem, the

  18. Boundary segmentation for fluorescence microscopy using steerable filters

    Science.gov (United States)

    Ho, David Joon; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.

    2017-02-01

    Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation, steerable filters to capture directional tendencies, and connected-component analysis. The results from several data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has better performance when compared to other popular image segmentation methods when using ground truth data obtained via manual segmentation.

  19. Automatic segmentation and 3D reconstruction of intravascular ultrasound images for a fast preliminar evaluation of vessel pathologies.

    Science.gov (United States)

    Sanz-Requena, Roberto; Moratal, David; García-Sánchez, Diego Ramón; Bodí, Vicente; Rieta, José Joaquín; Sanchis, Juan Manuel

    2007-03-01

    Intravascular ultrasound (IVUS) imaging is used along with X-ray coronary angiography to detect vessel pathologies. Manual analysis of IVUS images is slow and time-consuming and it is not feasible for clinical purposes. A semi-automated method is proposed to generate 3D reconstructions from IVUS video sequences, so that a fast diagnose can be easily done, quantifying plaque length and severity as well as plaque volume of the vessels under study. The methodology described in this work has four steps: a pre-processing of IVUS images, a segmentation of media-adventitia contour, a detection of intima and plaque and a 3D reconstruction of the vessel. Preprocessing is intended to remove noise from the images without blurring the edges. Segmentation of media-adventitia contour is achieved using active contours (snakes). In particular, we use the gradient vector flow (GVF) as external force for the snakes. The detection of lumen border is obtained taking into account gray-level information of the inner part of the previously detected contours. A knowledge-based approach is used to determine which level of gray corresponds statistically to the different regions of interest: intima, plaque and lumen. The catheter region is automatically discarded. An estimate of plaque type is also given. Finally, 3D reconstruction of all detected regions is made. The suitability of this methodology has been verified for the analysis and visualization of plaque length, stenosis severity, automatic detection of the most problematic regions, calculus of plaque volumes and a preliminary estimation of plaque type obtaining for automatic measures of lumen and vessel area an average error smaller than 1mm(2) (equivalent aproximately to 10% of the average measure), for calculus of plaque and lumen volume errors smaller than 0.5mm(3) (equivalent approximately to 20% of the average measure) and for plaque type estimates a mismatch of less than 8% in the analysed frames.

  20. Semi-automatic image analysis methodology for the segmentation of bubbles and drops in complex dispersions occurring in bioreactors

    Science.gov (United States)

    Taboada, B.; Vega-Alvarado, L.; Córdova-Aguilar, M. S.; Galindo, E.; Corkidi, G.

    2006-09-01

    Characterization of multiphase systems occurring in fermentation processes is a time-consuming and tedious process when manual methods are used. This work describes a new semi-automatic methodology for the on-line assessment of diameters of oil drops and air bubbles occurring in a complex simulated fermentation broth. High-quality digital images were obtained from the interior of a mechanically stirred tank. These images were pre-processed to find segments of edges belonging to the objects of interest. The contours of air bubbles and oil drops were then reconstructed using an improved Hough transform algorithm which was tested in two, three and four-phase simulated fermentation model systems. The results were compared against those obtained manually by a trained observer, showing no significant statistical differences. The method was able to reduce the total processing time for the measurements of bubbles and drops in different systems by 21-50% and the manual intervention time for the segmentation procedure by 80-100%.

  1. Automatic brain matter segmentation of computed tomography images using a statistical model: A tool to gain working time!

    Science.gov (United States)

    Bertè, Francesco; Lamponi, Giuseppe; Bramanti, Placido; Calabrò, Rocco S

    2015-10-01

    Brain computed tomography (CT) is useful diagnostic tool for the evaluation of several neurological disorders due to its accuracy, reliability, safety and wide availability. In this field, a potentially interesting research topic is the automatic segmentation and recognition of medical regions of interest (ROIs). Herein, we propose a novel automated method, based on the use of the active appearance model (AAM) for the segmentation of brain matter in CT images to assist radiologists in the evaluation of the images. The method described, that was applied to 54 CT images coming from a sample of outpatients affected by cognitive impairment, enabled us to obtain the generation of a model overlapping with the original image with quite good precision. Since CT neuroimaging is in widespread use for detecting neurological disease, including neurodegenerative conditions, the development of automated tools enabling technicians and physicians to reduce working time and reach a more accurate diagnosis is needed. © The Author(s) 2015.

  2. Scan-Less Line Field Optical Coherence Tomography, with Automatic Image Segmentation, as a Measurement Tool for Automotive Coatings

    Directory of Open Access Journals (Sweden)

    Samuel Lawman

    2017-04-01

    Full Text Available The measurement of the thicknesses of layers is important for the quality assurance of industrial coating systems. Current measurement techniques only provide a limited amount of information. Here, we show that spectral domain Line Field (LF Optical Coherence Tomography (OCT is able to return to the user a cross sectional B-Scan image in a single shot with no mechanical moving parts. To reliably extract layer thicknesses from such images of automotive paint systems, we present an automatic graph search image segmentation algorithm. To show that the algorithm works independently of the OCT device, the measurements are repeated with a separate time domain Full Field (FF OCT system. This gives matching mean thickness values within the standard deviations of the measured thicknesses across each B-Scan image. The combination of an LF-OCT with graph search segmentation is potentially a powerful technique for the quality assurance of non-opaque industrial coating layers.

  3. Automatic Segmentation and Online virtualCT in Head-and-Neck Adaptive Radiation Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Peroni, Marta, E-mail: marta.peroni@mail.polimi.it [Department of Bioengineering, Politecnico di Milano, Milano (Italy); Ciardo, Delia [Advanced Radiotherapy Center, European Institute of Oncology, Milano (Italy); Spadea, Maria Francesca [Department of Experimental and Clinical Medicine, Universita degli Studi Magna Graecia, Catanzaro (Italy); Riboldi, Marco [Department of Bioengineering, Politecnico di Milano, Milano (Italy); Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pavia (Italy); Comi, Stefania; Alterio, Daniela [Advanced Radiotherapy Center, European Institute of Oncology, Milano (Italy); Baroni, Guido [Department of Bioengineering, Politecnico di Milano, Milano (Italy); Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pavia (Italy); Orecchia, Roberto [Advanced Radiotherapy Center, European Institute of Oncology, Milano (Italy); Universita degli Studi di Milano, Milano (Italy); Medical Department, Centro Nazionale di Adroterapia Oncologica, Pavia (Italy)

    2012-11-01

    Purpose: The purpose of this work was to develop and validate an efficient and automatic strategy to generate online virtual computed tomography (CT) scans for adaptive radiation therapy (ART) in head-and-neck (HN) cancer treatment. Method: We retrospectively analyzed 20 patients, treated with intensity modulated radiation therapy (IMRT), for an HN malignancy. Different anatomical structures were considered: mandible, parotid glands, and nodal gross tumor volume (nGTV). We generated 28 virtualCT scans by means of nonrigid registration of simulation computed tomography (CTsim) and cone beam CT images (CBCTs), acquired for patient setup. We validated our approach by considering the real replanning CT (CTrepl) as ground truth. We computed the Dice coefficient (DSC), center of mass (COM) distance, and root mean square error (RMSE) between correspondent points located on the automatically segmented structures on CBCT and virtualCT. Results: Residual deformation between CTrepl and CBCT was below one voxel. Median DSC was around 0.8 for mandible and parotid glands, but only 0.55 for nGTV, because of the fairly homogeneous surrounding soft tissues and of its small volume. Median COM distance and RMSE were comparable with image resolution. No significant correlation between RMSE and initial or final deformation was found. Conclusion: The analysis provides evidence that deformable image registration may contribute significantly in reducing the need of full CT-based replanning in HN radiation therapy by supporting swift and objective decision-making in clinical practice. Further work is needed to strengthen algorithm potential in nGTV localization.

  4. Automatic segmentation and online virtualCT in head-and-neck adaptive radiation therapy.

    Science.gov (United States)

    Peroni, Marta; Ciardo, Delia; Spadea, Maria Francesca; Riboldi, Marco; Comi, Stefania; Alterio, Daniela; Baroni, Guido; Orecchia, Roberto

    2012-11-01

    The purpose of this work was to develop and validate an efficient and automatic strategy to generate online virtual computed tomography (CT) scans for adaptive radiation therapy (ART) in head-and-neck (HN) cancer treatment. We retrospectively analyzed 20 patients, treated with intensity modulated radiation therapy (IMRT), for an HN malignancy. Different anatomical structures were considered: mandible, parotid glands, and nodal gross tumor volume (nGTV). We generated 28 virtualCT scans by means of nonrigid registration of simulation computed tomography (CTsim) and cone beam CT images (CBCTs), acquired for patient setup. We validated our approach by considering the real replanning CT (CTrepl) as ground truth. We computed the Dice coefficient (DSC), center of mass (COM) distance, and root mean square error (RMSE) between correspondent points located on the automatically segmented structures on CBCT and virtualCT. Residual deformation between CTrepl and CBCT was below one voxel. Median DSC was around 0.8 for mandible and parotid glands, but only 0.55 for nGTV, because of the fairly homogeneous surrounding soft tissues and of its small volume. Median COM distance and RMSE were comparable with image resolution. No significant correlation between RMSE and initial or final deformation was found. The analysis provides evidence that deformable image registration may contribute significantly in reducing the need of full CT-based replanning in HN radiation therapy by supporting swift and objective decision-making in clinical practice. Further work is needed to strengthen algorithm potential in nGTV localization. Copyright © 2012 Elsevier Inc. All rights reserved.

  5. Determining the number of clusters for kernelized fuzzy C-means algorithms for automatic medical image segmentation

    Directory of Open Access Journals (Sweden)

    E.A. Zanaty

    2012-03-01

    Full Text Available In this paper, we determine the suitable validity criterion of kernelized fuzzy C-means and kernelized fuzzy C-means with spatial constraints for automatic segmentation of magnetic resonance imaging (MRI. For that; the original Euclidean distance in the FCM is replaced by a Gaussian radial basis function classifier (GRBF and the corresponding algorithms of FCM methods are derived. The derived algorithms are called as the kernelized fuzzy C-means (KFCM and kernelized fuzzy C-means with spatial constraints (SKFCM. These methods are implemented on eighteen indexes as validation to determine whether indexes are capable to acquire the optimal clusters number. The performance of segmentation is estimated by applying these methods independently on several datasets to prove which method can give good results and with which indexes. Our test spans various indexes covering the classical and the rather more recent indexes that have enjoyed noticeable success in that field. These indexes are evaluated and compared by applying them on various test images, including synthetic images corrupted with noise of varying levels, and simulated volumetric MRI datasets. Comparative analysis is also presented to show whether the validity index indicates the optimal clustering for our datasets.

  6. Automated segmentation of pigmented skin lesions in multispectral imaging

    International Nuclear Information System (INIS)

    Carrara, Mauro; Tomatis, Stefano; Bono, Aldo; Bartoli, Cesare; Moglia, Daniele; Lualdi, Manuela; Colombo, Ambrogio; Santinami, Mario; Marchesini, Renato

    2005-01-01

    The aim of this study was to develop an algorithm for the automatic segmentation of multispectral images of pigmented skin lesions. The study involved 1700 patients with 1856 cutaneous pigmented lesions, which were analysed in vivo by a novel spectrophotometric system, before excision. The system is able to acquire a set of 15 different multispectral images at equally spaced wavelengths between 483 and 951 nm. An original segmentation algorithm was developed and applied to the whole set of lesions and was able to automatically contour them all. The obtained lesion boundaries were shown to two expert clinicians, who, independently, rejected 54 of them. The 97.1% contour accuracy indicates that the developed algorithm could be a helpful and effective instrument for the automatic segmentation of skin pigmented lesions. (note)

  7. A Literature Review of Renal Surgical Anatomy and Surgical Strategies for Partial Nephrectomy.

    Science.gov (United States)

    Klatte, Tobias; Ficarra, Vincenzo; Gratzke, Christian; Kaouk, Jihad; Kutikov, Alexander; Macchi, Veronica; Mottrie, Alexandre; Porpiglia, Francesco; Porter, James; Rogers, Craig G; Russo, Paul; Thompson, R Houston; Uzzo, Robert G; Wood, Christopher G; Gill, Inderbir S

    2015-12-01

    A detailed understanding of renal surgical anatomy is necessary to optimize preoperative planning and operative technique and provide a basis for improved outcomes. To evaluate the literature regarding pertinent surgical anatomy of the kidney and related structures, nephrometry scoring systems, and current surgical strategies for partial nephrectomy (PN). A literature review was conducted. Surgical renal anatomy fundamentally impacts PN surgery. The renal artery divides into anterior and posterior divisions, from which approximately five segmental terminal arteries originate. The renal veins are not terminal. Variations in the vascular and lymphatic channels are common; thus, concurrent lymphadenectomy is not routinely indicated during PN for cT1 renal masses in the setting of clinically negative lymph nodes. Renal-protocol contrast-enhanced computed tomography or magnetic resonance imaging is used for standard imaging. Anatomy-based nephrometry scoring systems allow standardized academic reporting of tumor characteristics and predict PN outcomes (complications, remnant function, possibly histology). Anatomy-based novel surgical approaches may reduce ischemic time during PN; these include early unclamping, segmental clamping, tumor-specific clamping (zero ischemia), and unclamped PN. Cancer cure after PN relies on complete resection, which can be achieved by thin margins. Post-PN renal function is impacted by kidney quality, remnant quantity, and ischemia type and duration. Surgical renal anatomy underpins imaging, nephrometry scoring systems, and vascular control techniques that reduce global renal ischemia and may impact post-PN function. A contemporary ideal PN excises the tumor with a thin negative margin, delicately secures the tumor bed to maximize vascularized remnant parenchyma, and minimizes global ischemia to the renal remnant with minimal complications. In this report we review renal surgical anatomy. Renal mass imaging allows detailed delineation of the

  8. Head segmentation in vertebrates

    OpenAIRE

    Kuratani, Shigeru; Schilling, Thomas

    2008-01-01

    Classic theories of vertebrate head segmentation clearly exemplify the idealistic nature of comparative embryology prior to the 20th century. Comparative embryology aimed at recognizing the basic, primary structure that is shared by all vertebrates, either as an archetype or an ancestral developmental pattern. Modern evolutionary developmental (Evo-Devo) studies are also based on comparison, and therefore have a tendency to reduce complex embryonic anatomy into overly simplified patterns. Her...

  9. Automatic topics segmentation for TV news video

    Science.gov (United States)

    Hmayda, Mounira; Ejbali, Ridha; Zaied, Mourad

    2017-03-01

    Automatic identification of television programs in the TV stream is an important task for operating archives. This article proposes a new spatio-temporal approach to identify the programs in TV stream into two main steps: First, a reference catalogue for video features visual jingles built. We operate the features that characterize the instances of the same program type to identify the different types of programs in the flow of television. The role of video features is to represent the visual invariants for each visual jingle using appropriate automatic descriptors for each television program. On the other hand, programs in television streams are identified by examining the similarity of the video signal for visual grammars in the catalogue. The main idea of the identification process is to compare the visual similarity of the video signal features in the flow of television to the catalogue. After presenting the proposed approach, the paper overviews encouraging experimental results on several streams extracted from different channels and compounds of several programs.

  10. Comparison of a gross anatomy laboratory to online anatomy software for teaching anatomy.

    Science.gov (United States)

    Mathiowetz, Virgil; Yu, Chih-Huang; Quake-Rapp, Cindee

    2016-01-01

    This study was designed to assess the grades, self-perceived learning, and satisfaction between occupational therapy students who used a gross anatomy laboratory versus online anatomy software (AnatomyTV) as tools to learn anatomy at a large public university and a satellite campus in the mid-western United States. The goal was to determine if equivalent learning outcomes could be achieved regardless of learning tool used. In addition, it was important to determine why students chose the gross anatomy laboratory over online AnatomyTV. A two group, post-test only design was used with data gathered at the end of the course. Primary outcomes were students' grades, self-perceived learning, and satisfaction. In addition, a survey was used to collect descriptive data. One cadaver prosection was available for every four students in the gross anatomy laboratory. AnatomyTV was available online through the university library. At the conclusion of the course, the gross anatomy laboratory group had significantly higher grade percentage, self-perceived learning, and satisfaction than the AnatomyTV group. However, the practical significance of the difference is debatable. The significantly greater time spent in gross anatomy laboratory during the laboratory portion of the course may have affected the study outcomes. In addition, some students may find the difference in (B+) versus (A-) grade as not practically significant. Further research needs to be conducted to identify what specific anatomy teaching resources are most effective beyond prosection for students without access to a gross anatomy laboratory. © 2015 American Association of Anatomists.

  11. Symbolic Segmentation: A Corpus-Based Analysis of Melodic Phrases

    NARCIS (Netherlands)

    Rodríguez López, M.E.; Volk, Anja

    2014-01-01

    Gestalt-based segmentation models constitute the current state of the art in automatic segmentation of melodies. These models commonly assume that segment boundary perception is mainly triggered by local discontinuities, i.e. by abrupt changes in pitch and/or duration between neighbouring notes.

  12. Automatic Delineation of On-Line Head-And-Neck Computed Tomography Images: Toward On-Line Adaptive Radiotherapy

    International Nuclear Information System (INIS)

    Zhang Tiezhi; Chi Yuwei; Meldolesi, Elisa; Yan Di

    2007-01-01

    Purpose: To develop and validate a fully automatic region-of-interest (ROI) delineation method for on-line adaptive radiotherapy. Methods and Materials: On-line adaptive radiotherapy requires a robust and automatic image segmentation method to delineate ROIs in on-line volumetric images. We have implemented an atlas-based image segmentation method to automatically delineate ROIs of head-and-neck helical computed tomography images. A total of 32 daily computed tomography images from 7 head-and-neck patients were delineated using this automatic image segmentation method. Manually drawn contours on the daily images were used as references in the evaluation of automatically delineated ROIs. Two methods were used in quantitative validation: (1) the dice similarity coefficient index, which indicates the overlapping ratio between the manually and automatically delineated ROIs; and (2) the distance transformation, which yields the distances between the manually and automatically delineated ROI surfaces. Results: Automatic segmentation showed agreement with manual contouring. For most ROIs, the dice similarity coefficient indexes were approximately 0.8. Similarly, the distance transformation evaluation results showed that the distances between the manually and automatically delineated ROI surfaces were mostly within 3 mm. The distances between two surfaces had a mean of 1 mm and standard deviation of <2 mm in most ROIs. Conclusion: With atlas-based image segmentation, it is feasible to automatically delineate ROIs on the head-and-neck helical computed tomography images in on-line adaptive treatments

  13. Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

    International Nuclear Information System (INIS)

    Maduskar, Pragnya; Hogeweg, Laurens; Sánchez, Clara I.; Ginneken, Bram van; Jong, Pim A. de; Peters-Bax, Liesbeth; Dawson, Rodney; Ayles, Helen

    2014-01-01

    Purpose: Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs. Methods: A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihood value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers:k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space. Results: The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure Ω was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap Ω of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were

  14. Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

    Energy Technology Data Exchange (ETDEWEB)

    Maduskar, Pragnya, E-mail: pragnya.maduskar@radboudumc.nl; Hogeweg, Laurens; Sánchez, Clara I.; Ginneken, Bram van [Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, 6525 GA (Netherlands); Jong, Pim A. de [Department of Radiology, University Medical Center Utrecht, 3584 CX (Netherlands); Peters-Bax, Liesbeth [Department of Radiology, Radboud University Medical Center, Nijmegen, 6525 GA (Netherlands); Dawson, Rodney [University of Cape Town Lung Institute, Cape Town 7700 (South Africa); Ayles, Helen [Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London WC1E 7HT (United Kingdom)

    2014-07-15

    Purpose: Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs. Methods: A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihood value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers:k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space. Results: The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure Ω was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap Ω of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were

  15. Generic and robust method for automatic segmentation of PET images using an active contour model

    Energy Technology Data Exchange (ETDEWEB)

    Zhuang, Mingzan [Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen (Netherlands)

    2016-08-15

    Purpose: Although positron emission tomography (PET) images have shown potential to improve the accuracy of targeting in radiation therapy planning and assessment of response to treatment, the boundaries of tumors are not easily distinguishable from surrounding normal tissue owing to the low spatial resolution and inherent noisy characteristics of PET images. The objective of this study is to develop a generic and robust method for automatic delineation of tumor volumes using an active contour model and to evaluate its performance using phantom and clinical studies. Methods: MASAC, a method for automatic segmentation using an active contour model, incorporates the histogram fuzzy C-means clustering, and localized and textural information to constrain the active contour to detect boundaries in an accurate and robust manner. Moreover, the lattice Boltzmann method is used as an alternative approach for solving the level set equation to make it faster and suitable for parallel programming. Twenty simulated phantom studies and 16 clinical studies, including six cases of pharyngolaryngeal squamous cell carcinoma and ten cases of nonsmall cell lung cancer, were included to evaluate its performance. Besides, the proposed method was also compared with the contourlet-based active contour algorithm (CAC) and Schaefer’s thresholding method (ST). The relative volume error (RE), Dice similarity coefficient (DSC), and classification error (CE) metrics were used to analyze the results quantitatively. Results: For the simulated phantom studies (PSs), MASAC and CAC provide similar segmentations of the different lesions, while ST fails to achieve reliable results. For the clinical datasets (2 cases with connected high-uptake regions excluded) (CSs), CAC provides for the lowest mean RE (−8.38% ± 27.49%), while MASAC achieves the best mean DSC (0.71 ± 0.09) and mean CE (53.92% ± 12.65%), respectively. MASAC could reliably quantify different types of lesions assessed in this work

  16. Segmentation-Based And Segmentation-Free Methods for Spotting Handwritten Arabic Words

    OpenAIRE

    Ball , Gregory R.; Srihari , Sargur N.; Srinivasan , Harish

    2006-01-01

    http://www.suvisoft.com; Given a set of handwritten documents, a common goal is to search for a relevant subset. Attempting to find a query word or image in such a set of documents is called word spotting. Spotting handwritten words in documents written in the Latin alphabet, and more recently in Arabic, has received considerable attention. One issue is generating candidate word regions on a page. Attempting to definitely segment the document into such regions (automatic segmentation) can mee...

  17. Rhythm-based segmentation of Popular Chinese Music

    DEFF Research Database (Denmark)

    Jensen, Karl Kristoffer

    2005-01-01

    We present a new method to segment popular music based on rhythm. By computing a shortest path based on the self-similarity matrix calculated from a model of rhythm, segmenting boundaries are found along the di- agonal of the matrix. The cost of a new segment is opti- mized by matching manual...... and automatic segment boundaries. We compile a small song database of 21 randomly selected popular Chinese songs which come from Chinese Mainland, Taiwan and Hong Kong. The segmenting results on the small corpus show that 78% manual segmentation points are detected and 74% auto- matic segmentation points...

  18. Model-based segmentation of short-axis MR cardiac images

    NARCIS (Netherlands)

    Spreeuwers, Lieuwe Jan; Breeuwer, M.

