WorldWideScience

Sample records for automatic anatomy segmentation

  1. Automatic segmentation of intra-cochlear anatomy in post-implantation CT

    Science.gov (United States)

    Reda, Fitsum A.; Dawant, Benoit M.; McRackan, Theodore R.; Labadie, Robert F.; Noble, Jack H.

    2013-03-01

    A cochlear implant (CI) is a neural prosthetic device that restores hearing by directly stimulating the auditory nerve with an electrode array. In CI surgery, the surgeon threads the electrode array into the cochlea, blind to internal structures. We have recently developed algorithms for determining the position of CI electrodes relative to intra-cochlear anatomy using pre- and post-implantation CT. We are currently using this approach to develop a CI programming assistance system that uses knowledge of electrode position to determine a patient-customized CI sound processing strategy. However, this approach cannot be used for the majority of CI users because the cochlea is obscured by image artifacts produced by CI electrodes and acquisition of pre-implantation CT is not universal. In this study we propose an approach that extends our techniques so that intra-cochlear anatomy can be segmented for CI users for which pre-implantation CT was not acquired. The approach achieves automatic segmentation of intra-cochlear anatomy in post-implantation CT by exploiting intra-subject symmetry in cochlear anatomy across ears. We validated our approach on a dataset of 10 ears in which both pre- and post-implantation CTs were available. Our approach results in mean and maximum segmentation errors of 0.27 and 0.62 mm, respectively. This result suggests that our automatic segmentation approach is accurate enough for developing customized CI sound processing strategies for unilateral CI patients based solely on postimplantation CT scans.

  2. 3D automatic anatomy segmentation based on iterative graph-cut-ASM

    OpenAIRE

    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...

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

    International Nuclear Information System (INIS)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Xinjian; Bagci, Ulas [Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10 Room 1C515, Bethesda, Maryland 20892-1182 and Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi' an 710071 (China); Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10 Room 1C515, Bethesda, Maryland 20892-1182 (United States)

    2011-08-15

    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

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

    International Nuclear Information System (INIS)

    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)

    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. 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 analy

  8. Automatic Melody Segmentation

    OpenAIRE

    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 analysis is a widespread practice among musicians: performers use it to help them memorise pieces, music theorists and historians use it to compare works, music students use it to understand the composi...

  9. Automatic Speech Segmentation Based on HMM

    OpenAIRE

    M. Kroul

    2007-01-01

    This contribution deals with the problem of automatic phoneme segmentation using HMMs. Automatization of speech segmentation task is important for applications, where large amount of data is needed to process, so manual segmentation is out of the question. In this paper we focus on automatic segmentation of recordings, which will be used for triphone synthesis unit database creation. For speech synthesis, the speech unit quality is a crucial aspect, so the maximal accuracy in segmentation is ...

  10. Performance Evaluation of Automatic Anatomy Segmentation Algorithm on Repeat or Four-Dimensional Computed Tomography Images Using Deformable Image Registration Method

    International Nuclear Information System (INIS)

    Purpose: Auto-propagation of anatomic regions of interest from the planning computed tomography (CT) scan to the daily CT is an essential step in image-guided adaptive radiotherapy. The goal of this study was to quantitatively evaluate the performance of the algorithm in typical clinical applications. Methods and Materials: We had previously adopted an image intensity-based deformable registration algorithm to find the correspondence between two images. In the present study, the regions of interest delineated on the planning CT image were mapped onto daily CT or four-dimensional CT images using the same transformation. Postprocessing methods, such as boundary smoothing and modification, were used to enhance the robustness of the algorithm. Auto-propagated contours for 8 head-and-neck cancer patients with a total of 100 repeat CT scans, 1 prostate patient with 24 repeat CT scans, and 9 lung cancer patients with a total of 90 four-dimensional CT images were evaluated against physician-drawn contours and physician-modified deformed contours using the volume overlap index and mean absolute surface-to-surface distance. Results: The deformed contours were reasonably well matched with the daily anatomy on the repeat CT images. The volume overlap index and mean absolute surface-to-surface distance was 83% and 1.3 mm, respectively, compared with the independently drawn contours. Better agreement (>97% and <0.4 mm) was achieved if the physician was only asked to correct the deformed contours. The algorithm was also robust in the presence of random noise in the image. Conclusion: The deformable algorithm might be an effective method to propagate the planning regions of interest to subsequent CT images of changed anatomy, although a final review by physicians is highly recommended

  11. Automatic anatomy recognition of sparse objects

    Science.gov (United States)

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

    2015-03-01

    A general body-wide automatic anatomy recognition (AAR) methodology was proposed in our previous work based on hierarchical fuzzy models of multitudes of objects which was not tied to any specific organ system, body region, or image modality. That work revealed the challenges encountered in modeling, recognizing, and delineating sparse objects throughout the body (compared to their non-sparse counterparts) if the models are based on the object's exact geometric representations. The challenges stem mainly from the variation in sparse objects in their shape, topology, geographic layout, and relationship to other objects. That led to the idea of modeling sparse objects not from the precise geometric representations of their samples but by using a properly designed optimal super form. This paper presents the underlying improved methodology which includes 5 steps: (a) Collecting image data from a specific population group G and body region Β and delineating in these images the objects in Β to be modeled; (b) Building a super form, S-form, for each object O in Β; (c) Refining the S-form of O to construct an optimal (minimal) super form, S*-form, which constitutes the (fuzzy) model of O; (d) Recognizing objects in Β using the S*-form; (e) Defining confounding and background objects in each S*-form for each object and performing optimal delineation. Our evaluations based on 50 3D computed tomography (CT) image sets in the thorax on four sparse objects indicate that substantially improved performance (FPVF~2%, FNVF~10%, and success where the previous approach failed) can be achieved using the new approach.

  12. Neuroanatomical automatic segmentation in brain cancer patients

    OpenAIRE

    D’Haese, P.; Niermann, K; Cmelak, A.; Donnelly, E.; Duay, V.; Li, R; Dawant, B.

    2003-01-01

    Conformally prescribed radiation therapy for brain cancer requires precisely defining the target treatment area, as well as delineating vital brain structures which must be spared from radiotoxicity. The current clinical practice of manually segmenting brain structures can be complex and exceedingly time consuming. Automatic computeraided segmentation methods have been proposed to increase efficiency and reproducibility in developing radiation treatment plans. Previous studies have establishe...

  13. Semi-automatic knee cartilage segmentation

    Science.gov (United States)

    Dam, Erik B.; Folkesson, Jenny; Pettersen, Paola C.; Christiansen, Claus

    2006-03-01

    Osteo-Arthritis (OA) is a very common age-related cause of pain and reduced range of motion. A central effect of OA is wear-down of the articular cartilage that otherwise ensures smooth joint motion. Quantification of the cartilage breakdown is central in monitoring disease progression and therefore cartilage segmentation is required. Recent advances allow automatic cartilage segmentation with high accuracy in most cases. However, the automatic methods still fail in some problematic cases. For clinical studies, even if a few failing cases will be averaged out in the overall results, this reduces the mean accuracy and precision and thereby necessitates larger/longer studies. Since the severe OA cases are often most problematic for the automatic methods, there is even a risk that the quantification will introduce a bias in the results. Therefore, interactive inspection and correction of these problematic cases is desirable. For diagnosis on individuals, this is even more crucial since the diagnosis will otherwise simply fail. We introduce and evaluate a semi-automatic cartilage segmentation method combining an automatic pre-segmentation with an interactive step that allows inspection and correction. The automatic step consists of voxel classification based on supervised learning. The interactive step combines a watershed transformation of the original scan with the posterior probability map from the classification step at sub-voxel precision. We evaluate the method for the task of segmenting the tibial cartilage sheet from low-field magnetic resonance imaging (MRI) of knees. The evaluation shows that the combined method allows accurate and highly reproducible correction of the segmentation of even the worst cases in approximately ten minutes of interaction.

  14. Automatic evaluation of uterine cervix segmentations

    Science.gov (United States)

    Lotenberg, Shelly; Gordon, Shiri; Long, Rodney; Antani, Sameer; Jeronimo, Jose; Greenspan, Hayit

    2007-03-01

    In this work we focus on the generation of reliable ground truth data for a large medical repository of digital cervicographic images (cervigrams) collected by the National Cancer Institute (NCI). This work is part of an ongoing effort conducted by NCI together with the National Library of Medicine (NLM) at the National Institutes of Health (NIH) to develop a web-based database of the digitized cervix images in order to study the evolution of lesions related to cervical cancer. As part of this effort, NCI has gathered twenty experts to manually segment a set of 933 cervigrams into regions of medical and anatomical interest. This process yields a set of images with multi-expert segmentations. The objectives of the current work are: 1) generate multi-expert ground truth and assess the diffculty of segmenting an image, 2) analyze observer variability in the multi-expert data, and 3) utilize the multi-expert ground truth to evaluate automatic segmentation algorithms. The work is based on STAPLE (Simultaneous Truth and Performance Level Estimation), which is a well known method to generate ground truth segmentation maps from multiple experts' observations. We have analyzed both intra- and inter-expert variability within the segmentation data. We propose novel measures of "segmentation complexity" by which we can automatically identify cervigrams that were found difficult to segment by the experts, based on their inter-observer variability. Finally, the results are used to assess our own automated algorithm for cervix boundary detection.

  15. An Automatic Indirect Immunofluorescence Cell Segmentation System

    OpenAIRE

    2014-01-01

    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...

  16. CT segmentation of dental shapes by anatomy-driven reformation imaging and B-spline modelling.

    Science.gov (United States)

    Barone, S; Paoli, A; Razionale, A V

    2016-06-01

    Dedicated imaging methods are among the most important tools of modern computer-aided medical applications. In the last few years, cone beam computed tomography (CBCT) has gained popularity in digital dentistry for 3D imaging of jawbones and teeth. However, the anatomy of a maxillofacial region complicates the assessment of tooth geometry and anatomical location when using standard orthogonal views of the CT data set. In particular, a tooth is defined by a sub-region, which cannot be easily separated from surrounding tissues by only considering pixel grey-intensity values. For this reason, an image enhancement is usually necessary in order to properly segment tooth geometries. In this paper, an anatomy-driven methodology to reconstruct individual 3D tooth anatomies by processing CBCT data is presented. The main concept is to generate a small set of multi-planar reformation images along significant views for each target tooth, driven by the individual anatomical geometry of a specific patient. The reformation images greatly enhance the clearness of the target tooth contours. A set of meaningful 2D tooth contours is extracted and used to automatically model the overall 3D tooth shape through a B-spline representation. The effectiveness of the methodology has been verified by comparing some anatomy-driven reconstructions of anterior and premolar teeth with those obtained by using standard tooth segmentation tools. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26418417

  17. Automatic image segmentation by dynamic region merging.

    Science.gov (United States)

    Peng, Bo; Zhang, Lei; Zhang, David

    2011-12-01

    This paper addresses the automatic image segmentation problem in a region merging style. With an initially oversegmented image, in which many regions (or superpixels) with homogeneous color are detected, an image segmentation is performed by iteratively merging the regions according to a statistical test. There are two essential issues in a region-merging algorithm: order of merging and the stopping criterion. In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test and the minimal cost criterion. Starting from an oversegmented image, neighboring regions are progressively merged if there is an evidence for merging according to this predicate. We show that the merging order follows the principle of dynamic programming. This formulates the image segmentation as an inference problem, where the final segmentation is established based on the observed image. We also prove that the produced segmentation satisfies certain global properties. In addition, a faster algorithm is developed to accelerate the region-merging process, which maintains a nearest neighbor graph in each iteration. Experiments on real natural images are conducted to demonstrate the performance of the proposed dynamic region-merging algorithm. PMID:21609885

  18. 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.

  19. Towards fully automatic object detection and segmentation

    Science.gov (United States)

    Schramm, Hauke; Ecabert, Olivier; Peters, Jochen; Philomin, Vasanth; Weese, Juergen

    2006-03-01

    An automatic procedure for detecting and segmenting anatomical objects in 3-D images is necessary for achieving a high level of automation in many medical applications. Since today's segmentation techniques typically rely on user input for initialization, they do not allow for a fully automatic workflow. In this work, the generalized Hough transform is used for detecting anatomical objects with well defined shape in 3-D medical images. This well-known technique has frequently been used for object detection in 2-D images and is known to be robust and reliable. However, its computational and memory requirements are generally huge, especially in case of considering 3-D images and various free transformation parameters. Our approach limits the complexity of the generalized Hough transform to a reasonable amount by (1) using object prior knowledge during the preprocessing in order to suppress unlikely regions in the image, (2) restricting the flexibility of the applied transformation to only scaling and translation, and (3) using a simple shape model which does not cover any inter-individual shape variability. Despite these limitations, the approach is demonstrated to allow for a coarse 3-D delineation of the femur, vertebra and heart in a number of experiments. Additionally it is shown that the quality of the object localization is in nearly all cases sufficient to initialize a successful segmentation using shape constrained deformable models.

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

    International Nuclear Information System (INIS)

    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.

  1. 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.

  2. Automatic segmentation of mammogram and tomosynthesis images

    Science.gov (United States)

    Sargent, Dusty; Park, Sun Young

    2016-03-01

    Breast cancer is a one of the most common forms of cancer in terms of new cases and deaths both in the United States and worldwide. However, the survival rate with breast cancer is high if it is detected and treated before it spreads to other parts of the body. The most common screening methods for breast cancer are mammography and digital tomosynthesis, which involve acquiring X-ray images of the breasts that are interpreted by radiologists. The work described in this paper is aimed at optimizing the presentation of mammography and tomosynthesis images to the radiologist, thereby improving the early detection rate of breast cancer and the resulting patient outcomes. Breast cancer tissue has greater density than normal breast tissue, and appears as dense white image regions that are asymmetrical between the breasts. These irregularities are easily seen if the breast images are aligned and viewed side-by-side. However, since the breasts are imaged separately during mammography, the images may be poorly centered and aligned relative to each other, and may not properly focus on the tissue area. Similarly, although a full three dimensional reconstruction can be created from digital tomosynthesis images, the same centering and alignment issues can occur for digital tomosynthesis. Thus, a preprocessing algorithm that aligns the breasts for easy side-by-side comparison has the potential to greatly increase the speed and accuracy of mammogram reading. Likewise, the same preprocessing can improve the results of automatic tissue classification algorithms for mammography. In this paper, we present an automated segmentation algorithm for mammogram and tomosynthesis images that aims to improve the speed and accuracy of breast cancer screening by mitigating the above mentioned problems. Our algorithm uses information in the DICOM header to facilitate preprocessing, and incorporates anatomical region segmentation and contour analysis, along with a hidden Markov model (HMM) for

  3. An Ontology Based Approach for Automatically Annotating Document Segments

    Directory of Open Access Journals (Sweden)

    Maryam Hazman

    2012-03-01

    Full Text Available This paper presents an approach for automatically annotating document segments within information rich texts using a domain ontology. The work exploits the logical structure of input documents in order to achieve its task. The underlying assumption behind this work is that segments in such documents embody self contained informative units. Another assumption is that segment headings coupled with a documents hierarchical structure offer informal representations of segment content; and that matching segment headings to concepts in an ontology/thesaurus can result in the creation of formal labels/meta-data for these segments. A series of experiments was carried out using the presented approach on a set of Arabic agricultural extension documents. The results of carrying out these experiments demonstrate that the proposed approach is capable of automatically annotating segments with concepts that describe a segments content with a high degree of accuracy.

  4. Automatic topic segmentation and labeling in multiparty dialogue

    OpenAIRE

    Hsueh, Pei-Yun; Moore, Johanna D.

    2006-01-01

    This study concerns how to segment a scenario-driven multiparty dialogue and how to label these segments automatically. We apply approaches that have been proposed for identifying topic boundaries at a coarser level to the problem of identifying agenda-based topic boundaries in scenario-based meetings. We also develop conditional models to classify segments into topic classes. Experiments in topic segmentation show that a supervised classification approach that combines lexical and conversati...

  5. 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.

  6. Automatic texture segmentation for content-based image retrieval application

    OpenAIRE

    Fauzi, M.F.A.; Lewis, P. H.

    2006-01-01

    In this article, a brief review on texture segmentation is presented, before a novel automatic texture segmentation algorithm is developed. The algorithm is based on a modified discrete wavelet frames and the mean shift algorithm. The proposed technique is tested on a range of textured images including composite texture images, synthetic texture images, real scene images as well as our main source of images, the museum images of various kinds. An extension to the automatic texture segmentatio...

  7. AUTOMATIC SEGMENTATION OF PELVIS FOR BRACHYTHERAPY OF PROSTATE.

    Science.gov (United States)

    Kardell, M; Magnusson, M; Sandborg, M; Alm Carlsson, G; Jeuthe, J; Malusek, A

    2016-06-01

    Advanced model-based iterative reconstruction algorithms in quantitative computed tomography (CT) perform automatic segmentation of tissues to estimate material properties of the imaged object. Compared with conventional methods, these algorithms may improve quality of reconstructed images and accuracy of radiation treatment planning. Automatic segmentation of tissues is, however, a difficult task. The aim of this work was to develop and evaluate an algorithm that automatically segments tissues in CT images of the male pelvis. The newly developed algorithm (MK2014) combines histogram matching, thresholding, region growing, deformable model and atlas-based registration techniques for the segmentation of bones, adipose tissue, prostate and muscles in CT images. Visual inspection of segmented images showed that the algorithm performed well for the five analysed images. The tissues were identified and outlined with accuracy sufficient for the dual-energy iterative reconstruction algorithm whose aim is to improve the accuracy of radiation treatment planning in brachytherapy of the prostate. PMID:26567322

  8. Web-accessible cervigram automatic segmentation tool

    Science.gov (United States)

    Xue, Zhiyun; Antani, Sameer; Long, L. Rodney; Thoma, George R.

    2010-03-01

    Uterine cervix image analysis is of great importance to the study of uterine cervix cancer, which is among the leading cancers affecting women worldwide. In this paper, we describe our proof-of-concept, Web-accessible system for automated segmentation of significant tissue regions in uterine cervix images, which also demonstrates our research efforts toward promoting collaboration between engineers and physicians for medical image analysis projects. Our design and implementation unifies the merits of two commonly used languages, MATLAB and Java. It circumvents the heavy workload of recoding the sophisticated segmentation algorithms originally developed in MATLAB into Java while allowing remote users who are not experienced programmers and algorithms developers to apply those processing methods to their own cervicographic images and evaluate the algorithms. Several other practical issues of the systems are also discussed, such as the compression of images and the format of the segmentation results.

  9. Automatic Hair Segmentation in the Wild

    DEFF Research Database (Denmark)

    Julian, Pauline; Dehais, Christophe; Lauze, Francois Bernard;

    2010-01-01

    This paper presents an algorithm for segmenting the hair region in uncontrolled, real life conditions images. Our method is based on a simple statistical hair shape model representing the upper hair part. We detect this region by minimizing an energy which uses active shape and active contour. Th...

  10. 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.

  11. FULLY AUTOMATIC FRAMEWORK FOR SEGMENTATION OF BRAIN MRI IMAGE

    Institute of Scientific and Technical Information of China (English)

    Lin Pan; Zheng Chongxun; Yang Yong; Gu Jianwen

    2005-01-01

    Objective To propose an automatic framework for segmentation of brain image in this paper. Methods The brain MRI image segmentation framework consists of three-step segmentation procedures. First, Non-brain structures removal by level set method. Then, the non-uniformity correction method is based on computing estimates of tissue intensity variation. Finally, it uses a statistical model based on Markov random filed for MRI brain image segmentation. The brain tissue can be classified into cerebrospinal fluid, white matter and gray matter. Results To evaluate the proposed our method, we performed two sets of experiments, one on simulated MR and another on real MR brain data. Conclusion The efficacy of the brain MRI image segmentation framework has been demonstrated by the extensive experiments. In the future, we are also planning on a large-scale clinical evaluation of this segmentation framework.

  12. An automatic segmentation method for fast imaging in PET

    International Nuclear Information System (INIS)

    A new segmentation method has been developed for PET fast imaging. The technique automatically segments the transmission images into different anatomical regions, it efficiently reduced the PET transmission scan time. The result shows that this method gives only 3 min-scan time which is perfect for attenuation correction of the PET images instead of the original 15-30 min-scan time. This approach has been successfully tested both on phantom and clinical data

  13. Towards an automatic coronary artery segmentation algorithm.

    Science.gov (United States)

    Fallavollita, Pascal; Cheriet, Farida

    2006-01-01

    A method is presented that aims at minimizing image processing time during X-ray fluoroscopy interventions. First, an automatic frame extraction algorithm is proposed in order to extract relevant image frames with respect to their cardiac phase (systole or diastole). Secondly, a 4-step filter is suggested in order to enhance vessel contours. The reciprocal of the enhanced image is used as an alternative speed function to initialize the fast marching method. The complete algorithm was tested on eight clinical angiographic data sets and comparisons with two other vessel enhancement filters (Lorenz and Frangi) are made for the centerline extraction procedure. In order to assess the suitability of our filter the extracted centerline coordinates are compared with the manually traced axis. PMID:17946540

  14. Adaptive automatic segmentation of Leishmaniasis parasite in Indirect Immunofluorescence images.

    Science.gov (United States)

    Ouertani, F; Amiri, H; Bettaib, J; Yazidi, R; Ben Salah, A

    2014-01-01

    This paper describes the first steps for the automation of the serum titration process. In fact, this process requires an Indirect Immunofluorescence (IIF) diagnosis automation. We deal with the initial phase that represents the fluorescence images segmentation. Our approach consists of three principle stages: (1) a color based segmentation which aims at extracting the fluorescent foreground based on k-means clustering, (2) the segmentation of the fluorescent clustered image, and (3) a region-based feature segmentation, intended to remove the fluorescent noisy regions and to locate fluorescent parasites. We evaluated the proposed method on 40 IIF images. Experimental results show that such a method provides reliable and robust automatic segmentation of fluorescent Promastigote parasite. PMID:25571049

  15. Sequentiality of daily life physiology: an automatized segmentation approach.

    Science.gov (United States)

    Fontecave-Jallon, J; Baconnier, P; Tanguy, S; Eymaron, M; Rongier, C; Guméry, P Y

    2013-09-01

    Based on the hypotheses that (1) a physiological organization exists inside each activity of daily life and (2) the pattern of evolution of physiological variables is characteristic of each activity, pattern changes should be detected on daily life physiological recordings. The present study aims at investigating whether a simple segmentation method can be set up to detect pattern changes on physiological recordings carried out during daily life. Heart and breathing rates and skin temperature have been non-invasively recorded in volunteers following scenarios made of "daily life" steps (13 records). An observer, undergoing the scenario, wrote down annotations during the recording time. Two segmentation procedures have been compared to the annotations, a visual inspection of the signals and an automatic program based on a trends detection algorithm applied to one physiological signal (skin temperature). The annotations resulted in a total number of 213 segments defined on the 13 records, the best visual inspection detected less segments (120) than the automatic program (194). If evaluated in terms of the number of correspondences between the times marks given by annotations and those resulting from both physiologically based segmentations, the automatic program was better than the visual inspection. The mean time lags between annotation and program time marks remain variables time series recorded in common life conditions exhibit different successive patterns that can be detected by a simple trends detection algorithm. Theses sequences are coherent with the corresponding annotated activity. PMID:23943146

  16. Automatic Segmentation of Eight Tissue Classes in Neonatal Brain MRI

    Science.gov (United States)

    Anbeek, Petronella; Išgum, Ivana; van Kooij, Britt J. M.; Mol, Christian P.; Kersbergen, Karina J.; Groenendaal, Floris; Viergever, Max A.; de Vries, Linda S.; Benders, Manon J. N. L.

    2013-01-01

    Purpose Volumetric measurements of neonatal brain tissues may be used as a biomarker for later neurodevelopmental outcome. We propose an automatic method for probabilistic brain segmentation in neonatal MRIs. Materials and Methods In an IRB-approved study axial T1- and T2-weighted MR images were acquired at term-equivalent age for a preterm cohort of 108 neonates. A method for automatic probabilistic segmentation of the images into eight cerebral tissue classes was developed: cortical and central grey matter, unmyelinated and myelinated white matter, cerebrospinal fluid in the ventricles and in the extra cerebral space, brainstem and cerebellum. Segmentation is based on supervised pixel classification using intensity values and spatial positions of the image voxels. The method was trained and evaluated using leave-one-out experiments on seven images, for which an expert had set a reference standard manually. Subsequently, the method was applied to the remaining 101 scans, and the resulting segmentations were evaluated visually by three experts. Finally, volumes of the eight segmented tissue classes were determined for each patient. Results The Dice similarity coefficients of the segmented tissue classes, except myelinated white matter, ranged from 0.75 to 0.92. Myelinated white matter was difficult to segment and the achieved Dice coefficient was 0.47. Visual analysis of the results demonstrated accurate segmentations of the eight tissue classes. The probabilistic segmentation method produced volumes that compared favorably with the reference standard. Conclusion The proposed method provides accurate segmentation of neonatal brain MR images into all given tissue classes, except myelinated white matter. This is the one of the first methods that distinguishes cerebrospinal fluid in the ventricles from cerebrospinal fluid in the extracerebral space. This method might be helpful in predicting neurodevelopmental outcome and useful for evaluating neuroprotective clinical

  17. Automatic segmentation of eight tissue classes in neonatal brain MRI.

    Directory of Open Access Journals (Sweden)

    Petronella Anbeek

    Full Text Available PURPOSE: Volumetric measurements of neonatal brain tissues may be used as a biomarker for later neurodevelopmental outcome. We propose an automatic method for probabilistic brain segmentation in neonatal MRIs. MATERIALS AND METHODS: In an IRB-approved study axial T1- and T2-weighted MR images were acquired at term-equivalent age for a preterm cohort of 108 neonates. A method for automatic probabilistic segmentation of the images into eight cerebral tissue classes was developed: cortical and central grey matter, unmyelinated and myelinated white matter, cerebrospinal fluid in the ventricles and in the extra cerebral space, brainstem and cerebellum. Segmentation is based on supervised pixel classification using intensity values and spatial positions of the image voxels. The method was trained and evaluated using leave-one-out experiments on seven images, for which an expert had set a reference standard manually. Subsequently, the method was applied to the remaining 101 scans, and the resulting segmentations were evaluated visually by three experts. Finally, volumes of the eight segmented tissue classes were determined for each patient. RESULTS: The Dice similarity coefficients of the segmented tissue classes, except myelinated white matter, ranged from 0.75 to 0.92. Myelinated white matter was difficult to segment and the achieved Dice coefficient was 0.47. Visual analysis of the results demonstrated accurate segmentations of the eight tissue classes. The probabilistic segmentation method produced volumes that compared favorably with the reference standard. CONCLUSION: The proposed method provides accurate segmentation of neonatal brain MR images into all given tissue classes, except myelinated white matter. This is the one of the first methods that distinguishes cerebrospinal fluid in the ventricles from cerebrospinal fluid in the extracerebral space. This method might be helpful in predicting neurodevelopmental outcome and useful for evaluating

  18. 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

  19. Automatic anatomy recognition in post-tonsillectomy MR images of obese children with OSAS

    Science.gov (United States)

    Tong, Yubing; Udupa, Jayaram K.; Odhner, Dewey; Sin, Sanghun; Arens, Raanan

    2015-03-01

    Automatic Anatomy Recognition (AAR) is a recently developed approach for the automatic whole body wide organ segmentation. We previously tested that methodology on image cases with some pathology where the organs were not distorted significantly. In this paper, we present an advancement of AAR to handle organs which may have been modified or resected by surgical intervention. We focus on MRI of the neck in pediatric Obstructive Sleep Apnea Syndrome (OSAS). The proposed method consists of an AAR step followed by support vector machine techniques to detect the presence/absence of organs. The AAR step employs a hierarchical organization of the organs for model building. For each organ, a fuzzy model over a population is built. The model of the body region is then described in terms of the fuzzy models and a host of other descriptors which include parent to offspring relationship estimated over the population. Organs are recognized following the organ hierarchy by using an optimal threshold based search. The SVM step subsequently checks for evidence of the presence of organs. Experimental results show that AAR techniques can be combined with machine learning strategies within the AAR recognition framework for good performance in recognizing missing organs, in our case missing tonsils in post-tonsillectomy images as well as in simulating tonsillectomy images. The previous recognition performance is maintained achieving an organ localization accuracy of within 1 voxel when the organ is actually not removed. To our knowledge, no methods have been reported to date for handling significantly deformed or missing organs, especially in neck MRI.

  20. Fully automatic plaque segmentation in 3-D carotid ultrasound images.

    Science.gov (United States)

    Cheng, Jieyu; Li, He; Xiao, Feng; Fenster, Aaron; Zhang, Xuming; He, Xiaoling; Li, Ling; Ding, Mingyue

    2013-12-01

    Automatic segmentation of the carotid plaques from ultrasound images has been shown to be an important task for monitoring progression and regression of carotid atherosclerosis. Considering the complex structure and heterogeneity of plaques, a fully automatic segmentation method based on media-adventitia and lumen-intima boundary priors is proposed. This method combines image intensity with structure information in both initialization and a level-set evolution process. Algorithm accuracy was examined on the common carotid artery part of 26 3-D carotid ultrasound images (34 plaques ranging in volume from 2.5 to 456 mm(3)) by comparing the results of our algorithm with manual segmentations of two experts. Evaluation results indicated that the algorithm yielded total plaque volume (TPV) differences of -5.3 ± 12.7 and -8.5 ± 13.8 mm(3) and absolute TPV differences of 9.9 ± 9.5 and 11.8 ± 11.1 mm(3). Moreover, high correlation coefficients in generating TPV (0.993 and 0.992) between algorithm results and both sets of manual results were obtained. The automatic method provides a reliable way to segment carotid plaque in 3-D ultrasound images and can be used in clinical practice to estimate plaque measurements for management of carotid atherosclerosis. PMID:24063959

  1. Semi-automatic liver volume segmentation in computed tomography images

    International Nuclear Information System (INIS)

    Liver volume is a significant parameter in surgery for tumor extraction, transplants, and regeneration treatments. Generally, the volume estimation is obtained from manual segmentation performed by specialists, resulting in a tedious process with low reproducibility. In this work a semi-automatic method for the liver volume segmentation in CT images is presented. The method consist in manually superimpose a triangular surface on the images, and use a movement equation associated to each vertex to deform the surface and delimit the liver boundaries. Surface dynamics depend on intensity and gradient information, and neighboring relationships between vertices, until a fixed number of iterations is reached. Comparison between the obtained results and reference segmentation in 20 CT scans, show the surface adaptability to the shape and the diffuse boundaries of the liver, two of the principal segmentation problems.

  2. 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.

  3. Automatic Segmentation of Drosophila Neural Compartments Using GAL4 Expression Data Reveals Novel Visual Pathways.

    Science.gov (United States)

    Panser, Karin; Tirian, Laszlo; Schulze, Florian; Villalba, Santiago; Jefferis, Gregory S X E; Bühler, Katja; Straw, Andrew D

    2016-08-01

    Identifying distinct anatomical structures within the brain and developing genetic tools to target them are fundamental steps for understanding brain function. We hypothesize that enhancer expression patterns can be used to automatically identify functional units such as neuropils and fiber tracts. We used two recent, genome-scale Drosophila GAL4 libraries and associated confocal image datasets to segment large brain regions into smaller subvolumes. Our results (available at https://strawlab.org/braincode) support this hypothesis because regions with well-known anatomy, namely the antennal lobes and central complex, were automatically segmented into familiar compartments. The basis for the structural assignment is clustering of voxels based on patterns of enhancer expression. These initial clusters are agglomerated to make hierarchical predictions of structure. We applied the algorithm to central brain regions receiving input from the optic lobes. Based on the automated segmentation and manual validation, we can identify and provide promising driver lines for 11 previously identified and 14 novel types of visual projection neurons and their associated optic glomeruli. The same strategy can be used in other brain regions and likely other species, including vertebrates. PMID:27426516

  4. Segmentation precision of abdominal anatomy for MRI-based radiotherapy

    International Nuclear Information System (INIS)

    The limited soft tissue visualization provided by computed tomography, the standard imaging modality for radiotherapy treatment planning and daily localization, has motivated studies on the use of magnetic resonance imaging (MRI) for better characterization of treatment sites, such as the prostate and head and neck. However, no studies have been conducted on MRI-based segmentation for the abdomen, a site that could greatly benefit from enhanced soft tissue targeting. We investigated the interobserver and intraobserver precision in segmentation of abdominal organs on MR images for treatment planning and localization. Manual segmentation of 8 abdominal organs was performed by 3 independent observers on MR images acquired from 14 healthy subjects. Observers repeated segmentation 4 separate times for each image set. Interobserver and intraobserver contouring precision was assessed by computing 3-dimensional overlap (Dice coefficient [DC]) and distance to agreement (Hausdorff distance [HD]) of segmented organs. The mean and standard deviation of intraobserver and interobserver DC and HD values were DCintraobserver = 0.89 ± 0.12, HDintraobserver = 3.6 mm ± 1.5, DCinterobserver = 0.89 ± 0.15, and HDinterobserver = 3.2 mm ± 1.4. Overall, metrics indicated good interobserver/intraobserver precision (mean DC > 0.7, mean HD < 4 mm). Results suggest that MRI offers good segmentation precision for abdominal sites. These findings support the utility of MRI for abdominal planning and localization, as emerging MRI technologies, techniques, and onboard imaging devices are beginning to enable MRI-based radiotherapy

  5. Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields

    OpenAIRE

    Sheng-hui Liao; Shi-jian Liu; Bei-ji Zou; Xi Ding; Ye Liang; Jun-hui Huang

    2015-01-01

    An important preprocess in computer-aided orthodontics is to segment teeth from the dental models accurately, which should involve manual interactions as few as possible. But fully automatic partition of all teeth is not a trivial task, since teeth occur in different shapes and their arrangements vary substantially from one individual to another. The difficulty is exacerbated when severe teeth malocclusion and crowding problems occur, which is a common occurrence in clinical cases. Most publi...

  6. Morphometric Atlas Selection for Automatic Brachial Plexus Segmentation

    International Nuclear Information System (INIS)

    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

  7. Morphometric Atlas Selection for Automatic Brachial Plexus Segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Van de Velde, Joris, E-mail: joris.vandevelde@ugent.be [Department of Anatomy, Ghent University, Ghent (Belgium); Department of Radiotherapy, Ghent University, Ghent (Belgium); Wouters, Johan [Department of Anatomy, Ghent University, Ghent (Belgium); Vercauteren, Tom; De Gersem, Werner; Duprez, Fréderic; De Neve, Wilfried [Department of Radiotherapy, Ghent University, Ghent (Belgium); Van Hoof, Tom [Department of Anatomy, Ghent University, Ghent (Belgium)

    2015-07-01

    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.

  8. Automatic sputum color image segmentation for tuberculosis diagnosis

    Science.gov (United States)

    Forero-Vargas, Manuel G.; Sierra-Ballen, Eduard L.; Alvarez-Borrego, Josue; Pech-Pacheco, Jose L.; Cristobal-Perez, Gabriel; Alcala, Luis; Desco, Manuel

    2001-11-01

    Tuberculosis (TB) and other mycobacteriosis are serious illnesses which control is mainly based on presumptive diagnosis. Besides of clinical suspicion, the diagnosis of mycobacteriosis must be done through genus specific smears of clinical specimens. However, these techniques lack of sensitivity and consequently clinicians must wait culture results as much as two months. Computer analysis of digital images from these smears could improve sensitivity of the test and, moreover, decrease workload of the micobacteriologist. Bacteria segmentation of particular species entails a complex process. Bacteria shape is not enough as a discriminant feature, because there are many species that share the same shape. Therefore the segmentation procedure requires to be improved using the color image information. In this paper we present two segmentation procedures based on fuzzy rules and phase-only correlation techniques respectively that will provide the basis of a future automatic particle' screening.

  9. Automatic Image Segmentation Base on Human Color Perceptions

    Directory of Open Access Journals (Sweden)

    Yu Li-jie

    2009-10-01

    Full Text Available In this paper we propose a color image segmentation algorithm based on perceptual color vision model. First, the original image is divide into image blocks which are not overlapped; then, the mean and variance of every image back was calculated in CIEL*a*b* color space, and the image blocks were divided into homogeneous color blocks and texture blocks by the variance of it. The initial seed regions are automatically selected depending on calculating the homogeneous color blocks' color difference in CIEL*a*b* color space and spatial information. The color contrast gradient of the texture blocks need to calculate and the edge information are stored for regional growing. The fuzzy region growing algorithm and coloredge detection to obtain a final segmentation map. The experimental segmentation results hold favorable consistency in terms of human perception, and confirm effectiveness of the algorithm.

  10. 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.

  11. 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.

  12. 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

  13. Automatic breast density segmentation: an integration of different approaches

    International Nuclear Information System (INIS)

    Mammographic breast density has been found to be a strong risk factor for breast cancer. In most studies, it is assessed with a user-assisted threshold method, which is time consuming and subjective. In this study, we develop a breast density segmentation method that is fully automatic. The method is based on pixel classification in which different approaches known in the literature to segment breast density are integrated and extended. In addition, the method incorporates the knowledge of a trained observer, by using segmentations obtained by the user-assisted threshold method as training data. The method is trained and tested using 1300 digitized film mammographic images acquired with a variety of systems. Results show a high correspondence between the automated method and the user-assisted threshold method. Pearson's correlation coefficient between our method and the user-assisted method is R = 0.911 for percent density and R = 0.895 for dense area, which is substantially higher than the best correlation found in the literature (R = 0.70, R = 0.68). The area under the receiver operating characteristic curve obtained when discriminating between fatty and dense pixels is 0.987. A combination of segmentation strategies outperforms the application of single segmentation techniques.

  14. A Flexible Semi-Automatic Approach for Glioblastoma multiforme Segmentation

    CERN Document Server

    Egger, Jan; Kuhnt, Daniela; Kappus, Christoph; Carl, Barbara; Freisleben, Bernd; Nimsky, Christopher

    2011-01-01

    Gliomas are the most common primary brain tumors, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the preoperative tumor volume is essential. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming process that can be overcome with the help of segmentation methods. In this paper, a flexible semi-automatic approach for grade IV glioma segmentation is presented. The approach uses a novel segmentation scheme for spherical objects that creates a directed 3D graph. Thereafter, the minimal cost closed set on the graph is computed via a polynomial time s-t cut, creating an optimal segmentation of the tumor. The user can improve the results by specifying an arbitrary number of additional seed points to support the algorithm with grey value information and geometrical constraints. The presented method is tested on 12 magnetic resonance imaging datasets. The ground truth of the tumor boundaries are manually extracted by neurosurgeons. The...

  15. An Automatic Learning-Based Framework for Robust Nucleus Segmentation.

    Science.gov (United States)

    Xing, Fuyong; Xie, Yuanpu; Yang, Lin

    2016-02-01

    Computer-aided image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of diseases such as brain tumor, pancreatic neuroendocrine tumor (NET), and breast cancer. Automated nucleus segmentation is a prerequisite for various quantitative analyses including automatic morphological feature computation. However, it remains to be a challenging problem due to the complex nature of histopathology images. In this paper, we propose a learning-based framework for robust and automatic nucleus segmentation with shape preservation. Given a nucleus image, it begins with a deep convolutional neural network (CNN) model to generate a probability map, on which an iterative region merging approach is performed for shape initializations. Next, a novel segmentation algorithm is exploited to separate individual nuclei combining a robust selection-based sparse shape model and a local repulsive deformable model. One of the significant benefits of the proposed framework is that it is applicable to different staining histopathology images. Due to the feature learning characteristic of the deep CNN and the high level shape prior modeling, the proposed method is general enough to perform well across multiple scenarios. We have tested the proposed algorithm on three large-scale pathology image datasets using a range of different tissue and stain preparations, and the comparative experiments with recent state of the arts demonstrate the superior performance of the proposed approach. PMID:26415167

  16. Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields.

    Science.gov (United States)

    Liao, Sheng-hui; Liu, Shi-jian; Zou, Bei-ji; Ding, Xi; Liang, Ye; Huang, Jun-hui

    2015-01-01

    An important preprocess in computer-aided orthodontics is to segment teeth from the dental models accurately, which should involve manual interactions as few as possible. But fully automatic partition of all teeth is not a trivial task, since teeth occur in different shapes and their arrangements vary substantially from one individual to another. The difficulty is exacerbated when severe teeth malocclusion and crowding problems occur, which is a common occurrence in clinical cases. Most published methods in this area either are inaccurate or require lots of manual interactions. Motivated by the state-of-the-art general mesh segmentation methods that adopted the theory of harmonic field to detect partition boundaries, this paper proposes a novel, dental-targeted segmentation framework for dental meshes. With a specially designed weighting scheme and a strategy of a priori knowledge to guide the assignment of harmonic constraints, this method can identify teeth partition boundaries effectively. Extensive experiments and quantitative analysis demonstrate that the proposed method is able to partition high-quality teeth automatically with robustness and efficiency. PMID:26413507

  17. Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields

    Directory of Open Access Journals (Sweden)

    Sheng-hui Liao

    2015-01-01

    Full Text Available An important preprocess in computer-aided orthodontics is to segment teeth from the dental models accurately, which should involve manual interactions as few as possible. But fully automatic partition of all teeth is not a trivial task, since teeth occur in different shapes and their arrangements vary substantially from one individual to another. The difficulty is exacerbated when severe teeth malocclusion and crowding problems occur, which is a common occurrence in clinical cases. Most published methods in this area either are inaccurate or require lots of manual interactions. Motivated by the state-of-the-art general mesh segmentation methods that adopted the theory of harmonic field to detect partition boundaries, this paper proposes a novel, dental-targeted segmentation framework for dental meshes. With a specially designed weighting scheme and a strategy of a priori knowledge to guide the assignment of harmonic constraints, this method can identify teeth partition boundaries effectively. Extensive experiments and quantitative analysis demonstrate that the proposed method is able to partition high-quality teeth automatically with robustness and efficiency.

  18. Automatic Segmentation of Abdominal Adipose Tissue in MRI

    DEFF Research Database (Denmark)

    Mosbech, Thomas Hammershaimb; Pilgaard, Kasper; Vaag, Allan; Larsen, Rasmus

    This paper presents a method for automatically segmenting abdominal adipose tissue from 3-dimensional magnetic resonance images. We distinguish between three types of adipose tissue; visceral, deep subcutaneous and superficial subcutaneous. Images are pre-processed to remove the bias field effect...... 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...... are separated using deformable models, incorporating information from the clustering. The subcutaneous adipose tissue is subdivided into a deep and superficial part by means of dynamic programming applied to a spatial transformation of the image data. Regression analysis shows good correspondences...

  19. 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.

  20. Automatic segmentation of maxillofacial cysts in cone beam CT images.

    Science.gov (United States)

    Abdolali, Fatemeh; Zoroofi, Reza Aghaeizadeh; Otake, Yoshito; Sato, Yoshinobu

    2016-05-01

    Accurate segmentation of cysts and tumors is an essential step for diagnosis, monitoring and planning therapeutic intervention. This task is usually done manually, however manual identification and segmentation is tedious. In this paper, an automatic method based on asymmetry analysis is proposed which is general enough to segment various types of jaw cysts. The key observation underlying this approach is that normal head and face structure is roughly symmetric with respect to midsagittal plane: the left part and the right part can be divided equally by an axis of symmetry. Cysts and tumors typically disturb this symmetry. The proposed approach consists of three main steps as follows: At first, diffusion filtering is used for preprocessing and symmetric axis is detected. Then, each image is divided into two parts. In the second stage, free form deformation (FFD) is used to correct slight displacement of corresponding pixels of the left part and a reflected copy of the right part. In the final stage, intensity differences are analyzed and a number of constraints are enforced to remove false positive regions. The proposed method has been validated on 97 Cone Beam Computed Tomography (CBCT) sets containing various jaw cysts which were collected from various image acquisition centers. Validation is performed using three similarity indicators (Jaccard index, Dice's coefficient and Hausdorff distance). The mean Dice's coefficient of 0.83, 0.87 and 0.80 is achieved for Radicular, Dentigerous and KCOT classes, respectively. For most of the experiments done, we achieved high true positive (TP). This means that a large number of cyst pixels are correctly classified. Quantitative results of automatic segmentation show that the proposed method is more effective than one of the recent methods in the literature. PMID:27035862

  1. Automatic partitioning of head CTA for enabling segmentation

    Science.gov (United States)

    Suryanarayanan, Srikanth; Mullick, Rakesh; Mallya, Yogish; Kamath, Vidya; Nagaraj, Nithin

    2004-05-01

    Radiologists perform a CT Angiography procedure to examine vascular structures and associated pathologies such as aneurysms. Volume rendering is used to exploit volumetric capabilities of CT that provides complete interactive 3-D visualization. However, bone forms an occluding structure and must be segmented out. The anatomical complexity of the head creates a major challenge in the segmentation of bone and vessel. An analysis of the head volume reveals varying spatial relationships between vessel and bone that can be separated into three sub-volumes: "proximal", "middle", and "distal". The "proximal" and "distal" sub-volumes contain good spatial separation between bone and vessel (carotid referenced here). Bone and vessel appear contiguous in the "middle" partition that remains the most challenging region for segmentation. The partition algorithm is used to automatically identify these partition locations so that different segmentation methods can be developed for each sub-volume. The partition locations are computed using bone, image entropy, and sinus profiles along with a rule-based method. The algorithm is validated on 21 cases (varying volume sizes, resolution, clinical sites, pathologies) using ground truth identified visually. The algorithm is also computationally efficient, processing a 500+ slice volume in 6 seconds (an impressive 0.01 seconds / slice) that makes it an attractive algorithm for pre-processing large volumes. The partition algorithm is integrated into the segmentation workflow. Fast and simple algorithms are implemented for processing the "proximal" and "distal" partitions. Complex methods are restricted to only the "middle" partition. The partitionenabled segmentation has been successfully tested and results are shown from multiple cases.

  2. Atlas-based automatic segmentation of MR images: Validation study on the brainstem in radiotherapy context

    International Nuclear Information System (INIS)

    Purpose: Brain tumor radiotherapy requires the volume measurements and the localization of several individual brain structures. Any tool that can assist the physician to perform the delineation would then be of great help. Among segmentation methods, those that are atlas-based are appealing because they are able to segment several structures simultaneously, while preserving the anatomy topology. This study aims to evaluate such a method in a clinical context. Methods and materials: The brain atlas is made of two three-dimensional (3D) volumes: the first is an artificial 3D magnetic resonance imaging (MRI); the second consists of the segmented structures in this artificial MRI. The elastic registration of the artificial 3D MRI against a patient 3D MRI dataset yields an elastic transformation that can be applied to the labeled image. The elastic transformation is obtained by minimizing the sum of the square differences of the image intensities and derived from the optical flow principle. This automatic delineation (AD) enables the mapping of the segmented structures onto the patient MRI. Parameters of the AD have been optimized on a set of 20 patients. Results are obtained on a series of 6 patients' MRI. A comprehensive validation of the AD has been conducted on performance of atlas-based segmentation in a clinical context with volume, position, sensitivity, and specificity that are compared by a panel of seven experimented physicians for the brain tumor treatments. Results: Expert interobserver volume variability ranged from 16.70 cm3 to 41.26 cm3. For patients, the ratio of minimal to maximal volume ranged from 48% to 70%. Median volume varied from 19.47 cm3 to 27.66 cm3 and volume of the brainstem calculated by AD varied from 17.75 cm3 to 24.54 cm3. Medians of experts ranged, respectively, for sensitivity and specificity, from 0.75 to 0.98 and from 0.85 to 0.99. Median of AD were, respectively, 0.77 and 0.97. Mean of experts ranged, respectively, from 0.78 to 0

  3. Automatic segmentation of the striatum and globus pallidus using MIST: Multimodal Image Segmentation Tool.

    Science.gov (United States)

    Visser, Eelke; Keuken, Max C; Douaud, Gwenaëlle; Gaura, Veronique; Bachoud-Levi, Anne-Catherine; Remy, Philippe; Forstmann, Birte U; Jenkinson, Mark

    2016-01-15

    Accurate segmentation of the subcortical structures is frequently required in neuroimaging studies. Most existing methods use only a T1-weighted MRI volume to segment all supported structures and usually rely on a database of training data. We propose a new method that can use multiple image modalities simultaneously and a single reference segmentation for initialisation, without the need for a manually labelled training set. The method models intensity profiles in multiple images around the boundaries of the structure after nonlinear registration. It is trained using a set of unlabelled training data, which may be the same images that are to be segmented, and it can automatically infer the location of the physical boundary using user-specified priors. We show that the method produces high-quality segmentations of the striatum, which is clearly visible on T1-weighted scans, and the globus pallidus, which has poor contrast on such scans. The method compares favourably to existing methods, showing greater overlap with manual segmentations and better consistency. PMID:26477650

  4. Automatic segmentation of abdominal vessels for improved pancreas localization

    Science.gov (United States)

    Farag, Amal; Liu, Jiamin; Summers, Ronald M.

    2014-03-01

    Accurate automatic detection and segmentation of abdominal organs from CT images is important for quantitative and qualitative organ tissue analysis as well as computer-aided diagnosis. The large variability of organ locations, the spatial interaction between organs that appear similar in medical scans and orientation and size variations are among the major challenges making the task very difficult. The pancreas poses these challenges in addition to its flexibility which allows for the shape of the tissue to vastly change. Due to the close proximity of the pancreas to numerous surrounding organs within the abdominal cavity the organ shifts according to the conditions of the organs within the abdomen, as such the pancreas is constantly changing. Combining these challenges with typically found patient-to-patient variations and scanning conditions the pancreas becomes harder to localize. In this paper we focus on three abdominal vessels that almost always abut the pancreas tissue and as such useful landmarks to identify the relative location of the pancreas. The splenic and portal veins extend from the hila of the spleen and liver, respectively, travel through the abdominal cavity and join at a position close to the head of the pancreas known as the portal confluence. A third vein, the superior mesenteric vein, anastomoses with the other two veins at the portal confluence. An automatic segmentation framework for obtaining the splenic vein, portal confluence and superior mesenteric vein is proposed using 17 contrast enhanced computed-tomography datasets. The proposed method uses outputs from the multi-organ multi-atlas label fusion and Frangi vesselness filter to obtain automatic seed points for vessel tracking and generation of statistical models of the desired vessels. The approach shows ability to identify the vessels and improve localization of the pancreas within the abdomen.

  5. Automatic segmentation and classification of multiple sclerosis in multichannel MRI.

    Science.gov (United States)

    Akselrod-Ballin, Ayelet; Galun, Meirav; Gomori, John Moshe; Filippi, Massimo; Valsasina, Paola; Basri, Ronen; Brandt, Achi

    2009-10-01

    We introduce a multiscale approach that combines segmentation with classification to detect abnormal brain structures in medical imagery, and demonstrate its utility in automatically detecting multiple sclerosis (MS) lesions in 3-D multichannel magnetic resonance (MR) images. Our method uses segmentation to obtain a hierarchical decomposition of a multichannel, anisotropic MR scans. It then produces a rich set of features describing the segments in terms of intensity, shape, location, neighborhood relations, and anatomical context. These features are then fed into a decision forest classifier, trained with data labeled by experts, enabling the detection of lesions at all scales. Unlike common approaches that use voxel-by-voxel analysis, our system can utilize regional properties that are often important for characterizing abnormal brain structures. We provide experiments on two types of real MR images: a multichannel proton-density-, T2-, and T1-weighted dataset of 25 MS patients and a single-channel fluid attenuated inversion recovery (FLAIR) dataset of 16 MS patients. Comparing our results with lesion delineation by a human expert and with previously extensively validated results shows the promise of the approach. PMID:19758850

  6. Voxel classification and graph cuts for automated segmentation of pathological periprosthetic hip anatomy

    OpenAIRE

    Malan, D.F.; Botha, C.P.; Valstar, E.R.

    2012-01-01

    Purpose Automated patient-specific image-based segmentation of tissues surrounding aseptically loose hip prostheses is desired. For this we present an automated segmentation pipeline that labels periprosthetic tissues in computed tomography (CT). The intended application of this pipeline is in pre-operative planning. Methods Individual voxels were classified based on a set of automatically extracted image features. Minimum-cost graph cuts were computed on the classification results. The graph...

  7. 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.

  8. Automatic optic disc segmentation based on image brightness and contrast

    Science.gov (United States)

    Lu, Shijian; Liu, Jiang; Lim, Joo Hwee; Zhang, Zhuo; Tan, Ngan Meng; Wong, Wing Kee; Li, Huiqi; Wong, Tien Yin

    2010-03-01

    Untreated glaucoma leads to permanent damage of the optic nerve and resultant visual field loss, which can progress to blindness. As glaucoma often produces additional pathological cupping of the optic disc (OD), cupdisc- ratio is one measure that is widely used for glaucoma diagnosis. This paper presents an OD localization method that automatically segments the OD and so can be applied for the cup-disc-ratio based glaucoma diagnosis. The proposed OD segmentation method is based on the observations that the OD is normally much brighter and at the same time have a smoother texture characteristics compared with other regions within retinal images. Given a retinal image we first capture the ODs smooth texture characteristic by a contrast image that is constructed based on the local maximum and minimum pixel lightness within a small neighborhood window. The centre of the OD can then be determined according to the density of the candidate OD pixels that are detected by retinal image pixels of the lowest contrast. After that, an OD region is approximately determined by a pair of morphological operations and the OD boundary is finally determined by an ellipse that is fitted by the convex hull of the detected OD region. Experiments over 71 retinal images of different qualities show that the OD region overlapping reaches up to 90.37% according to the OD boundary ellipses determined by our proposed method and the one manually plotted by an ophthalmologist.

  9. Definition and automatic anatomy recognition of lymph node zones in the pelvis on CT images

    Science.gov (United States)

    Liu, Yu; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Guo, Shuxu; Attor, Rosemary; Reinicke, Danica; Torigian, Drew A.

    2016-03-01

    Currently, unlike IALSC-defined thoracic lymph node zones, no explicitly provided definitions for lymph nodes in other body regions are available. Yet, definitions are critical for standardizing the recognition, delineation, quantification, and reporting of lymphadenopathy in other body regions. Continuing from our previous work in the thorax, this paper proposes a standardized definition of the grouping of pelvic lymph nodes into 10 zones. We subsequently employ our earlier Automatic Anatomy Recognition (AAR) framework designed for body-wide organ modeling, recognition, and delineation to actually implement these zonal definitions where the zones are treated as anatomic objects. First, all 10 zones and key anatomic organs used as anchors are manually delineated under expert supervision for constructing fuzzy anatomy models of the assembly of organs together with the zones. Then, optimal hierarchical arrangement of these objects is constructed for the purpose of achieving the best zonal recognition. For actual localization of the objects, two strategies are used -- optimal thresholded search for organs and one-shot method for the zones where the known relationship of the zones to key organs is exploited. Based on 50 computed tomography (CT) image data sets for the pelvic body region and an equal division into training and test subsets, automatic zonal localization within 1-3 voxels is achieved.

  10. Automatic Scheme for Fused Medical Image Segmentation with Nonsubsampled Contourlet Transform

    Directory of Open Access Journals (Sweden)

    Ch.Hima Bindu

    2012-10-01

    Full Text Available Medical image segmentation has become an essential technique in clinical and research- oriented applications. Because manual segmentation methods are tedious, and semi-automatic segmentation lacks the flexibility, fully-automatic methods have become the preferred type of medical image segmentation. This work proposes a robust fully automatic segmentation scheme based on the modified contouring technique. The entire scheme consists of three stages. In the first stage, the Nonsubsampled Contourlet Transform (NSCT of image is computed. This is followed by the fusion of coefficients using fusion method. For that fused image local threshold is computed. This is followed by the second stage in which the initial points are determined by computation of global threshold. Finally, in the third stage, searching procedure is started from each initial point to obtain closed-loop contours. The whole process is fully automatic. This avoids the disadvantages of semi-automatic schemes such as manually selecting the initial contours and points.

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

    International Nuclear Information System (INIS)

    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

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

    International Nuclear Information System (INIS)

    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.)

  13. Automatic Segmentation of Vertebrae from Radiographs: A Sample-Driven Active Shape Model Approach

    DEFF Research Database (Denmark)

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

    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...

  14. Automatic Segmentation of News Items Based on Video and Audio Features

    Institute of Scientific and Technical Information of China (English)

    王伟强; 高文

    2002-01-01

    The automatic segmentation of news items is a key for implementing the automatic cataloging system of news video. This paper presents an approach which manages audio and video feature information to automatically segment news items. The integration of audio and visual analyses can overcome the weakness of the approach using only image analysis techniques. It makes the approach more adaptable to various situations of news items. The proposed approach detects silence segments in accompanying audio, and integrates them with shot segmentation results, as well as anchor shot detection results, to determine the boundaries among news items. Experimental results show that the integration of audio and video features is an effective approach to solving the problem of automatic segmentation of news items.

  15. Heart region segmentation from low-dose CT scans: an anatomy based approach

    Science.gov (United States)

    Reeves, Anthony P.; Biancardi, Alberto M.; Yankelevitz, David F.; Cham, Matthew D.; Henschke, Claudia I.

    2012-02-01

    Cardiovascular disease is a leading cause of death in developed countries. The concurrent detection of heart diseases during low-dose whole-lung CT scans (LDCT), typically performed as part of a screening protocol, hinges on the accurate quantification of coronary calcification. The creation of fully automated methods is ideal as complete manual evaluation is imprecise, operator dependent, time consuming and thus costly. The technical challenges posed by LDCT scans in this context are mainly twofold. First, there is a high level image noise arising from the low radiation dose technique. Additionally, there is a variable amount of cardiac motion blurring due to the lack of electrocardiographic gating and the fact that heart rates differ between human subjects. As a consequence, the reliable segmentation of the heart, the first stage toward the implementation of morphologic heart abnormality detection, is also quite challenging. An automated computer method based on a sequential labeling of major organs and determination of anatomical landmarks has been evaluated on a public database of LDCT images. The novel algorithm builds from a robust segmentation of the bones and airways and embodies a stepwise refinement starting at the top of the lungs where image noise is at its lowest and where the carina provides a good calibration landmark. The segmentation is completed at the inferior wall of the heart where extensive image noise is accommodated. This method is based on the geometry of human anatomy and does not involve training through manual markings. Using visual inspection by an expert reader as a gold standard, the algorithm achieved successful heart and major vessel segmentation in 42 of 45 low-dose CT images. In the 3 remaining cases, the cardiac base was over segmented due to incorrect hemidiaphragm localization.

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

    International Nuclear Information System (INIS)

    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. ((orig.))

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

    International Nuclear Information System (INIS)

    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

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

    International Nuclear Information System (INIS)

    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

  19. 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.

  20. 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)

    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

  1. Automatic tuning of MST segmentation of mammograms for registration and mass detection algorithms

    OpenAIRE

    Mariusz Bajger; Fei Ma; Bottema, Murk J.

    2009-01-01

    A technique utilizing an entropy measure is developed for automatically tuning the segmentation of screening mammograms by minimum spanning trees (MST). The lack of such technique has been a major obstacle in previous work to segment mammograms for registration and applying mass detection algorithms.

  2. Automatic Music Boundary Detection Using Short Segmental Acoustic Similarity in a Music Piece

    OpenAIRE

    Tanaka Kazuyo; Lee Shi-Wook; Itoh Yoshiaki; Iwabuchi Akira; Kojima Kazunori; Ishigame Masaaki

    2008-01-01

    The present paper proposes a new approach for detecting music boundaries, such as the boundary between music pieces or the boundary between a music piece and a speech section for automatic segmentation of musical video data and retrieval of a designated music piece. The proposed approach is able to capture each music piece using acoustic similarity defined for short-term segments in the music piece. The short segmental acoustic similarity is obtained by means of a new algorithm called segmen...

  3. Semi-Automatic Segmentation of Autosomal Dominant Polycystic Kidneys using Random Forests

    OpenAIRE

    Sharma, Kanishka; Peter, Loic; Rupprecht, Christian; Caroli, Anna; Wang, Lichao; Remuzzi, Andrea; Baust, Maximilian; Navab, Nassir

    2015-01-01

    This paper presents a method for 3D segmentation of kidneys from patients with autosomal dominant polycystic kidney disease (ADPKD) and severe renal insufficiency, using computed tomography (CT) data. ADPKD severely alters the shape of the kidneys due to non-uniform formation of cysts. As a consequence, fully automatic segmentation of such kidneys is very challenging. We present a segmentation method with minimal user interaction based on a random forest classifier. One of the major novelties...