    Reliable automatic segmentation of MR cardiac images is still an important problem in medical image processing. Although image data quality has improved considerably during the last years, this segmentation is still considered a difficult problem. Manual segmentation is hardly an option as this is

  19. Accuracy and reproducibility of a novel semi-automatic segmentation technique for MR volumetry of the pituitary gland

    Energy Technology Data Exchange (ETDEWEB)

    Renz, Diane M. [Charite University Medicine Berlin, Campus Virchow Clinic, Department of Radiology, Berlin (Germany); Hahn, Horst K.; Rexilius, Jan [Institute for Medical Image Computing, Fraunhofer MEVIS, Bremen (Germany); Schmidt, Peter [Friedrich-Schiller-University, Jena University Hospital, Institute of Diagnostic and Interventional Radiology, Department of Neuroradiology, Jena (Germany); Lentschig, Markus [MR- and PET/CT Centre Bremen, Bremen (Germany); Pfeil, Alexander [Friedrich-Schiller-University, Jena University Hospital, Department of Internal Medicine III, Jena (Germany); Sauner, Dieter [St. Georg Clinic Leipzig, Hospital Hubertusburg, Department of Radiology, Wermsdorf (Germany); Fitzek, Clemens [Asklepios Clinic Brandenburg, Department of Radiology and Neuroradiology, Brandenburg an der Havel (Germany); Mentzel, Hans-Joachim [Friedrich-Schiller-University, Jena University Hospital, Institute of Diagnostic and Interventional Radiology, Department of Pediatric Radiology, Jena (Germany); Kaiser, Werner A. [Friedrich-Schiller-University, Jena University Hospital, Institute of Diagnostic and Interventional Radiology, Jena (Germany); Reichenbach, Juergen R. [Friedrich-Schiller-University, Jena University Hospital, Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena (Germany); Boettcher, Joachim [SRH Clinic Gera, Institute of Diagnostic and Interventional Radiology, Gera (Germany)

    2011-04-15

    Although several reports about volumetric determination of the pituitary gland exist, volumetries have been solely performed by indirect measurements or manual tracing on the gland's boundaries. The purpose of this study was to evaluate the accuracy and reproducibility of a novel semi-automatic MR-based segmentation technique. In an initial technical investigation, T1-weighted 3D native magnetised prepared rapid gradient echo sequences (1.5 T) with 1 mm isotropic voxel size achieved high reliability and were utilised in different in vitro and in vivo studies. The computer-assisted segmentation technique was based on an interactive watershed transform after resampling and gradient computation. Volumetry was performed by three observers with different software and neuroradiologic experiences, evaluating phantoms of known volume (0.3, 0.9 and 1.62 ml) and healthy subjects (26 to 38 years; overall 135 volumetries). High accuracy of the volumetry was shown by phantom analysis; measurement errors were <4% with a mean error of 2.2%. In vitro, reproducibility was also promising with intra-observer variability of 0.7% for observer 1 and 0.3% for observers 2 and 3; mean inter-observer variability was in vitro 1.2%. In vivo, scan-rescan, intra-observer and inter-observer variability showed mean values of 3.2%, 1.8% and 3.3%, respectively. Unifactorial analysis of variance demonstrated no significant differences between pituitary volumes for various MR scans or software calculations in the healthy study groups (p > 0.05). The analysed semi-automatic MR volumetry of the pituitary gland is a valid, reliable and fast technique. Possible clinical applications are hyperplasia or atrophy of the gland in pathological circumstances either by a single assessment or by monitoring in follow-up studies. (orig.)

  20. Automatic noninvasive measurement of systolic blood pressure using photoplethysmography

    Directory of Open Access Journals (Sweden)

    Glik Zehava

    2009-10-01

    Full Text Available Abstract Background Automatic measurement of arterial blood pressure is important, but the available commercial automatic blood pressure meters, mostly based on oscillometry, are of low accuracy. Methods In this study, we present a cuff-based technique for automatic measurement of systolic blood pressure, based on photoplethysmographic signals measured simultaneously in fingers of both hands. After inflating the pressure cuff to a level above systolic blood pressure in a relatively slow rate, it is slowly deflated. The cuff pressure for which the photoplethysmographic signal reappeared during the deflation of the pressure-cuff was taken as the systolic blood pressure. The algorithm for the detection of the photoplethysmographic signal involves: (1 determination of the time-segments in which the photoplethysmographic signal distal to the cuff is expected to appear, utilizing the photoplethysmographic signal in the free hand, and (2 discrimination between random fluctuations and photoplethysmographic pattern. The detected pulses in the time-segments were identified as photoplethysmographic pulses if they met two criteria, based on the pulse waveform and on the correlation between the signal in each segment and the signal in the two neighboring segments. Results Comparison of the photoplethysmographic-based automatic technique to sphygmomanometry, the reference standard, shows that the standard deviation of their differences was 3.7 mmHg. For subjects with systolic blood pressure above 130 mmHg the standard deviation was even lower, 2.9 mmHg. These values are much lower than the 8 mmHg value imposed by AAMI standard for automatic blood pressure meters. Conclusion The photoplethysmographic-based technique for automatic measurement of systolic blood pressure, and the algorithm which was presented in this study, seems to be accurate.

  1. Sectional anatomy aid for improvement of decompression surgery approach to vertical segment of facial nerve.

    Science.gov (United States)

    Feng, Yan; Zhang, Yi Qun; Liu, Min; Jin, Limin; Huangfu, Mingmei; Liu, Zhenyu; Hua, Peiyan; Liu, Yulong; Hou, Ruida; Sun, Yu; Li, You Qiong; Wang, Yu Fa; Feng, Jia Chun

    2012-05-01

    The aim of this study was to find a surgical approach to a vertical segment of the facial nerve (VFN) with a relatively wide visual field and small lesion by studying the location and structure of VFN with cross-sectional anatomy. High-resolution spiral computed tomographic multiplane reformation was used to reform images that were parallel to the Frankfort horizontal plane. To locate the VFN, we measured the distances as follows: from the VFN to the paries posterior bony external acoustic meatus on 5 typical multiplane reformation images, to the promontorium tympani and the root of the tympanic ring on 2 typical images. The mean distances from the VFN to the paries posterior bony external acoustic meatus are as follows: 4.47 mm on images showing the top of the external acoustic meatus, 4.20 mm on images with the best view of the window niche, 3.35 mm on images that show the widest external acoustic meatus, 4.22 mm on images with the inferior margin of the sulcus tympanicus, and 5.49 mm on images that show the bottom of the external acoustic meatus. The VFN is approximately 4.20 mm lateral to the promontorium tympani on images with the best view of the window niche and 4.12 mm lateral to the root of the tympanic ring on images with the inferior margin of the sulcus tympanicus. The other results indicate that the area and depth of the surgical wound from the improved approach would be much smaller than that from the typical approach. The surgical approach to the horizontal segment of the facial nerve through the external acoustic meatus and the tympanic cavity could be improved by grinding off the external acoustic meatus to show the VFN. The VFN can be found by taking the promontorium tympani and tympanic ring as references. This improvement is of high potential to expand the visual field to the facial nerve, remarkably without significant injury to the patients compared with the typical approach through the mastoid process.

  2. Semiautomatic segmentation of liver metastases on volumetric CT images

    International Nuclear Information System (INIS)

    Yan, Jiayong; Schwartz, Lawrence H.; Zhao, Binsheng

    2015-01-01

    Purpose: Accurate segmentation and quantification of liver metastases on CT images are critical to surgery/radiation treatment planning and therapy response assessment. To date, there are no reliable methods to perform such segmentation automatically. In this work, the authors present a method for semiautomatic delineation of liver metastases on contrast-enhanced volumetric CT images. Methods: The first step is to manually place a seed region-of-interest (ROI) in the lesion on an image. This ROI will (1) serve as an internal marker and (2) assist in automatically identifying an external marker. With these two markers, lesion contour on the image can be accurately delineated using traditional watershed transformation. Density information will then be extracted from the segmented 2D lesion and help determine the 3D connected object that is a candidate of the lesion volume. The authors have developed a robust strategy to automatically determine internal and external markers for marker-controlled watershed segmentation. By manually placing a seed region-of-interest in the lesion to be delineated on a reference image, the method can automatically determine dual threshold values to approximately separate the lesion from its surrounding structures and refine the thresholds from the segmented lesion for the accurate segmentation of the lesion volume. This method was applied to 69 liver metastases (1.1–10.3 cm in diameter) from a total of 15 patients. An independent radiologist manually delineated all lesions and the resultant lesion volumes served as the “gold standard” for validation of the method’s accuracy. Results: The algorithm received a median overlap, overestimation ratio, and underestimation ratio of 82.3%, 6.0%, and 11.5%, respectively, and a median average boundary distance of 1.2 mm. Conclusions: Preliminary results have shown that volumes of liver metastases on contrast-enhanced CT images can be accurately estimated by a semiautomatic segmentation

  3. Automatic choroid cells segmentation and counting based on approximate convexity and concavity of chain code in fluorescence microscopic image

    Science.gov (United States)

    Lu, Weihua; Chen, Xinjian; Zhu, Weifang; Yang, Lei; Cao, Zhaoyuan; Chen, Haoyu

    2015-03-01

    In this paper, we proposed a method based on the Freeman chain code to segment and count rhesus choroid-retinal vascular endothelial cells (RF/6A) automatically for fluorescence microscopy images. The proposed method consists of four main steps. First, a threshold filter and morphological transform were applied to reduce the noise. Second, the boundary information was used to generate the Freeman chain codes. Third, the concave points were found based on the relationship between the difference of the chain code and the curvature. Finally, cells segmentation and counting were completed based on the characteristics of the number of the concave points, the area and shape of the cells. The proposed method was tested on 100 fluorescence microscopic cell images, and the average true positive rate (TPR) is 98.13% and the average false positive rate (FPR) is 4.47%, respectively. The preliminary results showed the feasibility and efficiency of the proposed method.

  4. SU-F-J-171: Robust Atlas Based Segmentation of the Prostate and Peripheral Zone Regions On MRI Utilizing Multiple MRI System Vendors

    International Nuclear Information System (INIS)

    Padgett, K; Pollack, A; Stoyanova, R; Swallen, A; Nelson, A

    2016-01-01

    Purpose: Automatically generated prostate MRI contours can be used to aid in image registration with CT or ultrasound and to reduce the burden of contouring for radiation treatment planning. In addition, prostate and zonal contours can assist to automate quantitative imaging features extraction and the analyses of longitudinal MRI studies. These potential gains are limited if the solutions are not compatible across different MRI vendors. The goal of this study is to characterize an atlas based automatic segmentation procedure of the prostate collected on MRI systems from multiple vendors. Methods: The prostate and peripheral zone (PZ) were manually contoured by an expert radiation oncologist on T2-weighted scans acquired on both GE (n=31) and Siemens (n=33) 3T MRI systems. A leave-one-out approach was utilized where the target subject is removed from the atlas before the segmentation algorithm is initiated. The atlas-segmentation method finds the best nine matched atlas subjects and then performs a normalized intensity-based free-form deformable registration of these subjects to the target subject. These nine contours are then merged into a single contour using Simultaneous Truth and Performance Level Estimation (STAPLE). Contour comparisons were made using Dice similarity coefficients (DSC) and Hausdorff distances. Results: Using the T2 FatSat (FS) GE datasets the atlas generated contours resulted in an average DSC of 0.83±0.06 for prostate, 0.57±0.12 for PZ and 0.75±0.09 for CG. Similar results were found when using the Siemens data with a DSC of 0.79±0.14 for prostate, 0.54±0.16 and 0.70±0.9. Contrast between prostate and surrounding anatomy and between the PZ and CG contours for both vendors demonstrated superior contrast separation; significance was found for all comparisons p-value < 0.0001. Conclusion: Atlas-based segmentation yielded promising results for all contours compared to expertly defined contours in both Siemens and GE 3T systems providing

  5. SU-F-J-171: Robust Atlas Based Segmentation of the Prostate and Peripheral Zone Regions On MRI Utilizing Multiple MRI System Vendors

    Energy Technology Data Exchange (ETDEWEB)

    Padgett, K; Pollack, A; Stoyanova, R [University of Miami, Miami, Florida (United States); Swallen, A; Nelson, A [MIM Software Inc., Cleveland, OH (United States)

    2016-06-15

    Purpose: Automatically generated prostate MRI contours can be used to aid in image registration with CT or ultrasound and to reduce the burden of contouring for radiation treatment planning. In addition, prostate and zonal contours can assist to automate quantitative imaging features extraction and the analyses of longitudinal MRI studies. These potential gains are limited if the solutions are not compatible across different MRI vendors. The goal of this study is to characterize an atlas based automatic segmentation procedure of the prostate collected on MRI systems from multiple vendors. Methods: The prostate and peripheral zone (PZ) were manually contoured by an expert radiation oncologist on T2-weighted scans acquired on both GE (n=31) and Siemens (n=33) 3T MRI systems. A leave-one-out approach was utilized where the target subject is removed from the atlas before the segmentation algorithm is initiated. The atlas-segmentation method finds the best nine matched atlas subjects and then performs a normalized intensity-based free-form deformable registration of these subjects to the target subject. These nine contours are then merged into a single contour using Simultaneous Truth and Performance Level Estimation (STAPLE). Contour comparisons were made using Dice similarity coefficients (DSC) and Hausdorff distances. Results: Using the T2 FatSat (FS) GE datasets the atlas generated contours resulted in an average DSC of 0.83±0.06 for prostate, 0.57±0.12 for PZ and 0.75±0.09 for CG. Similar results were found when using the Siemens data with a DSC of 0.79±0.14 for prostate, 0.54±0.16 and 0.70±0.9. Contrast between prostate and surrounding anatomy and between the PZ and CG contours for both vendors demonstrated superior contrast separation; significance was found for all comparisons p-value < 0.0001. Conclusion: Atlas-based segmentation yielded promising results for all contours compared to expertly defined contours in both Siemens and GE 3T systems providing

  6. Towards automatic patient positioning and scan planning using continuously moving table MR imaging.

    Science.gov (United States)

    Koken, Peter; Dries, Sebastian P M; Keupp, Jochen; Bystrov, Daniel; Pekar, Vladimir; Börnert, Peter

    2009-10-01

    A concept is proposed to simplify patient positioning and scan planning to improve ease of use and workflow in MR. After patient preparation in front of the scanner the operator selects the anatomy of interest by a single push-button action. Subsequently, the patient table is moved automatically into the scanner, while real-time 3D isotropic low-resolution continuously moving table scout scanning is performed using patient-independent MR system settings. With a real-time organ identification process running in parallel and steering the scanner, the target anatomy can be positioned fully automatically in the scanner's sensitive volume. The desired diagnostic examination of the anatomy of interest can be planned and continued immediately using the geometric information derived from the acquired 3D data. The concept was implemented and successfully tested in vivo in 12 healthy volunteers, focusing on the liver as the target anatomy. The positioning accuracy achieved was on the order of several millimeters, which turned out to be sufficient for initial planning purposes. Furthermore, the impact of nonoptimal system settings on the positioning performance, the signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) was investigated. The present work proved the basic concept of the proposed approach as an element of future scan automation. (c) 2009 Wiley-Liss, Inc.

  7. Automated segmentation of tumors on bone scans using anatomy-specific thresholding

    Science.gov (United States)

    Chu, Gregory H.; Lo, Pechin; Kim, Hyun J.; Lu, Peiyun; Ramakrishna, Bharath; Gjertson, David; Poon, Cheryce; Auerbach, Martin; Goldin, Jonathan; Brown, Matthew S.

    2012-03-01

    Quantification of overall tumor area on bone scans may be a potential biomarker for treatment response assessment and has, to date, not been investigated. Segmentation of bone metastases on bone scans is a fundamental step for this response marker. In this paper, we propose a fully automated computerized method for the segmentation of bone metastases on bone scans, taking into account characteristics of different anatomic regions. A scan is first segmented into anatomic regions via an atlas-based segmentation procedure, which involves non-rigidly registering a labeled atlas scan to the patient scan. Next, an intensity normalization method is applied to account for varying levels of radiotracer dosing levels and scan timing. Lastly, lesions are segmented via anatomic regionspecific intensity thresholding. Thresholds are chosen by receiver operating characteristic (ROC) curve analysis against manual contouring by board certified nuclear medicine physicians. A leave-one-out cross validation of our method on a set of 39 bone scans with metastases marked by 2 board-certified nuclear medicine physicians yielded a median sensitivity of 95.5%, and specificity of 93.9%. Our method was compared with a global intensity thresholding method. The results show a comparable sensitivity and significantly improved overall specificity, with a p-value of 0.0069.

  8. Comparison of a Gross Anatomy Laboratory to Online Anatomy Software for Teaching Anatomy

    Science.gov (United States)

    Mathiowetz, Virgil; Yu, Chih-Huang; Quake-Rapp, Cindee

    2016-01-01

    This study was designed to assess the grades, self-perceived learning, and satisfaction between occupational therapy students who used a gross anatomy laboratory versus online anatomy software (AnatomyTV) as tools to learn anatomy at a large public university and a satellite campus in the mid-western United States. The goal was to determine if…

  9. Fast automatic segmentation of anatomical structures in x-ray computed tomography images to improve fluorescence molecular tomography reconstruction.

    Science.gov (United States)

    Freyer, Marcus; Ale, Angelique; Schulz, Ralf B; Zientkowska, Marta; Ntziachristos, Vasilis; Englmeier, Karl-Hans

    2010-01-01

    The recent development of hybrid imaging scanners that integrate fluorescence molecular tomography (FMT) and x-ray computed tomography (XCT) allows the utilization of x-ray information as image priors for improving optical tomography reconstruction. To fully capitalize on this capacity, we consider a framework for the automatic and fast detection of different anatomic structures in murine XCT images. To accurately differentiate between different structures such as bone, lung, and heart, a combination of image processing steps including thresholding, seed growing, and signal detection are found to offer optimal segmentation performance. The algorithm and its utilization in an inverse FMT scheme that uses priors is demonstrated on mouse images.

  10. The evaluation of multi-structure, multi-atlas pelvic anatomy features in a prostate MR lymphography CAD system

    Science.gov (United States)

    Meijs, M.; Debats, O.; Huisman, H.

    2015-03-01

    In prostate cancer, the detection of metastatic lymph nodes indicates progression from localized disease to metastasized cancer. The detection of positive lymph nodes is, however, a complex and time consuming task for experienced radiologists. Assistance of a two-stage Computer-Aided Detection (CAD) system in MR Lymphography (MRL) is not yet feasible due to the large number of false positives in the first stage of the system. By introducing a multi-structure, multi-atlas segmentation, using an affine transformation followed by a B-spline transformation for registration, the organ location is given by a mean density probability map. The atlas segmentation is semi-automatically drawn with ITK-SNAP, using Active Contour Segmentation. Each anatomic structure is identified by a label number. Registration is performed using Elastix, using Mutual Information and an Adaptive Stochastic Gradient optimization. The dataset consists of the MRL scans of ten patients, with lymph nodes manually annotated in consensus by two expert readers. The feature map of the CAD system consists of the Multi-Atlas and various other features (e.g. Normalized Intensity and multi-scale Blobness). The voxel-based Gentleboost classifier is evaluated using ROC analysis with cross validation. We show in a set of 10 studies that adding multi-structure, multi-atlas anatomical structure likelihood features improves the quality of the lymph node voxel likelihood map. Multiple structure anatomy maps may thus make MRL CAD more feasible.

  11. CERES: A new cerebellum lobule segmentation method.

    Science.gov (United States)

    Romero, Jose E; Coupé, Pierrick; Giraud, Rémi; Ta, Vinh-Thong; Fonov, Vladimir; Park, Min Tae M; Chakravarty, M Mallar; Voineskos, Aristotle N; Manjón, Jose V

    2017-02-15

    The human cerebellum is involved in language, motor tasks and cognitive processes such as attention or emotional processing. Therefore, an automatic and accurate segmentation method is highly desirable to measure and understand the cerebellum role in normal and pathological brain development. In this work, we propose a patch-based multi-atlas segmentation tool called CERES (CEREbellum Segmentation) that is able to automatically parcellate the cerebellum lobules. The proposed method works with standard resolution magnetic resonance T1-weighted images and uses the Optimized PatchMatch algorithm to speed up the patch matching process. The proposed method was compared with related recent state-of-the-art methods showing competitive results in both accuracy (average DICE of 0.7729) and execution time (around 5 minutes). Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT.

    Science.gov (United States)

    Fu, Huazhu; Xu, Yanwu; Lin, Stephen; Zhang, Xiaoqin; Wong, Damon Wing Kee; Liu, Jiang; Frangi, Alejandro F; Baskaran, Mani; Aung, Tin

    2017-09-01

    Angle-closure glaucoma is a major cause of irreversible visual impairment and can be identified by measuring the anterior chamber angle (ACA) of the eye. The ACA can be viewed clearly through anterior segment optical coherence tomography (AS-OCT), but the imaging characteristics and the shapes and locations of major ocular structures can vary significantly among different AS-OCT modalities, thus complicating image analysis. To address this problem, we propose a data-driven approach for automatic AS-OCT structure segmentation, measurement, and screening. Our technique first estimates initial markers in the eye through label transfer from a hand-labeled exemplar data set, whose images are collected over different patients and AS-OCT modalities. These initial markers are then refined by using a graph-based smoothing method that is guided by AS-OCT structural information. These markers facilitate segmentation of major clinical structures, which are used to recover standard clinical parameters. These parameters can be used not only to support clinicians in making anatomical assessments, but also to serve as features for detecting anterior angle closure in automatic glaucoma screening algorithms. Experiments on Visante AS-OCT and Cirrus high-definition-OCT data sets demonstrate the effectiveness of our approach.

  13. Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

    International Nuclear Information System (INIS)

    Dewalle-Vignion, Anne-Sophie; Betrouni, Nacim; Vermandel, Maximilien; Baillet, Clio

    2015-01-01

    Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians’ manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging.Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used.Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results.The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging. (paper)

  14. Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

    Science.gov (United States)

    Dewalle-Vignion, Anne-Sophie; Betrouni, Nacim; Baillet, Clio; Vermandel, Maximilien

    2015-12-01

    Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians’ manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging. Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used. Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results. The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging.

  15. Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Images

    NARCIS (Netherlands)

    Lee, K.; Buitendijk, G.H.; Bogunovic, H.; Springelkamp, H.; Hofman, A.; Wahle, A.; Sonka, M.; Vingerling, J.R.; Klaver, C.C.W.; Abramoff, M.D.

    2016-01-01

    PURPOSE: To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies. METHODS: Six hundred ninety macular SD-OCT image volumes (6.0 x 6.0 x 2.3 mm3)

  16. First performance evaluation of software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine at CT.

    Science.gov (United States)

    Scholtz, Jan-Erik; Wichmann, Julian L; Kaup, Moritz; Fischer, Sebastian; Kerl, J Matthias; Lehnert, Thomas; Vogl, Thomas J; Bauer, Ralf W

    2015-03-01

    To evaluate software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine on CT in terms of accuracy, potential for time savings and workflow improvement. 77 patients (28 women, 49 men, mean age 65.3±14.4 years) with known or suspected spinal disorders (degenerative spine disease n=32; disc herniation n=36; traumatic vertebral fractures n=9) underwent 64-slice MDCT with thin-slab reconstruction. Time for automatic labeling of the thoracolumbar spine and reconstruction of double-angulated axial images of the pathological vertebrae was compared with manually performed reconstruction of anatomical aligned axial images. Reformatted images of both reconstruction methods were assessed by two observers regarding accuracy of symmetric depiction of anatomical structures. In 33 cases double-angulated axial images were created in 1 vertebra, in 28 cases in 2 vertebrae and in 16 cases in 3 vertebrae. Correct automatic labeling was achieved in 72 of 77 patients (93.5%). Errors could be manually corrected in 4 cases. Automatic labeling required 1min in average. In cases where anatomical aligned axial images of 1 vertebra were created, reconstructions made by hand were significantly faster (pquality with excellent inter-observer agreement. The evaluated software for automatic labeling and anatomically aligned, double-angulated axial image reconstruction of the thoracolumbar spine on CT is time-saving when reconstructions of 2 and more vertebrae are performed. Checking results of automatic labeling is necessary to prevent errors in labeling. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  17. Sulcal and gyral anatomy of the basal occipital-temporal lobe.

    Science.gov (United States)

    Chau, Anthony Minh Tien; Stewart, Fiona; Gragnaniello, Cristian

    2014-12-01

    The sulcal and gyral anatomy of the basal occipital-temporal lobe is highly variable and detailed descriptions of this region are limited and often inconsistent. The aim of this study was to describe the salient features of the sulcal and gyral anatomy of the basal occipital-temporal lobe. We studied the sulcal and gyral patterns of 30 formalin-fixed cerebral hemispheres. The major landmarks are the collateral sulcus (separated into the rhinal, proper, and caudal segments) and occipitotemporal sulcus (often interrupted), which were always present in this study. The bifurcation of the caudal collateral sulcus is a useful landmark. In relation to these sulci, we have described the surface anatomy and nominated landmarks of the medial (parahippocampal and lingual) and lateral (fusiform) occipitotemporal gyri. Understanding of the sulcal and gyral patterns of the basal occipital-temporal lobe may provide valuable information in its radiological and intraoperative interpretation.

  18. Segmentation of isolated MR images: development and comparison of neuronal networks

    International Nuclear Information System (INIS)

    Paredes, R.; Robles, M.; Marti-Bonmati, L.; Masia, L.

    1998-01-01

    Segmentation defines the capacity to differentiate among types of tissues. In MR. it is frequently applied to volumetric determinations. Digital images can be segmented in a number of ways; neuronal networks (NN) can be employed for this purpose. Our objective was to develop algorithms for automatic segmentation using NN and apply them to central nervous system MR images. The segmentation obtained with NN was compared with that resulting from other procedures (region-growing and K means). Each NN consisted of two layers: one based on unsupervised training, which was utilized for image segmentation in sets of K, and a second layer associating each set obtained by the preceding layer with the real set corresponding to the previously segmented objective image. This NN was trained with previously segmented images with supervised regions-growing algorithms and automatic K means. Thus, 4 different segmentation were obtained: region-growing, K means, NN with region-growing and NN with K means. The tissue volumes corresponding to cerebrospinal fluid, gray matter and white matter obtained with the 4 techniques were compared and the most representative segmented image was selected qualitatively by averaging the visual perception of 3 radiologists. The segmentation that best corresponded to the visual perception of the radiologists was that consisting of trained NN with region-growing. In comparison, the other 3 algorithms presented low percentage differences (mean, 3.44%). The mean percentage error for the 3 tissues from these algorithms was lower for region-growing segmentation (2.34%) than for trained NN with K means (3.31%) and for automatic K-means segmentation (4.66%). Thus, NN are reliable in the automation of isolated MR image segmentation. (Author) 12 refs

  19. User-assisted video segmentation system for visual communication

    Science.gov (United States)

    Wu, Zhengping; Chen, Chun

    2002-01-01

    Video segmentation plays an important role for efficient storage and transmission in visual communication. In this paper, we introduce a novel video segmentation system using point tracking and contour formation techniques. Inspired by the results from the study of the human visual system, we intend to solve the video segmentation problem into three separate phases: user-assisted feature points selection, feature points' automatic tracking, and contour formation. This splitting relieves the computer of ill-posed automatic segmentation problems, and allows a higher level of flexibility of the method. First, the precise feature points can be found using a combination of user assistance and an eigenvalue-based adjustment. Second, the feature points in the remaining frames are obtained using motion estimation and point refinement. At last, contour formation is used to extract the object, and plus a point insertion process to provide the feature points for next frame's tracking.

  20. Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells

    OpenAIRE

    Carolina Wählby; Joakim Lindblad; Mikael Vondrus; Ewert Bengtsson; Lennart Björkesten

    2002-01-01

    Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre?processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical ana...

  1. Model-based Vestibular Afferent Stimulation: Modular Workflow for Analyzing Stimulation Scenarios in Patient Specific and Statistical Vestibular Anatomy

    Directory of Open Access Journals (Sweden)

    Michael Handler

    2017-12-01

    Full Text Available Our sense of balance and spatial orientation strongly depends on the correct functionality of our vestibular system. Vestibular dysfunction can lead to blurred vision and impaired balance and spatial orientation, causing a significant decrease in quality of life. Recent studies have shown that vestibular implants offer a possible treatment for patients with vestibular dysfunction. The close proximity of the vestibular nerve bundles, the facial nerve and the cochlear nerve poses a major challenge to targeted stimulation of the vestibular system. Modeling the electrical stimulation of the vestibular system allows for an efficient analysis of stimulation scenarios previous to time and cost intensive in vivo experiments. Current models are based on animal data or CAD models of human anatomy. In this work, a (semi-automatic modular workflow is presented for the stepwise transformation of segmented vestibular anatomy data of human vestibular specimens to an electrical model and subsequently analyzed. The steps of this workflow include (i the transformation of labeled datasets to a tetrahedra mesh, (ii nerve fiber anisotropy and fiber computation as a basis for neuron models, (iii inclusion of arbitrary electrode designs, (iv simulation of quasistationary potential distributions, and (v analysis of stimulus waveforms on the stimulation outcome. Results obtained by the workflow based on human datasets and the average shape of a statistical model revealed a high qualitative agreement and a quantitatively comparable range compared to data from literature, respectively. Based on our workflow, a detailed analysis of intra- and extra-labyrinthine electrode configurations with various stimulation waveforms and electrode designs can be performed on patient specific anatomy, making this framework a valuable tool for current optimization questions concerning vestibular implants in humans.

  2. The Brain's Cutting-Room Floor: Segmentation of Narrative Cinema

    Science.gov (United States)

    Zacks, Jeffrey M.; Speer, Nicole K.; Swallow, Khena M.; Maley, Corey J.

    2010-01-01

    Observers segment ongoing activity into meaningful events. Segmentation is a core component of perception that helps determine memory and guide planning. The current study tested the hypotheses that event segmentation is an automatic component of the perception of extended naturalistic activity, and that the identification of event boundaries in such activities results in part from processing changes in the perceived situation. Observers may identify boundaries between events as a result of processing changes in the observed situation. To test this hypothesis and study this potential mechanism, we measured brain activity while participants viewed an extended narrative film. Large transient responses were observed when the activity was segmented, and these responses were mediated by changes in the observed activity, including characters and their interactions, interactions with objects, spatial location, goals, and causes. These results support accounts that propose event segmentation is automatic and depends on processing meaningful changes in the perceived situation; they are the first to show such effects for extended naturalistic human activity. PMID:20953234

  3. Combining prior day contours to improve automated prostate segmentation

    International Nuclear Information System (INIS)

    Godley, Andrew; Sheplan Olsen, Lawrence J.; Stephans, Kevin; Zhao Anzi

    2013-01-01

    Purpose: To improve the accuracy of automatically segmented prostate, rectum, and bladder contours required for online adaptive therapy. The contouring accuracy on the current image guidance [image guided radiation therapy (IGRT)] scan is improved by combining contours from earlier IGRT scans via the simultaneous truth and performance level estimation (STAPLE) algorithm. Methods: Six IGRT prostate patients treated with daily kilo-voltage (kV) cone-beam CT (CBCT) had their original plan CT and nine CBCTs contoured by the same physician. Three types of automated contours were produced for analysis. (1) Plan: By deformably registering the plan CT to each CBCT and then using the resulting deformation field to morph the plan contours to match the CBCT anatomy. (2) Previous: The contour set drawn by the physician on the previous day CBCT is similarly deformed to match the current CBCT anatomy. (3) STAPLE: The contours drawn by the physician, on each prior CBCT and the plan CT, are deformed to match the CBCT anatomy to produce multiple contour sets. These sets are combined using the STAPLE algorithm into one optimal set. Results: Compared to plan and previous, STAPLE improved the average Dice's coefficient (DC) with the original physician drawn CBCT contours to a DC as follows: Bladder: 0.81 ± 0.13, 0.91 ± 0.06, and 0.92 ± 0.06; Prostate: 0.75 ± 0.08, 0.82 ± 0.05, and 0.84 ± 0.05; and Rectum: 0.79 ± 0.06, 0.81 ± 0.06, and 0.85 ± 0.04, respectively. The STAPLE results are within intraobserver consistency, determined by the physician blindly recontouring a subset of CBCTs. Comparing plans recalculated using the physician and STAPLE contours showed an average disagreement less than 1% for prostate D98 and mean dose, and 5% and 3% for bladder and rectum mean dose, respectively. One scan takes an average of 19 s to contour. Using five scans plus STAPLE takes less than 110 s on a 288 core graphics processor unit. Conclusions: Combining the plan and all prior days via

  4. PSNet: prostate segmentation on MRI based on a convolutional neural network.

    Science.gov (United States)

    Tian, Zhiqiang; Liu, Lizhi; Zhang, Zhenfeng; Fei, Baowei

    2018-04-01

    Automatic segmentation of the prostate on magnetic resonance images (MRI) has many applications in prostate cancer diagnosis and therapy. We proposed a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage, which uses prostate MRI and the corresponding ground truths as inputs. The learned CNN model can be used to make an inference for pixel-wise segmentation. Experiments were performed on three data sets, which contain prostate MRI of 140 patients. The proposed CNN model of prostate segmentation (PSNet) obtained a mean Dice similarity coefficient of [Formula: see text] as compared to the manually labeled ground truth. Experimental results show that the proposed model could yield satisfactory segmentation of the prostate on MRI.

  5. Metric Learning for Hyperspectral Image Segmentation

    Science.gov (United States)

    Bue, Brian D.; Thompson, David R.; Gilmore, Martha S.; Castano, Rebecca

    2011-01-01

    We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.

  6. Semi-automatic geographic atrophy segmentation for SD-OCT images

    OpenAIRE

    Chen, Qiang; de Sisternes, Luis; Leng, Theodore; Zheng, Luoluo; Kutzscher, Lauren; Rubin, Daniel L.

    2013-01-01

    Geographic atrophy (GA) is a condition that is associated with retinal thinning and loss of the retinal pigment epithelium (RPE) layer. It appears in advanced stages of non-exudative age-related macular degeneration (AMD) and can lead to vision loss. We present a semi-automated GA segmentation algorithm for spectral-domain optical coherence tomography (SD-OCT) images. The method first identifies and segments a surface between the RPE and the choroid to generate retinal projection images in wh...

  7. High-Resolution Isotropic Three-Dimensional MR Imaging of the Extraforaminal Segments of the Cranial Nerves.

    Science.gov (United States)

    Wen, Jessica; Desai, Naman S; Jeffery, Dean; Aygun, Nafi; Blitz, Ari

    2018-02-01

    High-resolution isotropic 3-dimensional (D) MR imaging with and without contrast is now routinely used for imaging evaluation of cranial nerve anatomy and pathologic conditions. The anatomic details of the extraforaminal segments are well-visualized on these techniques. A wide range of pathologic entities may cause enhancement or displacement of the nerve, which is now visible to an extent not available on standard 2D imaging. This article highlights the anatomy of extraforaminal segments of the cranial nerves and uses select cases to illustrate the utility and power of these sequences, with a focus on constructive interference in steady-state. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Preoperative evaluation of hepatic arterial and portal venous anatomy using the time resolved echo-shared MR angiographic technique in living liver donors

    International Nuclear Information System (INIS)

    Lee, Min Woo; Lee, Jeong Min; Lee, Jae Young; Kim, Se Hyung; Park, Eun-Ah; Han, Joon Koo; Choi, Jin-Young; Choi, Byung Ihn; Kim, Young Jun; Suh, Kyung-Suk

    2007-01-01

    The purpose of this study was to determine whether MR angiography utilizing the time resolved echo-shared angiographic technique (TREAT) can provide an effective assessment of the hepatic artery (HA) and portal vein (PV) in living donor candidates. MR angiography (MRA)was performed in 27 patients (23 men and 4 women; mean age, 31 years) by using TREAT. Two blinded radiologists evaluated HA anatomy, origin of segment IV feeding artery and PV anatomy in consensus. Qualitative evaluations of MRA images were performed using the following criteria: (a) overall image quality, (b) presence of artifacts, and (c) degree of venous contamination of the arterial phase. Using intraoperative findings as a standard of reference, the accuracy for the HA anatomy, origin of segment IV feeding artery and PV anatomy on TREAT-MRA were 93% (25/27), 85% (23/27), and 96% (26/27), respectively. Overall image qualities were as follows: excellent (n=22, 81%), good (n=4, 15%), and fair (n=1, 4%). Significant artifacts or venous contamination of the arterial phase images was not noted in any patient. TREAT-MRA can provide a complete evaluation of HA and PV anatomy during preoperative evaluation of living liver donors. Furthermore, it provides a more detailed anatomy of the HA without venous contamination. (orig.)

  9. Adversarial training and dilated convolutions for brain MRI segmentation

    NARCIS (Netherlands)

    Moeskops, P.; Veta, M.; Lafarge, M.W.; Eppenhof, K.A.J.; Pluim, J.P.W.

    2017-01-01

    Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversarial networks have recently gained popularity because of their power in generating images that are difficult to

  10. Automatic detection of osteoporotic vertebral fractures in routine thoracic and abdominal MDCT

    Energy Technology Data Exchange (ETDEWEB)

    Baum, Thomas; Dobritz, Martin; Rummeny, Ernst J.; Noel, Peter B. [Technische Universitaet Muenchen, Institut fuer Radiologie, Klinikum rechts der Isar, Muenchen (Germany); Bauer, Jan S. [Technische Universitaet Muenchen, Abteilung fuer Neuroradiologie, Klinikum rechts der Isar, Muenchen (Germany); Klinder, Tobias; Lorenz, Cristian [Philips Research Laboratories, Hamburg (Germany)

    2014-04-15

    To develop a prototype algorithm for automatic spine segmentation in MDCT images and use it to automatically detect osteoporotic vertebral fractures. Cross-sectional routine thoracic and abdominal MDCT images of 71 patients including 8 males and 9 females with 25 osteoporotic vertebral fractures and longitudinal MDCT images of 9 patients with 18 incidental fractures in the follow-up MDCT were retrospectively selected. The spine segmentation algorithm localised and identified the vertebrae T5-L5. Each vertebra was automatically segmented by using corresponding vertebra surface shape models that were adapted to the original images. Anterior, middle, and posterior height of each vertebra was automatically determined; the anterior-posterior ratio (APR) and middle-posterior ratio (MPR) were computed. As the gold standard, radiologists graded vertebral fractures from T5 to L5 according to the Genant classification in consensus. Using ROC analysis to differentiate vertebrae without versus with prevalent fracture, AUC values of 0.84 and 0.83 were obtained for APR and MPR, respectively (p < 0.001). Longitudinal changes in APR and MPR were significantly different between vertebrae without versus with incidental fracture (ΔAPR: -8.5 % ± 8.6 % versus -1.6 % ± 4.2 %, p = 0.002; ΔMPR: -11.4 % ± 7.7 % versus -1.2 % ± 1.6 %, p < 0.001). This prototype algorithm may support radiologists in reporting currently underdiagnosed osteoporotic vertebral fractures so that appropriate therapy can be initiated. circle This spine segmentation algorithm automatically localised, identified, and segmented the vertebrae in MDCT images. (orig.)

  11. Optical Character Recognition Using Active Contour Segmentation

    Directory of Open Access Journals (Sweden)

    Nabeel Oudah

    2018-01-01

    Full Text Available Document analysis of images snapped by camera is a growing challenge. These photos are often poor-quality compound images, composed of various objects and text; this makes automatic analysis complicated. OCR is one of the image processing techniques which is used to perform automatic identification of texts. Existing image processing techniques need to manage many parameters in order to clearly recognize the text in such pictures. Segmentation is regarded one of these essential parameters. This paper discusses the accuracy of segmentation process and its effect over the recognition process. According to the proposed method, the images were firstly filtered using the wiener filter then the active contour algorithm could be applied in the segmentation process. The Tesseract OCR Engine was selected in order to evaluate the performance and identification accuracy of the proposed method. The results showed that a more accurate segmentation process shall lead to a more accurate recognition results. The rate of recognition accuracy was 0.95 for the proposed algorithm compared with 0.85 for the Tesseract OCR Engine.

  12. Automatic Segmentation and Deep Learning of Bird Sounds

    NARCIS (Netherlands)

    Koops, Hendrik Vincent; Van Balen, J.M.H.; Wiering, F.

    2015-01-01

    We present a study on automatic birdsong recognition with deep neural networks using the BIRDCLEF2014 dataset. Through deep learning, feature hierarchies are learned that represent the data on several levels of abstraction. Deep learning has been applied with success to problems in fields such as

  13. Automatic Segmentation of Abdominal Adipose Tissue in MRI

    DEFF Research Database (Denmark)

    Mosbech, Thomas Hammershaimb; Pilgaard, Kasper; Vaag, Allan

    2011-01-01

    of intensity in-homogeneities. This effect is estimated by a thin plate spline extended to fit two classes of automatically sampled intensity points in 3D. Adipose tissue pixels are labelled with fuzzy c-means clustering and locally determined thresholds. The visceral and subcutaneous adipose tissue...

  14. A new user-assisted segmentation and tracking technique for an object-based video editing system

    Science.gov (United States)

    Yu, Hong Y.; Hong, Sung-Hoon; Lee, Mike M.; Choi, Jae-Gark

    2004-03-01

    This paper presents a semi-automatic segmentation method which can be used to generate video object plane (VOP) for object based coding scheme and multimedia authoring environment. Semi-automatic segmentation can be considered as a user-assisted segmentation technique. A user can initially mark objects of interest around the object boundaries and then the user-guided and selected objects are continuously separated from the unselected areas through time evolution in the image sequences. The proposed segmentation method consists of two processing steps: partially manual intra-frame segmentation and fully automatic inter-frame segmentation. The intra-frame segmentation incorporates user-assistance to define the meaningful complete visual object of interest to be segmentation and decides precise object boundary. The inter-frame segmentation involves boundary and region tracking to obtain temporal coherence of moving object based on the object boundary information of previous frame. The proposed method shows stable efficient results that could be suitable for many digital video applications such as multimedia contents authoring, content based coding and indexing. Based on these results, we have developed objects based video editing system with several convenient editing functions.

  15. Clinical pilot study for the automatic segmentation and recognition of abdominal adipose tissue compartments from MRI data

    Energy Technology Data Exchange (ETDEWEB)

    Noel, P.B.; Bauer, J.S.; Ganter, C.; Markus, C.; Rummeny, E.J.; Engels, H.P. [Klinikum rechts der Isar, Technische Univ. Muenchen (Germany). Inst. fuer Radiologie; Hauner, H. [Klinikum rechts der Isar, Technische Univ. Muenchen (Germany). Else Kroener-Fresenius-Center for Nutritional Medicine

    2012-06-15

    Purpose: In the diagnosis and risk assessment of obesity, both the amount and distribution of adipose tissue compartments are critical factors. We present a hybrid method for the quantitative measurement of human body fat compartments. Materials and Methods: MRI imaging was performed on a 1.5 T scanner. In a pre-processing step, the images were corrected for bias field inhomogeneity. For segmentation and recognition a hybrid algorithm was developed to automatically differentiate between different adipose tissue compartments. The presented algorithm is designed with a combination of shape and intensity-based techniques. To incorporate the presented algorithm into the clinical routine, we developed a graphical user interface. Results from our methods were compared with the known volume of an adipose tissue phantom. To evaluate our method, we analyzed 40 clinical MRI scans of the abdominal region. Results: Relatively low segmentation errors were found for subcutaneous adipose tissue (3.56 %) and visceral adipose tissue (0.29 %) in phantom studies. The clinical results indicated high correlations between the distribution of adipose tissue compartments and obesity. Conclusion: We present an approach that rapidly identifies and quantifies adipose tissue depots of interest. With this method examination and analysis can be performed in a clinically feasible timeframe. (orig.)

  16. Clinical pilot study for the automatic segmentation and recognition of abdominal adipose tissue compartments from MRI data

    International Nuclear Information System (INIS)

    Noel, P.B.; Bauer, J.S.; Ganter, C.; Markus, C.; Rummeny, E.J.; Engels, H.P.; Hauner, H.

    2012-01-01

    Purpose: In the diagnosis and risk assessment of obesity, both the amount and distribution of adipose tissue compartments are critical factors. We present a hybrid method for the quantitative measurement of human body fat compartments. Materials and Methods: MRI imaging was performed on a 1.5 T scanner. In a pre-processing step, the images were corrected for bias field inhomogeneity. For segmentation and recognition a hybrid algorithm was developed to automatically differentiate between different adipose tissue compartments. The presented algorithm is designed with a combination of shape and intensity-based techniques. To incorporate the presented algorithm into the clinical routine, we developed a graphical user interface. Results from our methods were compared with the known volume of an adipose tissue phantom. To evaluate our method, we analyzed 40 clinical MRI scans of the abdominal region. Results: Relatively low segmentation errors were found for subcutaneous adipose tissue (3.56 %) and visceral adipose tissue (0.29 %) in phantom studies. The clinical results indicated high correlations between the distribution of adipose tissue compartments and obesity. Conclusion: We present an approach that rapidly identifies and quantifies adipose tissue depots of interest. With this method examination and analysis can be performed in a clinically feasible timeframe. (orig.)

  17. Revisiting the dose-effect correlations in irradiated head and neck cancer using automatic segmentation tools of the dental structures, mandible and maxilla

    International Nuclear Information System (INIS)

    Thariat, J.; Ramus, L.; Odin, G.; Vincent, S.; Orlanducci, M.H.; Dassonville, O.; Darcourt, V.; Lacout, A.; Marcy, P.Y.; Cagnol, G.; Malandain, G.

    2011-01-01

    Purpose. - Manual delineation of dental structures is too time-consuming to be feasible in routine practice. Information on dose risk levels is crucial for dentists following irradiation of the head and neck to avoid post-extraction osteoradionecrosis based on empirical dose-effects data established on bidimensional radiation therapy plans. Material and methods. - We present an automatic atlas-based segmentation framework of the dental structures, called Dentalmaps, constructed from a patient image-segmentation database. Results. - This framework is accurate (within 2 Gy accuracy) and relevant for the routine use. It has the potential to guide dental care in the context of new irradiation techniques. Conclusion. - This tool provides a user-friendly interface for dentists and radiation oncologists in the context of irradiated head and neck cancer patients. It will likely improve the knowledge of dose-effect correlations for dental complications and osteoradionecrosis. (authors)

  18. Automatic path proposal computation for CT-guided percutaneous liver biopsy.

    Science.gov (United States)

    Helck, A; Schumann, C; Aumann, J; Thierfelder, K; Strobl, F F; Braunagel, M; Niethammer, M; Clevert, D A; Hoffmann, R T; Reiser, M; Sandner, T; Trumm, C

    2016-12-01

    To evaluate feasibility of automatic software-based path proposals for CT-guided percutaneous biopsies. Thirty-three patients (60 [Formula: see text] 12 years) referred for CT-guided biopsy of focal liver lesions were consecutively included. Pre-interventional CT and dedicated software (FraunhoferMeVis Pathfinder) were used for (semi)automatic segmentation of relevant structures. The software subsequently generated three path proposals in downward quality for CT-guided biopsy. Proposed needle paths were compared with consensus proposal of two experts (comparable, less suitable, not feasible). In case of comparable results, equivalent approach to software-based path proposal was used. Quality of segmentation process was evaluated (Likert scale, 1 [Formula: see text] best, 6 [Formula: see text] worst), and time for processing was registered. All biopsies were performed successfully without complications. In 91 % one of the three automatic path proposals was rated comparable to experts' proposal. None of the first proposals was rated not feasible, and 76 % were rated comparable to the experts' proposal. 7 % automatic path proposals were rated not feasible, all being second choice ([Formula: see text]) or third choice ([Formula: see text]). In 79 %, segmentation at least was good. Average total time for establishing automatic path proposal was 42 [Formula: see text] 9 s. Automatic software-based path proposal for CT-guided liver biopsies in the majority provides path proposals that are easy to establish and comparable to experts' insertion trajectories.

  19. Multiple Scale Music Segmentation Using Rhythm, Timbre, and Harmony

    Directory of Open Access Journals (Sweden)

    Kristoffer Jensen

    2007-01-01

    Full Text Available The segmentation of music into intro-chorus-verse-outro, and similar segments, is a difficult topic. A method for performing automatic segmentation based on features related to rhythm, timbre, and harmony is presented, and compared, between the features and between the features and manual segmentation of a database of 48 songs. Standard information retrieval performance measures are used in the comparison, and it is shown that the timbre-related feature performs best.

  20. Detection and segmentation of virus plaque using HOG and SVM: toward automatic plaque assay.

    Science.gov (United States)

    Mao, Yihao; Liu, Hong; Ye, Rong; Shi, Yonghong; Song, Zhijian

    2014-01-01

    Plaque assaying, measurement of the number, diameter, and area of plaques in a Petri dish image, is a standard procedure gauging the concentration of phage in biology. This paper presented a novel and effective method for implementing automatic plaque assaying. The method was mainly comprised of the following steps: In the training stage, after pre-processing the images for noise suppression, an initial training set was readied by sampling positive (with a plaque at the center) and negative (plaque-free) patches from the training images, and extracting the HOG features from each patch. The linear SVM classifier was trained in a self-learnt supervised learning strategy to avoid possible missing detection. Specifically, the training set which contained positive and negative patches sampled manually from training images was used to train the preliminary classifier which exhaustively searched the training images to predict the label for the unlabeled patches. The mislabeled patches were evaluated by experts and relabeled. And all the newly labeled patches and their corresponding HOG features were added to the initial training set to train the final classifier. In the testing stage, a sliding-window technique was first applied to the unseen image for obtaining HOG features, which were inputted into the classifier to predict whether the patch was positive. Second, a locally adaptive Otsu method was performed on the positive patches to segment the plaques. Finally, after removing the outliers, the parameters of the plaques were measured in the segmented plaques. The experimental results demonstrated that the accuracy of the proposed method was similar to the one measured manually by experts, but it took less than 30 seconds.