  4. Automatic segmentation of right ventricular ultrasound images using sparse matrix transform and a level set

    International Nuclear Information System (INIS)

    An automatic segmentation framework is proposed to segment the right ventricle (RV) in echocardiographic images. The method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform, a training model, and a localized region-based level set. First, the sparse matrix transform extracts main motion regions of the myocardium as eigen-images by analyzing the statistical information of the images. Second, an RV training model is registered to the eigen-images in order to locate the position of the RV. Third, the training model is adjusted and then serves as an optimized initialization for the segmentation of each image. Finally, based on the initializations, a localized, region-based level set algorithm is applied to segment both epicardial and endocardial boundaries in each echocardiograph. Three evaluation methods were used to validate the performance of the segmentation framework. The Dice coefficient measures the overall agreement between the manual and automatic segmentation. The absolute distance and the Hausdorff distance between the boundaries from manual and automatic segmentation were used to measure the accuracy of the segmentation. Ultrasound images of human subjects were used for validation. For the epicardial and endocardial boundaries, the Dice coefficients were 90.8 ± 1.7% and 87.3 ± 1.9%, the absolute distances were 2.0 ± 0.42 mm and 1.79 ± 0.45 mm, and the Hausdorff distances were 6.86 ± 1.71 mm and 7.02 ± 1.17 mm, respectively. The automatic segmentation method based on a sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging. (paper)

  5. Automatic intraocular lens segmentation and detection in optical coherence tomography images.

    Science.gov (United States)

    Gillner, Melanie; Eppig, Timo; Langenbucher, Achim

    2014-05-01

    We present a new algorithm for automatic segmentation and detection of an accommodative intraocular lens implanted in a biomechanical eye model. We extracted lens curvature and position. The algorithm contains denoising and fan correction by a multi-level calibration routine. The segmentation is realized by an adapted canny edge detection algorithm followed by a detection of lens surface with an automatic region of interest search to suppress non-optical surfaces like the lens haptic. The optical distortion of lens back surface is corrected by inverse raytracing. Lens geometry was extracted by a spherical fit. We implemented and demonstrated a powerful algorithm for automatic segmentation, detection and surface analysis of intraocular lenses in vitro. The achieved accuracy is within the expected range determined by previous studies. Future improvements will include the transfer to clinical anterior segment OCT devices. PMID:23928353

  6. 3D Fast Automatic Segmentation of Kidney Based on Modified AAM and Random Forest.

    Science.gov (United States)

    Jin, Chao; Shi, Fei; Xiang, Dehui; Jiang, Xueqing; Zhang, Bin; Wang, Ximing; Zhu, Weifang; Gao, Enting; Chen, Xinjian

    2016-06-01

    In this paper, a fully automatic method is proposed to segment the kidney into multiple components: renal cortex, renal column, renal medulla and renal pelvis, in clinical 3D CT abdominal images. The proposed fast automatic segmentation method of kidney consists of two main parts: localization of renal cortex and segmentation of kidney components. In the localization of renal cortex phase, a method which fully combines 3D Generalized Hough Transform (GHT) and 3D Active Appearance Models (AAM) is applied to localize the renal cortex. In the segmentation of kidney components phase, a modified Random Forests (RF) method is proposed to segment the kidney into four components based on the result from localization phase. During the implementation, a multithreading technology is applied to speed up the segmentation process. The proposed method was evaluated on a clinical abdomen CT data set, including 37 contrast-enhanced volume data using leave-one-out strategy. The overall true-positive volume fraction and false-positive volume fraction were 93.15%, 0.37% for renal cortex segmentation; 83.09%, 0.97% for renal column segmentation; 81.92%, 0.55% for renal medulla segmentation; and 80.28%, 0.30% for renal pelvis segmentation, respectively. The average computational time of segmenting kidney into four components took 20 seconds. PMID:26742124

  7. 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.

  8. Automatic segmentation of heart cavities in multidimensional ultrasound images

    Science.gov (United States)

    Wolf, Ivo; Glombitza, Gerald; De Simone, Rosalyn; Meinzer, Hans-Peter

    2000-06-01

    We propose a segmentation method different from active contours, which can cope with incomplete edges. The algorithm has been developed to segment heart cavities, but may be extended to more complex object shapes. Due to the almost convex geometry of heart cavities we are using a polar coordinate system with its origin near the cavity's center. The image is scanned from the origin for potential edge points. In order to assess the likelihood of an edge point to belong to the myocardial wall, region based information, such as visibility and local wall thickness, is included. The local information (edge points) progressively is expanded by first grouping the edge points to line segments and then selecting a subgroup of segments to obtain the final closed contour. This is done by means of minimizing a cost function. The plausibility of the result is checked and, if needed, the contour is corrected and/or refined by searching for additional potential edge points. For multidimensional images the algorithm is applied slice-by-slice without the need of further user interaction. The new segmentation method has been applied to clinical ultrasound images, the result being that the myocardial wall correctly was detected in the vast majority of cases.

  9. 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 mu

  10. Knowledge Automatic Indexing Based on Concept Lexicon and Segmentation Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Lan-cheng; JIANG Dan; LE Jia-jin

    2005-01-01

    This paper is based on two existing theories about automatic indexing of thematic knowledge concept. The prohibit-word table with position information has been designed. The improved Maximum Matching-Minimum Backtracking method has been researched. Moreover it has been studied on improved indexing algorithm and application technology based on rules and thematic concept word table.

  11. Automatic segmentation of cerebral MR images using artificial neural networks

    International Nuclear Information System (INIS)

    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

  12. Automatic segmentation and diameter measurement of coronary artery vessels

    Science.gov (United States)

    Zhao, Kun; Tang, Zhenyu; Pauli, Josef

    2011-03-01

    This work presents a hybrid method for 2D artery vessel segmentation and diameter measurement in X-Ray angiograms. The proposed method is novel in that tracking-based and model-based approaches are combined. A robust and efficient tracking template, the "annular template", is devised for vessel tracking. It can readily be applied on X-Ray angiograms without any preprocessing. Starting from an initial tracking point given by the user the tracking algorithm iteratively repositions the annular template and thereby detects the vessel boundaries and possible bifurcations. With a user selected end point the tracking process results in a set of points that describes the contour and topology of an artery vessel segment between the initial and end points. A "boundary correction and interpolation" operation refines the extracted points which initialize the Snakes algorithm. Boundary correction adjusts the points to ensure that they lie on the vessel segment of interest. Boundary interpolation adds more points, so that there are sufficiently many points for the Snakes algorithm to generate a smooth and accurate vessel segmentation. After the application of Snakes the resulting points are sequentially connected to represent the vessel contour. Then, the diameters are measured along the extracted vessel contour. The segmentation and measurement results are compared with manually extracted and measured vessel segments. The average Precision, Recall and Jaccard Index of 21 vessel samples are 91.5%, 92.1% and 84.9%, respectively. Compared with ground truth measurements of diameters the average relative error is 8.2%, and the average absolute error is 1.13 pixels.

  13. Automatic tissue segmentation of breast biopsies imaged by QPI

    Science.gov (United States)

    Majeed, Hassaan; Nguyen, Tan; Kandel, Mikhail; Marcias, Virgilia; Do, Minh; Tangella, Krishnarao; Balla, Andre; Popescu, Gabriel

    2016-03-01

    The current tissue evaluation method for breast cancer would greatly benefit from higher throughput and less inter-observer variation. Since quantitative phase imaging (QPI) measures physical parameters of tissue, it can be used to find quantitative markers, eliminating observer subjectivity. Furthermore, since the pixel values in QPI remain the same regardless of the instrument used, classifiers can be built to segment various tissue components without need for color calibration. In this work we use a texton-based approach to segment QPI images of breast tissue into various tissue components (epithelium, stroma or lumen). A tissue microarray comprising of 900 unstained cores from 400 different patients was imaged using Spatial Light Interference Microscopy. The training data were generated by manually segmenting the images for 36 cores and labelling each pixel (epithelium, stroma or lumen.). For each pixel in the data, a response vector was generated by the Leung-Malik (LM) filter bank and these responses were clustered using the k-means algorithm to find the centers (called textons). A random forest classifier was then trained to find the relationship between a pixel's label and the histogram of these textons in that pixel's neighborhood. The segmentation was carried out on the validation set by calculating the texton histogram in a pixel's neighborhood and generating a label based on the model learnt during training. Segmentation of the tissue into various components is an important step toward efficiently computing parameters that are markers of disease. Automated segmentation, followed by diagnosis, can improve the accuracy and speed of analysis leading to better health outcomes.

  14. Automatic Segmentation for Reach/frequency Estimation of Newspaper Sections and Internet Papers

    DEFF Research Database (Denmark)

    Mortensen, Peter Stendahl; Arnaa, Kristian

    1999-01-01

    This paper will present a new way of estimating reach and frequency without asking the frequency question or conducting double interviewing. Instead, the sample is segmented automatically by a CHAID-analysis, maximising the differences in reading probabilities among the segments. Typically, many...... 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 of...

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

    DEFF Research Database (Denmark)

    Jørgensen, Peter Stanley; Hansen, Karin Vels; Larsen, Rasmus; Bowen, Jacob R.

    2010-01-01

    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...... the experimentally acquired data are used as the driving forces. The framework performs the segmentation in 3D rather than on a slice by slice basis. It naturally supplies sub-voxel precision of segmented surfaces and allows constraints on the surface curvature to enforce a smooth surface in the...

  16. Real-Time Automatic Artery Segmentation, Reconstruction and Registration for Ultrasound-Guided Regional Anaesthesia of the Femoral Nerve.

    Science.gov (United States)

    Smistad, Erik; Lindseth, Frank

    2016-03-01

    The goal is to create an assistant for ultrasound- guided femoral nerve block. By segmenting and visualizing the important structures such as the femoral artery, we hope to improve the success of these procedures. This article is the first step towards this goal and presents novel real-time methods for identifying and reconstructing the femoral artery, and registering a model of the surrounding anatomy to the ultrasound images. The femoral artery is modelled as an ellipse. The artery is first detected by a novel algorithm which initializes the artery tracking. This algorithm is completely automatic and requires no user interaction. Artery tracking is achieved with a Kalman filter. The 3D artery is reconstructed in real-time with a novel algorithm and a tracked ultrasound probe. A mesh model of the surrounding anatomy was created from a CT dataset. Registration of this model is achieved by landmark registration using the centerpoints from the artery tracking and the femoral artery centerline of the model. The artery detection method was able to automatically detect the femoral artery and initialize the tracking in all 48 ultrasound sequences. The tracking algorithm achieved an average dice similarity coefficient of 0.91, absolute distance of 0.33 mm, and Hausdorff distance 1.05 mm. The mean registration error was 2.7 mm, while the average maximum error was 12.4 mm. The average runtime was measured to be 38, 8, 46 and 0.2 milliseconds for the artery detection, tracking, reconstruction and registration methods respectively. PMID:26513782

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

    International Nuclear Information System (INIS)

    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)

  18. Experiments in Image Segmentation for Automatic US License Plate Recognition

    OpenAIRE

    Diaz Acosta, Beatriz

    2004-01-01

    License plate recognition/identification (LPR/I) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. In the United States, however, each state has its own standard-issue plates, plus several optional styles, which are referred to as special license plates or varieties. There is a clear absence of standardization and multi-colored, complex backgrounds are becoming more frequent in license plates. Commercially availab...

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

    International Nuclear Information System (INIS)

    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)

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

    Science.gov (United States)

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

    2012-12-01

    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.

  1. Algorithms for automatic segmentation of bovine embryos produced in vitro

    International Nuclear Information System (INIS)

    In vitro production has been employed in bovine embryos and quantification of lipids is fundamental to understand the metabolism of these embryos. This paper presents a unsupervised segmentation method for histological images of bovine embryos. In this method, the anisotropic filter was used in the differents RGB components. After pre-processing step, the thresholding technique based on maximum entropy was applied to separate lipid droplets in the histological slides in different stages: early cleavage, morula and blastocyst. In the postprocessing step, false positives are removed using the connected components technique that identify regions with excess of dye near pellucid zone. The proposed segmentation method was applied in 30 histological images of bovine embryos. Experiments were performed with the images and statistical measures of sensitivity, specificity and accuracy were calculated based on reference images (gold standard). The value of accuracy of the proposed method was 96% with standard deviation of 3%

  2. Automatic detection and Segmentation of the Tumor Tissue

    Czech Academy of Sciences Publication Activity Database

    Čáp, M.; Gescheidtová, E.; Marcon, P.; Bartušek, Karel

    Cambridge : The Electromagnetics Academy, 2013, s. 53-56. ISBN 978-1-934142-24-0. ISSN 1559-9450. [The 33rd PIERS in Taipei. Progress in Electromagnetics Research Symposium.. Taipei (TW), 25.03.2013-28.03.2013] R&D Projects: GA ČR GAP102/11/0318; GA ČR GAP102/12/1104 Institutional support: RVO:68081731 Keywords : segmentation * MRI * tumor tissue * high-grade glioms Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering

  3. Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs

    OpenAIRE

    Mohammad Awrangjeb; Fraser, Clive S.

    2014-01-01

    Automatic extraction of building roofs from remote sensing data is important for many applications, including 3D city modeling. This paper proposes a new method for automatic segmentation of raw LIDAR (light detection and ranging) data. Using the ground height from a DEM (digital elevation model), the raw LIDAR points are separated into two groups. The first group contains the ground points that form a “building mask”. The second group contains non-ground points that are clustered using the b...

  4. Carotid stenosis assessment with multi-detector CT angiography: comparison between manual and automatic segmentation methods.

    Science.gov (United States)

    Zhu, Chengcheng; Patterson, Andrew J; Thomas, Owen M; Sadat, Umar; Graves, Martin J; Gillard, Jonathan H

    2013-04-01

    Luminal stenosis is used for selecting the optimal management strategy for patients with carotid artery disease. The aim of this study is to evaluate the reproducibility of carotid stenosis quantification using manual and automated segmentation methods using submillimeter through-plane resolution Multi-Detector CT angiography (MDCTA). 35 patients having carotid artery disease with >30 % luminal stenosis as identified by carotid duplex imaging underwent contrast enhanced MDCTA. Two experienced CT readers quantified carotid stenosis from axial source images, reconstructed maximum intensity projection (MIP) and 3D-carotid geometry which was automatically segmented by an open-source toolkit (Vascular Modelling Toolkit, VMTK) using NASCET criteria. Good agreement among the measurement using axial images, MIP and automatic segmentation was observed. Automatic segmentation methods show better inter-observer agreement between the readers (intra-class correlation coefficient (ICC): 0.99 for diameter stenosis measurement) than manual measurement of axial (ICC = 0.82) and MIP (ICC = 0.86) images. Carotid stenosis quantification using an automatic segmentation method has higher reproducibility compared with manual methods. PMID:23135615

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

    International Nuclear Information System (INIS)

    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.

  6. Principles and methods for automatic and semi-automatic tissue segmentation in MRI data.

    Science.gov (United States)

    Wang, Lei; Chitiboi, Teodora; Meine, Hans; Günther, Matthias; Hahn, Horst K

    2016-04-01

    The development of magnetic resonance imaging (MRI) revolutionized both the medical and scientific worlds. A large variety of MRI options have generated a huge amount of image data to interpret. The investigation of a specific tissue in 3D or 4D MR images can be facilitated by image processing techniques, such as segmentation and registration. In this work, we provide a brief review of the principles and methods that are commonly applied to achieve superior tissue segmentation results in MRI. The impacts of MR image acquisition on segmentation outcome and the principles of selecting and exploiting segmentation techniques tailored for specific tissue identification tasks are discussed. In the end, two exemplary applications, breast and fibroglandular tissue segmentation in MRI and myocardium segmentation in short-axis cine and real-time MRI, are discussed to explain the typical challenges that can be posed in practical segmentation tasks in MRI data. The corresponding solutions that are adopted to deal with these challenges of the two practical segmentation tasks are thoroughly reviewed. PMID:26755062

  7. Automatic Active Contour-Based Segmentation and Classification of Carotid Artery Ultrasound Images

    OpenAIRE

    Chaudhry, Asmatullah; Hassan, Mehdi; Khan, Asifullah; Kim, Jin Young

    2013-01-01

    In this paper, we present automatic image segmentation and classification technique for carotid artery ultrasound images based on active contour approach. For early detection of the plaque in carotid artery to avoid serious brain strokes, active contour-based techniques have been applied successfully to segment out the carotid artery ultrasound images. Further, ultrasound images might be affected due to rotation, scaling, or translational factors during acquisition process. Keeping in view th...

  8. Automatic 3D Object Segmentation in Multiple Views using Volumetric Graph-Cuts

    OpenAIRE

    Campbell, N. D. F.; Vogiatzis, G.; Hernández, C.; Cipolla, R.

    2007-01-01

    We propose an algorithm for automatically obtaining a segmentation of a rigid object in a sequence of images that are calibrated for camera pose and intrinsic parameters. Until recently, the best segmentation results have been obtained by interactive methods that require manual labelling of image regions. Our method requires no user input but instead relies on the camera fixating on the object of interest during the sequence. We begin by learning a model of the object is colour, from the imag...

  9. Semi-Automatic Medical Image Segmentation with Adaptive Local Statistics in Conditional Random Fields Framework

    OpenAIRE

    Hu, Yu-Chi J.; Michael D. Grossberg; Mageras, Gikas S.

    2008-01-01

    Planning radiotherapy and surgical procedures usually require onerous manual segmentation of anatomical structures from medical images. In this paper we present a semi-automatic and accurate segmentation method to dramatically reduce the time and effort required of expert users. This is accomplished by giving a user an intuitive graphical interface to indicate samples of target and non-target tissue by loosely drawing a few brush strokes on the image. We use these brush strokes to provide the...

  10. 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

  11. Automatic organ segmentation on torso CT images by using content-based image retrieval

    Science.gov (United States)

    Zhou, Xiangrong; Watanabe, Atsuto; Zhou, Xinxin; Hara, Takeshi; Yokoyama, Ryujiro; Kanematsu, Masayuki; Fujita, Hiroshi

    2012-02-01

    This paper presents a fast and robust segmentation scheme that automatically identifies and extracts a massive-organ region on torso CT images. In contrast to the conventional algorithms that are designed empirically for segmenting a specific organ based on traditional image processing techniques, the proposed scheme uses a fully data-driven approach to accomplish a universal solution for segmenting the different massive-organ regions on CT images. Our scheme includes three processing steps: machine-learning-based organ localization, content-based image (reference) retrieval, and atlas-based organ segmentation techniques. We applied this scheme to automatic segmentations of heart, liver, spleen, left and right kidney regions on non-contrast CT images respectively, which are still difficult tasks for traditional segmentation algorithms. The segmentation results of these organs are compared with the ground truth that manually identified by a medical expert. The Jaccard similarity coefficient between the ground truth and automated segmentation result centered on 67% for heart, 81% for liver, 78% for spleen, 75% for left kidney, and 77% for right kidney. The usefulness of our proposed scheme was confirmed.

  12. Automatic phone segment alignment using statistical deviations from manual transcriptions

    Science.gov (United States)

    Hayakawa, Toru; Shirai, Katsuhiko; Kato, Hiroaki; Sagisaka, Yoshinori

    2002-11-01

    For precise temporal characteristic description, disagreements between manual labeling and automatic labeling were quantitatively analyzed with respect to the spectral feature extraction, adoption of acoustic matchers (HMM models), and acoustic matcher by itself. Error analysis shows that boundaries are shifted at phone boundaries where the speech spectrum changes quite rapidly. This disagreement results from the spectral feature extraction averaged over a given window. For the adoption of model, big errors are found at phone boundaries where the spectrum changes slowly. The third model-dependent errors are seen at phones whose duration cannot be shorter than the frame increment period times the HMM state number. To take into account these error factors individually to reduce the amount of alignment errors, we modified the automatic alignment results context-dependently using statistical characteristics of phone boundary displacement. This post-processing of boundary modification reduces boundary errors from 14.79 ms to 11.07 ms. Supplementary experiment shows that this improvement of about 4 ms corresponds to eight times of error reduction obtained by speaker adaptation of acoustic matchers. [Work supported by TAO, Japan.

  13. Weakly supervised automatic segmentation and 3D modeling of the knee joint from MR images

    Science.gov (United States)

    Amami, Amal; Ben Azouz, Zouhour

    2013-12-01

    Automatic segmentation and 3D modeling of the knee joint from MR images, is a challenging task. Most of the existing techniques require the tedious manual segmentation of a training set of MRIs. We present an approach that necessitates the manual segmentation of one MR image. It is based on a volumetric active appearance model. First, a dense tetrahedral mesh is automatically created on a reference MR image that is arbitrary selected. Second, a pairwise non-rigid registration between each MRI from a training set and the reference MRI is computed. The non-rigid registration is based on a piece-wise affine deformation using the created tetrahedral mesh. The minimum description length is then used to bring all the MR images into a correspondence. An average image and tetrahedral mesh, as well as a set of main modes of variations, are generated using the established correspondence. Any manual segmentation of the average MRI can be mapped to other MR images using the AAM. The proposed approach has the advantage of simultaneously generating 3D reconstructions of the surface as well as a 3D solid model of the knee joint. The generated surfaces and tetrahedral meshes present the interesting property of fulfilling a correspondence between different MR images. This paper shows preliminary results of the proposed approach. It demonstrates the automatic segmentation and 3D reconstruction of a knee joint obtained by mapping a manual segmentation of a reference image.

  14. Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring

    Science.gov (United States)

    Moghbel, Mehrdad; Mashohor, Syamsiah; Mahmud, Rozi; Saripan, M. Iqbal Bin

    2016-01-01

    Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset. PMID:27540353

  15. Heritability and reliability of automatically segmented human hippocampal formation subregions.

    Science.gov (United States)

    Whelan, Christopher D; Hibar, Derrek P; van Velzen, Laura S; Zannas, Anthony S; Carrillo-Roa, Tania; McMahon, Katie; Prasad, Gautam; Kelly, Sinéad; Faskowitz, Joshua; deZubiracay, Greig; Iglesias, Juan E; van Erp, Theo G M; Frodl, Thomas; Martin, Nicholas G; Wright, Margaret J; Jahanshad, Neda; Schmaal, Lianne; Sämann, Philipp G; Thompson, Paul M

    2016-03-01

    The human hippocampal formation can be divided into a set of cytoarchitecturally and functionally distinct subregions, involved in different aspects of memory formation. Neuroanatomical disruptions within these subregions are associated with several debilitating brain disorders including Alzheimer's disease, major depression, schizophrenia, and bipolar disorder. Multi-center brain imaging consortia, such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, are interested in studying disease effects on these subregions, and in the genetic factors that affect them. For large-scale studies, automated extraction and subsequent genomic association studies of these hippocampal subregion measures may provide additional insight. Here, we evaluated the test-retest reliability and transplatform reliability (1.5T versus 3T) of the subregion segmentation module in the FreeSurfer software package using three independent cohorts of healthy adults, one young (Queensland Twins Imaging Study, N=39), another elderly (Alzheimer's Disease Neuroimaging Initiative, ADNI-2, N=163) and another mixed cohort of healthy and depressed participants (Max Planck Institute, MPIP, N=598). We also investigated agreement between the most recent version of this algorithm (v6.0) and an older version (v5.3), again using the ADNI-2 and MPIP cohorts in addition to a sample from the Netherlands Study for Depression and Anxiety (NESDA) (N=221). Finally, we estimated the heritability (h(2)) of the segmented subregion volumes using the full sample of young, healthy QTIM twins (N=728). Test-retest reliability was high for all twelve subregions in the 3T ADNI-2 sample (intraclass correlation coefficient (ICC)=0.70-0.97) and moderate-to-high in the 4T QTIM sample (ICC=0.5-0.89). Transplatform reliability was strong for eleven of the twelve subregions (ICC=0.66-0.96); however, the hippocampal fissure was not consistently reconstructed across 1.5T and 3T field strengths (ICC=0

  16. 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. PMID:26553109

  17. AUTOMATIC RETINA EXUDATES SEGMENTATION WITHOUT A MANUALLY LABELLED TRAINING SET

    Energy Technology Data Exchange (ETDEWEB)

    Giancardo, Luca [ORNL; Meriaudeau, Fabrice [ORNL; Karnowski, Thomas Paul [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Tobin Jr, Kenneth William [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK)

    2011-01-01

    Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy which can be assessed by detecting exudates (a type of bright lesion) in fundus images. In this work, two new methods for the detection of exudates are presented which do not use a supervised learning step and therefore do not require ground-truthed lesion training sets which are time consuming to create, difficult to obtain, and prone to human error. We introduce a new dataset of fundus images from various ethnic groups and levels of DME which we have made publicly available. We evaluate our algorithm with this dataset and compare our results with two recent exudate segmentation algorithms. In all of our tests, our algorithms perform better or comparable with an order of magnitude reduction in computational time.

  18. An efficient conditional random field approach for automatic and interactive neuron segmentation.

    Science.gov (United States)

    Uzunbas, Mustafa Gokhan; Chen, Chao; Metaxas, Dimitris

    2016-01-01

    We present a new graphical-model-based method for automatic and interactive segmentation of neuron structures from electron microscopy (EM) images. For automated reconstruction, our learning based model selects a collection of nodes from a hierarchical merging tree as the proposed segmentation. More specifically, this is achieved by training a conditional random field (CRF) whose underlying graph is the watershed merging tree. The maximum a posteriori (MAP) prediction of the CRF is the output segmentation. Our results are comparable to the results of state-of-the-art methods. Furthermore, both the inference and the training are very efficient as the graph is tree-structured. The problem of neuron segmentation requires extremely high segmentation quality. Therefore, proofreading, namely, interactively correcting mistakes of the automatic method, is a necessary module in the pipeline. Based on our efficient tree-structured inference algorithm, we develop an interactive segmentation framework which only selects locations where the model is uncertain for a user to proofread. The uncertainty is measured by the marginals of the graphical model. Only giving a limited number of choices makes the user interaction very efficient. Based on user corrections, our framework modifies the merging tree and thus improves the segmentation globally. PMID:26210001

  19. Automatic Segmentation of Multi-Contrast MRI Using Statistical Region Merging

    Czech Academy of Sciences Publication Activity Database

    Dvořák, P.; Bartušek, Karel; Gescheidtová, E.

    Cambridge : The Electromagnetics Academy, 2014, s. 1865-1869. ISBN 978-1-934142-28-8. [PIERS 2014. Progress In Electromagnetics Research Symposium /35./. Guangzhou (CN), 25.08.2014-28.08.2014] R&D Projects: GA ČR GAP102/12/1104 Institutional support: RVO:68081731 Keywords : MRI * automatic segmentation Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering

  20. Blood Vessel Segmentation Using Moving-Window Robust Automatic Threshold Selection

    NARCIS (Netherlands)

    Wilkinson, Michael H.F.; Wijbenga, Tsjipke; Vries, Gijs de; Westenberg, Michel A.

    2003-01-01

    Two moving-window methods, using either flat or Gaussian weighted windows, for local thresholding with Robust Automatic Threshold Selection are developed. The results show that fast segmentation of blood vessels against a varying background and noise is possible at modest computational cost. Volumes

  1. Affinity-based constraint optimization for nearly-automatic vessel segmentation

    Science.gov (United States)

    Cooper, O.; Freiman, M.; Joskowicz, L.; Lischinski, D.

    2010-03-01

    We present an affinity-based optimization method for nearly-automatic vessels segmentation in CTA scans. The desired segmentation is modeled as a function that minimizes a quadratic affinity-based functional. The functional incorporates intensity and geometrical vessel shape information and a smoothing constraint. Given a few user-defined seeds, the minimum of the functional is obtained by solving a single set of linear equations. The binary segmentation is then obtained by applying a user-selected threshold. The advantages of our method are that it requires fewer initialization seeds, is robust, and yields better results than existing graph-based interactive segmentation methods. Experimental results on 20 vessel segments including the carotid arteries bifurcation and noisy parts of the carotid yield a mean symmetric surface error of 0.54mm (std=0.28).

  2. Automatic segmentation of vertebral contours from CT images using fuzzy corners.

    Science.gov (United States)

    Athertya, Jiyo S; Saravana Kumar, G

    2016-05-01

    Automatic segmentation of bone in computed tomography (CT) images is critical for the implementation of computer-assisted diagnosis which has increasing potential in the evaluation of various spine disorders. Of the many techniques available for delineating the region of interest (ROI), active contour methods (ACM) are well-established techniques that are used to segment medical images. The initialization for these methods is either through manual intervention or by applying a global threshold, thus making them semi-automatic in nature. The paper presents a methodology for automatic contour initialization in ACM and demonstrates the applicability of the method for medical image segmentation from spinal CT images. Initially, a set of feature markers from the image is extracted to construct an initial contour for the ACM. A fuzzified corner metric, based on image intensity, is proposed to identify the feature markers to be enclosed by the contour. A concave hull based on α shape, is constructed using these fuzzy corners to give the initial contour. The proposed method was evaluated against conventional feature detectors and other initialization methods. The results show the method׳s robust performance in the presence of simulated Gaussian noise levels. The method enables the ACM to efficiently converge to the ground truth segmentation. The reference standard for comparison was the annotated images from a radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the segmentation. PMID:27017068

  3. 3D automatic liver segmentation using feature-constrained Mahalanobis distance in CT images.

    Science.gov (United States)

    Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

    2016-08-01

    Automatic 3D liver segmentation is a fundamental step in the liver disease diagnosis and surgery planning. This paper presents a novel fully automatic algorithm for 3D liver segmentation in clinical 3D computed tomography (CT) images. Based on image features, we propose a new Mahalanobis distance cost function using an active shape model (ASM). We call our method MD-ASM. Unlike the standard active shape model (ST-ASM), the proposed method introduces a new feature-constrained Mahalanobis distance cost function to measure the distance between the generated shape during the iterative step and the mean shape model. The proposed Mahalanobis distance function is learned from a public database of liver segmentation challenge (MICCAI-SLiver07). As a refinement step, we propose the use of a 3D graph-cut segmentation. Foreground and background labels are automatically selected using texture features of the learned Mahalanobis distance. Quantitatively, the proposed method is evaluated using two clinical 3D CT scan databases (MICCAI-SLiver07 and MIDAS). The evaluation of the MICCAI-SLiver07 database is obtained by the challenge organizers using five different metric scores. The experimental results demonstrate the availability of the proposed method by achieving an accurate liver segmentation compared to the state-of-the-art methods. PMID:26501155

  4. Automatic segmentation method which divides a cerebral artery tree in time-of-flight MR-angiography into artery segments

    Science.gov (United States)

    Takemura, Akihiro; Suzuki, Masayuki; Harauchi, Hajime; Okumura, Yusuke; Umeda, Tokuo

    2006-03-01

    To achieve sufficient accuracy and robustness, 2D/3D registration methods between DSA and MRA of the cerebral artery require an automatic extraction method that can isolate wanted segments from the cerebral artery tree. Here, we described an automatic segmentation method that divides the cerebral artery tree in time-of-flight magnetic resonance angiography (TOF-MRA) into each artery. This method requires a 3D dataset of the cerebral artery tree obtained by TOF-MRA. The processes of this method are: 1) every branch in the cerebral artery tree is labeled with a unique index number, 2) the 3D center of the Circle of Willis is determined using 2D and 3D templates, and 3) the labeled branches are classified with reference to the 3D territory map of cerebral arteries centered on the Circle of Willis. This method classifies all branches into internal carotid arteries (ICA), basilar artery (BA), middle cerebral artery (MCA), a1 segment of anterior cerebral artery (ACA(A1)), other segments of the anterior cerebral artery (ACA), posterior communication artery (PcomA), and posterior cerebral artery (PCA). In the eleven cases examined, the numbers of correctly segmented pixels in each branch were counted and the percentages based on the total number of pixels of the artery were calculated. Manually classified arteries of each case were used as references. Mean percentages were: ACA, 87.6%; R-ACA(A1), 44.9%; L-ACA(A1), 30.4%; R-MC, 82.4%; L-MC, 79.0%; R-PcomA, 0.5%; L-PcomA, 0.0%; R-PCA, 77.2%; L-PCA, 80.0%; R-ICA, 78.6%; L-ICA, 93.05; BA, 77.1%; and total arteries, 78.9%.