  1. A general system for automatic biomedical image segmentation using intensity neighborhoods.

    Science.gov (United States)

    Chen, Cheng; Ozolek, John A; Wang, Wei; Rohde, Gustavo K

    2011-01-01

    Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications.

  2. A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods

    Directory of Open Access Journals (Sweden)

    Cheng Chen

    2011-01-01

    Full Text Available Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications.

  3. The brain’s cutting-room floor: segmentation of narrative cinema

    Directory of Open Access Journals (Sweden)

    Jeffrey M. Zacks

    2010-10-01

    Full Text Available Observers segment ongoing activity into meaningful events. Segmentation is a core component of perception that helps determine memory and guide planning. The current study tested the hypotheses that event segmentation is an automatic component of the perception of extended naturalistic activity, and that the identification of event boundaries in such activities results in part from processing changes in the perceived situation. Observers may identify boundaries between events as a result of processing changes in the observed situation. To test this hypothesis and study this potential mechanism, we measured brain activity while participants viewed an extended narrative film. Large transient responses were observed when the activity was segmented, and these responses were mediated by changes in the observed activity, including characters and their interactions, interactions with objects, spatial location, goals, and causes. These results support accounts that propose event segmentation is automatic and depends on processing meaningful changes in the perceived situation; they are the first to show such effects for extended naturalistic human activity.

  4. Segmentation of corpus callosum using diffusion tensor imaging: validation in patients with glioblastoma

    International Nuclear Information System (INIS)

    Nazem-Zadeh, Mohammad-Reza; Saksena, Sona; Babajani-Fermi, Abbas; Jiang, Quan; Soltanian-Zadeh, Hamid; Rosenblum, Mark; Mikkelsen, Tom; Jain, Rajan

    2012-01-01

    This paper presents a three-dimensional (3D) method for segmenting corpus callosum in normal subjects and brain cancer patients with glioblastoma. Nineteen patients with histologically confirmed treatment naïve glioblastoma and eleven normal control subjects underwent DTI on a 3T scanner. Based on the information inherent in diffusion tensors, a similarity measure was proposed and used in the proposed algorithm. In this algorithm, diffusion pattern of corpus callosum was used as prior information. Subsequently, corpus callosum was automatically divided into Witelson subdivisions. We simulated the potential rotation of corpus callosum under tumor pressure and studied the reproducibility of the proposed segmentation method in such cases. Dice coefficients, estimated to compare automatic and manual segmentation results for Witelson subdivisions, ranged from 94% to 98% for control subjects and from 81% to 95% for tumor patients, illustrating closeness of automatic and manual segmentations. Studying the effect of corpus callosum rotation by different Euler angles showed that although segmentation results were more sensitive to azimuth and elevation than skew, rotations caused by brain tumors do not have major effects on the segmentation results. The proposed method and similarity measure segment corpus callosum by propagating a hyper-surface inside the structure (resulting in high sensitivity), without penetrating into neighboring fiber bundles (resulting in high specificity)

  5. Generalized pixel profiling and comparative segmentation with application to arteriovenous malformation segmentation.

    Science.gov (United States)

    Babin, D; Pižurica, A; Bellens, R; De Bock, J; Shang, Y; Goossens, B; Vansteenkiste, E; Philips, W

    2012-07-01

    Extraction of structural and geometric information from 3-D images of blood vessels is a well known and widely addressed segmentation problem. The segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, with a special application in diagnostics and surgery on arteriovenous malformations (AVM). However, the techniques addressing the problem of the AVM inner structure segmentation are rare. In this work we present a novel method of pixel profiling with the application to segmentation of the 3-D angiography AVM images. Our algorithm stands out in situations with low resolution images and high variability of pixel intensity. Another advantage of our method is that the parameters are set automatically, which yields little manual user intervention. The results on phantoms and real data demonstrate its effectiveness and potentials for fine delineation of AVM structure. Copyright © 2012 Elsevier B.V. All rights reserved.

  6. Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks

    Directory of Open Access Journals (Sweden)

    Liang Chen

    2017-01-01

    Full Text Available Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-weighted MR imaging (DWI is sensitive to these lesions, localizing and quantifying them manually is costly and challenging for clinicians. In this paper, we propose a novel framework to automatically segment stroke lesions in DWI. Our framework consists of two convolutional neural networks (CNNs: one is an ensemble of two DeconvNets (Noh et al., 2015, which is the EDD Net; the second CNN is the multi-scale convolutional label evaluation net (MUSCLE Net, which aims to evaluate the lesions detected by the EDD Net in order to remove potential false positives. To the best of our knowledge, it is the first attempt to solve this problem and using both CNNs achieves very good results. Furthermore, we study the network architectures and key configurations in detail to ensure the best performance. It is validated on a large dataset comprising clinical acquired DW images from 741 subjects. A mean accuracy of Dice coefficient obtained is 0.67 in total. The mean Dice scores based on subjects with only small and large lesions are 0.61 and 0.83, respectively. The lesion detection rate achieved is 0.94.

  7. Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks.

    Science.gov (United States)

    Chen, Liang; Bentley, Paul; Rueckert, Daniel

    2017-01-01

    Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-weighted MR imaging (DWI) is sensitive to these lesions, localizing and quantifying them manually is costly and challenging for clinicians. In this paper, we propose a novel framework to automatically segment stroke lesions in DWI. Our framework consists of two convolutional neural networks (CNNs): one is an ensemble of two DeconvNets (Noh et al., 2015), which is the EDD Net; the second CNN is the multi-scale convolutional label evaluation net (MUSCLE Net), which aims to evaluate the lesions detected by the EDD Net in order to remove potential false positives. To the best of our knowledge, it is the first attempt to solve this problem and using both CNNs achieves very good results. Furthermore, we study the network architectures and key configurations in detail to ensure the best performance. It is validated on a large dataset comprising clinical acquired DW images from 741 subjects. A mean accuracy of Dice coefficient obtained is 0.67 in total. The mean Dice scores based on subjects with only small and large lesions are 0.61 and 0.83, respectively. The lesion detection rate achieved is 0.94.

  8. Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion.

    Directory of Open Access Journals (Sweden)

    Da Ma

    Full Text Available Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework.

  9. Grinding Parts For Automatic Welding

    Science.gov (United States)

    Burley, Richard K.; Hoult, William S.

    1989-01-01

    Rollers guide grinding tool along prospective welding path. Skatelike fixture holds rotary grinder or file for machining large-diameter rings or ring segments in preparation for welding. Operator grasps handles to push rolling fixture along part. Rollers maintain precise dimensional relationship so grinding wheel cuts precise depth. Fixture-mounted grinder machines surface to quality sufficient for automatic welding; manual welding with attendant variations and distortion not necessary. Developed to enable automatic welding of parts, manual welding of which resulted in weld bead permeated with microscopic fissures.

  10. Posterior subscapular dissection: An improved approach to the brachial plexus for human anatomy students.

    Science.gov (United States)

    Hager, Shaun; Backus, Timothy Charles; Futterman, Bennett; Solounias, Nikos; Mihlbachler, Matthew C

    2014-05-01

    Students of human anatomy are required to understand the brachial plexus, from the proximal roots extending from spinal nerves C5 through T1, to the distal-most branches that innervate the shoulder and upper limb. However, in human cadaver dissection labs, students are often instructed to dissect the brachial plexus using an antero-axillary approach that incompletely exposes the brachial plexus. This approach readily exposes the distal segments of the brachial plexus but exposure of proximal and posterior segments require extensive dissection of neck and shoulder structures. Therefore, the proximal and posterior segments of the brachial plexus, including the roots, trunks, divisions, posterior cord and proximally branching peripheral nerves often remain unobserved during study of the cadaveric shoulder and brachial plexus. Here we introduce a subscapular approach that exposes the entire brachial plexus, with minimal amount of dissection or destruction of surrounding structures. Lateral retraction of the scapula reveals the entire length of the brachial plexus in the subscapular space, exposing the brachial plexus roots and other proximal segments. Combining the subscapular approach with the traditional antero-axillary approach allows students to observe the cadaveric brachial plexus in its entirety. Exposure of the brachial dissection in the subscapular space requires little time and is easily incorporated into a preexisting anatomy lab curriculum without scheduling additional time for dissection. Copyright © 2014 Elsevier GmbH. All rights reserved.

  11. Visual analytics for the exploration and assessment of segmentation errors

    NARCIS (Netherlands)

    Raidou, R.G.; Marcelis, F.J.J.; Breeuwer, M.; Gröller, M.E.; Vilanova Bartroli, A.

    2016-01-01

    Several diagnostic and treatment procedures require the segmentation of anatomical structures from medical images. However, the automatic model-based methods that are often employed, may produce inaccurate segmentations. These, if used as input for diagnosis or treatment, can have detrimental

  12. Interactive segmentation for geographic atrophy in retinal fundus images.

    Science.gov (United States)

    Lee, Noah; Smith, R Theodore; Laine, Andrew F

    2008-10-01

    Fundus auto-fluorescence (FAF) imaging is a non-invasive technique for in vivo ophthalmoscopic inspection of age-related macular degeneration (AMD), the most common cause of blindness in developed countries. Geographic atrophy (GA) is an advanced form of AMD and accounts for 12-21% of severe visual loss in this disorder [3]. Automatic quantification of GA is important for determining disease progression and facilitating clinical diagnosis of AMD. The problem of automatic segmentation of pathological images still remains an unsolved problem. In this paper we leverage the watershed transform and generalized non-linear gradient operators for interactive segmentation and present an intuitive and simple approach for geographic atrophy segmentation. We compare our approach with the state of the art random walker [5] algorithm for interactive segmentation using ROC statistics. Quantitative evaluation experiments on 100 FAF images show a mean sensitivity/specificity of 98.3/97.7% for our approach and a mean sensitivity/specificity of 88.2/96.6% for the random walker algorithm.

  13. A segmentation approach for a delineation of terrestrial ecoregions

    Science.gov (United States)

    Nowosad, J.; Stepinski, T.

    2017-12-01

    Terrestrial ecoregions are the result of regionalization of land into homogeneous units of similar ecological and physiographic features. Terrestrial Ecoregions of the World (TEW) is a commonly used global ecoregionalization based on expert knowledge and in situ observations. Ecological Land Units (ELUs) is a global classification of 250 meters-sized cells into 4000 types on the basis of the categorical values of four environmental variables. ELUs are automatically calculated and reproducible but they are not a regionalization which makes them impractical for GIS-based spatial analysis and for comparison with TEW. We have regionalized terrestrial ecosystems on the basis of patterns of the same variables (land cover, soils, landform, and bioclimate) previously used in ELUs. Considering patterns of categorical variables makes segmentation and thus regionalization possible. Original raster datasets of the four variables are first transformed into regular grids of square-sized blocks of their cells called eco-sites. Eco-sites are elementary land units containing local patterns of physiographic characteristics and thus assumed to contain a single ecosystem. Next, eco-sites are locally aggregated using a procedure analogous to image segmentation. The procedure optimizes pattern homogeneity of all four environmental variables within each segment. The result is a regionalization of the landmass into land units characterized by uniform pattern of land cover, soils, landforms, climate, and, by inference, by uniform ecosystem. Because several disjoined segments may have very similar characteristics, we cluster the segments to obtain a smaller set of segment types which we identify with ecoregions. Our approach is automatic, reproducible, updatable, and customizable. It yields the first automatic delineation of ecoregions on the global scale. In the resulting vector database each ecoregion/segment is described by numerous attributes which make it a valuable GIS resource for

  14. A fully automatic, threshold-based segmentation method for the estimation of the Metabolic Tumor Volume from PET images: validation on 3D printed anthropomorphic oncological lesions

    Science.gov (United States)

    Gallivanone, F.; Interlenghi, M.; Canervari, C.; Castiglioni, I.

    2016-01-01

    18F-Fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) is a standard functional diagnostic technique to in vivo image cancer. Different quantitative paramters can be extracted from PET images and used as in vivo cancer biomarkers. Between PET biomarkers Metabolic Tumor Volume (MTV) has gained an important role in particular considering the development of patient-personalized radiotherapy treatment for non-homogeneous dose delivery. Different imaging processing methods have been developed to define MTV. The different proposed PET segmentation strategies were validated in ideal condition (e.g. in spherical objects with uniform radioactivity concentration), while the majority of cancer lesions doesn't fulfill these requirements. In this context, this work has a twofold objective: 1) to implement and optimize a fully automatic, threshold-based segmentation method for the estimation of MTV, feasible in clinical practice 2) to develop a strategy to obtain anthropomorphic phantoms, including non-spherical and non-uniform objects, miming realistic oncological patient conditions. The developed PET segmentation algorithm combines an automatic threshold-based algorithm for the definition of MTV and a k-means clustering algorithm for the estimation of the background. The method is based on parameters always available in clinical studies and was calibrated using NEMA IQ Phantom. Validation of the method was performed both in ideal (e.g. in spherical objects with uniform radioactivity concentration) and non-ideal (e.g. in non-spherical objects with a non-uniform radioactivity concentration) conditions. The strategy to obtain a phantom with synthetic realistic lesions (e.g. with irregular shape and a non-homogeneous uptake) consisted into the combined use of standard anthropomorphic phantoms commercially and irregular molds generated using 3D printer technology and filled with a radioactive chromatic alginate. The proposed segmentation algorithm was feasible in a

  15. Grammar-based Automatic 3D Model Reconstruction from Terrestrial Laser Scanning Data

    Science.gov (United States)

    Yu, Q.; Helmholz, P.; Belton, D.; West, G.

    2014-04-01

    The automatic reconstruction of 3D buildings has been an important research topic during the last years. In this paper, a novel method is proposed to automatically reconstruct the 3D building models from segmented data based on pre-defined formal grammar and rules. Such segmented data can be extracted e.g. from terrestrial or mobile laser scanning devices. Two steps are considered in detail. The first step is to transform the segmented data into 3D shapes, for instance using the DXF (Drawing Exchange Format) format which is a CAD data file format used for data interchange between AutoCAD and other program. Second, we develop a formal grammar to describe the building model structure and integrate the pre-defined grammars into the reconstruction process. Depending on the different segmented data, the selected grammar and rules are applied to drive the reconstruction process in an automatic manner. Compared with other existing approaches, our proposed method allows the model reconstruction directly from 3D shapes and takes the whole building into account.

  16. Multiscale CNNs for Brain Tumor Segmentation and Diagnosis.

    Science.gov (United States)

    Zhao, Liya; Jia, Kebin

    2016-01-01

    Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs). Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness.

  17. A Dirichlet process mixture model for automatic (18)F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions.

    Science.gov (United States)

    Giri, Maria Grazia; Cavedon, Carlo; Mazzarotto, Renzo; Ferdeghini, Marco

    2016-05-01

    The aim of this study was to implement a Dirichlet process mixture (DPM) model for automatic tumor edge identification on (18)F-fluorodeoxyglucose positron emission tomography ((18)F-FDG PET) images by optimizing the parameters on which the algorithm depends, to validate it experimentally, and to test its robustness. The DPM model belongs to the class of the Bayesian nonparametric models and uses the Dirichlet process prior for flexible nonparametric mixture modeling, without any preliminary choice of the number of mixture components. The DPM algorithm implemented in the statistical software package R was used in this work. The contouring accuracy was evaluated on several image data sets: on an IEC phantom (spherical inserts with diameter in the range 10-37 mm) acquired by a Philips Gemini Big Bore PET-CT scanner, using 9 different target-to-background ratios (TBRs) from 2.5 to 70; on a digital phantom simulating spherical/uniform lesions and tumors, irregular in shape and activity; and on 20 clinical cases (10 lung and 10 esophageal cancer patients). The influence of the DPM parameters on contour generation was studied in two steps. In the first one, only the IEC spheres having diameters of 22 and 37 mm and a sphere of the digital phantom (41.6 mm diameter) were studied by varying the main parameters until the diameter of the spheres was obtained within 0.2% of the true value. In the second step, the results obtained for this training set were applied to the entire data set to determine DPM based volumes of all available lesions. These volumes were compared to those obtained by applying already known algorithms (Gaussian mixture model and gradient-based) and to true values, when available. Only one parameter was found able to significantly influence segmentation accuracy (ANOVA test). This parameter was linearly connected to the uptake variance of the tested region of interest (ROI). In the first step of the study, a calibration curve was determined to

  18. A Dirichlet process mixture model for automatic 18F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions

    International Nuclear Information System (INIS)

    Giri, Maria Grazia; Cavedon, Carlo; Mazzarotto, Renzo; Ferdeghini, Marco

    2016-01-01

    Purpose: The aim of this study was to implement a Dirichlet process mixture (DPM) model for automatic tumor edge identification on 18 F-fluorodeoxyglucose positron emission tomography ( 18 F-FDG PET) images by optimizing the parameters on which the algorithm depends, to validate it experimentally, and to test its robustness. Methods: The DPM model belongs to the class of the Bayesian nonparametric models and uses the Dirichlet process prior for flexible nonparametric mixture modeling, without any preliminary choice of the number of mixture components. The DPM algorithm implemented in the statistical software package R was used in this work. The contouring accuracy was evaluated on several image data sets: on an IEC phantom (spherical inserts with diameter in the range 10–37 mm) acquired by a Philips Gemini Big Bore PET-CT scanner, using 9 different target-to-background ratios (TBRs) from 2.5 to 70; on a digital phantom simulating spherical/uniform lesions and tumors, irregular in shape and activity; and on 20 clinical cases (10 lung and 10 esophageal cancer patients). The influence of the DPM parameters on contour generation was studied in two steps. In the first one, only the IEC spheres having diameters of 22 and 37 mm and a sphere of the digital phantom (41.6 mm diameter) were studied by varying the main parameters until the diameter of the spheres was obtained within 0.2% of the true value. In the second step, the results obtained for this training set were applied to the entire data set to determine DPM based volumes of all available lesions. These volumes were compared to those obtained by applying already known algorithms (Gaussian mixture model and gradient-based) and to true values, when available. Results: Only one parameter was found able to significantly influence segmentation accuracy (ANOVA test). This parameter was linearly connected to the uptake variance of the tested region of interest (ROI). In the first step of the study, a calibration curve

  19. Automatic segmentation of cerebral MR images using artificial neural networks

    International Nuclear Information System (INIS)

    Alirezaie, J.; Jernigan, M.E.; Nahmias, C.

    1996-01-01

    In this paper we present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Our scheme utilizes the Self Organizing Feature Map (SOFM) artificial neural network for feature mapping and generates a set of codebook vectors. By extending the network with an additional layer the map will be classified and each tissue class will be labelled. An algorithm has been developed for extracting the cerebrum from the head scan prior to the segmentation. Extracting the cerebrum is performed by stripping away the skull pixels from the T2 image. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. To compare the results with other conventional approaches we applied the c-means algorithm to the problem

  20. Automatic Tortuosity-Based Retinopathy of Prematurity Screening System

    Science.gov (United States)

    Sukkaew, Lassada; Uyyanonvara, Bunyarit; Makhanov, Stanislav S.; Barman, Sarah; Pangputhipong, Pannet

    Retinopathy of Prematurity (ROP) is an infant disease characterized by increased dilation and tortuosity of the retinal blood vessels. Automatic tortuosity evaluation from retinal digital images is very useful to facilitate an ophthalmologist in the ROP screening and to prevent childhood blindness. This paper proposes a method to automatically classify the image into tortuous and non-tortuous. The process imitates expert ophthalmologists' screening by searching for clearly tortuous vessel segments. First, a skeleton of the retinal blood vessels is extracted from the original infant retinal image using a series of morphological operators. Next, we propose to partition the blood vessels recursively using an adaptive linear interpolation scheme. Finally, the tortuosity is calculated based on the curvature of the resulting vessel segments. The retinal images are then classified into two classes using segments characterized by the highest tortuosity. For an optimal set of training parameters the prediction is as high as 100%.

  1. Minimizing manual image segmentation turn-around time for neuronal reconstruction by embracing uncertainty.

    Directory of Open Access Journals (Sweden)

    Stephen M Plaza

    Full Text Available The ability to automatically segment an image into distinct regions is a critical aspect in many visual processing applications. Because inaccuracies often exist in automatic segmentation, manual segmentation is necessary in some application domains to correct mistakes, such as required in the reconstruction of neuronal processes from microscopic images. The goal of the automated segmentation tool is traditionally to produce the highest-quality segmentation, where quality is measured by the similarity to actual ground truth, so as to minimize the volume of manual correction necessary. Manual correction is generally orders-of-magnitude more time consuming than automated segmentation, often making handling large images intractable. Therefore, we propose a more relevant goal: minimizing the turn-around time of automated/manual segmentation while attaining a level of similarity with ground truth. It is not always necessary to inspect every aspect of an image to generate a useful segmentation. As such, we propose a strategy to guide manual segmentation to the most uncertain parts of segmentation. Our contributions include 1 a probabilistic measure that evaluates segmentation without ground truth and 2 a methodology that leverages these probabilistic measures to significantly reduce manual correction while maintaining segmentation quality.

  2. Reasonable threshold value used to segment the individual comet from the comet assay image

    International Nuclear Information System (INIS)

    Yan Xuekun; Chen Ying; Du Jie; Zhang Xueqing; Luo Yisheng

    2009-01-01

    Reasonable segmentation of the individual comet contour from the Comet Assay (CA) images is the precondition for all of parameters analysis during the automatic analyzing for the CA. The Otsu method and several arithmetic operators for image segmentation, such as Sobel, Prewitt, Roberts and Canny were used to segment the comet contour, and characters of the CA images were analyzed firstly. And then the segmentation methods which had been adopted in the software for CA automatic analysis, such as the CASP, the TriTek CometScore TM , were put for-ward and compared. At last, a two-step procedure for threshold calculation based on image-content analysis is adopted to segment the individual comet from the CA images, and several principles for the segmentation are put forward too.(authors)

  3. Larynx Anatomy

    Science.gov (United States)

    ... e.g. -historical Searches are case-insensitive Larynx Anatomy Add to My Pictures View /Download : Small: 648x576 ... View Download Large: 2700x2400 View Download Title: Larynx Anatomy Description: Anatomy of the larynx; drawing shows the ...

  4. Vulva Anatomy

    Science.gov (United States)

    ... e.g. -historical Searches are case-insensitive Vulva Anatomy Add to My Pictures View /Download : Small: 720x634 ... View Download Large: 3000x2640 View Download Title: Vulva Anatomy Description: Anatomy of the vulva; drawing shows the ...

  5. Pharynx Anatomy

    Science.gov (United States)

    ... e.g. -historical Searches are case-insensitive Pharynx Anatomy Add to My Pictures View /Download : Small: 720x576 ... View Download Large: 3000x2400 View Download Title: Pharynx Anatomy Description: Anatomy of the pharynx; drawing shows the ...

  6. Robust medical image segmentation for hyperthermia treatment planning

    International Nuclear Information System (INIS)

    Neufeld, E.; Chavannes, N.; Kuster, N.; Samaras, T.

    2005-01-01

    Full text: This work is part of an ongoing effort to develop a comprehensive hyperthermia treatment planning (HTP) tool. The goal is to unify all the steps necessary to perform treatment planning - from image segmentation to optimization of the energy deposition pattern - in a single tool. The bases of the HTP software are the routines and know-how developed in our TRINTY project that resulted the commercial EM platform SEMCAD-X. It incorporates the non-uniform finite-difference time-domain (FDTD) method, permitting the simulation of highly detailed models. Subsequently, in order to create highly resolved patient models, a powerful and robust segmentation tool is needed. A toolbox has been created that allows the flexible combination of various segmentation methods as well as several pre-and postprocessing functions. It works primarily with CT and MRI images, which it can read in various formats. A wide variety of segmentation methods has been implemented. This includes thresholding techniques (k-means classification, expectation maximization and modal histogram analysis for automatic threshold detection, multi-dimensional if required), region growing methods (with hysteretic behavior and simultaneous competitive growing), an interactive marker based watershed transformation, level-set methods (homogeneity and edge based, fast-marching), a flexible live-wire implementation as well as fuzzy connectedness. Due to the large number of tissues that need to be segmented for HTP, no methods that rely on prior knowledge have been implemented. Various edge extraction routines, distance transforms, smoothing techniques (convolutions, anisotropic diffusion, sigma filter...), connected component analysis, topologically flexible interpolation, image algebra and morphological operations are available. Moreover, contours or surfaces can be extracted, simplified and exported. Using these different techniques on several samples, the following conclusions have been drawn: Due to the

  7. Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy.

    Science.gov (United States)

    Wernitznig, Stefan; Sele, Mariella; Urschler, Martin; Zankel, Armin; Pölt, Peter; Rind, F Claire; Leitinger, Gerd

    2016-05-01

    Elucidating the anatomy of neuronal circuits and localizing the synaptic connections between neurons, can give us important insights in how the neuronal circuits work. We are using serial block-face scanning electron microscopy (SBEM) to investigate the anatomy of a collision detection circuit including the Lobula Giant Movement Detector (LGMD) neuron in the locust, Locusta migratoria. For this, thousands of serial electron micrographs are produced that allow us to trace the neuronal branching pattern. The reconstruction of neurons was previously done manually by drawing cell outlines of each cell in each image separately. This approach was very time consuming and troublesome. To make the process more efficient a new interactive software was developed. It uses the contrast between the neuron under investigation and its surrounding for semi-automatic segmentation. For segmentation the user sets starting regions manually and the algorithm automatically selects a volume within the neuron until the edges corresponding to the neuronal outline are reached. Internally the algorithm optimizes a 3D active contour segmentation model formulated as a cost function taking the SEM image edges into account. This reduced the reconstruction time, while staying close to the manual reference segmentation result. Our algorithm is easy to use for a fast segmentation process, unlike previous methods it does not require image training nor an extended computing capacity. Our semi-automatic segmentation algorithm led to a dramatic reduction in processing time for the 3D-reconstruction of identified neurons. Copyright © 2016 Elsevier B.V. All rights reserved.

  8. Three-Dimensional Segmentation of the Tumor in Computed Tomographic Images of Neuroblastoma

    OpenAIRE

    Deglint, Hanford J.; Rangayyan, Rangaraj M.; Ayres, Fábio J.; Boag, Graham S.; Zuffo, Marcelo K.

    2006-01-01

    Segmentation of the tumor in neuroblastoma is complicated by the fact that the mass is almost always heterogeneous in nature; furthermore, viable tumor, necrosis, and normal tissue are often intermixed. Tumor definition and diagnosis require the analysis of the spatial distribution and Hounsfield unit (HU) values of voxels in computed tomography (CT) images, coupled with a knowledge of normal anatomy. Segmentation and analysis of the tissue composition of the tumor can assist in quantitative ...

  9. Reliability of the Bony Anatomy in Image-Guided Stereotactic Radiotherapy of Brain Metastases

    International Nuclear Information System (INIS)

    Guckenberger, Matthias; Baier, Kurt; Guenther, Iris; Richter, Anne; Wilbert, Juergen; Sauer, Otto; Vordermark, Dirk; Flentje, Michael

    2007-01-01

    Purpose: To evaluate whether the position of brain metastases remains stable between planning and treatment in cranial stereotactic radiotherapy (SRT). Methods and Materials: Eighteen patients with 20 brain metastases were treated with single-fraction (17 lesions) or hypofractionated (3 lesions) image-guided SRT. Median time interval between planning and treatment was 8 days. Before treatment a cone-beam CT (CBCT) and a conventional CT after application of i.v. contrast were acquired. Setup errors using automatic bone registration (CBCT) and manual soft-tissue registration of the brain metastases (conventional CT) were compared. Results: Tumor size was not significantly different between planning and treatment. The three-dimensional setup error (mean ± SD) was 4.0 ± 2.1 mm and 3.5 ± 2.2 mm according to the bony anatomy and the lesion itself, respectively. A highly significant correlation between automatic bone match and soft-tissue registration was seen in all three directions (r ≥ 0.88). The three-dimensional distance between the isocenter according to bone match and soft-tissue registration was 1.7 ± 0.7 mm, maximum 2.8 mm. Treatment of intracranial pressure with steroids did not influence the position of the lesion relative to the bony anatomy. Conclusion: With a time interval of approximately 1 week between planning and treatment, the bony anatomy of the skull proved to be an excellent surrogate for the target position in image-guided SRT

  10. Hand Anatomy

    Science.gov (United States)

    ... All Topics A-Z Videos Infographics Symptom Picker Anatomy Bones Joints Muscles Nerves Vessels Tendons About Hand Surgery What is ... Hand Therapist? Media Find a Hand Surgeon Home Anatomy Bones Joints Muscles Nerves Vessels Tendons Anatomy The upper extremity is ...

  11. The place of surface anatomy in the medical literature and undergraduate anatomy textbooks.

    Science.gov (United States)

    Azer, Samy A

    2013-01-01

    The aims of this review were to examine the place of surface anatomy in the medical literature, particularly the methods and approaches used in teaching surface and living anatomy and assess commonly used anatomy textbooks in regard to their surface anatomy contents. PubMed and MEDLINE databases were searched using the following keywords "surface anatomy," "living anatomy," "teaching surface anatomy," "bony landmarks," "peer examination" and "dermatomes". The percentage of pages covering surface anatomy in each textbook was calculated as well as the number of images covering surface anatomy. Clarity, quality and adequacy of surface anatomy contents was also examined. The search identified 22 research papers addressing methods used in teaching surface anatomy, 31 papers that can help in the improvement of surface anatomy curriculum, and 12 anatomy textbooks. These teaching methods included: body painting, peer volunteer surface anatomy, use of a living anatomy model, real time ultrasound, virtual (visible) human dissector (VHD), full body digital x-ray of cadavers (Lodox(®) Statscan(®) images) combined with palpating landmarks on peers and the cadaver, as well as the use of collaborative, contextual and self-directed learning. Nineteen of these studies were published in the period from 2006 to 2013. The 31 papers covered evidence-based and clinically-applied surface anatomy. The percentage of surface anatomy in textbooks' contents ranged from 0 to 6.2 with an average of 3.4%. The number of medical illustrations on surface anatomy varied from 0 to 135. In conclusion, although there has been a progressive increase in publications addressing methods used in teaching surface anatomy over the last six to seven years, most anatomy textbooks do not provide students with adequate information about surface anatomy. Only three textbooks provided a solid explanation and foundation of understanding surface anatomy. © 2013 American Association of Anatomists.

  12. A digital interactive human brain atlas based on Chinese visible human datasets for anatomy teaching.

    Science.gov (United States)

    Li, Qiyu; Ran, Xu; Zhang, Shaoxiang; Tan, Liwen; Qiu, Mingguo

    2014-01-01

    As we know, the human brain is one of the most complicated organs in the human body, which is the key and difficult point in neuroanatomy and sectional anatomy teaching. With the rapid development and extensive application of imaging technology in clinical diagnosis, doctors are facing higher and higher requirement on their anatomy knowledge. Thus, to cultivate medical students to meet the needs of medical development today and to improve their ability to read and understand radiographic images have become urgent challenges for the medical teachers. In this context, we developed a digital interactive human brain atlas based on the Chinese visible human datasets for anatomy teaching (available for free download from http://www.chinesevisiblehuman.com/down/DHBA.rar). The atlas simultaneously provides views in all 3 primary planes of section. The main structures of the human brain have been anatomically labeled in all 3 views. It is potentially useful for anatomy browsing, user self-testing, and automatic student assessment. In a word, it is interactive, 3D, user friendly, and free of charge, which can provide a new, intuitive means for anatomy teaching.

  13. Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network.

    Science.gov (United States)

    Pal, Anabik; Garain, Utpal; Chandra, Aditi; Chatterjee, Raghunath; Senapati, Swapan

    2018-06-01

    Development of machine assisted tools for automatic analysis of psoriasis skin biopsy image plays an important role in clinical assistance. Development of automatic approach for accurate segmentation of psoriasis skin biopsy image is the initial prerequisite for developing such system. However, the complex cellular structure, presence of imaging artifacts, uneven staining variation make the task challenging. This paper presents a pioneering attempt for automatic segmentation of psoriasis skin biopsy images. Several deep neural architectures are tried for segmenting psoriasis skin biopsy images. Deep models are used for classifying the super-pixels generated by Simple Linear Iterative Clustering (SLIC) and the segmentation performance of these architectures is compared with the traditional hand-crafted feature based classifiers built on popularly used classifiers like K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). A U-shaped Fully Convolutional Neural Network (FCN) is also used in an end to end learning fashion where input is the original color image and the output is the segmentation class map for the skin layers. An annotated real psoriasis skin biopsy image data set of ninety (90) images is developed and used for this research. The segmentation performance is evaluated with two metrics namely, Jaccard's Coefficient (JC) and the Ratio of Correct Pixel Classification (RCPC) accuracy. The experimental results show that the CNN based approaches outperform the traditional hand-crafted feature based classification approaches. The present research shows that practical system can be developed for machine assisted analysis of psoriasis disease. Copyright © 2018 Elsevier B.V. All rights reserved.

  14. William, a voxel model of child anatomy from tomographic images for Monte Carlo dosimetry calculations

    International Nuclear Information System (INIS)

    Caon, M.

    2010-01-01

    Full text: Medical imaging provides two-dimensional pictures of the human internal anatomy from which may be constructed a three-dimensional model of organs and tissues suitable for calculation of dose from radiation. Diagnostic CT provides the greatest exposure to radiation per examination and the frequency of CT examination is high. Esti mates of dose from diagnostic radiography are still determined from data derived from geometric models (rather than anatomical models), models scaled from adult bodies (rather than bodies of children) and CT scanner hardware that is no longer used. The aim of anatomical modelling is to produce a mathematical representation of internal anatomy that has organs of realistic size, shape and positioning. The organs and tissues are represented by a great many cuboidal volumes (voxels). The conversion of medical images to voxels is called segmentation and on completion every pixel in an image is assigned to a tissue or organ. Segmentation is time consuming. An image processing pack age is used to identify organ boundaries in each image. Thirty to forty tomographic voxel models of anatomy have been reported in the literature. Each model is of an individual, or a composite from several individuals. Images of children are particularly scarce. So there remains a need for more paediatric anatomical models. I am working on segmenting ''William'' who is 368 PET-CT images from head to toe of a seven year old boy. William will be used for Monte Carlo dose calculations of dose from CT examination using a simulated modern CT scanner.

  15. NSCT BASED LOCAL ENHANCEMENT FOR ACTIVE CONTOUR BASED IMAGE SEGMENTATION APPLICATION

    Directory of Open Access Journals (Sweden)

    Hiren Mewada

    2010-08-01

    Full Text Available Because of cross-disciplinary nature, Active Contour modeling techniques have been utilized extensively for the image segmentation. In traditional active contour based segmentation techniques based on level set methods, the energy functions are defined based on the intensity gradient. This makes them highly sensitive to the situation where the underlying image content is characterized by image nonhomogeneities due to illumination and contrast condition. This is the most difficult problem to make them as fully automatic image segmentation techniques. This paper introduces one of the approaches based on image enhancement to this problem. The enhanced image is obtained using NonSubsampled Contourlet Transform, which improves the edges strengths in the direction where the illumination is not proper and then active contour model based on level set technique is utilized to segment the object. Experiment results demonstrate that proposed method can be utilized along with existing active contour model based segmentation method under situation characterized by intensity non-homogeneity to make them fully automatic.

  16. Unsupervised Performance Evaluation of Image Segmentation

    Directory of Open Access Journals (Sweden)

    Chabrier Sebastien

    2006-01-01

    Full Text Available We present in this paper a study of unsupervised evaluation criteria that enable the quantification of the quality of an image segmentation result. These evaluation criteria compute some statistics for each region or class in a segmentation result. Such an evaluation criterion can be useful for different applications: the comparison of segmentation results, the automatic choice of the best fitted parameters of a segmentation method for a given image, or the definition of new segmentation methods by optimization. We first present the state of art of unsupervised evaluation, and then, we compare six unsupervised evaluation criteria. For this comparative study, we use a database composed of 8400 synthetic gray-level images segmented in four different ways. Vinet's measure (correct classification rate is used as an objective criterion to compare the behavior of the different criteria. Finally, we present the experimental results on the segmentation evaluation of a few gray-level natural images.

  17. Statistical modeling of the eye for multimodal treatment planning for external beam radiation therapy of intraocular tumors.

    Science.gov (United States)

    Rüegsegger, Michael B; Bach Cuadra, Meritxell; Pica, Alessia; Amstutz, Christoph A; Rudolph, Tobias; Aebersold, Daniel; Kowal, Jens H

    2012-11-15

    Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. Cross-validation revealed a dice similarity of 95%±2% for the sclera and cornea and 91%±2% for the lens. Overall, mean segmentation error was found to be 0.3±0.1 mm. Average segmentation time was 14±2 s on a standard personal computer. Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes. Copyright © 2012 Elsevier Inc. All rights reserved.

  18. Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors

    Energy Technology Data Exchange (ETDEWEB)

    Rueegsegger, Michael B. [ARTORG Center for Biomedical Engineering Research, University of Bern (Switzerland); Bach Cuadra, Meritxell [Department of Radiology, University Hospital Center (CHUV) and University of Lausanne (UNIL), Signal Processing Laboratory - LTS5, Ecole Polytechnique Federale de Lausanne (Switzerland); Pica, Alessia [Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern (Switzerland); Amstutz, Christoph A. [Department of Ophthalmology, University Hospital Zurich (Switzerland); Rudolph, Tobias [ARTORG Center for Biomedical Engineering Research, University of Bern (Switzerland); Aebersold, Daniel [Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern (Switzerland); Kowal, Jens H., E-mail: jens.kowal@artorg.unibe.ch [ARTORG Center for Biomedical Engineering Research, University of Bern (Switzerland)

    2012-11-15

    Purpose: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. Methods and Materials: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. Results: Cross-validation revealed a dice similarity of 95% {+-} 2% for the sclera and cornea and 91% {+-} 2% for the lens. Overall, mean segmentation error was found to be 0.3 {+-} 0.1 mm. Average segmentation time was 14 {+-} 2 s on a standard personal computer. Conclusions: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.

  19. Automatic segmentation of thermal images of diabetic-at-risk feet using the snakes algorithm

    Science.gov (United States)

    Etehadtavakol, Mahnaz; Ng, E. Y. K.; Kaabouch, Naima

    2017-11-01

    Diabetes is a disease with multi-systemic problems. It is a leading cause of death, medical costs, and loss of productivity. Foot ulcers are one generally known problem of uncontrolled diabetes that can lead to amputation signs of foot ulcers are not always obvious. Sometimes, symptoms won't even show up until ulcer is infected. Hence, identification of pre-ulceration of the plantar surface of the foot in diabetics is beneficial. Thermography has the potential to identify regions of the plantar with no evidence of ulcer but yet risk. Thermography is a technique that is safe, easy, non-invasive, with no contact, and repeatable. In this study, 59 thermographic images of the plantar foot of patients with diabetic neuropathy are implemented using the snakes algorithm to separate two feet from background automatically and separating the right foot from the left on each image. The snakes algorithm both separates the right and left foot into segmented different clusters according to their temperatures. The hottest regions will have the highest risk of ulceration for each foot. This algorithm also worked perfectly for all the current images.

  20. Automatic computer aided analysis algorithms and system for adrenal tumors on CT images.

    Science.gov (United States)

    Chai, Hanchao; Guo, Yi; Wang, Yuanyuan; Zhou, Guohui

    2017-12-04

    The adrenal tumor will disturb the secreting function of adrenocortical cells, leading to many diseases. Different kinds of adrenal tumors require different therapeutic schedules. In the practical diagnosis, it highly relies on the doctor's experience to judge the tumor type by reading the hundreds of CT images. This paper proposed an automatic computer aided analysis method for adrenal tumors detection and classification. It consisted of the automatic segmentation algorithms, the feature extraction and the classification algorithms. These algorithms were then integrated into a system and conducted on the graphic interface by using MATLAB Graphic user interface (GUI). The accuracy of the automatic computer aided segmentation and classification reached 90% on 436 CT images. The experiments proved the stability and reliability of this automatic computer aided analytic system.

  1. Auto-Segmentation of Head and Neck Cancer using Textural features

    DEFF Research Database (Denmark)

    Hollensen, Christian; Jørgensen, Peter Stanley; Højgaard, Liselotte

    - and intra observer variability. Several automatic segmentation methods have been developed using positron emission tomography (PET) and/or computerised tomography (CT). The aim of the present study is to develop a model for 3-dimensional auto-segmentation, the level set method, to contour gross tumour...

  2. Fully automatic segmentation of arbitrarily shaped fiducial markers in cone-beam CT projections

    DEFF Research Database (Denmark)

    Bertholet, Jenny; Wan, Hanlin; Toftegaard, Jakob

    2017-01-01

    segmentation, the DPTB algorithm generates and uses a 3D marker model to create 2D templates at any projection angle. The 2D templates are used to segment the marker position as the position with highest normalized cross-correlation in a search area centered at the DP segmented position. The accuracy of the DP...... algorithm and the new DPTB algorithm was quantified as the 2D segmentation error (pixels) compared to a manual ground truth segmentation for 97 markers in the projection images of CBCT scans of 40 patients. Also the fraction of wrong segmentations, defined as 2D errors larger than 5 pixels, was calculated...

  3. Segmentation and Visualisation of Human Brain Structures

    Energy Technology Data Exchange (ETDEWEB)

    Hult, Roger

    2003-10-01

    In this thesis the focus is mainly on the development of segmentation techniques for human brain structures and of the visualisation of such structures. The images of the brain are both anatomical images (magnet resonance imaging (MRI) and autoradiography) and functional images that show blood flow (functional magnetic imaging (fMRI), positron emission tomography (PET), and single photon emission tomography (SPECT)). When working with anatomical images, the structures segmented are visible as different parts of the brain, e.g. the brain cortex, the hippocampus, or the amygdala. In functional images, the activity or the blood flow that be seen. Grey-level morphology methods are used in the segmentations to make tissue types in the images more homogenous and minimise difficulties with connections to outside tissue. A method for automatic histogram thresholding is also used. Furthermore, there are binary operations such as logic operation between masks and binary morphology operations. The visualisation of the segmented structures uses either surface rendering or volume rendering. For the visualisation of thin structures, surface rendering is the better choice since otherwise some voxels might be missed. It is possible to display activation from a functional image on the surface of a segmented cortex. A new method for autoradiographic images has been developed, which integrates registration, background compensation, and automatic thresholding to get faster and more reliable results than the standard techniques give.

  4. Segmentation and Visualisation of Human Brain Structures

    International Nuclear Information System (INIS)

    Hult, Roger

    2003-01-01

    In this thesis the focus is mainly on the development of segmentation techniques for human brain structures and of the visualisation of such structures. The images of the brain are both anatomical images (magnet resonance imaging (MRI) and autoradiography) and functional images that show blood flow (functional magnetic imaging (fMRI), positron emission tomography (PET), and single photon emission tomography (SPECT)). When working with anatomical images, the structures segmented are visible as different parts of the brain, e.g. the brain cortex, the hippocampus, or the amygdala. In functional images, the activity or the blood flow that be seen. Grey-level morphology methods are used in the segmentations to make tissue types in the images more homogenous and minimise difficulties with connections to outside tissue. A method for automatic histogram thresholding is also used. Furthermore, there are binary operations such as logic operation between masks and binary morphology operations. The visualisation of the segmented structures uses either surface rendering or volume rendering. For the visualisation of thin structures, surface rendering is the better choice since otherwise some voxels might be missed. It is possible to display activation from a functional image on the surface of a segmented cortex. A new method for autoradiographic images has been developed, which integrates registration, background compensation, and automatic thresholding to get faster and more reliable results than the standard techniques give

  5. Construction of Korean male tomographic model segmented from PET-CT data

    International Nuclear Information System (INIS)

    Lee, Choon Sik; Park, Sang Kyun; Lee, Jai Ki

    2004-01-01

    Tomographic human models provide currently the most realistic representation of human anatomy for radiation dosimetry calculation. Most of the models have been constructed by using computed tomographic (CT) or magnetic resonance (MR) images obtained from a single individual. Each scan has its inherent advantages and disadvantages. CT scan gives a considerable radiation dose to a subject, and MR scan takes too long time to get clear images of an immobile subject. An emerging source of medical images for the construction of tomographic models is PET-CT, which is performed when looking for cancer. In this study, a tomographic model of Korean adult male was developed by processing whole-body CT images of a PET-CT-scanned healthy volunteer. The 343 slices of the CT images were semi-automatically segmented layer by layer using a graphic software and screen digitizer. The 3rd Korean tomographic model, named KRMAN-2, consisting of 300x150x344 voxels of a size of 2x2x5mm 3 , was constructed. Examples of application to Monte Carlo radiation dosimetry calculation in idealized whole-body irradiations were given and discussed

  6. The speech signal segmentation algorithm using pitch synchronous analysis

    Directory of Open Access Journals (Sweden)

    Amirgaliyev Yedilkhan

    2017-03-01

    Full Text Available Parameterization of the speech signal using the algorithms of analysis synchronized with the pitch frequency is discussed. Speech parameterization is performed by the average number of zero transitions function and the signal energy function. Parameterization results are used to segment the speech signal and to isolate the segments with stable spectral characteristics. Segmentation results can be used to generate a digital voice pattern of a person or be applied in the automatic speech recognition. Stages needed for continuous speech segmentation are described.

  7. Effects of anatomy and diet on gastrointestinal pH in rodents.

    Science.gov (United States)

    Kohl, Kevin D; Stengel, Ashley; Samuni-Blank, Michal; Dearing, M Denise

    2013-04-01

    The pH of the gastrointestinal tract can have profound influences on digestive processes. Rodents exhibit wide variation in both stomach morphology and dietary strategies, both of which may influence gut pH. Various rodent species have evolved bilocular (or semi-segmented) stomachs that may allow for more microbial growth compared to unilocular (single-chambered) stomachs. Additionally, herbivory has evolved multiple times in rodents. The high dietary fiber typical of an herbivorous diet is known to induce secretion of bicarbonate in the gut. We predicted that stomach segmentation might facilitate the separation of contents in the proximal chamber from that of the gastric stomach, facilitating a chemical environment suitable to microbial growth. To investigate the effect of stomach anatomy and diet on gut pH, several species of rodent with varying stomach morphology were fed either a high or low-fiber diet for 7 days, and pH of the proximal stomach, gastric stomach, small intestine, and cecum were measured. We discovered that rodents with bilocular stomach anatomy maintained a larger pH gradient between the proximal and gastric stomach compartments, and were able to achieve a lower absolute gastric pH compared to those with unilocular stomachs. Dietary fiber increased the pH of the small intestine, but not in any other gut regions. The stomach pH data supports the century old hypothesis that bilocular stomach anatomy creates an environment in the proximal stomach that is suitable for microbial growth. Additionally, the alkaline small intestinal pH on a high fiber diet may enhance digestion. Copyright © 2013 Wiley Periodicals, Inc.