  5. A framework for automatic heart sound analysis without segmentation

    Directory of Open Access Journals (Sweden)

    Tungpimolrut Kanokvate

    2011-02-01

    Full Text Available Abstract Background A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs. Method Equal number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS. The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors. Result The proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR, and 0.90 under impulse noise up to 0.3 s duration. Conclusion The proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set.

  6. 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.

  7. 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).

  8. Automatic segmentation and co-registration of gated CT angiography datasets: measuring abdominal aortic pulsatility

    Science.gov (United States)

    Wentz, Robert; Manduca, Armando; Fletcher, J. G.; Siddiki, Hassan; Shields, Raymond C.; Vrtiska, Terri; Spencer, Garrett; Primak, Andrew N.; Zhang, Jie; Nielson, Theresa; McCollough, Cynthia; Yu, Lifeng

    2007-03-01

    Purpose: To develop robust, novel segmentation and co-registration software to analyze temporally overlapping CT angiography datasets, with an aim to permit automated measurement of regional aortic pulsatility in patients with abdominal aortic aneurysms. Methods: We perform retrospective gated CT angiography in patients with abdominal aortic aneurysms. Multiple, temporally overlapping, time-resolved CT angiography datasets are reconstructed over the cardiac cycle, with aortic segmentation performed using a priori anatomic assumptions for the aorta and heart. Visual quality assessment is performed following automatic segmentation with manual editing. Following subsequent centerline generation, centerlines are cross-registered across phases, with internal validation of co-registration performed by examining registration at the regions of greatest diameter change (i.e. when the second derivative is maximal). Results: We have performed gated CT angiography in 60 patients. Automatic seed placement is successful in 79% of datasets, requiring either no editing (70%) or minimal editing (less than 1 minute; 12%). Causes of error include segmentation into adjacent, high-attenuating, nonvascular tissues; small segmentation errors associated with calcified plaque; and segmentation of non-renal, small paralumbar arteries. Internal validation of cross-registration demonstrates appropriate registration in our patient population. In general, we observed that aortic pulsatility can vary along the course of the abdominal aorta. Pulsation can also vary within an aneurysm as well as between aneurysms, but the clinical significance of these findings remain unknown. Conclusions: Visualization of large vessel pulsatility is possible using ECG-gated CT angiography, partial scan reconstruction, automatic segmentation, centerline generation, and coregistration of temporally resolved datasets.

  9. Automatically Gradient Threshold Estimation of Anisotropic Diffusion for Meyer’s Watershed Algorithm Based Optimal Segmentation

    Directory of Open Access Journals (Sweden)

    Mithun Kumar PK

    2014-11-01

    Full Text Available Medical image segmentation is a fundamental task in the medical imaging field. Optimal segmentation is required for the accurate judgment or appropriate clinical diagnosis. In this paper, we proposed automatically gradient threshold estimator of anisotropic diffusion for Meyer’s Watershed algorithm based optimal segmentation. The Meyer’s Watershed algorithm is the most significant for a large number of regions separations but the over segmentation is the major drawback of the Meyer’s Watershed algorithm. We are able to remove over segmentation after using anisotropic diffusion as a preprocessing step of segmentation in the Meyer’s Watershed algorithm. We used a fixed window size for dynamically gradient threshold estimation. The gradient threshold is the most important parameter of the anisotropic diffusion for image smoothing. The proposed method is able to segment medical image accurately because of obtaining the enhancement image. The introducing method demonstrates better performance without loss of any clinical information while preserving edges. Our investigated method is more efficient and effective in order to segment the region of interests in the medical images indeed.

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

    International Nuclear Information System (INIS)

    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

  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. 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

    2016-01-01

    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 con-tours of the kidney, bladder, duodenum, and a liver tumor by an experienced radia-tion 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

  13. Automatic Segmentation of Heart Sounds (S1 & S2 Using Wavelet

    Directory of Open Access Journals (Sweden)

    MohammadAli Saghafi

    2009-03-01

    Full Text Available In this paper a new method approach is proposed for automatic segmentation of heart sounds (S1, S2 based on wavelet transform. Unlike many other approaches, this method does not use ECG as reference for detection. The applied criterion for segmentation is based on a very important physiologic attribute of heart which is the difference between the pressure of heart valves while opening and closing which causes high frequency components in heart sound. The main idea in this paper is to extract detail and approximation wavelet coefficients of heart sound (PCG to detect the heart cycle via the Shannon energy of coefficients and then segment S1 and S2. The results show the present algorithm is capable of accurate segmentation of 90% of first heart sounds (S1 and 88.9% of second heart sounds (S2.

  14. Automatic thalamus and hippocampus segmentation from MP2RAGE - comparison of publicly available methods and implications for DTI quantification

    DEFF Research Database (Denmark)

    Næss-Schmidt, Erhard; Tietze, Anna; Blicher, Jakob Udby;

    2016-01-01

    PURPOSE: In both structural and functional MRI, there is a need for accurate and reliable automatic segmentation of brain regions. Inconsistent segmentation reduces sensitivity and may bias results in clinical studies. The current study compares the performance of publicly available segmentation ...

  15. Using LSA and text segmentation to improve automatic Chinese dialogue text summarization

    Institute of Scientific and Technical Information of China (English)

    LIU Chuan-han; WANG Yong-cheng; ZHENG Fei; LIU De-rong

    2007-01-01

    Automatic Chinese text summarization for dialogue style is a relatively new research area. In this paper, Latent Semantic Analysis (LSA) is first used to extract semantic knowledge from a given document, all question paragraphs are identified,an automatic text segmentation approach analogous to TextTiling is exploited to improve the precision of correlating question paragraphs and answer paragraphs, and finally some "important" sentences are extracted from the generic content and the question-answer pairs to generate a complete summary. Experimental results showed that our approach is highly efficient and improves significantly the coherence of the summary while not compromising informativeness.

  16. Geometric characterization of a circumferential seam by automatic segmentation of digitized radioscopic images

    International Nuclear Information System (INIS)

    The present study deals with the nondestructive control of a circumferential seam by digital radioscopy. A series of images for one complete revolution of the welded component is available. We first resort to a joint approach by simulation and experimentation. This approach allows the detection of the molten zone limits for an initial image. We then develop a segmentation method that permits automatic extraction of the geometric characteristics of the set of images representative of the weld. These measures supply fast and automatic control of the weld quality. Results are shown for real components. (author)

  17. Automatic hip cartilage segmentation from 3D MR images using arc-weighted graph searching

    International Nuclear Information System (INIS)

    Accurate segmentation of hip joint cartilage from magnetic resonance (MR) images offers opportunities for quantitative investigations of pathoanatomical conditions such as osteoarthritis. In this paper, we present a fully automatic scheme for the segmentation of the individual femoral and acetabular cartilage plates in the human hip joint from high-resolution 3D MR images. The developed scheme uses an improved optimal multi-object multi-surface graph search framework with an arc-weighted graph representation that incorporates prior morphological knowledge as a basis for segmentation of the individual femoral and acetabular cartilage plates despite weak or incomplete boundary interfaces. This automated scheme was validated against manual segmentations from 3D true fast imaging with steady-state precession (TrueFISP) MR examinations of the right hip joints in 52 asymptomatic volunteers. Compared with expert manual segmentations of the combined, femoral and acetabular cartilage volumes, the automatic scheme obtained mean (± standard deviation) Dice’s similarity coefficients of 0.81 (± 0.03), 0.79 (± 0.03) and 0.72 (± 0.05). The corresponding mean absolute volume difference errors were 8.44% (± 6.36), 9.44% (± 7.19) and 9.05% (± 8.02). The mean absolute differences between manual and automated measures of cartilage thickness for femoral and acetabular cartilage plates were 0.13 mm (± 0.12) and 0.11 mm (± 0.11), respectively. (paper)

  18. A MULTILEVEL AUTOMATIC THRESHOLDING FOR IMAGE SEGMENTATION USING GENETIC ALGORITHM AND DWT

    Directory of Open Access Journals (Sweden)

    Rakesh Kumar

    2012-01-01

    Full Text Available In this paper, An Automatic Multilevel Thresholding Method for Image segmentation is proposed based on Discrete Wavelet Transforms and Genetic Algorithm. We have combined Genetic Algorithm with DWT to make Segmentation faster and adequate results. First the length of the histogram is reduced by using DWT. Using this Reduced Histogram, the number of Thresholds and Threshold Value are determined by Genetic Algorithm. The Thresholds are then projected in original Space. From the analysis of results, it can be concluded that the proposed method is fast and accurate.

  19. An Automatic Optic Disk Detection and Segmentation System using Multi-level Thresholding

    OpenAIRE

    KARASULU, B.

    2014-01-01

    Optic disk (OD) boundary localization is a substantial problem in ophthalmic image processing research area. In order to segment the region of OD, we developed an automatic system which involves a multi-level thresholding. The OD segmentation results of the system in terms of average precision, recall and accuracy for DRIVE database are 98.88%, 99.91%, 98.83%, for STARE database are 98.62%, 97.38%, 96.11%, and for DIARETDB1 database are 99.29%, 99.90%, 99.20%, respectively. Th...

  20. Automatic co-segmentation of lung tumor based on random forest in PET-CT images

    Science.gov (United States)

    Jiang, Xueqing; Xiang, Dehui; Zhang, Bin; Zhu, Weifang; Shi, Fei; Chen, Xinjian

    2016-03-01

    In this paper, a fully automatic method is proposed to segment the lung tumor in clinical 3D PET-CT images. The proposed method effectively combines PET and CT information to make full use of the high contrast of PET images and superior spatial resolution of CT images. Our approach consists of three main parts: (1) initial segmentation, in which spines are removed in CT images and initial connected regions achieved by thresholding based segmentation in PET images; (2) coarse segmentation, in which monotonic downhill function is applied to rule out structures which have similar standardized uptake values (SUV) to the lung tumor but do not satisfy a monotonic property in PET images; (3) fine segmentation, random forests method is applied to accurately segment the lung tumor by extracting effective features from PET and CT images simultaneously. We validated our algorithm on a dataset which consists of 24 3D PET-CT images from different patients with non-small cell lung cancer (NSCLC). The average TPVF, FPVF and accuracy rate (ACC) were 83.65%, 0.05% and 99.93%, respectively. The correlation analysis shows our segmented lung tumor volumes has strong correlation ( average 0.985) with the ground truth 1 and ground truth 2 labeled by a clinical expert.

  1. Semi-automatic segmentation of brain tumors using population and individual information.

    Science.gov (United States)

    Wu, Yao; Yang, Wei; Jiang, Jun; Li, Shuanqian; Feng, Qianjin; Chen, Wufan

    2013-08-01

    Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation. PMID:23319111

  2. The Research of ECG Signal Automatic Segmentation Algorithm Based on Fractal Dimension Trajectory

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    <正>In this paper a kind of ECG signal automatic segmentation algorithm based on ECG fractal dimension trajectory is put forward.First,the ECG signal will be analyzed,then constructing the fractal dimension trajectory of ECG signal according to the fractal dimension trajectory constructing algorithm,finally,obtaining ECG signal feature points and ECG automatic segmentation will be realized by the feature of ECG signal fractal dimension trajectory and the feature of ECG frequency domain characteristics.Through Matlab simulation of the algorithm,the results showed that by constructing the ECG fractal dimension trajectory enables ECG location of each component displayed clearly and obtains high success rate of sub-ECG,providing a basis to identify the various components of ECG signal accurately.

  3. Automatic detection and segmentation of stems of potted tomato plant using Kinect

    Science.gov (United States)

    Fu, Daichang; Xu, Lihong; Li, Dawei; Xin, Longjiao

    2014-04-01

    The automatic segmentation and recognition of greenhouse crop is an important aspect in digitized facility agriculture. Crop stems are closely related with the growth of the crop. Meanwhile, they are also an important physiological trait to identify the species of plants. For these reasons, this paper focuses on the digitization process to collect and analysis stems of greenhouse plants (tomatoes). An algorithm for automatic stem detection and extraction is proposed, based on a cheap and effective stereo vision system—Kinect. In order to demonstrate the usefulness and the potential applicability of our algorithm, a virtual tomato plant, whose stems are rendered by segmented stem texture samples, is reconstructed on OpenGL graphic platform.

  4. Automatic Segmentation Framework of Building Anatomical Mouse Model for Bioluminescence Tomography

    OpenAIRE

    Abdullah Alali

    2013-01-01

    Bioluminescence tomography is known as a highly ill-posed inverse problem. To improve the reconstruction performance by introducing anatomical structures as a priori knowledge, an automatic segmentation framework has been proposed in this paper to extract the mouse whole-body organs and tissues, which enables to build up a heterogeneous mouse model for reconstruction of bioluminescence tomography. Finally, an in vivo mouse experiment has been conducted to evaluate this framework by using an X...

  5. Word Chain-based Automatic Word Segmentation Method%基于词链的自动分词方法

    Institute of Scientific and Technical Information of China (English)

    杨建林; 张国梁

    2000-01-01

    An algorithm for automatic segmentation of Chinese word,which is an improved version of the minimum matching algorithm,is put forward.The key idea of the algorithm is to optimize the word bank and the matching process to enhance the speed and accuracy of word segmentation.By integrating the case bank for processing ambiguous word chain with relevant segmentation rules,the correctness of word segmentation is enhanced,which partly makes up the deficiency in processing natural language.

  6. 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

    2016-01-01

    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. PMID:27001047

  7. Automatic active contour-based segmentation and classification of carotid artery ultrasound images.

    Science.gov (United States)

    Chaudhry, Asmatullah; Hassan, Mehdi; Khan, Asifullah; Kim, Jin Young

    2013-12-01

    In this paper, we present automatic image segmentation and classification technique for carotid artery ultrasound images based on active contour approach. For early detection of the plaque in carotid artery to avoid serious brain strokes, active contour-based techniques have been applied successfully to segment out the carotid artery ultrasound images. Further, ultrasound images might be affected due to rotation, scaling, or translational factors during acquisition process. Keeping in view these facts, image alignment is used as a preprocessing step to align the carotid artery ultrasound images. In our experimental study, we exploit intima-media thickness (IMT) measurement to detect the presence of plaque in the artery. Support vector machine (SVM) classification is employed using these segmented images to distinguish the normal and diseased artery images. IMT measurement is used to form the feature vector. Our proposed approach segments the carotid artery images in an automatic way and further classifies them using SVM. Experimental results show the learning capability of SVM classifier and validate the usefulness of our proposed approach. Further, the proposed approach needs minimum interaction from a user for an early detection of plaque in carotid artery. Regarding the usefulness of the proposed approach in healthcare, it can be effectively used in remote areas as a preliminary clinical step even in the absence of highly skilled radiologists. PMID:23417308

  8. An Automatic Method for Geometric Segmentation of Masonry Arch Bridges for Structural Engineering Purposes

    Science.gov (United States)

    Riveiro, B.; DeJong, M.; Conde, B.

    2016-06-01

    Despite the tremendous advantages of the laser scanning technology for the geometric characterization of built constructions, there are important limitations preventing more widespread implementation in the structural engineering domain. Even though the technology provides extensive and accurate information to perform structural assessment and health monitoring, many people are resistant to the technology due to the processing times involved. Thus, new methods that can automatically process LiDAR data and subsequently provide an automatic and organized interpretation are required. This paper presents a new method for fully automated point cloud segmentation of masonry arch bridges. The method efficiently creates segmented, spatially related and organized point clouds, which each contain the relevant geometric data for a particular component (pier, arch, spandrel wall, etc.) of the structure. The segmentation procedure comprises a heuristic approach for the separation of different vertical walls, and later image processing tools adapted to voxel structures allows the efficient segmentation of the main structural elements of the bridge. The proposed methodology provides the essential processed data required for structural assessment of masonry arch bridges based on geometric anomalies. The method is validated using a representative sample of masonry arch bridges in Spain.

  9. 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.

  10. 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 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.

  11. Semi-automatic medical image segmentation with adaptive local statistics in Conditional Random Fields framework.

    Science.gov (United States)

    Hu, Yu-Chi J; Grossberg, Michael D; Mageras, Gikas S

    2008-01-01

    Planning radiotherapy and surgical procedures usually require onerous manual segmentation of anatomical structures from medical images. In this paper we present a semi-automatic and accurate segmentation method to dramatically reduce the time and effort required of expert users. This is accomplished by giving a user an intuitive graphical interface to indicate samples of target and non-target tissue by loosely drawing a few brush strokes on the image. We use these brush strokes to provide the statistical input for a Conditional Random Field (CRF) based segmentation. Since we extract purely statistical information from the user input, we eliminate the need of assumptions on boundary contrast previously used by many other methods, A new feature of our method is that the statistics on one image can be reused on related images without registration. To demonstrate this, we show that boundary statistics provided on a few 2D slices of volumetric medical data, can be propagated through the entire 3D stack of images without using the geometric correspondence between images. In addition, the image segmentation from the CRF can be formulated as a minimum s-t graph cut problem which has a solution that is both globally optimal and fast. The combination of a fast segmentation and minimal user input that is reusable, make this a powerful technique for the segmentation of medical images. PMID:19163362

  12. Segmentation of retinal blood vessels using normalized Gabor filters and automatic thresholding

    Directory of Open Access Journals (Sweden)

    Mandlenkosi Victor Gwetu

    2014-12-01

    Full Text Available Although computerized retinal image blood vessel segmentation has been extensively researched, there is still room for improvement in the quality of the segmented images. Since retinal image analysis is still widely used in the diagnosis of diabetic retinopathy, efficient and accurate image characterization techniques are required. Previous work has mainly focused on improving segmentation accuracy rates with little regard to the false positives that are produced by illumination variation. This research work presents a hybrid approach towards the segmentation of retinal blood vessels. New approaches towards the reduction of background illumination variation are proposed using normalized Gabor filtering. These are the base-offset encoding and a modified version of an existing zero-integral kernel technique. The valley emphasis automatic thresholding scheme is used to segment the Gabor response images. Experiments are conducted on the DRIVE and STARE retinal image data sets. Accuracy rates of up to 94% are achieved through the zero-integral and base offset methods. This is comparable with results from literature, where the same data sets are segmented using other classification techniques. The median-offset method is found to most effectively reduce background illumination variation.

  13. Automatic right ventricle (RV) segmentation by propagating a basal spatio-temporal characterization

    Science.gov (United States)

    Atehortúa, Angélica; Zuluaga, María. A.; Martínez, Fabio; Romero, Eduardo

    2015-12-01

    An accurate right ventricular (RV) function quantification is important to support the evaluation, diagnosis and prognosis of several cardiac pathologies and to complement the left ventricular function assessment. However, expert RV delineation is a time consuming task with high inter-and-intra observer variability. In this paper we present an automatic segmentation method of the RV in MR-cardiac sequences. Unlike atlas or multi-atlas methods, this approach estimates the RV using exclusively information from the sequence itself. For so doing, a spatio-temporal analysis segments the heart at the basal slice, segmentation that is then propagated to the apex by using a non-rigid-registration strategy. The proposed approach achieves an average Dice Score of 0:79 evaluated with a set of 48 patients.

  14. Automatic segmentation of the internal carotid arteries through the skull base

    Science.gov (United States)

    Manniesing, Rashindra; Niessen, Wiro J.

    2007-03-01

    An automatic method is presented to segment the internal carotid arteries through the difficult part of the skull base in CT angiography. The method uses the entropy per slice to select a cross sectional plane below the skull base. In this plane 2D circular structures are detected by the Hough transform. The center points are used to initialize a level set which evolves with a prior shape constraint on its topology. In contrast with some related vessel segmentation methods, our approach does not require the acquisition of an additional CT scan for bone masking. Experiments on twenty internal carotids in ten patients show that 19 seed points are correctly identified (95%) and 18 carotids (90%) are successfully segmented without any human interaction.

  15. Automatic segmentation and classification of human brain image based on a fuzzy brain atlas

    Science.gov (United States)

    Tan, Ou; Jia, Chunguang; Duan, Huilong; Lu, Weixue

    1998-09-01

    It is difficult to automatically segment and classify tomograph images of actual patient's brain. Therefore, many interactive operations are performed. It is very time consuming and its precision is much depended on the user. In this paper, we combine a brain atlas and 3D fuzzy image segmentation into the image matching. It can not only find out the precise boundary of anatomic structure but also save time of the interactive operation. At first, the anatomic information of atlas is mapped into tomograph images of actual brain with a two step image matching method. Then, based on the mapping result, a 3D fuzzy structure mask is calculated. With the fuzzy information of anatomic structure, a new method of fuzzy clustering based on genetic algorithm is used to segment and classify the real brain image. There is only a minimum requirement of interaction in the whole process, including removing the skull and selecting some intrinsic point pairs.

  16. Automatic segmentation and classification of mycobacterium tuberculosis with conventional light microscopy

    Science.gov (United States)

    Xu, Chao; Zhou, Dongxiang; Zhai, Yongping; Liu, Yunhui

    2015-12-01

    This paper realizes the automatic segmentation and classification of Mycobacterium tuberculosis with conventional light microscopy. First, the candidate bacillus objects are segmented by the marker-based watershed transform. The markers are obtained by an adaptive threshold segmentation based on the adaptive scale Gaussian filter. The scale of the Gaussian filter is determined according to the color model of the bacillus objects. Then the candidate objects are extracted integrally after region merging and contaminations elimination. Second, the shape features of the bacillus objects are characterized by the Hu moments, compactness, eccentricity, and roughness, which are used to classify the single, touching and non-bacillus objects. We evaluated the logistic regression, random forest, and intersection kernel support vector machines classifiers in classifying the bacillus objects respectively. Experimental results demonstrate that the proposed method yields to high robustness and accuracy. The logistic regression classifier performs best with an accuracy of 91.68%.

  17. 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.

  18. Automatic segmentation and classification of tendon nuclei from IHC stained images

    Science.gov (United States)

    Kuok, Chan-Pang; Wu, Po-Ting; Jou, I.-Ming; Su, Fong-Chin; Sun, Yung-Nien

    2015-12-01

    Immunohistochemical (IHC) staining is commonly used for detecting cells in microscopy. It is used for analyzing many types of diseases, e.g. breast cancer. Dispersion problem often exist at cell staining which will affect the accuracy of automatic counting. In this paper, we introduce a new method to overcome this problem. Otsu's thresholding method is first applied to exclude the background, so that only cells with dispersed staining are left at foreground, and then refinement will be applied by local adaptive thresholding method according to the irregularity index of the segmented shape at foreground. The segmentation results are also compared to the refinement results using Otsu's thresholding method. Cell classification based on the shape and color indices obtained from the segmentation result is applied to determine the cell condition into normal, abnormal and suspected abnormal cases.

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

    International Nuclear Information System (INIS)

    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 D2/D3 receptors in the brains of humans. In addition, a further test of the methodology was assessed by applying FCM segmentation to [123I]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 r2>0.99, P123I]IBZM data comparable with manual ROI analysis, with the binding ratios derived from both methods significantly correlated (r2=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.)

  20. Automatic corpus callosum segmentation using a deformable active Fourier contour model

    Science.gov (United States)

    Vachet, Clement; Yvernault, Benjamin; Bhatt, Kshamta; Smith, Rachel G.; Gerig, Guido; Cody Hazlett, Heather; Styner, Martin

    2012-03-01

    The corpus callosum (CC) is a structure of interest in many neuroimaging studies of neuro-developmental pathology such as autism. It plays an integral role in relaying sensory, motor and cognitive information from homologous regions in both hemispheres. We have developed a framework that allows automatic segmentation of the corpus callosum and its lobar subdivisions. Our approach employs constrained elastic deformation of flexible Fourier contour model, and is an extension of Szekely's 2D Fourier descriptor based Active Shape Model. The shape and appearance model, derived from a large mixed population of 150+ subjects, is described with complex Fourier descriptors in a principal component shape space. Using MNI space aligned T1w MRI data, the CC segmentation is initialized on the mid-sagittal plane using the tissue segmentation. A multi-step optimization strategy, with two constrained steps and a final unconstrained step, is then applied. If needed, interactive segmentation can be performed via contour repulsion points. Lobar connectivity based parcellation of the corpus callosum can finally be computed via the use of a probabilistic CC subdivision model. Our analysis framework has been integrated in an open-source, end-to-end application called CCSeg both with a command line and Qt-based graphical user interface (available on NITRC). A study has been performed to quantify the reliability of the semi-automatic segmentation on a small pediatric dataset. Using 5 subjects randomly segmented 3 times by two experts, the intra-class correlation coefficient showed a superb reliability (0.99). CCSeg is currently applied to a large longitudinal pediatric study of brain development in autism.

  1. 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)

    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

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

    Energy Technology Data Exchange (ETDEWEB)

    Zhou Jinghao; Kim, Sung; Jabbour, Salma; Goyal, Sharad; Haffty, Bruce; Chen, Ting; Levinson, Lydia; Metaxas, Dimitris; Yue, Ning J. [Department of Radiation Oncology, UMDNJ-Robert Wood Johnson Medical School, Cancer Institute of New Jersey, New Brunswick, New Jersey 08903 (United States); Department of Bioinformatics, UMDNJ-Robert Wood Johnson Medical School, Cancer Institute of New Jersey, New Brunswick, New Jersey 08903 (United States); Department of Radiation Oncology, UMDNJ-Robert Wood Johnson Medical School, Cancer Institute of New Jersey, New Brunswick, New Jersey 08903 (United States); Department of Computer Science, Rutgers, State University of New Jersey, Piscataway, New Jersey 08854 (United States); Department of Radiation Oncology, UMDNJ-Robert Wood Johnson Medical School, Cancer Institute of New Jersey, New Brunswick, New Jersey 08903 (United States)

    2010-03-15

    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

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

    OpenAIRE

    Daisne, Jean-François; Blumhofer, Andreas

    2013-01-01

    Background 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. Methods The updated Brai...

  4. Automatic lumbar vertebra segmentation from clinical CT for wedge compression fracture diagnosis

    Science.gov (United States)

    Ghosh, Subarna; Alomari, Raja'S.; Chaudhary, Vipin; Dhillon, Gurmeet

    2011-03-01

    Lumbar vertebral fractures vary greatly in types and causes and usually result from severe trauma or pathological conditions such as osteoporosis. Lumbar wedge compression fractures are amongst the most common ones where the vertebra is severely compressed forming a wedge shape and causing pain and pressure on the nerve roots and the spine. Since vertebral segmentation is the first step in any automated diagnosis task, we present a fully automated method for robustly localizing and segmenting the vertebrae for preparation of vertebral fracture diagnosis. Our segmentation method consists of five main steps towards the CAD(Computer-Aided Diagnosis) system: 1) Localization of the intervertebral discs. 2) Localization of the vertebral skeleton. 3) Segmentation of the individual vertebra. 4) Detection of the vertebrae center line and 5) Detection of the vertebrae major boundary points. Our segmentation results are promising with an average error of 1.5mm (modified Hausdorff distance metric) on 50 clinical CT cases i.e. a total of 250 lumbar vertebrae. We also present promising preliminary results for automatic wedge compression fracture diagnosis on 15 cases, 7 of which have one or more vertebral compression fracture, and obtain an accuracy of 97.33%.

  5. Semi-automatic segmentation of subcutaneous tumours from micro-computed tomography images

    International Nuclear Information System (INIS)

    This paper outlines the first attempt to segment the boundary of preclinical subcutaneous tumours, which are frequently used in cancer research, from micro-computed tomography (microCT) image data. MicroCT images provide low tissue contrast, and the tumour-to-muscle interface is hard to determine, however faint features exist which enable the boundary to be located. These are used as the basis of our semi-automatic segmentation algorithm. Local phase feature detection is used to highlight the faint boundary features, and a level set-based active contour is used to generate smooth contours that fit the sparse boundary features. The algorithm is validated against manually drawn contours and micro-positron emission tomography (microPET) images. When compared against manual expert segmentations, it was consistently able to segment at least 70% of the tumour region (n = 39) in both easy and difficult cases, and over a broad range of tumour volumes. When compared against tumour microPET data, it was able to capture over 80% of the functional microPET volume. Based on these results, we demonstrate the feasibility of subcutaneous tumour segmentation from microCT image data without the assistance of exogenous contrast agents. Our approach is a proof-of-concept that can be used as the foundation for further research, and to facilitate this, the code is open-source and available from www.setuvo.com. (paper)

  6. Automatic segmentation of the lumen of the carotid artery in ultrasound B-mode images

    Science.gov (United States)

    Santos, André M. F.; Tavares, Jão. Manuel R. S.; Sousa, Luísa; Santos, Rosa; Castro, Pedro; Azevedo, Elsa

    2013-02-01

    A new algorithm is proposed for the segmentation of the lumen and bifurcation boundaries of the carotid artery in B-mode ultrasound images. It uses the hipoechogenic characteristics of the lumen for the identification of the carotid boundaries and the echogenic characteristics for the identification of the bifurcation boundaries. The image to be segmented is processed with the application of an anisotropic diffusion filter for speckle removal and morphologic operators are employed in the detection of the artery. The obtained information is then used in the definition of two initial contours, one corresponding to the lumen and the other to the bifurcation boundaries, for the posterior application of the Chan-vese level set segmentation model. A set of longitudinal B-mode images of the common carotid artery (CCA) was acquired with a GE Healthcare Vivid-e ultrasound system (GE Healthcare, United Kingdom). All the acquired images include a part of the CCA and of the bifurcation that separates the CCA into the internal and external carotid arteries. In order to achieve the uppermost robustness in the imaging acquisition process, i.e., images with high contrast and low speckle noise, the scanner was adjusted differently for each acquisition and according to the medical exam. The obtained results prove that we were able to successfully apply a carotid segmentation technique based on cervical ultrasonography. The main advantage of the new segmentation method relies on the automatic identification of the carotid lumen, overcoming the limitations of the traditional methods.

  7. 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.

  8. 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.

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

    International Nuclear Information System (INIS)

    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

  10. Automatized segmentation of photovoltaic modules in IR-images with extreme noise

    Science.gov (United States)

    Vetter, Andreas; Hepp, Johannes; Brabec, Christoph J.

    2016-05-01

    Local electric defects may result in considerable performance losses in solar cells. Infrared thermography is an essential tool to detect these defects on photovoltaic modules. Accordingly, IR-thermography is frequently used in R&D labs of PV manufactures and, furthermore, outdoors in order to identify faulty modules in PV-power plants. Massive amount of data is acquired which needs to be analyzed. An automatized method for detecting solar modules in IR-images would enable a faster and automatized analysis of the data. However, IR-images tend to suffer from rather large noise, which makes an automatized segmentation challenging. The aim of this study was to establish a reliable segmentation algorithm for R&D labs. We propose an algorithm, which detects a solar cell or module within an IR-image with large noise. We tested the algorithm on images of 10 PV-samples characterized by highly sensitive dark lock-in thermography (DLIT). The algorithm proved to be very reliable in detecting correctly the solar module. In our study, we focused on thin film solar cells, however, a transfer of the algorithm to other cell types is straight forward.