  8. Interactive-cut: Real-time feedback segmentation for translational research.

    Science.gov (United States)

    Egger, Jan; Lüddemann, Tobias; Schwarzenberg, Robert; Freisleben, Bernd; Nimsky, Christopher

    2014-06-01

    In this contribution, a scale-invariant image segmentation algorithm is introduced that "wraps" the algorithm's parameters for the user by its interactive behavior, avoiding the definition of "arbitrary" numbers that the user cannot really understand. Therefore, we designed a specific graph-based segmentation method that only requires a single seed-point inside the target-structure from the user and is thus particularly suitable for immediate processing and interactive, real-time adjustments by the user. In addition, color or gray value information that is needed for the approach can be automatically extracted around the user-defined seed point. Furthermore, the graph is constructed in such a way, so that a polynomial-time mincut computation can provide the segmentation result within a second on an up-to-date computer. The algorithm presented here has been evaluated with fixed seed points on 2D and 3D medical image data, such as brain tumors, cerebral aneurysms and vertebral bodies. Direct comparison of the obtained automatic segmentation results with costlier, manual slice-by-slice segmentations performed by trained physicians, suggest a strong medical relevance of this interactive approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Interactive lung segmentation in abnormal human and animal chest CT scans

    International Nuclear Information System (INIS)

    Kockelkorn, Thessa T. J. P.; Viergever, Max A.; Schaefer-Prokop, Cornelia M.; Bozovic, Gracijela; Muñoz-Barrutia, Arrate; Rikxoort, Eva M. van; Brown, Matthew S.; Jong, Pim A. de; Ginneken, Bram van

    2014-01-01

    Purpose: Many medical image analysis systems require segmentation of the structures of interest as a first step. For scans with gross pathology, automatic segmentation methods may fail. The authors’ aim is to develop a versatile, fast, and reliable interactive system to segment anatomical structures. In this study, this system was used for segmenting lungs in challenging thoracic computed tomography (CT) scans. Methods: In volumetric thoracic CT scans, the chest is segmented and divided into 3D volumes of interest (VOIs), containing voxels with similar densities. These VOIs are automatically labeled as either lung tissue or nonlung tissue. The automatic labeling results can be corrected using an interactive or a supervised interactive approach. When using the supervised interactive system, the user is shown the classification results per slice, whereupon he/she can adjust incorrect labels. The system is retrained continuously, taking the corrections and approvals of the user into account. In this way, the system learns to make a better distinction between lung tissue and nonlung tissue. When using the interactive framework without supervised learning, the user corrects all incorrectly labeled VOIs manually. Both interactive segmentation tools were tested on 32 volumetric CT scans of pigs, mice and humans, containing pulmonary abnormalities. Results: On average, supervised interactive lung segmentation took under 9 min of user interaction. Algorithm computing time was 2 min on average, but can easily be reduced. On average, 2.0% of all VOIs in a scan had to be relabeled. Lung segmentation using the interactive segmentation method took on average 13 min and involved relabeling 3.0% of all VOIs on average. The resulting segmentations correspond well to manual delineations of eight axial slices per scan, with an average Dice similarity coefficient of 0.933. Conclusions: The authors have developed two fast and reliable methods for interactive lung segmentation in

  10. Mammogram segmentation using maximal cell strength updation in cellular automata.

    Science.gov (United States)

    Anitha, J; Peter, J Dinesh

    2015-08-01

    Breast cancer is the most frequently diagnosed type of cancer among women. Mammogram is one of the most effective tools for early detection of the breast cancer. Various computer-aided systems have been introduced to detect the breast cancer from mammogram images. In a computer-aided diagnosis system, detection and segmentation of breast masses from the background tissues is an important issue. In this paper, an automatic segmentation method is proposed to identify and segment the suspicious mass regions of mammogram using a modified transition rule named maximal cell strength updation in cellular automata (CA). In coarse-level segmentation, the proposed method performs an adaptive global thresholding based on the histogram peak analysis to obtain the rough region of interest. An automatic seed point selection is proposed using gray-level co-occurrence matrix-based sum average feature in the coarse segmented image. Finally, the method utilizes CA with the identified initial seed point and the modified transition rule to segment the mass region. The proposed approach is evaluated over the dataset of 70 mammograms with mass from mini-MIAS database. Experimental results show that the proposed approach yields promising results to segment the mass region in the mammograms with the sensitivity of 92.25% and accuracy of 93.48%.

  11. Segmenting Student Markets with a Student Satisfaction and Priorities Survey.

    Science.gov (United States)

    Borden, Victor M. H.

    1995-01-01

    A market segmentation analysis of 872 university students compared 2 hierarchical clustering procedures for deriving market segments: 1 using matching-type measures and an agglomerative clustering algorithm, and 1 using the chi-square based automatic interaction detection. Results and implications for planning, evaluating, and improving academic…

  12. Automatic labeling of MR brain images through extensible learning and atlas forests.

    Science.gov (United States)

    Xu, Lijun; Liu, Hong; Song, Enmin; Yan, Meng; Jin, Renchao; Hung, Chih-Cheng

    2017-12-01

    Multiatlas-based method is extensively used in MR brain images segmentation because of its simplicity and robustness. This method provides excellent accuracy although it is time consuming and limited in terms of obtaining information about new atlases. In this study, an automatic labeling of MR brain images through extensible learning and atlas forest is presented to address these limitations. We propose an extensible learning model which allows the multiatlas-based framework capable of managing the datasets with numerous atlases or dynamic atlas datasets and simultaneously ensure the accuracy of automatic labeling. Two new strategies are used to reduce the time and space complexity and improve the efficiency of the automatic labeling of brain MR images. First, atlases are encoded to atlas forests through random forest technology to reduce the time consumed for cross-registration between atlases and target image, and a scatter spatial vector is designed to eliminate errors caused by inaccurate registration. Second, an atlas selection method based on the extensible learning model is used to select atlases for target image without traversing the entire dataset and then obtain the accurate labeling. The labeling results of the proposed method were evaluated in three public datasets, namely, IBSR, LONI LPBA40, and ADNI. With the proposed method, the dice coefficient metric values on the three datasets were 84.17 ± 4.61%, 83.25 ± 4.29%, and 81.88 ± 4.53% which were 5% higher than those of the conventional method, respectively. The efficiency of the extensible learning model was evaluated by state-of-the-art methods for labeling of MR brain images. Experimental results showed that the proposed method could achieve accurate labeling for MR brain images without traversing the entire datasets. In the proposed multiatlas-based method, extensible learning and atlas forests were applied to control the automatic labeling of brain anatomies on large atlas datasets or dynamic

  13. [Application of GVF snake model in segmentation of whole body bone SPECT image].

    Science.gov (United States)

    Zhu, Chunmei; Tian, Lianfang; Chen, Ping; Wang, Lifei; Ye, Guangchun; Mao, Zongyuan

    2008-02-01

    Limited by the imaging principle of whole body bone SPECT image, the gray value of bladder area is quite high, which affects the image's brightness, contrast and readability. In the meantime, the similarity between bladder area and focus makes it difficult for some images to be segmented automatically. In this paper, an improved Snake model, GVF Snake, is adopted to automatically segment bladder area, preparing for further processing of whole body bone SPECT images.

  14. A digital database of wrist bone anatomy and carpal kinematics.

    Science.gov (United States)

    Moore, Douglas C; Crisco, Joseph J; Trafton, Theodore G; Leventhal, Evan L

    2007-01-01

    The skeletal wrist consists of eight small, intricately shaped carpal bones. The motion of these bones is complex, occurs in three dimensions, and remains incompletely defined. Our previous efforts have been focused on determining the in vivo three-dimensional (3-D) kinematics of the normal and abnormal carpus. In so doing we have developed an extensive database of carpal bone anatomy and kinematics from a large number of healthy subjects. The purpose of this paper is to describe that database and to make it available to other researchers. CT volume images of both wrists from 30 healthy volunteers (15 males and 15 females) were acquired in multiple wrist positions throughout the normal range of wrist motion. The outer cortical surfaces of the carpal bones, radius and ulna, and proximal metacarpals were segmented and the 3-D motion of each bone was calculated for each wrist position. The database was constructed to include high-resolution surface models, measures of bone volume and shape, and the 3-D kinematics of each segmented bone. The database does not include soft tissues of the wrist. While there are numerous digital anatomical databases, this one is unique in that it includes a large number of subjects and it contains in vivo kinematic data as well as the bony anatomy.

  15. A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.

    Directory of Open Access Journals (Sweden)

    Yan Liu

    Full Text Available Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS treatment planning. In this work, we developed a deep learning convolutional neural network (CNN algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.

  16. Skeleton-based Hierarchical Shape Segmentation

    NARCIS (Netherlands)

    Reniers, Dennie; Telea, Alexandru

    2007-01-01

    We present an effective framework for segmenting 3D shapes into meaningful components using the curve skeleton. Our algorithm identifies a number of critical points on the curve skeleton, either fully automatically as the junctions of the curve skeleton, or based on user input. We use these points

  17. 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set.

    Science.gov (United States)

    Popuri, Karteek; Cobzas, Dana; Murtha, Albert; Jägersand, Martin

    2012-07-01

    Brain tumor segmentation is a required step before any radiation treatment or surgery. When performed manually, segmentation is time consuming and prone to human errors. Therefore, there have been significant efforts to automate the process. But, automatic tumor segmentation from MRI data is a particularly challenging task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. In our work, we propose an automatic brain tumor segmentation method that addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multidimensional feature set. Then, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this work is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned region statistics in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters from the normal brain region to be in the tumor region. This leads to a better disambiguation of the tumor from brain tissue. We evaluated the performance of our automatic segmentation method on 15 real MRI scans of brain tumor patients, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Validation with the expert segmentation labels yielded encouraging results: Jaccard (58%), Precision (81%), Recall (67%), Hausdorff distance (24 mm). Using priors on the brain/tumor appearance, our proposed automatic 3D variational

  18. Automatic Moving Object Segmentation for Freely Moving Cameras

    Directory of Open Access Journals (Sweden)

    Yanli Wan

    2014-01-01

    Full Text Available This paper proposes a new moving object segmentation algorithm for freely moving cameras which is very common for the outdoor surveillance system, the car build-in surveillance system, and the robot navigation system. A two-layer based affine transformation model optimization method is proposed for camera compensation purpose, where the outer layer iteration is used to filter the non-background feature points, and the inner layer iteration is used to estimate a refined affine model based on the RANSAC method. Then the feature points are classified into foreground and background according to the detected motion information. A geodesic based graph cut algorithm is then employed to extract the moving foreground based on the classified features. Unlike the existing global optimization or the long term feature point tracking based method, our algorithm only performs on two successive frames to segment the moving foreground, which makes it suitable for the online video processing applications. The experiment results demonstrate the effectiveness of our algorithm in both of the high accuracy and the fast speed.

  19. Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

    Science.gov (United States)

    Nguyen, Thanh; Bui, Vy; Lam, Van; Raub, Christopher B; Chang, Lin-Ching; Nehmetallah, George

    2017-06-26

    We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

  20. A Dirichlet process mixture model for automatic {sup 18}F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions

    Energy Technology Data Exchange (ETDEWEB)

    Giri, Maria Grazia, E-mail: mariagrazia.giri@ospedaleuniverona.it; Cavedon, Carlo [Medical Physics Unit, University Hospital of Verona, P.le Stefani 1, Verona 37126 (Italy); Mazzarotto, Renzo [Radiation Oncology Unit, University Hospital of Verona, P.le Stefani 1, Verona 37126 (Italy); Ferdeghini, Marco [Nuclear Medicine Unit, University Hospital of Verona, P.le Stefani 1, Verona 37126 (Italy)

    2016-05-15

    Purpose: The aim of this study was to implement a Dirichlet process mixture (DPM) model for automatic tumor edge identification on {sup 18}F-fluorodeoxyglucose positron emission tomography ({sup 18}F-FDG PET) images by optimizing the parameters on which the algorithm depends, to validate it experimentally, and to test its robustness. Methods: The DPM model belongs to the class of the Bayesian nonparametric models and uses the Dirichlet process prior for flexible nonparametric mixture modeling, without any preliminary choice of the number of mixture components. The DPM algorithm implemented in the statistical software package R was used in this work. The contouring accuracy was evaluated on several image data sets: on an IEC phantom (spherical inserts with diameter in the range 10–37 mm) acquired by a Philips Gemini Big Bore PET-CT scanner, using 9 different target-to-background ratios (TBRs) from 2.5 to 70; on a digital phantom simulating spherical/uniform lesions and tumors, irregular in shape and activity; and on 20 clinical cases (10 lung and 10 esophageal cancer patients). The influence of the DPM parameters on contour generation was studied in two steps. In the first one, only the IEC spheres having diameters of 22 and 37 mm and a sphere of the digital phantom (41.6 mm diameter) were studied by varying the main parameters until the diameter of the spheres was obtained within 0.2% of the true value. In the second step, the results obtained for this training set were applied to the entire data set to determine DPM based volumes of all available lesions. These volumes were compared to those obtained by applying already known algorithms (Gaussian mixture model and gradient-based) and to true values, when available. Results: Only one parameter was found able to significantly influence segmentation accuracy (ANOVA test). This parameter was linearly connected to the uptake variance of the tested region of interest (ROI). In the first step of the study, a

  1. Hierarchical layered and semantic-based image segmentation using ergodicity map

    Science.gov (United States)

    Yadegar, Jacob; Liu, Xiaoqing

    2010-04-01

    Image segmentation plays a foundational role in image understanding and computer vision. Although great strides have been made and progress achieved on automatic/semi-automatic image segmentation algorithms, designing a generic, robust, and efficient image segmentation algorithm is still challenging. Human vision is still far superior compared to computer vision, especially in interpreting semantic meanings/objects in images. We present a hierarchical/layered semantic image segmentation algorithm that can automatically and efficiently segment images into hierarchical layered/multi-scaled semantic regions/objects with contextual topological relationships. The proposed algorithm bridges the gap between high-level semantics and low-level visual features/cues (such as color, intensity, edge, etc.) through utilizing a layered/hierarchical ergodicity map, where ergodicity is computed based on a space filling fractal concept and used as a region dissimilarity measurement. The algorithm applies a highly scalable, efficient, and adaptive Peano- Cesaro triangulation/tiling technique to decompose the given image into a set of similar/homogenous regions based on low-level visual cues in a top-down manner. The layered/hierarchical ergodicity map is built through a bottom-up region dissimilarity analysis. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level of detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanisms for contextual topological object/region relationship generation. Experiments have been conducted within the maritime image environment where the segmented layered semantic objects include the basic level objects (i.e. sky/land/water) and deeper level objects in the sky/land/water surfaces. Experimental results demonstrate the proposed algorithm has the capability to robustly and efficiently segment images into layered semantic objects

  2. Automatic generation of pictorial transcripts of video programs

    Science.gov (United States)

    Shahraray, Behzad; Gibbon, David C.

    1995-03-01

    An automatic authoring system for the generation of pictorial transcripts of video programs which are accompanied by closed caption information is presented. A number of key frames, each of which represents the visual information in a segment of the video (i.e., a scene), are selected automatically by performing a content-based sampling of the video program. The textual information is recovered from the closed caption signal and is initially segmented based on its implied temporal relationship with the video segments. The text segmentation boundaries are then adjusted, based on lexical analysis and/or caption control information, to account for synchronization errors due to possible delays in the detection of scene boundaries or the transmission of the caption information. The closed caption text is further refined through linguistic processing for conversion to lower- case with correct capitalization. The key frames and the related text generate a compact multimedia presentation of the contents of the video program which lends itself to efficient storage and transmission. This compact representation can be viewed on a computer screen, or used to generate the input to a commercial text processing package to generate a printed version of the program.

  3. Localized Segment Based Processing for Automatic Building Extraction from LiDAR Data

    Science.gov (United States)

    Parida, G.; Rajan, K. S.

    2017-05-01

    The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

  4. LOCALIZED SEGMENT BASED PROCESSING FOR AUTOMATIC BUILDING EXTRACTION FROM LiDAR DATA

    Directory of Open Access Journals (Sweden)

    G. Parida

    2017-05-01

    Full Text Available The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

  5. Automatic evaluation of vessel diameter variation from 2D X-ray angiography.

    Science.gov (United States)

    M'hiri, Faten; Duong, Luc; Desrosiers, Christian; Dahdah, Nagib; Miró, Joaquim; Cheriet, Mohamed

    2017-11-01

    Early detection of blood vessel pathologies can be made through the evaluation of functional and structural abnormalities in the arteries, including the arterial distensibility measure. We propose a feasibility study on computing arterial distensibility automatically from monoplane 2D X-ray sequences for both small arteries (such as coronary arteries) and larger arteries (such as the aorta). To compute the distensibility measure, three steps were developed: First, the segment of an artery is extracted using our graph-based segmentation method. Then, the same segment is tracked in the moving sequence using our spatio-temporal segmentation method: the Temporal Vessel Walker. Finally, the diameter of the artery is measured automatically at each frame of the sequence based on the segmentation results. The method was evaluated using one simulated sequence and 4 patients' angiograms depicting the coronary arteries and three depicting the ascending aorta. Results of the simulated sequence achieved a Dice index of 98%, with a mean squared error in diameter measurement of [Formula: see text] mm. Results obtained from patients' X-ray sequences are consistent with manual assessment of the diameter by experts. The proposed method measures changes in diameter of a specific segment of a blood vessel during the cardiac sequence, automatically based on monoplane 2D X-ray sequence. Such information might become a key to help physicians in the detection of variations of arterial stiffness associated with early stages of various vasculopathies.

  6. Multiscale CNNs for Brain Tumor Segmentation and Diagnosis

    Directory of Open Access Journals (Sweden)

    Liya Zhao

    2016-01-01

    Full Text Available Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs. Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness.

  7. Automatic assessment of volume asymmetries applied to hip abductor muscles in patients with hip arthroplasty

    Science.gov (United States)

    Klemt, Christian; Modat, Marc; Pichat, Jonas; Cardoso, M. J.; Henckel, Joahnn; Hart, Alister; Ourselin, Sebastien

    2015-03-01

    Metal-on-metal (MoM) hip arthroplasties have been utilised over the last 15 years to restore hip function for 1.5 million patients worldwide. Althoug widely used, this hip arthroplasty releases metal wear debris which lead to muscle atrophy. The degree of muscle wastage differs across patients ranging from mild to severe. The longterm outcomes for patients with MoM hip arthroplasty are reduced for increasing degrees of muscle atrophy, highlighting the need to automatically segment pathological muscles. The automated segmentation of pathological soft tissues is challenging as these lack distinct boundaries and morphologically differ across subjects. As a result, there is no method reported in the literature which has been successfully applied to automatically segment pathological muscles. We propose the first automated framework to delineate severely atrophied muscles by applying a novel automated segmentation propagation framework to patients with MoM hip arthroplasty. The proposed algorithm was used to automatically quantify muscle wastage in these patients.

  8. Comparison of liver volumetry on contrast-enhanced CT images: one semiautomatic and two automatic approaches.

    Science.gov (United States)

    Cai, Wei; He, Baochun; Fan, Yingfang; Fang, Chihua; Jia, Fucang

    2016-11-08

    This study was to evaluate the accuracy, consistency, and efficiency of three liver volumetry methods- one interactive method, an in-house-developed 3D medical Image Analysis (3DMIA) system, one automatic active shape model (ASM)-based segmentation, and one automatic probabilistic atlas (PA)-guided segmentation method on clinical contrast-enhanced CT images. Forty-two datasets, including 27 normal liver and 15 space-occupying liver lesion patients, were retrospectively included in this study. The three methods - one semiautomatic 3DMIA, one automatic ASM-based, and one automatic PA-based liver volumetry - achieved an accuracy with VD (volume difference) of -1.69%, -2.75%, and 3.06% in the normal group, respectively, and with VD of -3.20%, -3.35%, and 4.14% in the space-occupying lesion group, respectively. However, the three methods achieved an efficiency of 27.63 mins, 1.26 mins, 1.18 mins on average, respectively, compared with the manual volumetry, which took 43.98 mins. The high intraclass correlation coefficient between the three methods and the manual method indicated an excel-lent agreement on liver volumetry. Significant differences in segmentation time were observed between the three methods (3DMIA, ASM, and PA) and the manual volumetry (p volumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (p < 0.001). The semiautomatic interactive 3DMIA, automatic ASM-based, and automatic PA-based liver volum-etry agreed well with manual gold standard in both the normal liver group and the space-occupying lesion group. The ASM- and PA-based automatic segmentation have better efficiency in clinical use. © 2016 The Authors.

  9. Chest wall segmentation in automated 3D breast ultrasound scans.

    Science.gov (United States)

    Tan, Tao; Platel, Bram; Mann, Ritse M; Huisman, Henkjan; Karssemeijer, Nico

    2013-12-01

    In this paper, we present an automatic method to segment the chest wall in automated 3D breast ultrasound images. Determining the location of the chest wall in automated 3D breast ultrasound images is necessary in computer-aided detection systems to remove automatically detected cancer candidates beyond the chest wall and it can be of great help for inter- and intra-modal image registration. We show that the visible part of the chest wall in an automated 3D breast ultrasound image can be accurately modeled by a cylinder. We fit the surface of our cylinder model to a set of automatically detected rib-surface points. The detection of the rib-surface points is done by a classifier using features representing local image intensity patterns and presence of rib shadows. Due to attenuation of the ultrasound signal, a clear shadow is visible behind the ribs. Evaluation of our segmentation method is done by computing the distance of manually annotated rib points to the surface of the automatically detected chest wall. We examined the performance on images obtained with the two most common 3D breast ultrasound devices in the market. In a dataset of 142 images, the average mean distance of the annotated points to the segmented chest wall was 5.59 ± 3.08 mm. Copyright © 2012 Elsevier B.V. All rights reserved.

  10. Automated segmentation and recognition of abdominal wall muscles in X-ray torso CT images and its application in abdominal CAD

    International Nuclear Information System (INIS)

    Zhou, X.; Kamiya, N.; Hara, T.; Fujita, H.; Chen, H.; Yokoyama, R.; Hoshi, H.

    2007-01-01

    The information of abdominal wall is very important for the planning of surgical operation and abdominal organ recognition. In research fields of computer assisted radiology and surgery and computer-aided diagnosis, the segmentation and recognition of the abdominal wall muscles in CT images is a necessary pre-processing step. Due to the complexity of the abdominal wall structure and indistinctive in CT images, the automated segmentation of abdominal wall muscles is a difficult issue and has not been solved completely. We propose an approach to segment the abdominal wall muscles and divide it into three categories (front abdominal muscles including rectus abdominis; left and right side abdominal muscles including external oblique, internal oblique and transversus abdominis muscles) automatically. The approach, first, makes an initial classification of bone, fat, and muscles and organs based on the CT number. Then a layer structure is generated to describe the 3-D anatomical structures of human torso by stretching the torso region onto a thin-plate for easy recognition. The abdominal wall muscles are recognized on the layer structures using the spatial relations to the skeletal structure and CT numbers. Finally, the recognized regions are mapped back to the 3-D CT images using an inverse transformation of the stretching process. This method is applied to 20 cases of torso CT images and evaluations are based on visual comparison of the recognition results and the original CT images by an expert in anatomy. The results show that our approach can segment and recognize abdominal wall muscle regions effectively. (orig.)

  11. Automatic quantification of mammary glands on non-contrast x-ray CT by using a novel segmentation approach

    Science.gov (United States)

    Zhou, Xiangrong; Kano, Takuya; Cai, Yunliang; Li, Shuo; Zhou, Xinxin; Hara, Takeshi; Yokoyama, Ryujiro; Fujita, Hiroshi

    2016-03-01

    This paper describes a brand new automatic segmentation method for quantifying volume and density of mammary gland regions on non-contrast CT images. The proposed method uses two processing steps: (1) breast region localization, and (2) breast region decomposition to accomplish a robust mammary gland segmentation task on CT images. The first step detects two minimum bounding boxes of left and right breast regions, respectively, based on a machine-learning approach that adapts to a large variance of the breast appearances on different age levels. The second step divides the whole breast region in each side into mammary gland, fat tissue, and other regions by using spectral clustering technique that focuses on intra-region similarities of each patient and aims to overcome the image variance caused by different scan-parameters. The whole approach is designed as a simple structure with very minimum number of parameters to gain a superior robustness and computational efficiency for real clinical setting. We applied this approach to a dataset of 300 CT scans, which are sampled with the equal number from 30 to 50 years-old-women. Comparing to human annotations, the proposed approach can measure volume and quantify distributions of the CT numbers of mammary gland regions successfully. The experimental results demonstrated that the proposed approach achieves results consistent with manual annotations. Through our proposed framework, an efficient and effective low cost clinical screening scheme may be easily implemented to predict breast cancer risk, especially on those already acquired scans.