  11. Automatic Registration Method for Optical Remote Sensing Images with Large Background Variations Using Line Segments

    Directory of Open Access Journals (Sweden)

    Xiaolong Shi

    2016-05-01

    Full Text Available Image registration is an essential step in the process of image fusion, environment surveillance and change detection. Finding correct feature matches during the registration process proves to be difficult, especially for remote sensing images with large background variations (e.g., images taken pre and post an earthquake or flood. Traditional registration methods based on local intensity probably cannot maintain steady performances, as differences are significant in the same area of the corresponding images, and ground control points are not always available in many disaster images. In this paper, an automatic image registration method based on the line segments on the main shape contours (e.g., coastal lines, long roads and mountain ridges is proposed for remote sensing images with large background variations because the main shape contours can hold relatively more invariant information. First, a line segment detector called EDLines (Edge Drawing Lines, which was proposed by Akinlar et al. in 2011, is used to extract line segments from two corresponding images, and a line validation step is performed to remove meaningless and fragmented line segments. Then, a novel line segment descriptor with a new histogram binning strategy, which is robust to global geometrical distortions, is generated for each line segment based on the geometrical relationships,including both the locations and orientations of theremaining line segments relative to it. As a result of the invariance of the main shape contours, correct line segment matches will have similar descriptors and can be obtained by cross-matching among the descriptors. Finally, a spatial consistency measure is used to remove incorrect matches, and transformation parameters between the reference and sensed images can be figured out. Experiments with images from different types of satellite datasets, such as Landsat7, QuickBird, WorldView, and so on, demonstrate that the proposed algorithm is

  12. Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs

    Directory of Open Access Journals (Sweden)

    Mohammad Awrangjeb

    2014-04-01

    Full Text Available Automatic extraction of building roofs from remote sensing data is important for many applications, including 3D city modeling. This paper proposes a new method for automatic segmentation of raw LIDAR (light detection and ranging data. Using the ground height from a DEM (digital elevation model, the raw LIDAR points are separated into two groups. The first group contains the ground points that form a “building mask”. The second group contains non-ground points that are clustered using the building mask. A cluster of points usually represents an individual building or tree. During segmentation, the planar roof segments are extracted from each cluster of points and refined using rules, such as the coplanarity of points and their locality. Planes on trees are removed using information, such as area and point height difference. Experimental results on nine areas of six different data sets show that the proposed method can successfully remove vegetation and, so, offers a high success rate for building detection (about 90% correctness and completeness and roof plane extraction (about 80% correctness and completeness, when LIDAR point density is as low as four points/m2. Thus, the proposed method can be exploited in various applications.

  13. 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

  14. Anatomy of the ostia venae hepaticae and the retrohepatic segment of the inferior vena cava.

    OpenAIRE

    Camargo, A M; Teixeira, G G; Ortale, J R

    1996-01-01

    In 30 normal adult livers the retrohepatic segment of inferior vena cava had a length of 6.7 cm and was totally encircled by liver substance in 30% of cases. Altogether 442 ostia venae hepaticae were found, averaging 14.7 per liver and classified as large, medium, small and minimum. The localisation of the openings was studied according to the division of the wall of the retrohepatic segment of the inferior vena cava into 16 areas.

  15. Anatomy of the ostia venae hepaticae and the retrohepatic segment of the inferior vena cava.

    Science.gov (United States)

    Camargo, A M; Teixeira, G G; Ortale, J R

    1996-02-01

    In 30 normal adult livers the retrohepatic segment of inferior vena cava had a length of 6.7 cm and was totally encircled by liver substance in 30% of cases. Altogether 442 ostia venae hepaticae were found, averaging 14.7 per liver and classified as large, medium, small and minimum. The localisation of the openings was studied according to the division of the wall of the retrohepatic segment of the inferior vena cava into 16 areas. PMID:8655416

  16. Automatic Segmentation of the Cerebellum in Ultrasound Volumes of the Fetal Brain

    OpenAIRE

    G. Velásquez Rodríguez; F. Arámbula Cosío; M.E. Guzmán Huerta; L. Camargo Marín; H. Borboa Olivares; Boris Escalante Ramírez

    2015-01-01

    The size of the cerebellum in ultrasound volumes of the fetal brain has shown a high correlation with gestational age, which makes it a valuable feature to detect fetal growth restrictions. Manual annotation of the 3D surface of the cerebellum in an ultrasound volume is a time consuming task, which needs to be performed by a highly trained expert. In order to assist the experts in the evaluation of cerebellar dimensions, we developed an automatic scheme for the segmentation of the 3D surface ...

  17. An Automatic Optic Disk Detection and Segmentation System using Multi-level Thresholding

    Directory of Open Access Journals (Sweden)

    KARASULU, B.

    2014-05-01

    Full Text Available Optic disk (OD boundary localization is a substantial problem in ophthalmic image processing research area. In order to segment the region of OD, we developed an automatic system which involves a multi-level thresholding. The OD segmentation results of the system in terms of average precision, recall and accuracy for DRIVE database are 98.88%, 99.91%, 98.83%, for STARE database are 98.62%, 97.38%, 96.11%, and for DIARETDB1 database are 99.29%, 99.90%, 99.20%, respectively. The experimental results show that our system works properly on retinal image databases with diseased retinas, diabetic signs, and a large degree of quality variability.

  18. An adaptive spatial clustering method for automatic brain MR image segmentation

    Institute of Scientific and Technical Information of China (English)

    Jingdan Zhang; Daoqing Dai

    2009-01-01

    In this paper, an adaptive spatial clustering method is presented for automatic brain MR image segmentation, which is based on a competitive learning algorithm-self-organizing map (SOM). We use a pattern recognition approach in terms of feature generation and classifier design. Firstly, a multi-dimensional feature vector is constructed using local spatial information. Then, an adaptive spatial growing hierarchical SOM (ASGHSOM) is proposed as the classifier, which is an extension of SOM, fusing multi-scale segmentation with the competitive learning clustering algorithm to overcome the problem of overlapping grey-scale intensities on boundary regions. Furthermore, an adaptive spatial distance is integrated with ASGHSOM, in which local spatial information is considered in the cluster-ing process to reduce the noise effect and the classification ambiguity. Our proposed method is validated by extensive experiments using both simulated and real MR data with varying noise level, and is compared with the state-of-the-art algorithms.

  19. An efficient two-objective automatic SAR image segmentation framework using artificial immune system

    Science.gov (United States)

    Yang, Dongdong; Niu, Ruican; Fei, Rong; Jiang, Qiaoyong; Li, Hongye; Cao, Zijian

    2015-12-01

    Here, an efficient multi-objective automatic segmentation framework (MASF) is formulated and applied to synthetic aperture radar (SAR) image unsupervised classification. In the framework, three important issues are presented: 1) two reasonable image preprocessing techniques, including spatial filtering and watershed operator, are discussed at the initial stage of the framework; 2)then, an efficient immune multi-objective optimization algorithm with uniform clone, adaptive selection by online nondominated solutions, and dynamic deletion in diversity maintenance is proposed; 3 two very simple, but very efficient conflicting clustering validity indices are incorporated into the framework and simultaneously optimized. Two simulated SAR data and two complicated real images are used to quantitatively validate its effectiveness. In addition, four other state-of-the-art image segmentation methods are employed for comparison.

  20. 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. PMID:24759696

  1. Performing Label-Fusion-Based Segmentation Using Multiple Automatically Generated Templates

    Science.gov (United States)

    Chakravarty, M. Mallar; Steadman, Patrick; van Eede, Matthijs C.; Calcott, Rebecca D.; Gu, Victoria; Shaw, Philip; Raznahan, Armin; Collins, D. Louis; Lerch, Jason P.

    2016-01-01

    Classically, model-based segmentation procedures match magnetic resonance imaging (MRI) volumes to an expertly labeled atlas using nonlinear registration. The accuracy of these techniques are limited due to atlas biases, misregistration, and resampling error. Multi-atlas-based approaches are used as a remedy and involve matching each subject to a number of manually labeled templates. This approach yields numerous independent segmentations that are fused using a voxel-by-voxel label-voting procedure. In this article, we demonstrate how the multi-atlas approach can be extended to work with input atlases that are unique and extremely time consuming to construct by generating a library of multiple automatically generated templates of different brains (MAGeT Brain). We demonstrate the efficacy of our method for the mouse and human using two different nonlinear registration algorithms (ANIMAL and ANTs). The input atlases consist a high-resolution mouse brain atlas and an atlas of the human basal ganglia and thalamus derived from serial histological data. MAGeT Brain segmentation improves the identification of the mouse anterior commissure (mean Dice Kappa values (κ = 0.801), but may be encountering a ceiling effect for hippocampal segmentations. Applying MAGeT Brain to human subcortical structures improves segmentation accuracy for all structures compared to regular model-based techniques (κ = 0.845, 0.752, and 0.861 for the striatum, globus pallidus, and thalamus, respectively). Experiments performed with three manually derived input templates suggest that MAGeT Brain can approach or exceed the accuracy of multi-atlas label-fusion segmentation (κ = 0.894, 0.815, and 0.895 for the striatum, globus pallidus, and thalamus, respectively). PMID:22611030

  2. 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.

  3. Automatic segmentation of the fetal cerebellum using spherical harmonics and gray level profiles

    Science.gov (United States)

    Velásquez-Rodríguez, Gustavo; Arámbula Cosío, Fernando; Escalate Ramírez, Boris

    2015-12-01

    The cerebellum is an important structure to determine the gestational age, cerebellar diameter obtained by ultrasound volumes of the fetal brain has shown a high correlation with gestational age, therefore is useful to determine fetal growth restrictions. The manual annotation of 3D surfaces from the fetal brain is time consuming and needs to be done by a highly trained expert. To help with the annotation in the evaluation of cerebellar diameter, we developed a new automatic scheme for the segmentation of the 3D surface of the cerebellum in ultrasound volumes, using a spherical harmonics model and the optimization of an objective function based on gray level voxel profiles. The results on 10 ultrasound volumes of the fetal brain show an accuracy in the segmentation of the cerebellum (mean Dice coefficient of 0.7544). The method reported shows potential to effectively assist the experts in the assessment of fetal growth in ultrasound volumes. We consider the proposed cerebellum segmentation method a contribution for the SPHARM segmentations models, because it is capable to run without hardware restriction, (GPU), and gives adequate results in a reasonable amount of time.

  4. Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts.

    Science.gov (United States)

    Wu, Weiwei; Zhou, Zhuhuang; Wu, Shuicai; Zhang, Yanhua

    2016-01-01

    Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases. PMID:27127536

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

    International Nuclear Information System (INIS)

    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)

  6. Automatic segmentation and classification of the reflected laser dots during analytic measurement of mirror surfaces

    Science.gov (United States)

    Wang, ZhenZhou

    2016-08-01

    In the past research, we have proposed a one-shot-projection method for analytic measurement of the shapes of the mirror surfaces, which utilizes the information of two captured laser dots patterns to reconstruct the mirror surfaces. Yet, the automatic image processing algorithms to extract the laser dots patterns have not been addressed. In this paper, a series of automatic image processing algorithms are proposed to segment and classify the projected laser dots robustly and efficiently during measuring the shapes of mirror surfaces and each algorithm is indispensible for the finally achieved accuracy. Firstly, the captured image is modeled and filtered by the designed frequency domain filter. Then, it is segmented by a robust threshold selection method. A novel iterative erosion method is proposed to separate connected dots. Novel methods to remove noise blob and retrieve missing dots are also proposed. An effective registration method is used to help to select the used SNF laser and the dot generation pattern by analyzing if the dot pattern obeys the principle of central projection well. Experimental results verified the effectiveness of all the proposed algorithms.

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

    International Nuclear Information System (INIS)

    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

  8. Automatic 3D segmentation of spinal cord MRI using propagated deformable models

    Science.gov (United States)

    De Leener, B.; Cohen-Adad, J.; Kadoury, S.

    2014-03-01

    Spinal cord diseases or injuries can cause dysfunction of the sensory and locomotor systems. Segmentation of the spinal cord provides measures of atrophy and allows group analysis of multi-parametric MRI via inter-subject registration to a template. All these measures were shown to improve diagnostic and surgical intervention. We developed a framework to automatically segment the spinal cord on T2-weighted MR images, based on the propagation of a deformable model. The algorithm is divided into three parts: first, an initialization step detects the spinal cord position and orientation by using the elliptical Hough transform on multiple adjacent axial slices to produce an initial tubular mesh. Second, a low-resolution deformable model is iteratively propagated along the spinal cord. To deal with highly variable contrast levels between the spinal cord and the cerebrospinal fluid, the deformation is coupled with a contrast adaptation at each iteration. Third, a refinement process and a global deformation are applied on the low-resolution mesh to provide an accurate segmentation of the spinal cord. Our method was evaluated against a semi-automatic edge-based snake method implemented in ITK-SNAP (with heavy manual adjustment) by computing the 3D Dice coefficient, mean and maximum distance errors. Accuracy and robustness were assessed from 8 healthy subjects. Each subject had two volumes: one at the cervical and one at the thoracolumbar region. Results show a precision of 0.30 +/- 0.05 mm (mean absolute distance error) in the cervical region and 0.27 +/- 0.06 mm in the thoracolumbar region. The 3D Dice coefficient was of 0.93 for both regions.

  9. Sensitivity field distributions for segmental bioelectrical impedance analysis based on real human anatomy

    Science.gov (United States)

    Danilov, A. A.; Kramarenko, V. K.; Nikolaev, D. V.; Rudnev, S. G.; Salamatova, V. Yu; Smirnov, A. V.; Vassilevski, Yu V.

    2013-04-01

    In this work, an adaptive unstructured tetrahedral mesh generation technology is applied for simulation of segmental bioimpedance measurements using high-resolution whole-body model of the Visible Human Project man. Sensitivity field distributions for a conventional tetrapolar, as well as eight- and ten-electrode measurement configurations are obtained. Based on the ten-electrode configuration, we suggest an algorithm for monitoring changes in the upper lung area.

  10. Sensitivity field distributions for segmental bioelectrical impedance analysis based on real human anatomy

    International Nuclear Information System (INIS)

    In this work, an adaptive unstructured tetrahedral mesh generation technology is applied for simulation of segmental bioimpedance measurements using high-resolution whole-body model of the Visible Human Project man. Sensitivity field distributions for a conventional tetrapolar, as well as eight- and ten-electrode measurement configurations are obtained. Based on the ten-electrode configuration, we suggest an algorithm for monitoring changes in the upper lung area.

  11. A semi-automatic method for developing an anthropomorphic numerical model of dielectric anatomy by MRI

    International Nuclear Information System (INIS)

    Complex permittivity values have a dominant role in the overall consideration of interaction between radiofrequency electromagnetic fields and living matter, and in related applications such as electromagnetic dosimetry. There are still some concerns about the accuracy of published data and about their variability due to the heterogeneous nature of biological tissues. The aim of this study is to provide an alternative semi-automatic method by which numerical dielectric human models for dosimetric studies can be obtained. Magnetic resonance imaging (MRI) tomography was used to acquire images. A new technique was employed to correct nonuniformities in the images and frequency-dependent transfer functions to correlate image intensity with complex permittivity were used. The proposed method provides frequency-dependent models in which permittivity and conductivity vary with continuity-even in the same tissue-reflecting the intrinsic realistic spatial dispersion of such parameters. The human model is tested with an FDTD (finite difference time domain) algorithm at different frequencies; the results of layer-averaged and whole-body-averaged SAR (specific absorption rate) are compared with published work, and reasonable agreement has been found. Due to the short time needed to obtain a whole body model, this semi-automatic method may be suitable for efficient study of various conditions that can determine large differences in the SAR distribution, such as body shape, posture, fat-to-muscle ratio, height and weight

  12. Automatic FDG-PET-based tumor and metastatic lymph node segmentation in cervical cancer

    Science.gov (United States)

    Arbonès, Dídac R.; Jensen, Henrik G.; Loft, Annika; Munck af Rosenschöld, Per; Hansen, Anders Elias; Igel, Christian; Darkner, Sune

    2014-03-01

    Treatment of cervical cancer, one of the three most commonly diagnosed cancers worldwide, often relies on delineations of the tumour and metastases based on PET imaging using the contrast agent 18F-Fluorodeoxyglucose (FDG). We present a robust automatic algorithm for segmenting the gross tumour volume (GTV) and metastatic lymph nodes in such images. As the cervix is located next to the bladder and FDG is washed out through the urine, the PET-positive GTV and the bladder cannot be easily separated. Our processing pipeline starts with a histogram-based region of interest detection followed by level set segmentation. After that, morphological image operations combined with clustering, region growing, and nearest neighbour labelling allow to remove the bladder and to identify the tumour and metastatic lymph nodes. The proposed method was applied to 125 patients and no failure could be detected by visual inspection. We compared our segmentations with results from manual delineations of corresponding MR and CT images, showing that the detected GTV lays at least 97.5% within the MR/CT delineations. We conclude that the algorithm has a very high potential for substituting the tedious manual delineation of PET positive areas.

  13. Automatic histogram-based segmentation of white matter hyperintensities using 3D FLAIR images

    Science.gov (United States)

    Simões, Rita; Slump, Cornelis; Moenninghoff, Christoph; Wanke, Isabel; Dlugaj, Martha; Weimar, Christian

    2012-03-01

    White matter hyperintensities are known to play a role in the cognitive decline experienced by patients suffering from neurological diseases. Therefore, accurately detecting and monitoring these lesions is of importance. Automatic methods for segmenting white matter lesions typically use multimodal MRI data. Furthermore, many methods use a training set to perform a classification task or to determine necessary parameters. In this work, we describe and evaluate an unsupervised segmentation method that is based solely on the histogram of FLAIR images. It approximates the histogram by a mixture of three Gaussians in order to find an appropriate threshold for white matter hyperintensities. We use a context-sensitive Expectation-Maximization method to determine the Gaussian mixture parameters. The segmentation is subsequently corrected for false positives using the knowledge of the location of typical FLAIR artifacts. A preliminary validation with the ground truth on 6 patients revealed a Similarity Index of 0.73 +/- 0.10, indicating that the method is comparable to others in the literature which require multimodal MRI and/or a preliminary training step.

  14. Automatic delineation of tumor volumes by co-segmentation of combined PET/MR data

    International Nuclear Information System (INIS)

    Combined PET/MRI may be highly beneficial for radiotherapy treatment planning in terms of tumor delineation and characterization. To standardize tumor volume delineation, an automatic algorithm for the co-segmentation of head and neck (HN) tumors based on PET/MR data was developed. Ten HN patient datasets acquired in a combined PET/MR system were available for this study. The proposed algorithm uses both the anatomical T2-weighted MR and FDG-PET data. For both imaging modalities tumor probability maps were derived, assigning each voxel a probability of being cancerous based on its signal intensity. A combination of these maps was subsequently segmented using a threshold level set algorithm. To validate the method, tumor delineations from three radiation oncologists were available. Inter-observer variabilities and variabilities between the algorithm and each observer were quantified by means of the Dice similarity index and a distance measure. Inter-observer variabilities and variabilities between observers and algorithm were found to be comparable, suggesting that the proposed algorithm is adequate for PET/MR co-segmentation. Moreover, taking into account combined PET/MR data resulted in more consistent tumor delineations compared to MR information only. (paper)

  15. Representation learning: a unified deep learning framework for automatic prostate MR segmentation.

    Science.gov (United States)

    Liao, Shu; Gao, Yaozong; Oto, Aytekin; Shen, Dinggang

    2013-01-01

    Image representation plays an important role in medical image analysis. The key to the success of different medical image analysis algorithms is heavily dependent on how we represent the input data, namely features used to characterize the input image. In the literature, feature engineering remains as an active research topic, and many novel hand-crafted features are designed such as Haar wavelet, histogram of oriented gradient, and local binary patterns. However, such features are not designed with the guidance of the underlying dataset at hand. To this end, we argue that the most effective features should be designed in a learning based manner, namely representation learning, which can be adapted to different patient datasets at hand. In this paper, we introduce a deep learning framework to achieve this goal. Specifically, a stacked independent subspace analysis (ISA) network is adopted to learn the most effective features in a hierarchical and unsupervised manner. The learnt features are adapted to the dataset at hand and encode high level semantic anatomical information. The proposed method is evaluated on the application of automatic prostate MR segmentation. Experimental results show that significant segmentation accuracy improvement can be achieved by the proposed deep learning method compared to other state-of-the-art segmentation approaches. PMID:24579148

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

    International Nuclear Information System (INIS)

    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

  17. Automatic Segmentation of Wrist Bones in CT Using a Statistical Wrist Shape + Pose Model.

    Science.gov (United States)

    Anas, Emran Mohammad Abu; Rasoulian, Abtin; Seitel, Alexander; Darras, Kathryn; Wilson, David; John, Paul St; Pichora, David; Mousavi, Parvin; Rohling, Robert; Abolmaesumi, Purang

    2016-08-01

    Segmentation of the wrist bones in CT images has been frequently used in different clinical applications including arthritis evaluation, bone age assessment and image-guided interventions. The major challenges include non-uniformity and spongy textures of the bone tissue as well as narrow inter-bone spaces. In this work, we propose an automatic wrist bone segmentation technique for CT images based on a statistical model that captures the shape and pose variations of the wrist joint across 60 example wrists at nine different wrist positions. To establish the correspondences across the training shapes at neutral positions, the wrist bone surfaces are jointly aligned using a group-wise registration framework based on a Gaussian Mixture Model. Principal component analysis is then used to determine the major modes of shape variations. The variations in poses not only across the population but also across different wrist positions are incorporated in two pose models. An intra-subject pose model is developed by utilizing the similarity transforms at all wrist positions across the population. Further, an inter-subject pose model is used to model the pose variations across different wrist positions. For segmentation of the wrist bones in CT images, the developed model is registered to the edge point cloud extracted from the CT volume through an expectation maximization based probabilistic approach. Residual registration errors are corrected by application of a non-rigid registration technique. We validate the proposed segmentation method by registering the wrist model to a total of 66 unseen CT volumes of average voxel size of 0.38 mm. We report a mean surface distance error of 0.33 mm and a mean Jaccard index of 0.86. PMID:26890640

  18. Semi-Automatic Segmentation of Optic Radiations and LGN, and Their Relationship to EEG Alpha Waves

    Science.gov (United States)

    Descoteaux, Maxime; Bernier, Michaël; Garyfallidis, Eleftherios; Whittingstall, Kevin

    2016-01-01

    At rest, healthy human brain activity is characterized by large electroencephalography (EEG) fluctuations in the 8-13 Hz range, commonly referred to as the alpha band. Although it is well known that EEG alpha activity varies across individuals, few studies have investigated how this may be related to underlying morphological variations in brain structure. Specifically, it is generally believed that the lateral geniculate nucleus (LGN) and its efferent fibres (optic radiation, OR) play a key role in alpha activity, yet it is unclear whether their shape or size variations contribute to its inter-subject variability. Given the widespread use of EEG alpha in basic and clinical research, addressing this is important, though difficult given the problems associated with reliably segmenting the LGN and OR. For this, we employed a multi-modal approach and combined diffusion magnetic resonance imaging (dMRI), functional magnetic resonance imaging (fMRI) and EEG in 20 healthy subjects to measure structure and function, respectively. For the former, we developed a new, semi-automated approach for segmenting the OR and LGN, from which we extracted several structural metrics such as volume, position and diffusivity. Although these measures corresponded well with known morphology based on previous post-mortem studies, we nonetheless found that their inter-subject variability was not significantly correlated to alpha power or peak frequency (p >0.05). Our results therefore suggest that alpha variability may be mediated by an alternative structural source and our proposed methodology may in general help in better understanding the influence of anatomy on function such as measured by EEG or fMRI. PMID:27383146

  19. Semi-Automatic Segmentation of Optic Radiations and LGN, and Their Relationship to EEG Alpha Waves.

    Directory of Open Access Journals (Sweden)

    Emmanuelle Renauld

    Full Text Available At rest, healthy human brain activity is characterized by large electroencephalography (EEG fluctuations in the 8-13 Hz range, commonly referred to as the alpha band. Although it is well known that EEG alpha activity varies across individuals, few studies have investigated how this may be related to underlying morphological variations in brain structure. Specifically, it is generally believed that the lateral geniculate nucleus (LGN and its efferent fibres (optic radiation, OR play a key role in alpha activity, yet it is unclear whether their shape or size variations contribute to its inter-subject variability. Given the widespread use of EEG alpha in basic and clinical research, addressing this is important, though difficult given the problems associated with reliably segmenting the LGN and OR. For this, we employed a multi-modal approach and combined diffusion magnetic resonance imaging (dMRI, functional magnetic resonance imaging (fMRI and EEG in 20 healthy subjects to measure structure and function, respectively. For the former, we developed a new, semi-automated approach for segmenting the OR and LGN, from which we extracted several structural metrics such as volume, position and diffusivity. Although these measures corresponded well with known morphology based on previous post-mortem studies, we nonetheless found that their inter-subject variability was not significantly correlated to alpha power or peak frequency (p >0.05. Our results therefore suggest that alpha variability may be mediated by an alternative structural source and our proposed methodology may in general help in better understanding the influence of anatomy on function such as measured by EEG or fMRI.

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

    International Nuclear Information System (INIS)

    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

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

    International Nuclear Information System (INIS)

    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

  2. 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.

  3. Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO).

    Science.gov (United States)

    Estrada, Rolando; Tomasi, Carlo; Cabrera, Michelle T; Wallace, David K; Freedman, Sharon F; Farsiu, Sina

    2012-02-01

    We present a methodology for extracting the vascular network in the human retina using Dijkstra's shortest-path algorithm. Our method preserves vessel thickness, requires no manual intervention, and follows vessel branching naturally and efficiently. To test our method, we constructed a retinal video indirect ophthalmoscopy (VIO) image database from pediatric patients and compared the segmentations achieved by our method and state-of-the-art approaches to a human-drawn gold standard. Our experimental results show that our algorithm outperforms prior state-of-the-art methods, for both single VIO frames and automatically generated, large field-of-view enhanced mosaics. We have made the corresponding dataset and source code freely available online. PMID:22312585

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

    International Nuclear Information System (INIS)

    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

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

    International Nuclear Information System (INIS)

    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

  7. Automatic region-of-interest segmentation and registration of dynamic contrast-enhanced images of colorectal tumors

    International Nuclear Information System (INIS)

    Dynamic contrast-enhanced (DCE) images can be acquired at multiple time points and multiple slice locations of a tumor. Image segmentation and registration are important preprocessing steps that can improve subsequent analysis of DCE images by kinetic modeling. An automatic system for region-of-interest segmentation and registration of DCE images is presented. Tissue segmentation is performed using a combination of thresholding and morphological operations, and further refined using shape information from consecutive images. The segmented regions are subsequently registered based on a mutual information method that accounts for possible tissue movement between slices. The proposed segmentation and registration methods are applied on actual DCE CT datasets to illustrate feasibility of practical implementation in the clinic. (paper)

  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

    2016-07-01

    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. 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.

  10. Automatic Segmentation and Online virtualCT in Head-and-Neck Adaptive Radiation Therapy

    International Nuclear Information System (INIS)

    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.

  11. Automatic segmentation of subfigure image panels for multimodal biomedical document retrieval

    Science.gov (United States)

    Cheng, Beibei; Antani, Sameer; Stanley, R. Joe; Thoma, George R.

    2011-01-01

    Biomedical images are often referenced for clinical decision support (CDS), educational purposes, and research. The task of automatically finding the images in a scientific article that are most useful for the purpose of determining relevance to a clinical situation is traditionally done using text and is quite challenging. We propose to improve this by associating image features from the entire image and from relevant regions of interest with biomedical concepts described in the figure caption or discussion in the article. However, images used in scientific article figures are often composed of multiple panels where each sub-figure (panel) is referenced in the caption using alphanumeric labels, e.g. Figure 1(a), 2(c), etc. It is necessary to separate individual panels from a multi-panel figure as a first step toward automatic annotation of images. In this work we present methods that add make robust our previous efforts reported here. Specifically, we address the limitation in segmenting figures that do not exhibit explicit inter-panel boundaries, e.g. illustrations, graphs, and charts. We present a novel hybrid clustering algorithm based on particle swarm optimization (PSO) with fuzzy logic controller (FLC) to locate related figure components in such images. Results from our evaluation are very promising with 93.64% panel detection accuracy for regular (non-illustration) figure images and 92.1% accuracy for illustration images. A computational complexity analysis also shows that PSO is an optimal approach with relatively low computation time. The accuracy of separating these two type images is 98.11% and is achieved using decision tree.

  12. Myocardial Iron Loading Assessment by Automatic Left Ventricle Segmentation with Morphological Operations and Geodesic Active Contour on T2* images

    Science.gov (United States)

    Luo, Yun-Gang; Ko, Jacky Kl; Shi, Lin; Guan, Yuefeng; Li, Linong; Qin, Jing; Heng, Pheng-Ann; Chu, Winnie Cw; Wang, Defeng

    2015-07-01

    Myocardial iron loading thalassemia patients could be identified using T2* magnetic resonance images (MRI). To quantitatively assess cardiac iron loading, we proposed an effective algorithm to segment aligned free induction decay sequential myocardium images based on morphological operations and geodesic active contour (GAC). Nine patients with thalassemia major were recruited (10 male and 16 female) to undergo a thoracic MRI scan in the short axis view. Free induction decay images were registered for T2* mapping. The GAC were utilized to segment aligned MR images with a robust initialization. Segmented myocardium regions were divided into sectors for a region-based quantification of cardiac iron loading. Our proposed automatic segmentation approach achieve a true positive rate at 84.6% and false positive rate at 53.8%. The area difference between manual and automatic segmentation was 25.5% after 1000 iterations. Results from T2* analysis indicated that regions with intensity lower than 20 ms were suffered from heavy iron loading in thalassemia major patients. The proposed method benefited from abundant edge information of the free induction decay sequential MRI. Experiment results demonstrated that the proposed method is feasible in myocardium segmentation and was clinically applicable to measure myocardium iron loading.

  13. Automatic plaque characterization and vessel wall segmentation in magnetic resonance images of atherosclerotic carotid arteries

    Science.gov (United States)

    Adame, Isabel M.; van der Geest, Rob J.; Wasserman, Bruce A.; Mohamed, Mona; Reiber, Johan H. C.; Lelieveldt, Boudewijn P. F.

    2004-05-01

    Composition and structure of atherosclerotic plaque is a primary focus of cardiovascular research. In vivo MRI provides a meanse to non-invasively image and assess the morphological features of athersclerotic and normal human carotid arteries. To quantitatively assess the vulnerability and the type of plaque, the contours of the lumen, outer boundary of the vessel wall and plaque components, need to be traced. To achieve this goal, we have developed an automated contou detection technique, which consists of three consecutive steps: firstly, the outer boundary of the vessel wall is detected by means of an ellipse-fitting procedure in order to obtain smoothed shapes; secondly, the lumen is segnented using fuzzy clustering. Thre region to be classified is that within the outer vessel wall boundary obtained from the previous step; finally, for plaque detection we follow the same approach as for lumen segmentation: fuzzy clustering. However, plaque is more difficult to segment, as the pixel gray value can differ considerably from one region to another, even when it corresponds to the same type of tissue. That makes further processing necessary. All these three steps might be carried out combining information from different sequences (PD-, T2-, T1-weighted images, pre- and post-contrast), to improve the contour detection. The algorithm has been validated in vivo on 58 high-resolution PD and T1 weighted MR images (19 patients). The results demonstrate excellent correspondence between automatic and manual area measurements: lumen (r=0.94), outer (r=0.92), and acceptable for fibrous cap thickness (r=0.76).

  14. 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)

    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

  15. 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.

  16. Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis

    Directory of Open Access Journals (Sweden)

    Christian Held

    2013-01-01

    Full Text Available Introduction: Research and diagnosis in medicine and biology often require the assessment of a large amount of microscopy image data. Although on the one hand, digital pathology and new bioimaging technologies find their way into clinical practice and pharmaceutical research, some general methodological issues in automated image analysis are still open. Methods: In this study, we address the problem of fitting the parameters in a microscopy image segmentation pipeline. We propose to fit the parameters of the pipeline′s modules with optimization algorithms, such as, genetic algorithms or coordinate descents, and show how visual exploration of the parameter space can help to identify sub-optimal parameter settings that need to be avoided. Results: This is of significant help in the design of our automatic parameter fitting framework, which enables us to tune the pipeline for large sets of micrographs. Conclusion: The underlying parameter spaces pose a challenge for manual as well as automated parameter optimization, as the parameter spaces can show several local performance maxima. Hence, optimization strategies that are not able to jump out of local performance maxima, like the hill climbing algorithm, often result in a local maximum.

  17. Evaluation of advanced automatic PET segmentation methods using nonspherical thin-wall inserts

    Energy Technology Data Exchange (ETDEWEB)

    Berthon, B., E-mail: BerthonB@cardiff.ac.uk; Marshall, C. [Wales Research and Diagnostic Positron Emission Tomography Imaging Centre, Cardiff CF14 4XN (United Kingdom); Evans, M. [Velindre Cancer Centre, Cardiff CF14 2TL (United Kingdom); Spezi, E. [Department of Medical Physics, Velindre Cancer Centre, Cardiff CF14 2TL (United Kingdom)

    2014-02-15

    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 < 0.5), caused by a failure to recover the object geometry. The region-growing method reached high levels of accuracy for most inserts (DSC > 0.76 except for tori) but showed the largest

  18. Evaluation of advanced automatic PET segmentation methods using nonspherical thin-wall inserts

    International Nuclear Information System (INIS)

    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

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

    Directory of Open Access Journals (Sweden)

    Denis Pavliha

    Full Text Available 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.

  20. Ambiguous Segment Elimination in Automatic Word Segmentation Model%自动分词模型中的歧义字段消除探讨

    Institute of Scientific and Technical Information of China (English)

    苏惠明

    2012-01-01

    Web technology is widely used in the research and application field of online information service system, intelligent question-answering system. In intelligent question-answering system, the research on the automatic word segmentation model and method is so much. However, the problems have not been resolved. The article makes a research on the algorithm of automatic word segmentation model and the elimination of ambiguous segment based on principle and statistics.%Web技术在在线信息服务系统的研究和应用领域中,智能答疑系统得到了越来越广泛的应用.在智能答疑系统中,对汉语自动分词的模型和方法已经有了很多的研究,然而始终不能得到完善的解决.本文利用基于规则和基于统计的歧义消除策略对自动分词模型中的算法和歧义字段的消除作出了一定的研究.

  1. Liver segmentation in MRI: a fully automatic method based on stochastic partitions

    OpenAIRE

    López-Mir, Fernando; Naranjo Ornedo, Valeriana; Angulo, J.; Alcañiz Raya, Mariano Luis; Luna, L.

    2014-01-01

    There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marke...

  2. An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images

    Science.gov (United States)

    Wang, Liansheng; Li, Shusheng; Chen, Rongzhen; Liu, Sze-Yu; Chen, Jyh-Cheng

    2016-01-01

    Accurate segmentation and classification of different anatomical structures of teeth from medical images plays an essential role in many clinical applications. Usually, the anatomical structures of teeth are manually labelled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing three dimensional (3D) information, and classify the tooth by employing unsupervised learning Pulse Coupled Neural Networks (PCNN) model. In order to evaluate the proposed method, the experiments are conducted on the different datasets of mandibular molars and the experimental results show that our method can achieve better accuracy and robustness compared to other four state of the art clustering methods. PMID:27322421

  3. Automatic image segmentation for treatment planning in radiotherapy; Segmentation automatique des images pour la planifi cation dosimetrique en radiotherapie

    Energy Technology Data Exchange (ETDEWEB)

    Pasquiera, D. [Centre Galilee, polyclinique de la Louviere, 59 - Lille (France); Peyrodie, L. [Ecole des hautes etudes d' ingenieur, 59 - Lille (France); Laboratoire d' automatique, genie informatique et signal (LAGIS), Cite scientifi que, 59 - Villeneuve d' Ascq (France); Denis, F. [Centre Jean-Bernard, 72 - Le Mans (France); Pointreau, Y.; Bera, G. [Clinique d' oncologie radiotherapie, Centre Henry-S.-Kaplan, CHU Bretonneau, 37 - Tours (France); Lartigau, E. [Departement universitaire de radiotherapie, Centre O. Lambret, Universite Lille 2, 59 - Lille (France)

    2010-07-01

    One drawback of the growth in conformal radiotherapy and image-guided radiotherapy is the increased time needed to define the volumes of interest. This also results in inter- and intra-observer variability. However, developments in computing and image processing have enabled these tasks to be partially or totally automated. This article will provide a detailed description of the main principles of image segmentation in radiotherapy, its applications and the most recent results in a clinical context. (authors)

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

    OpenAIRE

    2014-01-01

    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 an...

  5. Accurate and Fully Automatic Hippocampus Segmentation Using Subject-Specific 3D Optimal Local Maps Into a Hybrid Active Contour Model

    OpenAIRE

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

    2014-01-01

    Assessing the structural integrity of the hippocampus (HC) is an essential step toward prevention, diagnosis, and follow-up of various brain disorders due to the implication of the structural changes of the HC in those disorders. In this respect, the development of automatic segmentation methods that can accurately, reliably, and reproducibly segment the HC has attracted considerable attention over the past decades. This paper presents an innovative 3-D fully automatic method to be used on to...

  6. Automatic Segmentation of Colon in 3D CT Images and Removal of Opacified Fluid Using Cascade Feed Forward Neural Network

    Directory of Open Access Journals (Sweden)

    K. Gayathri Devi

    2015-01-01

    Full Text Available Purpose. Colon segmentation is an essential step in the development of computer-aided diagnosis systems based on computed tomography (CT images. The requirement for the detection of the polyps which lie on the walls of the colon is much needed in the field of medical imaging for diagnosis of colorectal cancer. Methods. The proposed work is focused on designing an efficient automatic colon segmentation algorithm from abdominal slices consisting of colons, partial volume effect, bowels, and lungs. The challenge lies in determining the exact colon enhanced with partial volume effect of the slice. In this work, adaptive thresholding technique is proposed for the segmentation of air packets, machine learning based cascade feed forward neural network enhanced with boundary detection algorithms are used which differentiate the segments of the lung and the fluids which are sediment at the side wall of colon and by rejecting bowels based on the slice difference removal method. The proposed neural network method is trained with Bayesian regulation algorithm to determine the partial volume effect. Results. Experiment was conducted on CT database images which results in 98% accuracy and minimal error rate. Conclusions. The main contribution of this work is the exploitation of neural network algorithm for removal of opacified fluid to attain desired colon segmentation result.

  7. Automatic Tracing and Segmentation of Rat Mammary Fat Pads in MRI Image Sequences Based on Cartoon-Texture Model

    Institute of Scientific and Technical Information of China (English)

    TU Shengxian; ZHANG Su; CHEN Yazhu; Freedman Matthew T; WANG Bin; XUAN Jason; WANG Yue

    2009-01-01

    The growth patterns of mammary fat pads and glandular tissues inside the fat pads may be related with the risk factors of breast cancer.Quantitative measurements of this relationship are available after segmentation of mammary pads and glandular tissues.Rat fat pads may lose continuity along image sequences or adjoin similar intensity areas like epidermis and subcutaneous regions.A new approach for automatic tracing and segmentation of fat pads in magnetic resonance imaging (MRI) image sequences is presented,which does not require that the number of pads be constant or the spatial location of pads be adjacent among image slices.First,each image is decomposed into cartoon image and texture image based on cartoon-texture model.They will be used as smooth image and feature image for segmentation and for targeting pad seeds,respectively.Then,two-phase direct energy segmentation based on Chan-Vese active contour model is applied to partitioning the cartoon image into a set of regions,from which the pad boundary is traced iteratively from the pad seed.A tracing algorithm based on scanning order is proposed to accurately trace the pad boundary,which effectively removes the epidermis attached to the pad without any post processing as well as solves the problem of over-segmentation of some small holes inside the pad.The experimental results demonstrate the utility of this approach in accurate delineation of various numbers of mammary pads from several sets of MRI images.

  8. AN AUTOMATIC SEGMENTATION METHOD FOR MOVING OBJECTS BASED ON THE SPATIAL-TEMPORAL INFORMATION OF VIDEO

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The new MPEG-4 video coding standard enables content-based functions. In order to support the new standard, frames should be decomposed into Video Object Planes (VOP),each VOP representing a moving object. This paper proposes an image segmentation method to separate moving objects from image sequences. The proposed method utilizes the spatial-temporal information. Spatial segmentation is applied to divide each image into connected areas and to find precise object boundaries of moving objects. To locate moving objects in image sequences,two consecutive image frames in the temporal direction are examined and a hypothesis testing is performed with Neyman-Pearson criterion. Spatial segmentation produces a spatial segmentation mask, and temporal segmentation yields a change detection mask that indicates moving objects and the background. Then spatial-temporal merging can be used to get the final results. This method has been tested on several images. Experimental results show that this segmentation method is efficient.

  9. Development of image-processing software for automatic segmentation of brain tumors in MR images

    International Nuclear Information System (INIS)

    Most of the commercially available software for brain tumor segmentation have limited functionality and frequently lack the careful validation that is required for clinical studies. We have developed an image-analysis software package called 'Prometheus,' which performs neural system-based segmentation operations on MR images using pre-trained information. The software also has the capability to improve its segmentation performance by using the training module of the neural system. The aim of this article is to present the design and modules of this software. The segmentation module of Prometheus can be used primarily for image analysis in MR images. Prometheus was validated against manual segmentation by a radiologist and its mean sensitivity and specificity was found to be 85. 7 4.89% and 93. 2±2.87%, respectively. Similarly, the mean segmentation accuracy and mean correspondence ratio was found to be 92. 35±3. 37% and 0. 78±0. 046, respectively. (author)

  10. Automatic Detection, Segmentation and Classification of Retinal Horizontal Neurons in Large-scale 3D Confocal Imagery

    Energy Technology Data Exchange (ETDEWEB)

    Karakaya, Mahmut [ORNL; Kerekes, Ryan A [ORNL; Gleason, Shaun Scott [ORNL; Martins, Rodrigo [St. Jude Children' s Research Hospital; Dyer, Michael [St. Jude Children' s Research Hospital

    2011-01-01

    Automatic analysis of neuronal structure from wide-field-of-view 3D image stacks of retinal neurons is essential for statistically characterizing neuronal abnormalities that may be causally related to neural malfunctions or may be early indicators for a variety of neuropathies. In this paper, we study classification of neuron fields in large-scale 3D confocal image stacks, a challenging neurobiological problem because of the low spatial resolution imagery and presence of intertwined dendrites from different neurons. We present a fully automated, four-step processing approach for neuron classification with respect to the morphological structure of their dendrites. In our approach, we first localize each individual soma in the image by using morphological operators and active contours. By using each soma position as a seed point, we automatically determine an appropriate threshold to segment dendrites of each neuron. We then use skeletonization and network analysis to generate the morphological structures of segmented dendrites, and shape-based features are extracted from network representations of each neuron to characterize the neuron. Based on qualitative results and quantitative comparisons, we show that we are able to automatically compute relevant features that clearly distinguish between normal and abnormal cases for postnatal day 6 (P6) horizontal neurons.

  11. Segmentation of retinal blood vessels using normalized Gabor filters and automatic thresholding

    OpenAIRE

    Mandlenkosi Victor Gwetu; Jules Raymond Tapamo; Serestina Viriri

    2014-01-01

    Although computerized retinal image blood vessel segmentation has been extensively researched, there is still room for improvement in the quality of the segmented images. Since retinal image analysis is still widely used in the diagnosis of diabetic retinopathy, efficient and accurate image characterization techniques are required. Previous work has mainly focused on improving segmentation accuracy rates with little regard to the false positives that are produced by illumination variation. T...

  12. An entropy-based approach to automatic image segmentation of satellite images

    Science.gov (United States)

    Barbieri, Andre L.; de Arruda, G. F.; Rodrigues, Francisco A.; Bruno, Odemir M.; Costa, Luciano da Fontoura

    2011-02-01

    An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation.

  13. An entropy-based approach to automatic image segmentation of satellite images

    CERN Document Server

    Barbieri, A L; Rodrigues, F A; Bruno, O M; Costa, L da F

    2009-01-01

    An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation.

  14. Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images

    Science.gov (United States)

    Keller, Brenton; Cunefare, David; Grewal, Dilraj S.; Mahmoud, Tamer H.; Izatt, Joseph A.; Farsiu, Sina

    2016-07-01

    We introduce a metric in graph search and demonstrate its application for segmenting retinal optical coherence tomography (OCT) images of macular pathology. Our proposed "adjusted mean arc length" (AMAL) metric is an adaptation of the lowest mean arc length search technique for automated OCT segmentation. We compare this method to Dijkstra's shortest path algorithm, which we utilized previously in our popular graph theory and dynamic programming segmentation technique. As an illustrative example, we show that AMAL-based length-adaptive segmentation outperforms the shortest path in delineating the retina/vitreous boundary of patients with full-thickness macular holes when compared with expert manual grading.

  15. 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)

    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

  16. Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data

    International Nuclear Information System (INIS)

    The combination of positron emission tomography (PET) and magnetic resonance (MR) tomography in a single device is anticipated to be the next step following PET/CT for future molecular imaging application. Compared to CT, the main advantages of MR are versatile soft tissue contrast and its capability to acquire functional information without ionizing radiation. However, MR is not capable of measuring a physical quantity that would allow a direct derivation of the attenuation values for high-energy photons. To overcome this problem, we propose a fully automated approach that uses a dedicated T1-weighted MR sequence in combination with a customized image processing technique to derive attenuation maps for whole-body PET. The algorithm automatically identifies the outer contour of the body and the lungs using region-growing techniques in combination with an intensity analysis for automatic threshold estimation. No user interaction is required to generate the attenuation map. The accuracy of the proposed MR-based attenuation correction (AC) approach was evaluated in a clinical study using whole-body PET/CT and MR images of the same patients (n = 15). The segmentation of the body and lung contour (L-R directions) was evaluated via a four-point scale in comparison to the original MR image (mean values >3.8). PET images were reconstructed using elastically registered MR-based and CT-based (segmented and non-segmented) attenuation maps. The MR-based AC showed similar behaviour as CT-based AC and similar accuracy as offered by segmented CT-based AC. Standardized uptake value (SUV) comparisons with reference to CT-based AC using predefined attenuation coefficients showed the largest difference for bone lesions (mean value ± standard variation of SUVmax: -3.0% ± 3.9% for MR; -6.5% ± 4.1% for segmented CT). A blind comparison of PET images corrected with segmented MR-based, CT-based and segmented CT-based AC afforded identical lesion detectability, but slight differences in

  17. An automatic integrated image segmentation, registration and change detection method for water-body extraction using HSR images and GIS data

    OpenAIRE

    H.G. Sui; Chen, G.; Hua, L.

    2013-01-01

    Automatic water-body extraction from remote sense images is a challenging problem. Using GIS data to update and extract waterbody is an old but active topic. However, automatic registration and change detection of the two data sets often presents difficulties. In this paper, a novel automatic water-body extraction method is proposed. The core idea is to integrate image segmentation, image registration and change detection with GIS data as a whole processing. A new iterative segmentat...

  18. Development, Implementation and Evaluation of Segmentation Algorithms for the Automatic Classification of Cervical Cells

    Science.gov (United States)

    Macaulay, Calum Eric

    Cancer of the uterine cervix is one of the most common cancers in women. An effective screening program for pre-cancerous and cancerous lesions can dramatically reduce the mortality rate for this disease. In British Columbia where such a screening program has been in place for some time, 2500 to 3000 slides of cervical smears need to be examined daily. More than 35 years ago, it was recognized that an automated pre-screening system could greatly assist people in this task. Such a system would need to find and recognize stained cells, segment the images of these cells into nucleus and cytoplasm, numerically describe the characteristics of the cells, and use these features to discriminate between normal and abnormal cells. The thrust of this work was (1) to research and develop new segmentation methods and compare their performance to those in the literature, (2) to determine dependence of the numerical cell descriptors on the segmentation method used, (3) to determine the dependence of cell classification accuracy on the segmentation used, and (4) to test the hypothesis that using numerical cell descriptors one can correctly classify the cells. The segmentation accuracies of 32 different segmentation procedures were examined. It was found that the best nuclear segmentation procedure was able to correctly segment 98% of the nuclei of a 1000 and a 3680 image database. Similarly the best cytoplasmic segmentation procedure was found to correctly segment 98.5% of the cytoplasm of the same 1000 image database. Sixty-seven different numerical cell descriptors (features) were calculated for every segmented cell. On a database of 800 classified cervical cells these features when used in a linear discriminant function analysis could correctly classify 98.7% of the normal cells and 97.0% of the abnormal cells. While some features were found to vary a great deal between segmentation procedures, the classification accuracy of groups of features was found to be independent of the

  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. Automatic Spatially-Adaptive Balancing of Energy Terms for Image Segmentation

    CERN Document Server

    Rao, Josna; Abugharbieh, Rafeef

    2009-01-01

    Image segmentation techniques are predominately based on parameter-laden optimization. The objective function typically involves weights for balancing competing image fidelity and segmentation regularization cost terms. Setting these weights suitably has been a painstaking, empirical process. Even if such ideal weights are found for a novel image, most current approaches fix the weight across the whole image domain, ignoring the spatially-varying properties of object shape and image appearance. We propose a novel technique that autonomously balances these terms in a spatially-adaptive manner through the incorporation of image reliability in a graph-based segmentation framework. We validate on synthetic data achieving a reduction in mean error of 47% (p-value << 0.05) when compared to the best fixed parameter segmentation. We also present results on medical images (including segmentations of the corpus callosum and brain tissue in MRI data) and on natural images.

  1. 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.

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

    OpenAIRE

    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

    Background 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. ...

  3. 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. PMID:26279309

  4. Development of a semi-automatic segmentation method for retinal OCT images tested in patients with diabetic macular edema.

    Directory of Open Access Journals (Sweden)

    Yijun Huang

    Full Text Available PURPOSE: To develop EdgeSelect, a semi-automatic method for the segmentation of retinal layers in spectral domain optical coherence tomography images, and to compare the segmentation results with a manual method. METHODS: SD-OCT (Heidelberg Spectralis scans of 28 eyes (24 patients with diabetic macular edema and 4 normal subjects were imported into a customized MATLAB application, and were manually segmented by three graders at the layers corresponding to the inner limiting membrane (ILM, the inner segment/ellipsoid interface (ISe, the retinal/retinal pigment epithelium interface (RPE, and the Bruch's membrane (BM. The scans were then segmented independently by the same graders using EdgeSelect, a semi-automated method allowing the graders to guide/correct the layer segmentation interactively. The inter-grader reproducibility and agreement in locating the layer positions between the manual and EdgeSelect methods were assessed and compared using the Wilcoxon signed rank test. RESULTS: The inter-grader reproducibility using the EdgeSelect method for retinal layers varied from 0.15 to 1.21 µm, smaller than those using the manual method (3.36-6.43 µm. The Wilcoxon test indicated the EdgeSelect method had significantly better reproducibility than the manual method. The agreement between the manual and EdgeSelect methods in locating retinal layers ranged from 0.08 to 1.32 µm. There were small differences between the two methods in locating the ILM (p = 0.012 and BM layers (p<0.001, but these were statistically indistinguishable in locating the ISe (p = 0.896 and RPE layers (p = 0.771. CONCLUSIONS: The EdgeSelect method resulted in better reproducibility and good agreement with a manual method in a set of eyes of normal subjects and with retinal disease, suggesting that this approach is feasible for OCT image analysis in clinical trials.

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

    International Nuclear Information System (INIS)

    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

  6. Automatic segmentation of Leishmania parasite in microscopic images using a modified CV level set method

    Science.gov (United States)

    Farahi, Maria; Rabbani, Hossein; Talebi, Ardeshir; Sarrafzadeh, Omid; Ensafi, Shahab

    2015-12-01

    Visceral Leishmaniasis is a parasitic disease that affects liver, spleen and bone marrow. According to World Health Organization report, definitive diagnosis is possible just by direct observation of the Leishman body in the microscopic image taken from bone marrow samples. We utilize morphological and CV level set method to segment Leishman bodies in digital color microscopic images captured from bone marrow samples. Linear contrast stretching method is used for image enhancement and morphological method is applied to determine the parasite regions and wipe up unwanted objects. Modified global and local CV level set methods are proposed for segmentation and a shape based stopping factor is used to hasten the algorithm. Manual segmentation is considered as ground truth to evaluate the proposed method. This method is tested on 28 samples and achieved 10.90% mean of segmentation error for global model and 9.76% for local model.

  7. Automatic segmentation of the lumen of the carotid artery in ultrasound B-mode images

    OpenAIRE

    Santos, AMF; tavares, jmrs; Sousa, L. de; Santos, R.; Castro, P.; Azevedo, E.

    2013-01-01

    A new algorithm is proposed for the segmentation of the lumen and bifurcation boundaries of the carotid artery in B-mode ultrasound images. It uses the hipoechogenic characteristics of the lumen for the identification of the carotid boundaries and the echogenic characteristics for the identification of the bifurcation boundaries. The image to be segmented is processed with the application of an anisotropic diffusion filter for speckle removal and morphologic operators are employed in the dete...

  8. Development of image-processing software for automatic segmentation of brain tumors in MR images

    Science.gov (United States)

    Vijayakumar, C.; Gharpure, Damayanti Chandrashekhar

    2011-01-01

    Most of the commercially available software for brain tumor segmentation have limited functionality and frequently lack the careful validation that is required for clinical studies. We have developed an image-analysis software package called ‘Prometheus,’ which performs neural system–based segmentation operations on MR images using pre-trained information. The software also has the capability to improve its segmentation performance by using the training module of the neural system. The aim of this article is to present the design and modules of this software. The segmentation module of Prometheus can be used primarily for image analysis in MR images. Prometheus was validated against manual segmentation by a radiologist and its mean sensitivity and specificity was found to be 85.71±4.89% and 93.2±2.87%, respectively. Similarly, the mean segmentation accuracy and mean correspondence ratio was found to be 92.35±3.37% and 0.78±0.046, respectively. PMID:21897560

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

    International Nuclear Information System (INIS)

    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.)

  10. Fully automatic segmentation of short and long axis cine cardiac MR

    OpenAIRE

    Breeuwer Marcel; Hautvast Gilion; Mory Benoit; Ciofolo-Veit Cybele; Fradkin Maxim

    2009-01-01

    The authors would like to thank the editor, BioMed Central, for the open access to the publication Detailed abstract + poster International audience We propose a fully automatic method for delineation of the endo- and epicardial contours in SA and LA cine CMR images in order to provide automatic, accurate quantitative left-ventricular functional assessment. The electronic version of this abstract is the complete one and can be found online at: http://jcmr-online.com/content/11/S1/P226

  11. An automatic segmentation method for building facades from vehicle-borne LiDAR point cloud data based on fundamental geographical data

    Science.gov (United States)

    Li, Yongqiang; Mao, Jie; Cai, Lailiang; Zhang, Xitong; Li, Lixue

    2016-03-01

    In this paper, the author proposed a segmentation method based on the fundamental geographic data, the algorithm describes as following: Firstly, convert the coordinate system of fundamental geographic data to that of vehicle- borne LiDAR point cloud though some data preprocessing work, and realize the coordinate system between them; Secondly, simplify the feature of fundamental geographic data, extract effective contour information of the buildings, then set a suitable buffer threshold value for building contour, and segment out point cloud data of building facades automatically; Thirdly, take a reasonable quality assessment mechanism, check and evaluate of the segmentation results, control the quality of segmentation result. Experiment shows that the proposed method is simple and effective. The method also has reference value for the automatic segmentation for surface features of other types of point cloud.

  12. Automatic segmentation of lymph vessel wall using optimal surface graph cut and hidden Markov Models.

    Science.gov (United States)

    Jones, Jonathan-Lee; Essa, Ehab; Xie, Xianghua

    2015-08-01

    We present a novel method to segment the lymph vessel wall in confocal microscopy images using Optimal Surface Segmentation (OSS) and hidden Markov Models (HMM). OSS is used to preform a pre-segmentation on the images, to act as the initial state for the HMM. We utilize a steerable filter to determine edge based filters for both of these segmentations, and use these features to build Gaussian probability distributions for both the vessel walls and the background. From this we infer the emission probability for the HMM, and the transmission probability is learned using a Baum-Welch algorithm. We transform the segmentation problem into one of cost minimization, with each node in the graph corresponding to one state, and the weight for each node being defined using its emission probability. We define the inter-relations between neighboring nodes using the transmission probability. Having constructed the problem, it is solved using the Viterbi algorithm, allowing the vessel to be reconstructed. The optimal solution can be found in polynomial time. We present qualitative and quantitative analysis to show the performance of the proposed method. PMID:26736778

  13. 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. PMID:17215103

  14. Automated segmentation method for the 3D ultrasound carotid image based on geometrically deformable model with automatic merge function

    Science.gov (United States)

    Li, Xiang; Wang, Zigang; Lu, Hongbing; Liang, Zhengrong

    2002-05-01

    Stenosis of the carotid is the most common cause of the stroke. The accurate measurement of the volume of the carotid and visualization of its shape are helpful in improving diagnosis and minimizing the variability of assessment of the carotid disease. Due to the complex anatomic structure of the carotid, it is mandatory to define the initial contours in every slice, which is very difficult and usually requires tedious manual operations. The purpose of this paper is to propose an automatic segmentation method, which automatically provides the contour of the carotid from the 3-D ultrasound image and requires minimum user interaction. In this paper, we developed the Geometrically Deformable Model (GDM) with automatic merge function. In our algorithm, only two initial contours in the topmost slice and four parameters are needed in advance. Simulated 3-D ultrasound image was used to test our algorithm. 3-D display of the carotid obtained by our algorithm showed almost identical shape with true 3-D carotid image. In addition, experimental results also demonstrated that error of the volume measurement of the carotid based on the three different initial contours is less that 1% and its speed was a very fast.

  15. 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)

    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.

  16. Automatic CT Brain Image Segmentation Using Two Level Multiresolution Mixture Model of EM

    Science.gov (United States)

    Jiji, G. Wiselin; Dehmeshki, Jamshid

    2014-04-01

    Tissue classification in computed tomography (CT) brain images is an important issue in the analysis of several brain dementias. A combination of different approaches for the segmentation of brain images is presented in this paper. A multi resolution algorithm is proposed along with scaled versions using Gaussian filter and wavelet analysis that extends expectation maximization (EM) algorithm. It is found that it is less sensitive to noise and got more accurate image segmentation than traditional EM. Moreover the algorithm has been applied on 20 sets of CT of the human brain and compared with other works. The segmentation results show the advantages of the proposed work have achieved more promising results and the results have been tested with Doctors.

  17. A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography.

    Science.gov (United States)

    Boegel, Marco; Hoelter, Philip; Redel, Thomas; Maier, Andreas; Hornegger, Joachim; Doerfler, Arnd

    2015-08-01

    Subarachnoid hemorrhage due to a ruptured cerebral aneurysm is still a devastating disease. Planning of endovascular aneurysm therapy is increasingly based on hemodynamic simulations necessitating reliable vessel segmentation and accurate assessment of vessel diameters. In this work, we propose a fully-automatic, locally adaptive, gradient-based thresholding algorithm. Our approach consists of two steps. First, we estimate the parameters of a global thresholding algorithm using an iterative process. Then, a locally adaptive version of the approach is applied using the estimated parameters. We evaluated both methods on 8 clinical 3D DSA cases. Additionally, we propose a way to select a reference segmentation based on 2D DSA measurements. For large vessels such as the internal carotid artery, our results show very high sensitivity (97.4%), precision (98.7%) and Dice-coefficient (98.0%) with our reference segmentation. Similar results (sensitivity: 95.7%, precision: 88.9% and Dice-coefficient: 90.7%) are achieved for smaller vessels of approximately 1mm diameter. PMID:26736679

  18. Evaluation of a Novel Approach for Automatic Volume Determination of Glioblastomas Based on Several Manual Expert Segmentations

    CERN Document Server

    Egger, Jan; Kuhnt, Daniela; Carl, Barbara; Kappus, Christoph; Freisleben, Bernd; Nimsky, Christopher

    2011-01-01

    The glioblastoma multiforme is the most common malignant primary brain tumor and is one of the highest malignant human neoplasms. During the course of disease, the evaluation of tumor volume is an essential part of the clinical follow-up. However, manual segmentation for acquisition of tumor volume is a time-consuming process. In this paper, a new approach for the automatic segmentation and volume determination of glioblastomas (glioblastoma multiforme) is presented and evaluated. The approach uses a user-defined seed point inside the glioma to set up a directed 3D graph. The nodes of the graph are obtained by sampling along rays that are sent through the surface points of a polyhedron. After the graph has been constructed, the minimal s-t cut is calculated to separate the glioblastoma from the background. For evaluation, 12 Magnetic Resonance Imaging (MRI) data sets were manually segmented slice by slice, by neurosurgeons with several years of experience in the resection of gliomas. Afterwards, the manual se...

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

    DEFF Research Database (Denmark)

    Petersen, Jens; Feragen, Aasa; Lo, P.; Owen, Megan; Wille, M.M.W.; Thomsen, Laura; Dirksen, Asger; de Bruijne, Marleen

    Purpose: Assessing airway dimensions and attenuation from CT images is useful in the study of diseases affecting the airways such as Chronic Obstructive Pulmonary Disease (COPD). Measurements can be compared between patients and over time if specific airway segments can be identified. However, ma...