  12. Probabilistic Segmentation of Folk Music Recordings

    Directory of Open Access Journals (Sweden)

    Ciril Bohak

    2016-01-01

    Full Text Available The paper presents a novel method for automatic segmentation of folk music field recordings. The method is based on a distance measure that uses dynamic time warping to cope with tempo variations and a dynamic programming approach to handle pitch drifting for finding similarities and estimating the length of repeating segment. A probabilistic framework based on HMM is used to find segment boundaries, searching for optimal match between the expected segment length, between-segment similarities, and likely locations of segment beginnings. Evaluation of several current state-of-the-art approaches for segmentation of commercial music is presented and their weaknesses when dealing with folk music are exposed, such as intolerance to pitch drift and variable tempo. The proposed method is evaluated and its performance analyzed on a collection of 206 folk songs of different ensemble types: solo, two- and three-voiced, choir, instrumental, and instrumental with singing. It outperforms current commercial music segmentation methods for noninstrumental music and is on a par with the best for instrumental recordings. The method is also comparable to a more specialized method for segmentation of solo singing folk music recordings.

  13. Automatic tissue segmentation of head and neck MR images for hyperthermia treatment planning

    International Nuclear Information System (INIS)

    Fortunati, Valerio; Niessen, Wiro J; Veenland, Jifke F; Van Walsum, Theo; Verhaart, René F; Paulides, Margarethus M

    2015-01-01

    A hyperthermia treatment requires accurate, patient-specific treatment planning. This planning is based on 3D anatomical models which are generally derived from computed tomography. Because of its superior soft tissue contrast, magnetic resonance imaging (MRI) information can be introduced to improve the quality of these 3D patient models and therefore the treatment planning itself. Thus, we present here an automatic atlas-based segmentation algorithm for MR images of the head and neck.Our method combines multiatlas local weighting fusion with intensity modelling. The accuracy of the method was evaluated using a leave-one-out cross validation experiment over a set of 11 patients for which manual delineation were available.The accuracy of the proposed method was high both in terms of the Dice similarity coefficient (DSC) and the 95th percentile Hausdorff surface distance (HSD) with median DSC higher than 0.8 for all tissues except sclera. For all tissues, except the spine tissues, the accuracy was approaching the interobserver agreement/variability both in terms of DSC and HSD. The positive effect of adding the intensity modelling to the multiatlas fusion decreased when a more accurate atlas fusion method was used.Using the proposed approach we improved the performance of the approach previously presented for H and N hyperthermia treatment planning, making the method suitable for clinical application. (paper)

  14. Automatic tissue segmentation of head and neck MR images for hyperthermia treatment planning

    Science.gov (United States)

    Fortunati, Valerio; Verhaart, René F.; Niessen, Wiro J.; Veenland, Jifke F.; Paulides, Margarethus M.; van Walsum, Theo

    2015-08-01

    A hyperthermia treatment requires accurate, patient-specific treatment planning. This planning is based on 3D anatomical models which are generally derived from computed tomography. Because of its superior soft tissue contrast, magnetic resonance imaging (MRI) information can be introduced to improve the quality of these 3D patient models and therefore the treatment planning itself. Thus, we present here an automatic atlas-based segmentation algorithm for MR images of the head and neck. Our method combines multiatlas local weighting fusion with intensity modelling. The accuracy of the method was evaluated using a leave-one-out cross validation experiment over a set of 11 patients for which manual delineation were available. The accuracy of the proposed method was high both in terms of the Dice similarity coefficient (DSC) and the 95th percentile Hausdorff surface distance (HSD) with median DSC higher than 0.8 for all tissues except sclera. For all tissues, except the spine tissues, the accuracy was approaching the interobserver agreement/variability both in terms of DSC and HSD. The positive effect of adding the intensity modelling to the multiatlas fusion decreased when a more accurate atlas fusion method was used. Using the proposed approach we improved the performance of the approach previously presented for H&N hyperthermia treatment planning, making the method suitable for clinical application.

  15. Track segment synthesis method for NTA film

    International Nuclear Information System (INIS)

    Kumazawa, Shigeru

    1980-03-01

    A method is presented for synthesizing track segments extracted from a gray-level digital picture of NTA film in automatic counting system. In order to detect each track in an arbitrary direction, even if it has some gaps, as a set of the track segments, the method links extracted segments along the track, in succession, to the linked track segments, according to whether each extracted segment bears a similarity of direction to the track or not and whether it is connected with the linked track segments or not. In the case of a large digital picture, the method is applied to each subpicture, which is a strip of the picture, and then concatenates subsets of track segments linked at each subpicture as a set of track segments belonging to a track. The method was applied to detecting tracks in various directions over the eight 364 x 40-pixel subpictures with the gray scale of 127/pixel (picture element) of the microphotograph of NTA film. It was proved to be able to synthesize track segments correctly for every track in the picture. (author)

  16. Integer anatomy

    Energy Technology Data Exchange (ETDEWEB)

    Doolittle, R. [ONR, Arlington, VA (United States)

    1994-11-15

    The title integer anatomy is intended to convey the idea of a systematic method for displaying the prime decomposition of the integers. Just as the biological study of anatomy does not teach us all things about behavior of species neither would we expect to learn everything about the number theory from a study of its anatomy. But, some number-theoretic theorems are illustrated by inspection of integer anatomy, which tend to validate the underlying structure and the form as developed and displayed in this treatise. The first statement to be made in this development is: the way structure of the natural numbers is displayed depends upon the allowed operations.

  17. Automatic segmentation of tumor-laden lung volumes from the LIDC database

    Science.gov (United States)

    O'Dell, Walter G.

    2012-03-01

    The segmentation of the lung parenchyma is often a critical pre-processing step prior to application of computer-aided detection of lung nodules. Segmentation of the lung volume can dramatically decrease computation time and reduce the number of false positive detections by excluding from consideration extra-pulmonary tissue. However, while many algorithms are capable of adequately segmenting the healthy lung, none have been demonstrated to work reliably well on tumor-laden lungs. Of particular challenge is to preserve tumorous masses attached to the chest wall, mediastinum or major vessels. In this role, lung volume segmentation comprises an important computational step that can adversely affect the performance of the overall CAD algorithm. An automated lung volume segmentation algorithm has been developed with the goals to maximally exclude extra-pulmonary tissue while retaining all true nodules. The algorithm comprises a series of tasks including intensity thresholding, 2-D and 3-D morphological operations, 2-D and 3-D floodfilling, and snake-based clipping of nodules attached to the chest wall. It features the ability to (1) exclude trachea and bowels, (2) snip large attached nodules using snakes, (3) snip small attached nodules using dilation, (4) preserve large masses fully internal to lung volume, (5) account for basal aspects of the lung where in a 2-D slice the lower sections appear to be disconnected from main lung, and (6) achieve separation of the right and left hemi-lungs. The algorithm was developed and trained to on the first 100 datasets of the LIDC image database.

  18. TU-AB-BRA-11: Evaluation of Fully Automatic Volumetric GBM Segmentation in the TCGA-GBM Dataset: Prognosis and Correlation with VASARI Features

    International Nuclear Information System (INIS)

    Rios Velazquez, E; Meier, R; Dunn, W; Gutman, D; Alexander, B; Wiest, R; Reyes, M; Bauer, S; Aerts, H

    2015-01-01

    Purpose: Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. Methods: MRI sets of 67 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA), including necrosis, edema, contrast enhancing and non-enhancing tumor. Spearman’s correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Results: Auto-segmented sub-volumes showed high agreement with manually delineated volumes (range (r): 0.65 – 0.91). Also showed higher correlation with VASARI features (auto r = 0.35, 0.60 and 0.59; manual r = 0.29, 0.50, 0.43, for contrast-enhancing, necrosis and edema, respectively). The contrast-enhancing volume and post-contrast abnormal volume showed the highest C-index (0.73 and 0.72), comparable to manually defined volumes (p = 0.22 and p = 0.07, respectively). The non-enhancing region defined by BraTumIA showed a significantly higher prognostic value (CI = 0.71) than the edema (CI = 0.60), both of which could not be distinguished by manual delineation. Conclusion: BraTumIA tumor sub-compartments showed higher correlation with VASARI data, and equivalent performance in terms of prognosis compared to manual sub-volumes. This method can enable more reproducible definition and quantification of imaging based biomarkers and has a large potential in high-throughput medical imaging research

  19. TU-AB-BRA-11: Evaluation of Fully Automatic Volumetric GBM Segmentation in the TCGA-GBM Dataset: Prognosis and Correlation with VASARI Features

    Energy Technology Data Exchange (ETDEWEB)

    Rios Velazquez, E [Dana-Farber Cancer Institute | Harvard Medical School, Boston, MA (United States); Meier, R [Institute for Surgical Technology and Biomechanics, Bern, NA (Switzerland); Dunn, W; Gutman, D [Emory University School of Medicine, Atlanta, GA (United States); Alexander, B [Dana- Farber Cancer Institute, Brigham and Womens Hospital, Harvard Medic, Boston, MA (United States); Wiest, R; Reyes, M [Institute for Surgical Technology and Biomechanics, University of Bern, Bern, NA (Switzerland); Bauer, S [Institute for Surgical Technology and Biomechanics, Support Center for Adva, Bern, NA (Switzerland); Aerts, H [Dana-Farber/Brigham Womens Cancer Center, Boston, MA (United States)

    2015-06-15

    Purpose: Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. Methods: MRI sets of 67 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA), including necrosis, edema, contrast enhancing and non-enhancing tumor. Spearman’s correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Results: Auto-segmented sub-volumes showed high agreement with manually delineated volumes (range (r): 0.65 – 0.91). Also showed higher correlation with VASARI features (auto r = 0.35, 0.60 and 0.59; manual r = 0.29, 0.50, 0.43, for contrast-enhancing, necrosis and edema, respectively). The contrast-enhancing volume and post-contrast abnormal volume showed the highest C-index (0.73 and 0.72), comparable to manually defined volumes (p = 0.22 and p = 0.07, respectively). The non-enhancing region defined by BraTumIA showed a significantly higher prognostic value (CI = 0.71) than the edema (CI = 0.60), both of which could not be distinguished by manual delineation. Conclusion: BraTumIA tumor sub-compartments showed higher correlation with VASARI data, and equivalent performance in terms of prognosis compared to manual sub-volumes. This method can enable more reproducible definition and quantification of imaging based biomarkers and has a large potential in high-throughput medical imaging research.

  20. Digital dissection system for medical school anatomy training

    Science.gov (United States)

    Augustine, Kurt E.; Pawlina, Wojciech; Carmichael, Stephen W.; Korinek, Mark J.; Schroeder, Kathryn K.; Segovis, Colin M.; Robb, Richard A.

    2003-05-01

    images are captured automatically, and then processed to generate a Quicktime VR sequence, which permits users to view an object from multiple angles by rotating it on the screen. This provides 3-D visualizations of anatomy for students without the need for special '3-D glasses' that would be impractical to use in a laboratory setting. In addition, a digital video camera may be mounted on the rig for capturing video recordings of selected dissection procedures being carried out by expert anatomists for playback by the students. Anatomists from the Department of Anatomy at Mayo have captured several sets of dissection sequences and processed them into Quicktime VR sequences. The students are able to look at these specimens from multiple angles using this VR technology. In addition, the student may zoom in to obtain high-resolution close-up views of the specimen. They may interactively view the specimen at varying stages of dissection, providing a way to quickly and intuitively navigate through the layers of tissue. Electronic media has begun to impact all areas of education, but a 3-D interactive visualization of specimen dissections in the laboratory environment is a unique and powerful means of teaching anatomy. When fully implemented, anatomy education will be enhanced significantly by comparison to traditional methods.

  1. Automatic Genre Classification of Musical Signals

    Science.gov (United States)

    Barbedo, Jayme Garcia sArnal; Lopes, Amauri

    2006-12-01

    We present a strategy to perform automatic genre classification of musical signals. The technique divides the signals into 21.3 milliseconds frames, from which 4 features are extracted. The values of each feature are treated over 1-second analysis segments. Some statistical results of the features along each analysis segment are used to determine a vector of summary features that characterizes the respective segment. Next, a classification procedure uses those vectors to differentiate between genres. The classification procedure has two main characteristics: (1) a very wide and deep taxonomy, which allows a very meticulous comparison between different genres, and (2) a wide pairwise comparison of genres, which allows emphasizing the differences between each pair of genres. The procedure points out the genre that best fits the characteristics of each segment. The final classification of the signal is given by the genre that appears more times along all signal segments. The approach has shown very good accuracy even for the lowest layers of the hierarchical structure.

  2. Anatomy of the cerebellopontine angle; Anatomie des Kleinhirnbrueckenwinkels

    Energy Technology Data Exchange (ETDEWEB)

    Grunwald, I.Q.; Papanagiotou, P.; Politi, M.; Reith, W. [Universitaetsklinikum des Saarlandes, Homburg/Saar (Germany). Klinik fuer Diagnostische und Interventionelle Neuroradiologie; Nabhan, A. [Universitaetsklinikum des Saarlandes, Homburg/Saar (Germany). Neurochirurgische Klinik

    2006-03-15

    The cerebellopontine angle (CPA) is an anatomically complex region of the brain. In this article we describe the anatomy of the CPA cisterns, of the internal auditory canal, the topography of the cerebellum and brainstem, and the neurovascular structures of this area. (orig.) [German] Der Kleinhirnbrueckenwinkel ist eine umschriebene anatomische Region. Im diesem Artikel werden die Subarachnoidalraeume im Kleinhirnbrueckenwinkel, die Anatomie der Felsenbeinflaeche, Anatomie und Topographie des Kleinhirns und des Hirnstamms, die arteriellen Beziehungen und venoese Drainage des Kleinhirnbrueckenwinkels besprochen. (orig.)

  3. Safe Zone Quantification of the Third Sacral Segment in Normal and Dysmorphic Sacra.

    Science.gov (United States)

    Hwang, John S; Reilly, Mark C; Shaath, Mohammad K; Changoor, Stuart; Eastman, Jonathan; Routt, Milton Lee Chip; Sirkin, Michael S; Adams, Mark R

    2018-04-01

    To quantify the osseous anatomy of the dysmorphic third sacral segment and assess its ability to accommodate internal fixation. Retrospective chart review of a trauma database. University Level 1 Trauma Center. Fifty-nine patients over the age of 18 with computed tomography scans of the pelvis separated into 2 groups: a group with normal pelvic anatomy and a group with sacral dysmorphism. The sacral osseous area was measured on computed tomography scans in the axial, coronal, and sagittal planes in normal and dysmorphic pelves. These measurements were used to determine the possibility of accommodating a transiliac transsacral screw in the third sacral segment. In the normal group, the S3 coronal transverse width averaged 7.71 mm and the S3 axial transverse width averaged 7.12 mm. The mean S3 cross-sectional area of the normal group was 55.8 mm. The dysmorphic group was found to have a mean S3 coronal transverse width of 9.49 mm, an average S3 axial transverse width of 9.14 mm, and an S3 cross-sectional area of 77.9 mm. The third sacral segment of dysmorphic sacra has a larger osseous pathway available to safely accommodate a transiliac transsacral screw when compared with normal sacra. The S3 segment of dysmorphic sacra can serve as an additional site for screw placement when treating unstable posterior pelvic ring fractures.

  4. Thymus Gland Anatomy

    Science.gov (United States)

    ... historical Searches are case-insensitive Thymus Gland, Adult, Anatomy Add to My Pictures View /Download : Small: 720x576 ... Large: 3000x2400 View Download Title: Thymus Gland, Adult, Anatomy Description: Anatomy of the thymus gland; drawing shows ...

  5. Normal Pancreas Anatomy

    Science.gov (United States)

    ... hyphen, e.g. -historical Searches are case-insensitive Pancreas Anatomy Add to My Pictures View /Download : Small: ... 1586x1534 View Download Large: 3172x3068 View Download Title: Pancreas Anatomy Description: Anatomy of the pancreas; drawing shows ...

  6. Automatic segmentation of blood vessels from retinal fundus images ...

    Indian Academy of Sciences (India)

    The retinal blood vessels were segmented through color space conversion and color channel extraction, image pre-processing, Gabor filtering, image postprocessing, feature construction through application of principal component analysis, k-means clustering and first level classification using Naïve–Bayes classification ...

  7. Segmentation of elongated structures in medical images

    NARCIS (Netherlands)

    Staal, Jozef Johannes

    2004-01-01

    The research described in this thesis concerns the automatic detection, recognition and segmentation of elongated structures in medical images. For this purpose techniques have been developed to detect subdimensional pointsets (e.g. ridges, edges) in images of arbitrary dimension. These

  8. Lung segmentation from HRCT using united geometric active contours

    Science.gov (United States)

    Liu, Junwei; Li, Chuanfu; Xiong, Jin; Feng, Huanqing

    2007-12-01

    Accurate lung segmentation from high resolution CT images is a challenging task due to various detail tracheal structures, missing boundary segments and complex lung anatomy. One popular method is based on gray-level threshold, however its results are usually rough. A united geometric active contours model based on level set is proposed for lung segmentation in this paper. Particularly, this method combines local boundary information and region statistical-based model synchronously: 1) Boundary term ensures the integrality of lung tissue.2) Region term makes the level set function evolve with global characteristic and independent on initial settings. A penalizing energy term is introduced into the model, which forces the level set function evolving without re-initialization. The method is found to be much more efficient in lung segmentation than other methods that are only based on boundary or region. Results are shown by 3D lung surface reconstruction, which indicates that the method will play an important role in the design of computer-aided diagnostic (CAD) system.

  9. Fast prostate segmentation for brachytherapy based on joint fusion of images and labels

    Science.gov (United States)

    Nouranian, Saman; Ramezani, Mahdi; Mahdavi, S. Sara; Spadinger, Ingrid; Morris, William J.; Salcudean, Septimiu E.; Abolmaesumi, Purang

    2014-03-01

    Brachytherapy as one of the treatment methods for prostate cancer takes place by implantation of radioactive seeds inside the gland. The standard of care for this treatment procedure is to acquire transrectal ultrasound images of the prostate which are segmented in order to plan the appropriate seed placement. The segmentation process is usually performed either manually or semi-automatically and is associated with subjective errors because the prostate visibility is limited in ultrasound images. The current segmentation process also limits the possibility of intra-operative delineation of the prostate to perform real-time dosimetry. In this paper, we propose a computationally inexpensive and fully automatic segmentation approach that takes advantage of previously segmented images to form a joint space of images and their segmentations. We utilize joint Independent Component Analysis method to generate a model which is further employed to produce a probability map of the target segmentation. We evaluate this approach on the transrectal ultrasound volume images of 60 patients using a leave-one-out cross-validation approach. The results are compared with the manually segmented prostate contours that were used by clinicians to plan brachytherapy procedures. We show that the proposed approach is fast with comparable accuracy and precision to those found in previous studies on TRUS segmentation.

  10. Colour transformations and K-means segmentation for automatic cloud detection

    Directory of Open Access Journals (Sweden)

    Martin Blazek

    2015-08-01

    Full Text Available The main aim of this work is to find simple criteria for automatic recognition of several meteorological phenomena using optical digital sensors (e.g., Wide-Field cameras, automatic DSLR cameras or robotic telescopes. The output of those sensors is commonly represented in RGB channels containing information about both colour and luminosity even when normalised. Transformation into other colour spaces (e.g., CIE 1931 xyz, CIE L*a*b*, YCbCr can separate colour from luminosity, which is especially useful in the image processing of automatic cloud boundary recognition. Different colour transformations provide different sectorization of cloudy images. Hence, the analysed meteorological phenomena (cloud types, clear sky project differently into the colour diagrams of each international colour systems. In such diagrams, statistical tools can be applied in search of criteria which could determine clear sky from a covered one and possibly even perform a meteorological classification of cloud types. For the purpose of this work, a database of sky images (both clear and cloudy, with emphasis on a variety of different observation conditions (e.g., time, altitude, solar angle, etc. was acquired. The effectiveness of several colour transformations for meteorological application is discussed and the representation of different clouds (or clear sky in those colour systems is analysed. Utilisation of this algorithm would be useful in all-sky surveys, supplementary meteorological observations, solar cell effectiveness predictions or daytime astronomical solar observations.

  11. Liver segment IV hypoplasia as a risk factor for bile duct injury.

    Science.gov (United States)

    Mercado, Miguel Angel; Franssen, Bernardo; Arriola, Juan Carlos; Garcia-Badiola, Artemio; Arámburo, Rigoberto; Elnecavé, Alejandro; Cortés-González, Rubén

    2011-09-01

    Bile duct injury remains constant in the era of laparoscopic cholecystectomy and misidentification of structures remains one of the most common causes of such injuries. Abnormalities in liver segment IV, which is fully visible during laparoscopic cholecystectomy, may contribute to misidentification as proposed herein. We describe the case of a 36-year-old female who had a bile duct injury during a laparoscopic cholecystectomy where the surgeon noticed an unusually small distance between the gallbladder and the round ligament. We define hypoplasia of liver segment IV as well as describe the variation of the biliary anatomy in the case. We also intend to fit it in a broader spectrum of developmental anomalies that have both hyopoplasia of some portion of the liver and variations in gallbladder and bile duct anatomy that may contribute to bile duct injury. To our knowledge, hypoplasia of liver segment IV has not been suggested in the literature as a risk factor for bile duct injury except in the extreme case of a left-sided gallbladder. Surgeons should be vigilant during laparoscopic cholecystectomy when they become aware of an unusually small distance between the gallbladder bed and the round ligament prior to beginning their dissection, variations in the common bile duct and cystic duct should be expected.

  12. Fuzzy clustering-based segmented attenuation correction in whole-body PET

    CERN Document Server

    Zaidi, H; Boudraa, A; Slosman, DO

    2001-01-01

    Segmented-based attenuation correction is now a widely accepted technique to reduce noise contribution of measured attenuation correction. In this paper, we present a new method for segmenting transmission images in positron emission tomography. This reduces the noise on the correction maps while still correcting for differing attenuation coefficients of specific tissues. Based on the Fuzzy C-Means (FCM) algorithm, the method segments the PET transmission images into a given number of clusters to extract specific areas of differing attenuation such as air, the lungs and soft tissue, preceded by a median filtering procedure. The reconstructed transmission image voxels are therefore segmented into populations of uniform attenuation based on the human anatomy. The clustering procedure starts with an over-specified number of clusters followed by a merging process to group clusters with similar properties and remove some undesired substructures using anatomical knowledge. The method is unsupervised, adaptive and a...

  13. Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.

    Science.gov (United States)

    Pereira, Sérgio; Meier, Raphael; McKinley, Richard; Wiest, Roland; Alves, Victor; Silva, Carlos A; Reyes, Mauricio

    2018-02-01

    Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes". In this paper, we propose a methodology to enhance the interpretability of automatically extracted machine learning features. The proposed system is composed of a Restricted Boltzmann Machine for unsupervised feature learning, and a Random Forest classifier, which are combined to jointly consider existing correlations between imaging data, features, and target variables. We define two levels of interpretation: global and local. The former is devoted to understanding if the system learned the relevant relations in the data correctly, while the later is focused on predictions performed on a voxel- and patient-level. In addition, we propose a novel feature importance strategy that considers both imaging data and target variables, and we demonstrate the ability of the approach to leverage the interpretability of the obtained representation for the task at hand. We evaluated the proposed methodology in brain tumor segmentation and penumbra estimation in ischemic stroke lesions. We show the ability of the proposed methodology to unveil information regarding relationships between imaging modalities and extracted features and their usefulness for the task at hand. In both clinical scenarios, we demonstrate that the proposed methodology enhances the interpretability of automatically learned features, highlighting specific learning patterns that resemble how an expert extracts relevant data from medical images. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning

    OpenAIRE

    Salehi, Seyed Sadegh Mohseni; Hashemi, Seyed Raein; Velasco-Annis, Clemente; Ouaalam, Abdelhakim; Estroff, Judy A.; Erdogmus, Deniz; Warfield, Simon K.; Gholipour, Ali

    2017-01-01

    Brain segmentation is a fundamental first step in neuroimage analysis. In the case of fetal MRI, it is particularly challenging and important due to the arbitrary orientation of the fetus, organs that surround the fetal head, and intermittent fetal motion. Several promising methods have been proposed but are limited in their performance in challenging cases and in real-time segmentation. We aimed to develop a fully automatic segmentation method that independently segments sections of the feta...