  20. 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.

  1. 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)

  2. 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.

  3. Scene segmentation from motion in multispectral imagery to aid automatic human gait recognition

    Science.gov (United States)

    Pearce, Daniel; Harvey, Christophe; Day, Simon; Goffredo, Michela

    2007-10-01

    Primarily focused at military and security environments where there is a need to identify humans covertly and remotely; this paper outlines how recovering human gait biometrics from a multi-spectral imaging system can overcome the failings of traditional biometrics to fulfil those needs. With the intention of aiding single camera human gait recognition, an algorithm was developed to accurately segment a walking human from multi-spectral imagery. 16-band imagery from the image replicating imaging spectrometer (IRIS) camera system is used to overcome some of the common problems associated with standard change detection techniques. Fusing the concepts of scene segmentation by spectral characterisation and background subtraction by image differencing gives a uniquely robust approach. This paper presents the results of real trials with human subjects and a prototype IRIS camera system, and compares performance to typical broadband camera systems.

  4. A software tool for automatic classification and segmentation of 2D/3D medical images

    International Nuclear Information System (INIS)

    Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided

  5. Automatic segmentation of the optic nerve head for deformation measurements in video rate optical coherence tomography

    Science.gov (United States)

    Hidalgo-Aguirre, Maribel; Gitelman, Julian; Lesk, Mark Richard; Costantino, Santiago

    2015-11-01

    Optical coherence tomography (OCT) imaging has become a standard diagnostic tool in ophthalmology, providing essential information associated with various eye diseases. In order to investigate the dynamics of the ocular fundus, we present a simple and accurate automated algorithm to segment the inner limiting membrane in video-rate optic nerve head spectral domain (SD) OCT images. The method is based on morphological operations including a two-step contrast enhancement technique, proving to be very robust when dealing with low signal-to-noise ratio images and pathological eyes. An analysis algorithm was also developed to measure neuroretinal tissue deformation from the segmented retinal profiles. The performance of the algorithm is demonstrated, and deformation results are presented for healthy and glaucomatous eyes.

  6. Automatic Moving Object Segmentation from Video Sequences Using Alternate Flashing System

    Directory of Open Access Journals (Sweden)

    Chang-Su Kim

    2010-01-01

    Full Text Available A novel algorithm to extract moving objects from video sequences is proposed in this paper. The proposed algorithm employs a flashing system to obtain an alternate series of lit and unlit frames from a single camera. For each unlit frame, the proposed algorithm synthesizes the corresponding lit frame using a motion-compensated interpolation scheme. Then, by comparing the unlit frame with the lit frame, we construct the sensitivity map, which provides depth cues. In addition to the sensitivity term, color, coherence, and smoothness terms are employed to define an energy function, which is minimized to yield segmentation results. Moreover, we develop a faster version of the proposed algorithm, which reduces the computational complexity significantly at the cost of slight performance degradation. Experiments on various test sequences show that the proposed algorithm provides high-quality segmentation results.

  7. Automatic segmentation of blood vessels from retinal fundus images through image processing and data mining techniques

    Indian Academy of Sciences (India)

    R Geetharamani; Lakshmi Balasubramanian

    2015-09-01

    Machine Learning techniques have been useful in almost every field of concern. Data Mining, a branch of Machine Learning is one of the most extensively used techniques. The ever-increasing demands in the field of medicine are being addressed by computational approaches in which Big Data analysis, image processing and data mining are on top priority. These techniques have been exploited in the domain of ophthalmology for better retinal fundus image analysis. Blood vessels, one of the most significant retinal anatomical structures are analysed for diagnosis of many diseases like retinopathy, occlusion and many other vision threatening diseases. Vessel segmentation can also be a pre-processing step for segmentation of other retinal structures like optic disc, fovea, microneurysms, etc. In this paper, blood vessel segmentation is attempted through image processing and data mining techniques. 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 algorithm and second level classification using C4.5 enhanced with bagging techniques. Association of every pixel against the feature vector necessitates Big Data analysis. The proposed methodology was evaluated on a publicly available database, STARE. The results reported 95.05% accuracy on entire dataset; however the accuracy was 95.20% on normal images and 94.89% on pathological images. A comparison of these results with the existing methodologies is also reported. This methodology can help ophthalmologists in better and faster analysis and hence early treatment to the patients.

  8. AUTOMATIC SEGMENTATION ALGORITHM FOR THE LUMEN OF THE CAROTID ARTERY IN ULTRASOUND B-MODE IMAGES

    OpenAIRE

    Santos, AMF; João Manuel R. S. Tavares; Sousa, L. de; Santos, R.; Castro, P.; Azevedo, E.

    2012-01-01

    A new algorithm is proposed for the identification and segmentation of the lumen and bifurcation boundaries of the carotid artery in 2D longitudinal ultrasound B-mode images. It uses the hipoechogenic characteristics defining the lumen of the carotid for its identification and echogenic characteristics for the identification of the bifurcation. The input image is preprocessed with the application of an anisotropic diffusion filter for speckle removal, and morphologic operators for the detecti...

  9. Automatic segmentation of the carotid artery in ultrasound B-mode images

    OpenAIRE

    André M.F. Santos; João Manuel R. S. Tavares; Elsa Azevedo; Luísa Sousa

    2013-01-01

    B-mode ultrasound imaging is well-known and used in the medical imaging field; however, it presents various difficulties, specifically in tasks of image segmentation and surface reconstruction, due to intrinsic adverse characteristics, such like low contrast and noise [1,2]. Despite this, B-mode ultrasound imaging has been used in the diagnosis of several cardiac diseases, particularly, carotid artery diseases like atherosclerosis, known as the quot;hardening of the artery, after the accumula...

  10. Automatic 3D segmentation of the prostate on magnetic resonance images for radiotherapy planning

    OpenAIRE

    Alvarez Jiménez, Charlems

    2015-01-01

    Abstract. Accurate segmentation of the prostate, the seminal vesicles, the bladder and the rectum is a crucial step for planning radiotherapy (RT) procedures. Modern radiotherapy protocols have included the delineation of the pelvic organs in magnetic resonance images (MRI), as the guide to the therapeutic beam irradiation over the target organ. However, this task is highly inter and intra-expert variable and may take about 20 minutes per patient, even for trained experts, constituting an imp...

  11. Automatic training and reliability estimation for 3D ASM applied to cardiac MRI segmentation

    Science.gov (United States)

    Tobon-Gomez, Catalina; Sukno, Federico M.; Butakoff, Constantine; Huguet, Marina; Frangi, Alejandro F.

    2012-07-01

    Training active shape models requires collecting manual ground-truth meshes in a large image database. While shape information can be reused across multiple imaging modalities, intensity information needs to be imaging modality and protocol specific. In this context, this study has two main purposes: (1) to test the potential of using intensity models learned from MRI simulated datasets and (2) to test the potential of including a measure of reliability during the matching process to increase robustness. We used a population of 400 virtual subjects (XCAT phantom), and two clinical populations of 40 and 45 subjects. Virtual subjects were used to generate simulated datasets (MRISIM simulator). Intensity models were trained both on simulated and real datasets. The trained models were used to segment the left ventricle (LV) and right ventricle (RV) from real datasets. Segmentations were also obtained with and without reliability information. Performance was evaluated with point-to-surface and volume errors. Simulated intensity models obtained average accuracy comparable to inter-observer variability for LV segmentation. The inclusion of reliability information reduced volume errors in hypertrophic patients (EF errors from 17 ± 57% to 10 ± 18% LV MASS errors from -27 ± 22 g to -14 ± 25 g), and in heart failure patients (EF errors from -8 ± 42% to -5 ± 14%). The RV model of the simulated images needs further improvement to better resemble image intensities around the myocardial edges. Both for real and simulated models, reliability information increased segmentation robustness without penalizing accuracy.

  12. A pre-clinical assessment of an atlas-based automatic segmentation tool for the head and neck

    International Nuclear Information System (INIS)

    Background and purpose: Accurate conformal radiotherapy treatment requires manual delineation of target volumes and organs at risk (OAR) that is both time-consuming and subject to large inter-user variability. One solution is atlas-based automatic segmentation (ABAS) where a priori information is used to delineate various organs of interest. The aim of the present study is to establish the accuracy of one such tool for the head and neck (H and N) using two different evaluation methods. Materials and methods: Two radiotherapy centres were provided with an ABAS tool that was used to outline the brainstem, parotids and mandible on several patients. The results were compared to manual delineations for the first centre (EM1) and reviewed/edited for the second centre (EM2), both of which were deemed as equally valid gold standards. The contours were compared in terms of their volume, sensitivity and specificity with the results being interpreted using the Dice similarity coefficient and a receiver operator characteristic (ROC) curve. Results: Automatic segmentation took typically ∼7 min for each patient on a standard PC. The results indicated that the atlas contour volume was generally within ±1SD of each gold standard apart from the parotids for EM1 and brainstem for EM2 that were over- and under-estimated, respectively (within ±2SD). The similarity of the atlas contours with their respective gold standard was satisfactory with an average Dice coefficient for all OAR of 0.68 ± 0.25 for EM1 and 0.82 ± 0.13 for EM2. All data had satisfactory sensitivity and specificity resulting in a favourable position in ROC space. Conclusions: These tests have shown that the ABAS tool exhibits satisfactory sensitivity and specificity for the OAR investigated. There is, however, a systematic over-segmentation of the parotids (EM1) and under-segmentation of the brainstem (EM2) that require careful review and editing in the majority of cases. Such issues have been discussed with the

  13. Automatic segmentation of magnetic resonance images of the trans-femoral residual limb.

    Science.gov (United States)

    Douglas, T S; Solomonidis, S E; Lee, V S; Spence, W D; Sandham, W A; Hadley, D M

    1998-12-01

    An automatic algorithm for the extraction of the skin and bone boundaries from axial magnetic resonance images of the residual limb of trans-femoral amputees is presented. The method makes use of K-means clustering and mathematical morphology. Statistical analysis of the results indicates that the computer-generated boundaries compare favourably to those drawn by human observers. The boundaries may be used in biomechanical modelling of the interaction between the residual limb and the prosthetic socket. The limb/socket interface determines the quality of prosthetic fit, therefore knowledge of this interface is important for the improvement of socket design in order to achieve patient comfort and mobility. PMID:10223645

  14. A new graph based text segmentation using Wikipedia for automatic text summarization

    Directory of Open Access Journals (Sweden)

    Mohsen Pourvali

    2012-01-01

    Full Text Available The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of automatically creating a compressed version of a given document that provides useful information to users, and multi-document summarization is to produce a summary delivering the majority of information content from a set of documents about an explicit or implicit main topic. According to the input text, in this paper we use the knowledge base of Wikipedia and the words of the main text to create independent graphs. We will then determine the important of graphs. Then we are specified importance of graph and sentences that have topics with high importance. Finally, we extract sentences with high importance. The experimental results on an open benchmark datasets from DUC01 and DUC02 show that our proposed approach can improve the performance compared to state-of-the-art summarization approaches

  15. A Multiphase Validation of Atlas-Based Automatic and Semiautomatic Segmentation Strategies for Prostate MRI

    International Nuclear Information System (INIS)

    Purpose: To perform a rigorous technological assessment and statistical validation of a software technology for anatomic delineations of the prostate on MRI datasets. Methods and Materials: A 3-phase validation strategy was used. Phase I consisted of anatomic atlas building using 100 prostate cancer MRI data sets to provide training data sets for the segmentation algorithms. In phase II, 2 experts contoured 15 new MRI prostate cancer cases using 3 approaches (manual, N points, and region of interest). In phase III, 5 new physicians with variable MRI prostate contouring experience segmented the same 15 phase II datasets using 3 approaches: manual, N points with no editing, and full autosegmentation with user editing allowed. Statistical analyses for time and accuracy (using Dice similarity coefficient) endpoints used traditional descriptive statistics, analysis of variance, analysis of covariance, and pooled Student t test. Results: In phase I, average (SD) total and per slice contouring time for the 2 physicians was 228 (75), 17 (3.5), 209 (65), and 15 seconds (3.9), respectively. In phase II, statistically significant differences in physician contouring time were observed based on physician, type of contouring, and case sequence. The N points strategy resulted in superior segmentation accuracy when initial autosegmented contours were compared with final contours. In phase III, statistically significant differences in contouring time were observed based on physician, type of contouring, and case sequence again. The average relative timesaving for N points and autosegmentation were 49% and 27%, respectively, compared with manual contouring. The N points and autosegmentation strategies resulted in average Dice values of 0.89 and 0.88, respectively. Pre- and postedited autosegmented contours demonstrated a higher average Dice similarity coefficient of 0.94. Conclusion: The software provided robust contours with minimal editing required. Observed time savings were seen

  16. Comparison of two algorithms in the automatic segmentation of blood vessels in fundus images

    Science.gov (United States)

    LeAnder, Robert; Chowdary, Myneni Sushma; Mokkapati, Swapnasri; Umbaugh, Scott E.

    2008-03-01

    Effective timing and treatment are critical to saving the sight of patients with diabetes. Lack of screening, as well as a shortage of ophthalmologists, help contribute to approximately 8,000 cases per year of people who lose their sight to diabetic retinopathy, the leading cause of new cases of blindness [1] [2]. Timely treatment for diabetic retinopathy prevents severe vision loss in over 50% of eyes tested [1]. Fundus images can provide information for detecting and monitoring eye-related diseases, like diabetic retinopathy, which if detected early, may help prevent vision loss. Damaged blood vessels can indicate the presence of diabetic retinopathy [9]. So, early detection of damaged vessels in retinal images can provide valuable information about the presence of disease, thereby helping to prevent vision loss. Purpose: The purpose of this study was to compare the effectiveness of two blood vessel segmentation algorithms. Methods: Fifteen fundus images from the STARE database were used to develop two algorithms using the CVIPtools software environment. Another set of fifteen images were derived from the first fifteen and contained ophthalmologists' hand-drawn tracings over the retinal vessels. The ophthalmologists' tracings were used as the "gold standard" for perfect segmentation and compared with the segmented images that were output by the two algorithms. Comparisons between the segmented and the hand-drawn images were made using Pratt's Figure of Merit (FOM), Signal-to-Noise Ratio (SNR) and Root Mean Square (RMS) Error. Results: Algorithm 2 has an FOM that is 10% higher than Algorithm 1. Algorithm 2 has a 6%-higher SNR than Algorithm 1. Algorithm 2 has only 1.3% more RMS error than Algorithm 1. Conclusions: Algorithm 1 extracted most of the blood vessels with some missing intersections and bifurcations. Algorithm 2 extracted all the major blood vessels, but eradicated some vessels as well. Algorithm 2 outperformed Algorithm 1 in terms of visual clarity, FOM

  17. Pharynx Anatomy

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    ... 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 ...

  18. Vulva Anatomy

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    ... 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 ...

  19. 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 ...

  20. Clinical pilot study for the automatic segmentation and recognition of abdominal adipose tissue compartments from MRI data

    International Nuclear Information System (INIS)

    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.)

  1. Automatic 4D reconstruction of patient-specific cardiac mesh with 1-to-1 vertex correspondence from segmented contours lines.

    Directory of Open Access Journals (Sweden)

    Chi Wan Lim

    Full Text Available We propose an automatic algorithm for the reconstruction of patient-specific cardiac mesh models with 1-to-1 vertex correspondence. In this framework, a series of 3D meshes depicting the endocardial surface of the heart at each time step is constructed, based on a set of border delineated magnetic resonance imaging (MRI data of the whole cardiac cycle. The key contribution in this work involves a novel reconstruction technique to generate a 4D (i.e., spatial-temporal model of the heart with 1-to-1 vertex mapping throughout the time frames. The reconstructed 3D model from the first time step is used as a base template model and then deformed to fit the segmented contours from the subsequent time steps. A method to determine a tree-based connectivity relationship is proposed to ensure robust mapping during mesh deformation. The novel feature is the ability to handle intra- and inter-frame 2D topology changes of the contours, which manifests as a series of merging and splitting of contours when the images are viewed either in a spatial or temporal sequence. Our algorithm has been tested on five acquisitions of cardiac MRI and can successfully reconstruct the full 4D heart model in around 30 minutes per subject. The generated 4D heart model conforms very well with the input segmented contours and the mesh element shape is of reasonably good quality. The work is important in the support of downstream computational simulation activities.

  2. Automatic segmentation of histological structures in normal and neoplastic mammary gland tissue sections

    Energy Technology Data Exchange (ETDEWEB)

    Fernandez-Gonzalez, Rodrigo; Deschamps, Thomas; Idica, Adam K.; Malladi, Ravi; Ortiz de Solorzano, Carlos

    2003-01-18

    In this paper we present a scheme for real time segmentation of histological structures in microscopic images of normal and neoplastic mammary gland sections. Paraffin embedded or frozen tissue blocks are sliced, and sections are stained with hematoxylin and eosin (H&E). The sections are then imaged using conventional bright field microscopy. The background of the images is corrected by arithmetic manipulation using a ''phantom.'' Then we use the fast marching method with a speed function that depends on the brightness gradient of the image to obtain a preliminary approximation to the boundaries of the structures of interest within a region of interest (ROI) of the entire section manually selected by the user. We use the result of the fast marching method as the initial condition for the level set motion equation. We run this last method for a few steps and obtain the final result of the segmentation. These results can be connected from section to section to build a three-dimensional reconstruction of the entire tissue block that we are studying.

  3. Automatic Detection and Segmentation of Columns in As-Built Buildings from Point Clouds

    Directory of Open Access Journals (Sweden)

    Lucía Díaz-Vilariño

    2015-11-01

    Full Text Available Over the past few years, there has been an increasing need for tools that automate the processing of as-built 3D laser scanner data. Given that a fast and active dimensional analysis of constructive components is essential for construction monitoring, this paper is particularly focused on the detection and segmentation of columns in building interiors from incomplete point clouds acquired with a Terrestrial Laser Scanner. The methodology addresses two types of columns: round cross-section and rectangular cross-section. Considering columns as vertical elements, the global strategy for segmentation involves the rasterization of a point cloud onto the XY plane and the implementation of a model-driven approach based on the Hough Transform. The methodology is tested in two real case studies, and experiments are carried out under different levels of data completeness. The results show the robustness of the methodology to the presence of clutter and partial occlusion, typical in building indoors, even though false positives can be obtained if other elements with the same shape and size as columns are present in the raster.

  4. A fully-automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder

    Directory of Open Access Journals (Sweden)

    Igual Laura

    2011-12-01

    Full Text Available Abstract Background Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations. Method We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure. Results We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis. Conclusion CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities

  5. Anatomy & Physiology

    Science.gov (United States)

    ... Surveillance Modules » Anatomy & Physiology Cancer Registration & Surveillance Modules Anatomy & Physiology Intro to the Human Body Body Functions & Life Process Anatomical Terminology Review ...

  6. Derivation of high-resolution MRI atlases of the human cerebellum at 3T and segmentation using multiple automatically generated templates.

    Science.gov (United States)

    Park, Min Tae M; Pipitone, Jon; Baer, Lawrence H; Winterburn, Julie L; Shah, Yashvi; Chavez, Sofia; Schira, Mark M; Lobaugh, Nancy J; Lerch, Jason P; Voineskos, Aristotle N; Chakravarty, M Mallar

    2014-07-15

    The cerebellum has classically been linked to motor learning and coordination. However, there is renewed interest in the role of the cerebellum in non-motor functions such as cognition and in the context of different neuropsychiatric disorders. The contribution of neuroimaging studies to advancing understanding of cerebellar structure and function has been limited, partly due to the cerebellum being understudied as a result of contrast and resolution limitations of standard structural magnetic resonance images (MRI). These limitations inhibit proper visualization of the highly compact and detailed cerebellar foliations. In addition, there is a lack of robust algorithms that automatically and reliably identify the cerebellum and its subregions, further complicating the design of large-scale studies of the cerebellum. As such, automated segmentation of the cerebellar lobules would allow detailed population studies of the cerebellum and its subregions. In this manuscript, we describe a novel set of high-resolution in vivo atlases of the cerebellum developed by pairing MR imaging with a carefully validated manual segmentation protocol. Using these cerebellar atlases as inputs, we validate a novel automated segmentation algorithm that takes advantage of the neuroanatomical variability that exists in a given population under study in order to automatically identify the cerebellum, and its lobules. Our automatic segmentation results demonstrate good accuracy in the identification of all lobules (mean Kappa [κ]=0.731; range 0.40-0.89), and the entire cerebellum (mean κ=0.925; range 0.90-0.94) when compared to "gold-standard" manual segmentations. These results compare favorably in comparison to other publically available methods for automatic segmentation of the cerebellum. The completed cerebellar atlases are available freely online (http://imaging-genetics.camh.ca/cerebellum) and can be customized to the unique neuroanatomy of different subjects using the proposed

  7. A Novel Region-Growing Based Semi-Automatic Segmentation Protocol for Three-Dimensional Condylar Reconstruction Using Cone Beam Computed Tomography (CBCT)

    NARCIS (Netherlands)

    Xi, Tong; Schreurs, Ruud; Heerink, Wout J.; Berge, Stefaan J.; Maal, Thomas J. J.

    2014-01-01

    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. Bilatera

  8. 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)

    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 cm3: 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

  9. Identification and automatic segmentation of multiphasic cell growth using a linear hybrid model.

    Science.gov (United States)

    Hartmann, András; Neves, Ana Rute; Lemos, João M; Vinga, Susana

    2016-09-01

    This article considers a new mathematical model for the description of multiphasic cell growth. A linear hybrid model is proposed and it is shown that the two-parameter logistic model with switching parameters can be represented by a Switched affine AutoRegressive model with eXogenous inputs (SARX). The growth phases are modeled as continuous processes, while the switches between the phases are considered to be discrete events triggering a change in growth parameters. This framework provides an easily interpretable model, because the intrinsic behavior is the same along all the phases but with a different parameterization. Another advantage of the hybrid model is that it offers a simpler alternative to recent more complex nonlinear models. The growth phases and parameters from datasets of different microorganisms exhibiting multiphasic growth behavior such as Lactococcus lactis, Streptococcus pneumoniae, and Saccharomyces cerevisiae, were inferred. The segments and parameters obtained from the growth data are close to the ones determined by the experts. The fact that the model could explain the data from three different microorganisms and experiments demonstrates the strength of this modeling approach for multiphasic growth, and presumably other processes consisting of multiple phases. PMID:27424949

  10. Automatic Segmentation of Whole Breast Using Atlas Approach and Deformable Image Registration

    International Nuclear Information System (INIS)

    Purpose: To compare interobserver variations in delineating the whole breast for treatment planning using two contouring methods. Methods and Materials: Autosegmented contours were generated by a deformable image registration-based breast segmentation method (DEF-SEG) by mapping the whole breast clinical target volume (CTVwb) from a template case to a new patient case. Eight breast radiation oncologists modified the autosegmented contours as necessary to achieve a clinically appropriate CTVwb and then recontoured the same case from scratch for comparison. The times to complete each approach, as well as the interobserver variations, were analyzed. The template case was also mapped to 10 breast cancer patients with a body mass index of 19.1-35.9 kg/m2. The three-dimensional surface-to-surface distances and volume overlapping analyses were computed to quantify contour variations. Results: The median time to edit the DEF-SEG-generated CTVwb was 12.9 min (range, 3.4-35.9) compared with 18.6 min (range, 8.9-45.2) to contour the CTVwb from scratch (30% faster, p = 0.028). The mean surface-to-surface distance was noticeably reduced from 1.6 mm among the contours generated from scratch to 1.0 mm using the DEF-SEG method (p = 0.047). The deformed contours in 10 patients achieved 94% volume overlap before correction and required editing of 5% (range, 1-10%) of the contoured volume. Conclusion: Significant interobserver variations suggested a lack of consensus regarding the CTVwb, even among breast cancer specialists. Using the DEF-SEG method produced more consistent results and required less time. The DEF-SEG method can be successfully applied to patients with different body mass indexes.

  11. 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)

    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.

  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. PMID:26917105

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

    International Nuclear Information System (INIS)

    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

  14. 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

  15. Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound

    OpenAIRE

    Milletari, Fausto; Ahmadi, Seyed-Ahmad; Kroll, Christine; Plate, Annika; Rozanski, Verena; Maiostre, Juliana; Levin, Johannes; Dietrich, Olaf; Ertl-Wagner, Birgit; Bötzel, Kai; Navab, Nassir

    2016-01-01

    In this work we propose a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs). Our method is based on Hough voting, a strategy that allows for fully automatic localisation and segmentation of the anatomies of interest. This approach does not only use the CNN classification outcomes, but it also implements voting by exploiting the features produced by the deepest portion of the network. We show that this learning-based segme...

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

    Science.gov (United States)

    Faganeli, J; Jager, F

    2010-03-01

    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%. PMID:20130344

  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)

    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. In vivo semi-automatic segmentation of multicontrast cardiovascular magnetic resonance for prospective cohort studies on plaque tissue composition: initial experience.

    Science.gov (United States)

    Yoneyama, Taku; Sun, Jie; Hippe, Daniel S; Balu, Niranjan; Xu, Dongxiang; Kerwin, William S; Hatsukami, Thomas S; Yuan, Chun

    2016-01-01

    Automatic in vivo segmentation of multicontrast (multisequence) carotid magnetic resonance for plaque composition has been proposed as a substitute for manual review to save time and reduce inter-reader variability in large-scale or multicenter studies. Using serial images from a prospective longitudinal study, we sought to compare a semi-automatic approach versus expert human reading in analyzing carotid atherosclerosis progression. Baseline and 6-month follow-up multicontrast carotid images from 59 asymptomatic subjects with 16-79 % carotid stenosis were reviewed by both trained radiologists with 2-4 years of specialized experience in carotid plaque characterization with MRI and a previously reported automatic atherosclerotic plaque segmentation algorithm, referred to as morphology-enhanced probabilistic plaque segmentation (MEPPS). Agreement on measurements from individual time points, as well as on compositional changes, was assessed using the intraclass correlation coefficient (ICC). There was good agreement between manual and MEPPS reviews on individual time points for calcification (CA) (area: ICC; 0.85-0.91; volume: ICC; 0.92-0.95) and lipid-rich necrotic core (LRNC) (area: ICC; 0.78-0.82; volume: ICC; 0.84-0.86). For compositional changes, agreement was good for CA volume change (ICC; 0.78) and moderate for LRNC volume change (ICC; 0.49). Factors associated with LRNC progression as detected by MEPPS review included intraplaque hemorrhage (positive association) and reduction in low-density lipoprotein cholesterol (negative association), which were consistent with previous findings from manual review. Automatic classifier for plaque composition produced results similar to expert manual review in a prospective serial MRI study of carotid atherosclerosis progression. Such automatic classification tools may be beneficial in large-scale multicenter studies by reducing image analysis time and avoiding bias between human reviewers. PMID:26169389

  19. 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 ...

  20. Tooth anatomy

    Science.gov (United States)

    ... page: //medlineplus.gov/ency/article/002214.htm Tooth anatomy To use the sharing features on this page, ... upper jawbone is called the maxilla. Images Tooth anatomy References Lingen MW. Head and neck. In: Kumar ...

  1. 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 ...

  2. Robottens Anatomi

    DEFF Research Database (Denmark)

    Antabi, Mimo

    Artiklen "Robottens Anatomi - mellem kunst og videnskab". Handler om brugen af robotter i kunstens og videnskabens verden.......Artiklen "Robottens Anatomi - mellem kunst og videnskab". Handler om brugen af robotter i kunstens og videnskabens verden....

  3. Hand Anatomy

    Science.gov (United States)

    ... Topics A-Z Videos Infographics Symptom Picker Hand Anatomy Hand Safety Fireworks Safety Lawnmower Safety Snowblower safety ... Topics A-Z Videos Infographics Symptom Picker Hand Anatomy Hand Safety Fireworks Safety Lawnmower Safety Snowblower safety ...

  4. Heart Anatomy

    Science.gov (United States)

    ... Incredible Machine Bonus poster (PDF) The Human Heart Anatomy Blood The Conduction System The Coronary Arteries The ... of the Leg Vasculature of the Torso Heart anatomy illustrations and animations for grades K-6. Heart ...

  5. FISICO: Fast Image SegmentatIon COrrection

    Science.gov (United States)

    Valenzuela, Waldo; Ferguson, Stephen J.; Ignasiak, Dominika; Diserens, Gaëlle; Häni, Levin; Wiest, Roland; Vermathen, Peter; Boesch, Chris

    2016-01-01

    Background and Purpose In clinical diagnosis, medical image segmentation plays a key role in the analysis of pathological regions. Despite advances in automatic and semi-automatic segmentation techniques, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a lower number of interactions, and a user-independent solution to reduce the time frame between image acquisition and diagnosis. Methods We present a new interactive method for correcting image segmentations. Our method provides 3D shape corrections through 2D interactions. This approach enables an intuitive and natural corrections of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle and knee joint segmentations from MR images. Results Experimental results show that full segmentation corrections could be performed within an average correction time of 5.5±3.3 minutes and an average of 56.5±33.1 user interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.02 for both anatomies. In addition, for users with different levels of expertise, our method yields a correction time and number of interaction decrease from 38±19.2 minutes to 6.4±4.3 minutes, and 339±157.1 to 67.7±39.6 interactions, respectively. PMID:27224061

  6. FISICO: Fast Image SegmentatIon COrrection.

    Directory of Open Access Journals (Sweden)

    Waldo Valenzuela

    Full Text Available In clinical diagnosis, medical image segmentation plays a key role in the analysis of pathological regions. Despite advances in automatic and semi-automatic segmentation techniques, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a lower number of interactions, and a user-independent solution to reduce the time frame between image acquisition and diagnosis.We present a new interactive method for correcting image segmentations. Our method provides 3D shape corrections through 2D interactions. This approach enables an intuitive and natural corrections of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle and knee joint segmentations from MR images.Experimental results show that full segmentation corrections could be performed within an average correction time of 5.5±3.3 minutes and an average of 56.5±33.1 user interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.02 for both anatomies. In addition, for users with different levels of expertise, our method yields a correction time and number of interaction decrease from 38±19.2 minutes to 6.4±4.3 minutes, and 339±157.1 to 67.7±39.6 interactions, respectively.

  7. EVENT SEGMENTATION

    OpenAIRE

    Zacks, Jeffrey M.; Swallow, Khena M.

    2007-01-01

    One way to understand something is to break it up into parts. New research indicates that segmenting ongoing activity into meaningful events is a core component of ongoing perception, with consequences for memory and learning. Behavioral and neuroimaging data suggest that event segmentation is automatic and that people spontaneously segment activity into hierarchically organized parts and sub-parts. This segmentation depends on the bottom-up processing of sensory features such as movement, an...