  15. Automatic, anatomically selective, artifact-free enhancement of digital chest radiographs

    International Nuclear Information System (INIS)

    Sezan, M.I.; Tekalp, A.M.; Schaetzing, R.

    1988-01-01

    The authors propose a technique for automatic, anatomically selective, artifact-free enhancement of digital chest radiographs. Anatomically selective enhancement is motivated by the different enhancement requirements of the lung field and the mediastinum. A recent peak detection algorithm is applied to the image histogram to automatically determine a gray-level threshold between the lung and mediastinum fields. The gray-level threshold facilitates anatomically selective gray-scale modification and unsharp masking. Further, in an attempt to suppress possible white-band artifacts due to unsharp masking at sharp edges, local-contrast adaptivity is incorporated into anatomically selective unsharp masking by designing an anatomy-sensitive emphasis parameter that varied asymmetrically with positive and negative values of the local image contrast

  16. Automatic generation of statistical pose and shape models for articulated joints.

    Science.gov (United States)

    Xin Chen; Graham, Jim; Hutchinson, Charles; Muir, Lindsay

    2014-02-01

    Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for simultaneous registration and segmentation of multiple 3-D (CT or MR) volumes of different subjects at various articulated positions. The framework starts with a pose model generated from 3-D volumes captured at different articulated positions of a single subject (template). This initial pose model is used to register the template volume to image volumes from new subjects. During this process, the Grow-Cut algorithm is used in an iterative refinement of the segmentation of the bone along with the pose parameters. As each new subject is registered and segmented, the pose model is updated, improving the accuracy of successive registrations. We applied the algorithm to CT images of the wrist from 25 subjects, each at five different wrist positions and demonstrated that it performed robustly and accurately. More importantly, the resulting segmentations allowed a statistical pose model of the carpal bones to be generated automatically without interaction. The evaluation results show that our proposed framework achieved accurate registration with an average mean target registration error of 0.34 ±0.27 mm. The automatic segmentation results also show high consistency with the ground truth obtained semi-automatically. Furthermore, we demonstrated the capability of the resulting statistical pose and shape models by using them to generate a measurement tool for scaphoid-lunate dissociation diagnosis, which achieved 90% sensitivity and specificity.

  17. Automatic Segmentation for Reach/frequency Estimation of Newspaper Sections and Internet Papers

    DEFF Research Database (Denmark)

    Mortensen, Peter Stendahl; Arnaa, Kristian

    1999-01-01

    segments are created, individualising the reading probabilities more than when using frequency groups. Two examples are presented: First, an experiment in which heavy users of the Internet are sampled on the Internet itself. The readers of each "Internet paper" are segmented by variables on their use...

  18. Towards automatic music transcription: note extraction based on independent subspace analysis

    Science.gov (United States)

    Wellhausen, Jens; Hoynck, Michael

    2005-01-01

    Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

  19. An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal Images

    Directory of Open Access Journals (Sweden)

    Rasha Al Shehhi

    2016-01-01

    Full Text Available This paper presents a hierarchical graph-based segmentation for blood vessel detection in digital retinal images. This segmentation employs some of perceptual Gestalt principles: similarity, closure, continuity, and proximity to merge segments into coherent connected vessel-like patterns. The integration of Gestalt principles is based on object-based features (e.g., color and black top-hat (BTH morphology and context and graph-analysis algorithms (e.g., Dijkstra path. The segmentation framework consists of two main steps: preprocessing and multiscale graph-based segmentation. Preprocessing is to enhance lighting condition, due to low illumination contrast, and to construct necessary features to enhance vessel structure due to sensitivity of vessel patterns to multiscale/multiorientation structure. Graph-based segmentation is to decrease computational processing required for region of interest into most semantic objects. The segmentation was evaluated on three publicly available datasets. Experimental results show that preprocessing stage achieves better results compared to state-of-the-art enhancement methods. The performance of the proposed graph-based segmentation is found to be consistent and comparable to other existing methods, with improved capability of detecting small/thin vessels.

  20. [Imaging anatomy of the cranial nerves using 3.0 Tesla MRI: a practical review for clinicians].

    Science.gov (United States)

    Chávez-Barba, Oscar; Martínez-Martínez, Lidieth; Cazares-Arellano, José Luis; Martínez-López, Manuel; Roldan-Valadez, Ernesto

    2011-01-01

    Magnetic resonance (MR) imaging is the method of choice to evaluate the cranial nerves (CN). These nerves constitute a group of structures that have acquired during their phylogenetic development a high degree of specialization. There are 12 pairs of CN to which we use their specific name or number. The olfactory (I) and optic (II) pairs are not real nerves but tracts from the encephalon. The spinal nerve (XI) derives from superior cervical segment of the spine. The other 9 pairs of CN are related with the brain stem. Although the skull base foramina can be seen on computed tomography, the nerves themselves can only be visualized in detail on MR. That means, in order to see the different segments of nerves I to XII, the right sequences must be used. It is important to provide detailed clinical information to the radiologist so that a tailored MR study can be performed. In this review, the basic imaging anatomy of the 12 CN is discussed and illustrated briefly with an emphasis on more advanced extra-axial anatomy, illustrated with high-resolution MR images. Clinicians looking for complete anatomic descriptions and/or MR illustrations are advised to consult specialized textbooks considering it is not possible to describe all of the anatomy in one article. This manuscript is intended to be a practical review for clinicians.

  1. Single-shot T1 mapping of the corpus callosum: A rapid characterization of fiber bundle anatomy

    Directory of Open Access Journals (Sweden)

    Sabine eHofer

    2015-05-01

    Full Text Available Using diffusion-tensor MRI and fiber tractography the topographic organization of the corpus callosum (CC has been described to comprise 5 segments with fibers projecting into prefrontal (I, premotor and supplementary motor (II, primary motor (III, and primary sensory areas (IV, as well as into parietal, temporal, and occipital cortical areas (V. In order to more rapidly characterize the underlying anatomy of these segments, this study used a novel single-shot T1 mapping method to quantitatively determine T1 relaxation times in the human CC. A region-of-interest analysis revealed a tendency for the lowest T1 relaxation times in the genu and the highest T1 relaxation times in the somatomotor region of the CC. This observation separates regions dominated by myelinated fibers with large diameters (somatomotor area from densely packed smaller axonal bundles (genu with less myelin. The results indicate that characteristic T1 relaxation times in callosal profiles provide an additional means to monitor differences in fiber anatomy, fiber density, and gray matter in respective neocortical areas. In conclusion, rapid T1 mapping allows for a characterization of the axonal architecture in an individual CC in less than 10 s. The approach emerges as a valuable means for studying neocortical brain anatomy with possible implications for the diagnosis of neurodegenerative processes.

  2. Automatic Model Generation Framework for Computational Simulation of Cochlear Implantation.

    Science.gov (United States)

    Mangado, Nerea; Ceresa, Mario; Duchateau, Nicolas; Kjer, Hans Martin; Vera, Sergio; Dejea Velardo, Hector; Mistrik, Pavel; Paulsen, Rasmus R; Fagertun, Jens; Noailly, Jérôme; Piella, Gemma; González Ballester, Miguel Ángel

    2016-08-01

    Recent developments in computational modeling of cochlear implantation are promising to study in silico the performance of the implant before surgery. However, creating a complete computational model of the patient's anatomy while including an external device geometry remains challenging. To address such a challenge, we propose an automatic framework for the generation of patient-specific meshes for finite element modeling of the implanted cochlea. First, a statistical shape model is constructed from high-resolution anatomical μCT images. Then, by fitting the statistical model to a patient's CT image, an accurate model of the patient-specific cochlea anatomy is obtained. An algorithm based on the parallel transport frame is employed to perform the virtual insertion of the cochlear implant. Our automatic framework also incorporates the surrounding bone and nerve fibers and assigns constitutive parameters to all components of the finite element model. This model can then be used to study in silico the effects of the electrical stimulation of the cochlear implant. Results are shown on a total of 25 models of patients. In all cases, a final mesh suitable for finite element simulations was obtained, in an average time of 94 s. The framework has proven to be fast and robust, and is promising for a detailed prognosis of the cochlear implantation surgery.

  3. Automatic failure identification of the nuclear power plant pellet fuel

    International Nuclear Information System (INIS)

    Oliveira, Adriano Fortunato de

    2010-01-01

    This paper proposed the development of an automatic technique for evaluating defects to help in the stage of fabrication of fuel elements. Was produced an intelligent image analysis for automatic recognition of defects in uranium pellets. Therefore, an Artificial Neural Network (ANN) was trained using segments of histograms of pellets, containing examples of both normal (no fault) and of defectives pellets (with major defects normally found). The images of the pellets were segmented into 11 shares. Histograms were made of these segments and trained the ANN. Besides automating the process, the system was able to obtain this classification accuracy of 98.33%. Although this percentage represents a significant advance ever in the quality control process, the use of more advanced techniques of photography and lighting will reduce it to insignificant levels with low cost. Technologically, the method developed, should it ever be implemented, will add substantial value in terms of process quality control and production outages in relation to domestic manufacturing of nuclear fuel. (author)

  4. SUPERVISED AUTOMATIC HISTOGRAM CLUSTERING AND WATERSHED SEGMENTATION. APPLICATION TO MICROSCOPIC MEDICAL COLOR IMAGES

    Directory of Open Access Journals (Sweden)

    Olivier Lezoray

    2011-05-01

    Full Text Available In this paper, an approach to the segmentation of microscopic color images is addressed, and applied to medical images. The approach combines a clustering method and a region growing method. Each color plane is segmented independently relying on a watershed based clustering of the plane histogram. The marginal segmentation maps intersect in a label concordance map. The latter map is simplified based on the assumption that the color planes are correlated. This produces a simplified label concordance map containing labeled and unlabeled pixels. The formers are used as an image of seeds for a color watershed. This fast and robust segmentation scheme is applied to several types of medical images.

  5. Comparison of liver volumetry on contrast‐enhanced CT images: one semiautomatic and two automatic approaches

    Science.gov (United States)

    Cai, Wei; He, Baochun; Fang, Chihua

    2016-01-01

    This study was to evaluate the accuracy, consistency, and efficiency of three liver volumetry methods— one interactive method, an in‐house‐developed 3D medical Image Analysis (3DMIA) system, one automatic active shape model (ASM)‐based segmentation, and one automatic probabilistic atlas (PA)‐guided segmentation method on clinical contrast‐enhanced CT images. Forty‐two datasets, including 27 normal liver and 15 space‐occupying liver lesion patients, were retrospectively included in this study. The three methods — one semiautomatic 3DMIA, one automatic ASM‐based, and one automatic PA‐based liver volumetry — achieved an accuracy with VD (volume difference) of −1.69%,−2.75%, and 3.06% in the normal group, respectively, and with VD of −3.20%,−3.35%, and 4.14% in the space‐occupying lesion group, respectively. However, the three methods achieved an efficiency of 27.63 mins, 1.26 mins, 1.18 mins on average, respectively, compared with the manual volumetry, which took 43.98 mins. The high intraclass correlation coefficient between the three methods and the manual method indicated an excellent agreement on liver volumetry. Significant differences in segmentation time were observed between the three methods (3DMIA, ASM, and PA) and the manual volumetry (pvolumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (pvolumetry agreed well with manual gold standard in both the normal liver group and the space‐occupying lesion group. The ASM‐ and PA‐based automatic segmentation have better efficiency in clinical use. PACS number(s): 87.55.‐x PMID:27929487

  6. Eye Anatomy

    Science.gov (United States)

    ... News About Us Donate In This Section Eye Anatomy en Español email Send this article to a ... You at Risk For Glaucoma? Childhood Glaucoma Eye Anatomy Five Common Glaucoma Tests Glaucoma Facts and Stats ...

  7. Transfer learning improves supervised image segmentation across imaging protocols

    DEFF Research Database (Denmark)

    van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.

    2015-01-01

    with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two MRI brain-segmentation tasks with multi-site data: white matter, gray matter, and CSF segmentation; and white-matter- /MS-lesion segmentation......The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform...... well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore...

  8. AN ITERATIVE SEGMENTATION METHOD FOR REGION OF INTEREST EXTRACTION

    Directory of Open Access Journals (Sweden)

    Volkan CETIN

    2013-01-01

    Full Text Available In this paper, a method is presented for applications which include mammographic image segmentation and region of interest extraction. Segmentation is a very critical and difficult stage to accomplish in computer aided detection systems. Although the presented segmentation method is developed for mammographic images, it can be used for any medical image which resembles the same statistical characteristics with mammograms. Fundamentally, the method contains iterative automatic thresholding and masking operations which is applied to the original or enhanced mammograms. Also the effect of image enhancement to the segmentation process was observed. A version of histogram equalization was applied to the images for enhancement. Finally, the results show that enhanced version of the proposed segmentation method is preferable because of its better success rate.

  9. Inference of segmented color and texture description by tensor voting.

    Science.gov (United States)

    Jia, Jiaya; Tang, Chi-Keung

    2004-06-01

    A robust synthesis method is proposed to automatically infer missing color and texture information from a damaged 2D image by (N)D tensor voting (N > 3). The same approach is generalized to range and 3D data in the presence of occlusion, missing data and noise. Our method translates texture information into an adaptive (N)D tensor, followed by a voting process that infers noniteratively the optimal color values in the (N)D texture space. A two-step method is proposed. First, we perform segmentation based on insufficient geometry, color, and texture information in the input, and extrapolate partitioning boundaries by either 2D or 3D tensor voting to generate a complete segmentation for the input. Missing colors are synthesized using (N)D tensor voting in each segment. Different feature scales in the input are automatically adapted by our tensor scale analysis. Results on a variety of difficult inputs demonstrate the effectiveness of our tensor voting approach.

  10. Automatic registration method for multisensor datasets adopted for dimensional measurements on cutting tools

    International Nuclear Information System (INIS)

    Shaw, L; Mehari, F; Weckenmann, A; Ettl, S; Häusler, G

    2013-01-01

    Multisensor systems with optical 3D sensors are frequently employed to capture complete surface information by measuring workpieces from different views. During coarse and fine registration the resulting datasets are afterward transformed into one common coordinate system. Automatic fine registration methods are well established in dimensional metrology, whereas there is a deficit in automatic coarse registration methods. The advantage of a fully automatic registration procedure is twofold: it enables a fast and contact-free alignment and further a flexible application to datasets of any kind of optical 3D sensor. In this paper, an algorithm adapted for a robust automatic coarse registration is presented. The method was originally developed for the field of object reconstruction or localization. It is based on a segmentation of planes in the datasets to calculate the transformation parameters. The rotation is defined by the normals of three corresponding segmented planes of two overlapping datasets, while the translation is calculated via the intersection point of the segmented planes. First results have shown that the translation is strongly shape dependent: 3D data of objects with non-orthogonal planar flanks cannot be registered with the current method. In the novel supplement for the algorithm, the translation is additionally calculated via the distance between centroids of corresponding segmented planes, which results in more than one option for the transformation. A newly introduced measure considering the distance between the datasets after coarse registration evaluates the best possible transformation. Results of the robust automatic registration method are presented on the example of datasets taken from a cutting tool with a fringe-projection system and a focus-variation system. The successful application in dimensional metrology is proven with evaluations of shape parameters based on the registered datasets of a calibrated workpiece. (paper)

  11. Automatic IVUS segmentation of atherosclerotic plaque with Stop & Go snake

    NARCIS (Netherlands)

    Brunenberg, E.J.L.; Pujol, O.; Haar Romenij, ter B.M.; Radeva, P.; Lelieveldt, B.P.F.; Haverkort, B.; de Laat, C.T.A.M.; Heijnsdijk, J.W.J.

    2006-01-01

    Since the upturn of intravascular ultrasound (IVUS)as an imaging technique for the coronary artery system, much research has been done to simplify the complicated analysis of the resulting images. In this study, an attempt to develop an automatic tissue characterization algorithm for IVUS images was

  12. Multi-modal RGB–Depth–Thermal Human Body Segmentation

    DEFF Research Database (Denmark)

    Palmero, Cristina; Clapés, Albert; Bahnsen, Chris

    2016-01-01

    This work addresses the problem of human body segmentation from multi-modal visual cues as a first stage of automatic human behavior analysis. We propose a novel RGB-Depth-Thermal dataset along with a multi-modal seg- mentation baseline. The several modalities are registered us- ing a calibration...... to other state-of-the-art meth- ods, obtaining an overlap above 75% on the novel dataset when compared to the manually annotated ground-truth of human segmentations....

  13. Automatically measuring brain ventricular volume within PACS using artificial intelligence.

    Science.gov (United States)

    Yepes-Calderon, Fernando; Nelson, Marvin D; McComb, J Gordon

    2018-01-01

    The picture archiving and communications system (PACS) is currently the standard platform to manage medical images but lacks analytical capabilities. Staying within PACS, the authors have developed an automatic method to retrieve the medical data and access it at a voxel level, decrypted and uncompressed that allows analytical capabilities while not perturbing the system's daily operation. Additionally, the strategy is secure and vendor independent. Cerebral ventricular volume is important for the diagnosis and treatment of many neurological disorders. A significant change in ventricular volume is readily recognized, but subtle changes, especially over longer periods of time, may be difficult to discern. Clinical imaging protocols and parameters are often varied making it difficult to use a general solution with standard segmentation techniques. Presented is a segmentation strategy based on an algorithm that uses four features extracted from the medical images to create a statistical estimator capable of determining ventricular volume. When compared with manual segmentations, the correlation was 94% and holds promise for even better accuracy by incorporating the unlimited data available. The volume of any segmentable structure can be accurately determined utilizing the machine learning strategy presented and runs fully automatically within the PACS.

  14. Evaluation of semi-automatic arterial stenosis quantification

    International Nuclear Information System (INIS)

    Hernandez Hoyos, M.; Universite Claude Bernard Lyon 1, 69 - Villeurbanne; Univ. de los Andes, Bogota; Serfaty, J.M.; Douek, P.C.; Universite Claude Bernard Lyon 1, 69 - Villeurbanne; Hopital Cardiovasculaire et Pneumologique L. Pradel, Bron; Maghiar, A.; Mansard, C.; Orkisz, M.; Magnin, I.; Universite Claude Bernard Lyon 1, 69 - Villeurbanne

    2006-01-01

    Object: To assess the accuracy and reproducibility of semi-automatic vessel axis extraction and stenosis quantification in 3D contrast-enhanced Magnetic Resonance Angiography (CE-MRA) of the carotid arteries (CA). Materials and methods: A total of 25 MRA datasets was used: 5 phantoms with known stenoses, and 20 patients (40 CAs) drawn from a multicenter trial database. Maracas software extracted vessel centerlines and quantified the stenoses, based on boundary detection in planes perpendicular to the centerline. Centerline accuracy was visually scored. Semi-automatic measurements were compared with: (1) theoretical phantom morphometric values, and (2) stenosis degrees evaluated by two independent radiologists. Results: Exploitable centerlines were obtained in 97% of CA and in all phantoms. In phantoms, the software achieved a better agreement with theoretic stenosis degrees (weighted kappa Κ W = 0.91) than the radiologists (Κ W = 0.69). In patients, agreement between software and radiologists varied from Κ W =0.67 to 0.90. In both, Maracas was substantially more reproducible than the readers. Mean operating time was within 1 min/ CA. Conclusion: Maracas software generates accurate 3D centerlines of vascular segments with minimum user intervention. Semi-automatic quantification of CA stenosis is also accurate, except in very severe stenoses that cannot be segmented. It substantially reduces the inter-observer variability. (orig.)

  15. Paraganglioma Anatomy

    Science.gov (United States)

    ... e.g. -historical Searches are case-insensitive Paraganglioma Anatomy Add to My Pictures View /Download : Small: 648x576 ... View Download Large: 2700x2400 View Download Title: Paraganglioma Anatomy Description: Paraganglioma of the head and neck; drawing ...

  16. Facial anatomy.

    Science.gov (United States)

    Marur, Tania; Tuna, Yakup; Demirci, Selman

    2014-01-01

    Dermatologic problems of the face affect both function and aesthetics, which are based on complex anatomical features. Treating dermatologic problems while preserving the aesthetics and functions of the face requires knowledge of normal anatomy. When performing successfully invasive procedures of the face, it is essential to understand its underlying topographic anatomy. This chapter presents the anatomy of the facial musculature and neurovascular structures in a systematic way with some clinically important aspects. We describe the attachments of the mimetic and masticatory muscles and emphasize their functions and nerve supply. We highlight clinically relevant facial topographic anatomy by explaining the course and location of the sensory and motor nerves of the face and facial vasculature with their relations. Additionally, this chapter reviews the recent nomenclature of the branching pattern of the facial artery. © 2013 Elsevier Inc. All rights reserved.

  17. An Alternative to Chaid Segmentation Algorithm Based on Entropy.

    Directory of Open Access Journals (Sweden)

    María Purificación Galindo Villardón

    2010-07-01

    Full Text Available The CHAID (Chi-Squared Automatic Interaction Detection treebased segmentation technique has been found to be an effective approach for obtaining meaningful segments that are predictive of a K-category (nominal or ordinal criterion variable. CHAID was designed to detect, in an automatic way, the  nteraction between several categorical or ordinal predictors in explaining a categorical response, but, this may not be true when Simpson’s paradox is present. This is due to the fact that CHAID is a forward selection algorithm based on the marginal counts. In this paper we propose a backwards elimination algorithm that starts with the full set of predictors (or full tree and eliminates predictors progressively. The elimination procedure is based on Conditional Independence contrasts using the concept of entropy. The proposed procedure is compared to CHAID.

  18. An interactive medical image segmentation framework using iterative refinement.

    Science.gov (United States)

    Kalshetti, Pratik; Bundele, Manas; Rahangdale, Parag; Jangra, Dinesh; Chattopadhyay, Chiranjoy; Harit, Gaurav; Elhence, Abhay

    2017-04-01

    Segmentation is often performed on medical images for identifying diseases in clinical evaluation. Hence it has become one of the major research areas. Conventional image segmentation techniques are unable to provide satisfactory segmentation results for medical images as they contain irregularities. They need to be pre-processed before segmentation. In order to obtain the most suitable method for medical image segmentation, we propose MIST (Medical Image Segmentation Tool), a two stage algorithm. The first stage automatically generates a binary marker image of the region of interest using mathematical morphology. This marker serves as the mask image for the second stage which uses GrabCut to yield an efficient segmented result. The obtained result can be further refined by user interaction, which can be done using the proposed Graphical User Interface (GUI). Experimental results show that the proposed method is accurate and provides satisfactory segmentation results with minimum user interaction on medical as well as natural images. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Segmentation of deformable organs from medical images using particle swarm optimization and nonlinear shape priors

    Science.gov (United States)

    Afifi, Ahmed; Nakaguchi, Toshiya; Tsumura, Norimichi

    2010-03-01

    In many medical applications, the automatic segmentation of deformable organs from medical images is indispensable and its accuracy is of a special interest. However, the automatic segmentation of these organs is a challenging task according to its complex shape. Moreover, the medical images usually have noise, clutter, or occlusion and considering the image information only often leads to meager image segmentation. In this paper, we propose a fully automated technique for the segmentation of deformable organs from medical images. In this technique, the segmentation is performed by fitting a nonlinear shape model with pre-segmented images. The kernel principle component analysis (KPCA) is utilized to capture the complex organs deformation and to construct the nonlinear shape model. The presegmentation is carried out by labeling each pixel according to its high level texture features extracted using the overcomplete wavelet packet decomposition. Furthermore, to guarantee an accurate fitting between the nonlinear model and the pre-segmented images, the particle swarm optimization (PSO) algorithm is employed to adapt the model parameters for the novel images. In this paper, we demonstrate the competence of proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans of different patients.

  20. Teaching Anatomy: need or taste?

    OpenAIRE

    Farrokhi, Ahmad; Nejad, Masoume Soleymani

    2017-01-01

    Abstract Background: Anatomy is one of the core sections of Basic Medical Sciences. Given the central role of anatomy, the development of medical knowledge and reach new horizons in science is not possible without relying on anatomy. Since in the anatomy science, students are familiar with the basic terms of medical language, the anatomy's hard to know and have a negative attitude towards this course. With these conditions, anatomy professors have an important role in providing incentives...