  8. Quantitative normal thoracic anatomy at CT.

    Science.gov (United States)

    Matsumoto, Monica M S; Udupa, Jayaram K; Tong, Yubing; Saboury, Babak; Torigian, Drew A

    2016-07-01

    Automatic anatomy recognition (AAR) methodologies for a body region require detailed understanding of the morphology, architecture, and geographical layout of the organs within the body region. The aim of this paper was to quantitatively characterize the normal anatomy of the thoracic region for AAR. Contrast-enhanced chest CT images from 41 normal male subjects, each with 11 segmented objects, were considered in this study. The individual objects were quantitatively characterized in terms of their linear size, surface area, volume, shape, CT attenuation properties, inter-object distances, size and shape correlations, size-to-distance correlations, and distance-to-distance correlations. A heat map visualization approach was used for intuitively portraying the associations between parameters. Numerous new observations about object geography and relationships were made. Some objects, such as the pericardial region, vary far less than others in size across subjects. Distance relationships are more consistent when involving an object such as trachea and bronchi than other objects. Considering the inter-object distance, some objects have a more prominent correlation, such as trachea and bronchi, right and left lungs, arterial system, and esophagus. The proposed method provides new, objective, and usable knowledge about anatomy whose utility in building body-wide models toward AAR has been demonstrated in other studies. PMID:27065241

  9. Accurate and Fully Automatic Hippocampus Segmentation Using Subject-Specific 3D Optimal Local Maps Into a Hybrid Active Contour Model.

    Science.gov (United States)

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

    2014-01-01

    Assessing the structural integrity of the hippocampus (HC) is an essential step toward prevention, diagnosis, and follow-up of various brain disorders due to the implication of the structural changes of the HC in those disorders. In this respect, the development of automatic segmentation methods that can accurately, reliably, and reproducibly segment the HC has attracted considerable attention over the past decades. This paper presents an innovative 3-D fully automatic method to be used on top of the multiatlas concept for the HC segmentation. The method is based on a subject-specific set of 3-D optimal local maps (OLMs) that locally control the influence of each energy term of a hybrid active contour model (ACM). The complete set of the OLMs for a set of training images is defined simultaneously via an optimization scheme. At the same time, the optimal ACM parameters are also calculated. Therefore, heuristic parameter fine-tuning is not required. Training OLMs are subsequently combined, by applying an extended multiatlas concept, to produce the OLMs that are anatomically more suitable to the test image. The proposed algorithm was tested on three different and publicly available data sets. Its accuracy was compared with that of state-of-the-art methods demonstrating the efficacy and robustness of the proposed method. PMID:27170866

  10. 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.

  11. SU-E-J-142: Performance Study of Automatic Image-Segmentation Algorithms in Motion Tracking Via MR-IGRT

    International Nuclear Information System (INIS)

    Purpose: Evaluate commonly used segmentation algorithms on a commercially available real-time MR image guided radiotherapy (MR-IGRT) system (ViewRay), compare the strengths and weaknesses of each method, with the purpose of improving motion tracking for more accurate radiotherapy. Methods: MR motion images of bladder, kidney, duodenum, and liver tumor were acquired for three patients using a commercial on-board MR imaging system and an imaging protocol used during MR-IGRT. A series of 40 frames were selected for each case to cover at least 3 respiratory cycles. Thresholding, Canny edge detection, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE), along with the ViewRay treatment planning and delivery system (TPDS) were included in the comparisons. To evaluate the segmentation results, an expert manual contouring of the organs or tumor from a physician was used as a ground-truth. Metrics value of sensitivity, specificity, Jaccard similarity, and Dice coefficient were computed for comparison. Results: In the segmentation of single image frame, all methods successfully segmented the bladder and kidney, but only FKM, KHM and TPDS were able to segment the liver tumor and the duodenum. For segmenting motion image series, the TPDS method had the highest sensitivity, Jarccard, and Dice coefficients in segmenting bladder and kidney, while FKM and KHM had a slightly higher specificity. A similar pattern was observed when segmenting the liver tumor and the duodenum. The Canny method is not suitable for consistently segmenting motion frames in an automated process, while thresholding and RD-LSE cannot consistently segment a liver tumor and the duodenum. Conclusion: The study compared six different segmentation methods and showed the effectiveness of the ViewRay TPDS algorithm in segmenting motion images during MR-IGRT. Future studies include a selection of conformal segmentation methods based on image/organ-specific information

  12. SU-E-J-142: Performance Study of Automatic Image-Segmentation Algorithms in Motion Tracking Via MR-IGRT

    Energy Technology Data Exchange (ETDEWEB)

    Feng, Y; Olsen, J.; Parikh, P.; Noel, C; Wooten, H; Du, D; Mutic, S; Hu, Y [Washington University, St. Louis, MO (United States); Kawrakow, I; Dempsey, J [Washington University, St. Louis, MO (United States); ViewRay Co., Oakwood Village, OH (United States)

    2014-06-01

    Purpose: Evaluate commonly used segmentation algorithms on a commercially available real-time MR image guided radiotherapy (MR-IGRT) system (ViewRay), compare the strengths and weaknesses of each method, with the purpose of improving motion tracking for more accurate radiotherapy. Methods: MR motion images of bladder, kidney, duodenum, and liver tumor were acquired for three patients using a commercial on-board MR imaging system and an imaging protocol used during MR-IGRT. A series of 40 frames were selected for each case to cover at least 3 respiratory cycles. Thresholding, Canny edge detection, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE), along with the ViewRay treatment planning and delivery system (TPDS) were included in the comparisons. To evaluate the segmentation results, an expert manual contouring of the organs or tumor from a physician was used as a ground-truth. Metrics value of sensitivity, specificity, Jaccard similarity, and Dice coefficient were computed for comparison. Results: In the segmentation of single image frame, all methods successfully segmented the bladder and kidney, but only FKM, KHM and TPDS were able to segment the liver tumor and the duodenum. For segmenting motion image series, the TPDS method had the highest sensitivity, Jarccard, and Dice coefficients in segmenting bladder and kidney, while FKM and KHM had a slightly higher specificity. A similar pattern was observed when segmenting the liver tumor and the duodenum. The Canny method is not suitable for consistently segmenting motion frames in an automated process, while thresholding and RD-LSE cannot consistently segment a liver tumor and the duodenum. Conclusion: The study compared six different segmentation methods and showed the effectiveness of the ViewRay TPDS algorithm in segmenting motion images during MR-IGRT. Future studies include a selection of conformal segmentation methods based on image/organ-specific information

  13. 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)

    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)

  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. The development and application of an automatic boundary segmentation methodology to evaluate the vaporizing characteristics of diesel spray under engine-like conditions

    International Nuclear Information System (INIS)

    Studying the vaporizing characteristics of diesel spray could greatly help to reduce engine emission and improve performance. The high-speed schlieren imaging method is an important optical technique for investigating the macroscopic vaporizing morphological evolution of liquid fuel, and pre-combustion constant volume combustion bombs are often used to simulate the high pressure and high temperature conditions occurring in diesel engines. Complicated background schlieren noises make it difficult to segment the spray region in schlieren spray images. To tackle this problem, this paper develops a vaporizing spray boundary segmentation methodology based on an automatic threshold determination algorithm. The methodology was also used to quantify the macroscopic characteristics of vaporizing sprays including tip penetration, near-field and far-field angles, and projected spray area and spray volume. The spray boundary segmentation methodology was realized in a MATLAB-based program. Comparisons were made between the spray characteristics obtained using the program method and those acquired using a manual method and the Hiroyasu prediction model. It is demonstrated that the methodology can segment and measure vaporizing sprays precisely and efficiently. Furthermore, the experimental results show that the spray angles were slightly affected by the injection pressure at high temperature and high pressure and under inert conditions. A higher injection pressure leads to longer spray tip penetration and a larger projected area and volume, while elevating the temperature of the environment can significantly promote the evaporation of cold fuel. (paper)

  16. Atlas Based Automatic Liver 3D CT Image Segmentation%基于图谱的肝脏CT三维自动分割研究

    Institute of Scientific and Technical Information of China (English)

    刘伟; 贾富仓; 胡庆茂; 王俊

    2011-01-01

    目的 在肝脏外科手术或肝脏病理研究中,计算肝脏体积是重要步骤.由于肝脏外形复杂、临近组织灰度值与之接近等特点,肝脏的自动医学图像分割仍是医学图像处理中的难点之一.方法 本文采用图谱结合3D非刚性配准的方法,同时加入肝脏区域搜索算法,实现了鲁棒性较高的肝脏自动分割程序.首先,利用20套训练图像创建图谱,然后程序自动搜索肝脏区域,最后将图谱与待分割CT图像依次进行仿射配准和B样条配准.配准以后的图谱肝脏轮廓即可表示为目标肝脏分割轮廓,进而计算出肝脏体积.结果 评估结果显示,上述方法在肝脏体积误差方面表现出色,达到77分,但在局部(主要在肝脏尖端)出现较大的误差.结论 该方法分割临床肝脏CT图像具有可行性.%Objective Liver segmentation is an important step for the planning and navigation in liver surgery. Accurate, fast and robust automatic segmentation methods for clinical routine data are urgently needed. Because of the liver- s characteristics, such as the complexity of the external form, the similarity between the intensities of the liver and the tissues around it, automatic segmentation of the liver is one of the difficulties in medical image processing. Methods In this paper, 3D non-rigid registration from a refined atlas to liver CT images is used for segmentation. Firstly, twenty sets of training images are utilized to create an atlas. Then the liver initial region is searched and located automatically. After that threshold filtering is used to enhance the robustness of segmentation. Finally, this atlas is non-rigidly registered to the liver in CT images with affine and B-spline in succession. The registered segmentation of liver- s atlas represented the segmentation of the target liver, and then the liver volume was calculated. Results The evaluation show that the proposed method works well in liver volume error, with the 77 score

  17. Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images

    International Nuclear Information System (INIS)

    We present a statistical shape model approach for automated segmentation of the proximal humerus and scapula with subsequent bone-cartilage interface (BCI) extraction from 3D magnetic resonance (MR) images of the shoulder region. Manual and automated bone segmentations from shoulder MR examinations from 25 healthy subjects acquired using steady-state free precession sequences were compared with the Dice similarity coefficient (DSC). The mean DSC scores between the manual and automated segmentations of the humerus and scapula bone volumes surrounding the BCI region were 0.926  ±  0.050 and 0.837  ±  0.059, respectively. The mean DSC values obtained for BCI extraction were 0.806  ±  0.133 for the humerus and 0.795  ±  0.117 for the scapula. The current model-based approach successfully provided automated bone segmentation and BCI extraction from MR images of the shoulder. In future work, this framework appears to provide a promising avenue for automated segmentation and quantitative analysis of cartilage in the glenohumeral joint. (paper)

  18. Semi-automatic segmentation and quantification of the internal carotid artery from 3D contrast-enhanced MR angiograms

    Science.gov (United States)

    van Bemmel, Cornelis M.; Niessen, Wiro J.

    2004-05-01

    A technique is presented for segmentation and quantification of stenosed internal carotid arteries in three-dimensional contrast-enhanced magnetic resonance angiography. Segmentation with sub-voxel accuracy of the internal carotid arteries (ICAs) has been achieved via level-set techniques in which the central axis serves as initialization. The central axis is determined with minmal user-interaction, viz. two user-defined points. Quantification is performed by measuring the cross-sectional area in the stenosis and at a reference segment in planes perpendicular to the central axis. The technique was applied to 52 ICAs. It is demonstrated that the method's reproducibility is better than the intra-observer agreement. Furthermore, the agreement between the presented method and the observers is better than the inter-observer agreement.

  19. 基于特征区域自动分割的人脸表情识别%Facial Expression Recognition Based on Feature Regions Automatic Segmentation

    Institute of Scientific and Technical Information of China (English)

    张腾飞; 闵锐; 王保云

    2011-01-01

    针对目前三维人脸表情区域分割方法复杂、费时问题,提出一种人脸表情区域自动分割方法,通过投影、曲率计算的方法检测人脸的部分特征点,以上述特征点为基础进行人脸表情区域的自动分割.为得到更加丰富的表情特征,结合人脸表情识别编码规则对提取到的特征矩阵进行扩充,利用分类器进行人脸表情的识别.通过对三维人脸表情数据库部分样本的识别结果表明,该方法可以取得较高的识别率.%To improve 3D facial expression feature regions segmentation, an automatic feature regions segmentation method is presented.The facial feature points are detected by conducting projection and curvature calculation, and are used as the basis of facial expression feature regions automatic segmentation.To obtain more abundant facial expression information, the Facial Action Coding System(FACS) coding roles is introduced to extend the extracted characteristic matrix.And facial expressions can be recognized by combining classifiers.Experimental results of 3D facial expression samples show that the method is effective with high recognition rate.

  20. Semi-automatic 3D segmentation of carotid lumen in contrast-enhanced computed tomography angiography images.

    Science.gov (United States)

    Hemmati, Hamidreza; Kamli-Asl, Alireza; Talebpour, Alireza; Shirani, Shapour

    2015-12-01

    The atherosclerosis disease is one of the major causes of the death in the world. Atherosclerosis refers to the hardening and narrowing of the arteries by plaques. Carotid stenosis is a narrowing or constriction of carotid artery lumen usually caused by atherosclerosis. Carotid artery stenosis can increase risk of brain stroke. Contrast-enhanced Computed Tomography Angiography (CTA) is a minimally invasive method for imaging and quantification of the carotid plaques. Manual segmentation of carotid lumen in CTA images is a tedious and time consuming procedure which is subjected to observer variability. As a result, there is a strong and growing demand for developing computer-aided carotid segmentation procedures. In this study, a novel method is presented for carotid artery lumen segmentation in CTA data. First, the mean shift smoothing is used for uniformity enhancement of gray levels. Then with the help of three seed points, the centerlines of the arteries are extracted by a 3D Hessian based fast marching shortest path algorithm. Finally, a 3D Level set function is performed for segmentation. Results on 14 CTA volumes data show 85% of Dice similarity and 0.42 mm of mean absolute surface distance measures. Evaluation shows that the proposed method requires minimal user intervention, low dependence to gray levels changes in artery path, resistance to extreme changes in carotid diameter and carotid branch locations. The proposed method has high accuracy and can be used in qualitative and quantitative evaluation. PMID:26429385

  1. Automatic optimal filament segmentation with sub-pixel accuracy using generalized linear models and B-spline level-sets.

    Science.gov (United States)

    Xiao, Xun; Geyer, Veikko F; Bowne-Anderson, Hugo; Howard, Jonathon; Sbalzarini, Ivo F

    2016-08-01

    Biological filaments, such as actin filaments, microtubules, and cilia, are often imaged using different light-microscopy techniques. Reconstructing the filament curve from the acquired images constitutes the filament segmentation problem. Since filaments have lower dimensionality than the image itself, there is an inherent trade-off between tracing the filament with sub-pixel accuracy and avoiding noise artifacts. Here, we present a globally optimal filament segmentation method based on B-spline vector level-sets and a generalized linear model for the pixel intensity statistics. We show that the resulting optimization problem is convex and can hence be solved with global optimality. We introduce a simple and efficient algorithm to compute such optimal filament segmentations, and provide an open-source implementation as an ImageJ/Fiji plugin. We further derive an information-theoretic lower bound on the filament segmentation error, quantifying how well an algorithm could possibly do given the information in the image. We show that our algorithm asymptotically reaches this bound in the spline coefficients. We validate our method in comprehensive benchmarks, compare with other methods, and show applications from fluorescence, phase-contrast, and dark-field microscopy. PMID:27104582

  2. SU-E-J-238: Monitoring Lymph Node Volumes During Radiotherapy Using Semi-Automatic Segmentation of MRI Images

    International Nuclear Information System (INIS)

    Purpose: Identification and image-based monitoring of lymph nodes growing due to disease, could be an attractive alternative to prophylactic head and neck irradiation. We evaluated the accuracy of the user-interactive Grow Cut algorithm for volumetric segmentation of radiotherapy relevant lymph nodes from MRI taken weekly during radiotherapy. Method: The algorithm employs user drawn strokes in the image to volumetrically segment multiple structures of interest. We used a 3D T2-wturbo spin echo images with an isotropic resolution of 1 mm3 and FOV of 492×492×300 mm3 of head and neck cancer patients who underwent weekly MR imaging during the course of radiotherapy. Various lymph node (LN) levels (N2, N3, N4'5) were individually contoured on the weekly MR images by an expert physician and used as ground truth in our study. The segmentation results were compared with the physician drawn lymph nodes based on DICE similarity score. Results: Three head and neck patients with 6 weekly MR images were evaluated. Two patients had level 2 LN drawn and one patient had level N2, N3 and N4'5 drawn on each MR image. The algorithm took an average of a minute to segment the entire volume (512×512×300 mm3). The algorithm achieved an overall DICE similarity score of 0.78. The time taken for initializing and obtaining the volumetric mask was about 5 mins for cases with only N2 LN and about 15 mins for the case with N2,N3 and N4'5 level nodes. The longer initialization time for the latter case was due to the need for accurate user inputs to separate overlapping portions of the different LN. The standard deviation in segmentation accuracy at different time points was utmost 0.05. Conclusions: Our initial evaluation of the grow cut segmentation shows reasonably accurate and consistent volumetric segmentations of LN with minimal user effort and time

  3. Robottens Anatomi

    DEFF Research Database (Denmark)

    Antabi, Mimo

    Rapport der beskriver de samlede erfaringer fra arbejdet med produktionen af teaterforestillingen Robottens Anatomi. Indehoder bl.a. interviews med medvirkende, bidrag fra instruktør, synopsis, beskrivelse af scenografi mv.......Rapport der beskriver de samlede erfaringer fra arbejdet med produktionen af teaterforestillingen Robottens Anatomi. Indehoder bl.a. interviews med medvirkende, bidrag fra instruktør, synopsis, beskrivelse af scenografi mv....

  4. A novel automatic algorithm for the segmentation of the lumen of the carotid artery in ultrasound B-mode images

    OpenAIRE

    Santos, AMF; dos santos, rm; castro, pmac; Azevedo, E.; Sousa, L. de; tavares, jmrs

    2013-01-01

    A novel algorithm is proposed for the segmentation of the lumen and bifurcation boundaries of the carotid artery in B-mode ultrasound images. It uses the image contrast characteristics of the lumen and bifurcation of the carotid artery in relation to other tissues and structures for their identification. The relevant ultrasound data regarding the artery presented in the input image is identified using morphologic operators and processed by an anisotropic diffusion filter for speckle noise rem...

  5. Automatic segmentation of the hippocampus for preterm neonates from early-in-life to term-equivalent age

    Directory of Open Access Journals (Sweden)

    Ting Guo

    2015-01-01

    Conclusions: MAGeT-Brain is capable of segmenting hippocampi accurately in preterm neonates, even at early-in-life. Hippocampal asymmetry with a larger right side is demonstrated on early-in-life images, suggesting that this phenomenon has its onset in the 3rd trimester of gestation. Hippocampal volume assessed at the time of early-in-life and term-equivalent age is linearly associated with GA at birth, whereby smaller volumes are associated with earlier birth.

  6. Optimal Elasticity cut-off value for discriminating Healthy to Pathological Fibrotic patients employing Fuzzy C-Means automatic segmentation in Liver Shear Wave Elastography images

    Science.gov (United States)

    Gatos, Ilias; Tsantis, Stavros; Skouroliakou, Aikaterini; Theotokas, Ioannis; Zoumpoulis, Pavlos S.; Kagadis, George C.

    2015-09-01

    The aim of the present study is to determine an optimal elasticity cut-off value for discriminating Healthy from Pathological fibrotic patients by means of Fuzzy C-Means automatic segmentation and maximum participation cluster mean value employment in Shear Wave Elastography (SWE) images. The clinical dataset comprised 32 subjects (16 Healthy and 16 histological or Fibroscan verified Chronic Liver Disease). An experienced Radiologist performed SWE measurement placing a region of interest (ROI) on each subject's right liver lobe providing a SWE image for each patient. Subsequently Fuzzy C-Means clustering was performed on every SWE image utilizing 5 clusters. Mean Stiffness value and pixels number of each cluster were calculated. The mean stiffness value feature of the cluster with maximum pixels number was then fed as input for ROC analysis. The selected Mean Stiffness value feature an Area Under the Curve (AUC) of 0.8633 with Optimum Cut-off value of 7.5 kPa with sensitivity and specificity values of 0.8438 and 0.875 and balanced accuracy of 0.8594. Examiner's classification measurements exhibited sensitivity, specificity and balanced accuracy value of 0.8125 with 7.1 kPa cutoff value. A new promising automatic algorithm was implemented with more objective criteria of defining optimum elasticity cut-off values for discriminating fibrosis stages for SWE. More subjects are needed in order to define if this algorithm is an objective tool to outperform manual ROI selection.

  7. Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer

    International Nuclear Information System (INIS)

    To validate, in the context of adaptive radiotherapy, three commercial software solutions for atlas-based segmentation. Fifteen patients, five for each group, with cancer of the Head&Neck, pleura, and prostate were enrolled in the study. In addition to the treatment planning CT (pCT) images, one replanning CT (rCT) image set was acquired for each patient during the RT course. Three experienced physicians outlined on the pCT and rCT all the volumes of interest (VOIs). We used three software solutions (VelocityAI 2.6.2 (V), MIM 5.1.1 (M) by MIMVista and ABAS 2.0 (A) by CMS-Elekta) to generate the automatic contouring on the repeated CT. All the VOIs obtained with automatic contouring (AC) were successively corrected manually. We recorded the time needed for: 1) ex novo ROIs definition on rCT; 2) generation of AC by the three software solutions; 3) manual correction of AC. To compare the quality of the volumes obtained automatically by the software and manually corrected with those drawn from scratch on rCT, we used the following indexes: overlap coefficient (DICE), sensitivity, inclusiveness index, difference in volume, and displacement differences on three axes (x, y, z) from the isocenter. The time saved by the three software solutions for all the sites, compared to the manual contouring from scratch, is statistically significant and similar for all the three software solutions. The time saved for each site are as follows: about an hour for Head&Neck, about 40 minutes for prostate, and about 20 minutes for mesothelioma. The best DICE similarity coefficient index was obtained with the manual correction for: A (contours for prostate), A and M (contours for H&N), and M (contours for mesothelioma). From a clinical point of view, the automated contouring workflow was shown to be significantly shorter than the manual contouring process, even though manual correction of the VOIs is always needed

  8. Automatic recognition and validation of the common carotid artery wall segmentation in 100 longitudinal ultrasound images: an integrated approach using feature selection, fitting and classification

    Science.gov (United States)

    Molinari, Filippo; Zeng, Guang; Suri, Jasjit S.

    2010-03-01

    Most of the algorithms for the common carotid artery (CCA) segmentation require human interaction. The aim of this study is to show a novel accurate algorithm for the computer-based automated tracing of CCA in longitudinal B-Mode ultrasound images. One hundred ultrasound B-Mode longitudinal images of the CCA were processed to delineate the region of interest containing the artery. The algorithm is based on geometric feature extraction, line fitting, and classification. Output of the algorithm is the tracings of the near and far adventitia layers. Performance of the algorithm was validated against human tracings (ground truth) and benchmarked with a previously developed automated technique. Ninety-eight images were correctly processed, resulting in an overall system error (with respect to ground truth) equal to 0.18 +/- 0.17 mm (near adventitia) and 0.17 +/- 0.24 mm (far adventitia). In far adventitia detection, our novel technique outperformed the current standard method, which showed overall system errors equal to 0.07 +/- 0.07 mm and 0.49 +/- 0.27 mm for near and far adventitia, respectively. We also showed that our new technique is quite insensitive to noise and has performance independent on the subset of images used for training the classifiers. Superior architecture of this methodology could constitute a general basis for the development of completely automatic CCA segmentation strategies.

  9. Automatic construction of patient-specific finite-element mesh of the spine from IVDs and vertebra segmentations

    Science.gov (United States)

    Castro-Mateos, Isaac; Pozo, Jose M.; Lazary, Aron; Frangi, Alejandro F.

    2016-03-01

    Computational medicine aims at developing patient-specific models to help physicians in the diagnosis and treatment selection for patients. The spine, and other skeletal structures, is an articulated object, composed of rigid bones (vertebrae) and non-rigid parts (intervertebral discs (IVD), ligaments and muscles). These components are usually extracted from different image modalities, involving patient repositioning. In the case of the spine, these models require the segmentation of IVDs from MR and vertebrae from CT. In the literature, there exists a vast selection of segmentations methods, but there is a lack of approaches to align the vertebrae and IVDs. This paper presents a method to create patient-specific finite element meshes for biomechanical simulations, integrating rigid and non-rigid parts of articulated objects. First, the different parts are aligned in a complete surface model. Vertebrae extracted from CT are rigidly repositioned in between the IVDs, initially using the IVDs location and then refining the alignment using the MR image with a rigid active shape model algorithm. Finally, a mesh morphing algorithm, based on B-splines, is employed to map a template finite-element (volumetric) mesh to the patient-specific surface mesh. This morphing reduces possible misalignments and guarantees the convexity of the model elements. Results show that the accuracy of the method to align vertebrae into MR, together with IVDs, is similar to that of the human observers. Thus, this method is a step forward towards the automation of patient-specific finite element models for biomechanical simulations.

  10. Template-based CTA to x-ray angio rigid registration of coronary arteries in frequency domain with automatic x-ray segmentation

    International Nuclear Information System (INIS)

    Purpose: A key challenge for image guided coronary interventions is accurate and absolutely robust image registration bringing together preinterventional information extracted from a three-dimensional (3D) patient scan and live interventional image information. In this paper, the authors present a novel scheme for 3D to two-dimensional (2D) rigid registration of coronary arteries extracted from preoperative image scan (3D) and a single segmented intraoperative x-ray angio frame in frequency and spatial domains for real-time angiography interventions by C-arm fluoroscopy.Methods: Most existing rigid registration approaches require a close initialization due to the abundance of local minima and high complexity of search algorithms. The authors' method eliminates this requirement by transforming the projections into translation-invariant Fourier domain for estimating the 3D pose. For 3D rotation recovery, template Digitally Reconstructed Radiographs (DRR) as candidate poses of 3D vessels of segmented computed tomography angiography are produced by rotating the camera (image intensifier) around the DICOM angle values with a specific range as in C-arm setup. The authors have compared the 3D poses of template DRRs with the segmented x-ray after equalizing the scales in three domains, namely, Fourier magnitude, Fourier phase, and Fourier polar. The best rotation pose candidate was chosen by one of the highest similarity measures returned by the methods in these domains. It has been noted in literature that frequency domain methods are robust against noise and occlusion which was also validated by the authors' results. 3D translation of the volume was then recovered by distance-map based BFGS optimization well suited to convex structure of the authors' objective function without local minima due to distance maps. A novel automatic x-ray vessel segmentation was also performed in this study.Results: Final results were evaluated in 2D projection space for patient data; and

  11. Template-based CTA to x-ray angio rigid registration of coronary arteries in frequency domain with automatic x-ray segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Aksoy, Timur; Unal, Gozde [Sabanci University, Tuzla, Istanbul 34956 (Turkey); Demirci, Stefanie; Navab, Nassir [Computer Aided Medical Procedures (CAMP), Technical University of Munich, Garching, 85748 (Germany); Degertekin, Muzaffer [Yeditepe University Hospital, Istanbul 34752 (Turkey)

    2013-10-15

    Purpose: A key challenge for image guided coronary interventions is accurate and absolutely robust image registration bringing together preinterventional information extracted from a three-dimensional (3D) patient scan and live interventional image information. In this paper, the authors present a novel scheme for 3D to two-dimensional (2D) rigid registration of coronary arteries extracted from preoperative image scan (3D) and a single segmented intraoperative x-ray angio frame in frequency and spatial domains for real-time angiography interventions by C-arm fluoroscopy.Methods: Most existing rigid registration approaches require a close initialization due to the abundance of local minima and high complexity of search algorithms. The authors' method eliminates this requirement by transforming the projections into translation-invariant Fourier domain for estimating the 3D pose. For 3D rotation recovery, template Digitally Reconstructed Radiographs (DRR) as candidate poses of 3D vessels of segmented computed tomography angiography are produced by rotating the camera (image intensifier) around the DICOM angle values with a specific range as in C-arm setup. The authors have compared the 3D poses of template DRRs with the segmented x-ray after equalizing the scales in three domains, namely, Fourier magnitude, Fourier phase, and Fourier polar. The best rotation pose candidate was chosen by one of the highest similarity measures returned by the methods in these domains. It has been noted in literature that frequency domain methods are robust against noise and occlusion which was also validated by the authors' results. 3D translation of the volume was then recovered by distance-map based BFGS optimization well suited to convex structure of the authors' objective function without local minima due to distance maps. A novel automatic x-ray vessel segmentation was also performed in this study.Results: Final results were evaluated in 2D projection space for

  12. Computer-assisted segmentation of CT images by statistical region merging for the production of voxel models of anatomy for CT dosimetry

    Czech Academy of Sciences Publication Activity Database

    Caon, M.; Sedlář, Jiří; Bajger, M.; Lee, G.

    2014-01-01

    Roč. 37, č. 2 (2014), s. 393-403. ISSN 0158-9938 Institutional support: RVO:67985556 Keywords : Voxel model * Image segmentation * Statistical region merging * CT dosimetry Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.882, year: 2014 http://library.utia.cas.cz/separaty/2014/ZOI/sedlar-0428537.pdf

  13. Combining registration and active shape models for the automatic segmentation of the lymph node regions in head and neck CT images

    International Nuclear Information System (INIS)

    Purpose: Intensity-modulated radiation therapy (IMRT) is the state of the art technique for head and neck cancer treatment. It requires precise delineation of the target to be treated and structures to be spared, which is currently done manually. The process is a time-consuming task of which the delineation of lymph node regions is often the longest step. Atlas-based delineation has been proposed as an alternative, but, in the authors' experience, this approach is not accurate enough for routine clinical use. Here, the authors improve atlas-based segmentation results obtained for level II-IV lymph node regions using an active shape model (ASM) approach. Methods: An average image volume was first created from a set of head and neck patient images with minimally enlarged nodes. The average image volume was then registered using affine, global, and local nonrigid transformations to the other volumes to establish a correspondence between surface points in the atlas and surface points in each of the other volumes. Once the correspondence was established, the ASMs were created for each node level. The models were then used to first constrain the results obtained with an atlas-based approach and then to iteratively refine the solution. Results: The method was evaluated through a leave-one-out experiment. The ASM- and atlas-based segmentations were compared to manual delineations via the Dice similarity coefficient (DSC) for volume overlap and the Euclidean distance between manual and automatic 3D surfaces. The mean DSC value obtained with the ASM-based approach is 10.7% higher than with the atlas-based approach; the mean and median surface errors were decreased by 13.6% and 12.0%, respectively. Conclusions: The ASM approach is effective in reducing segmentation errors in areas of low CT contrast where purely atlas-based methods are challenged. Statistical analysis shows that the improvements brought by this approach are significant.

  14. Stedets Anatomi

    DEFF Research Database (Denmark)

    Larsen, Lasse Juel

    Titlen på denne ph.d.-afhandling, Stedets Anatomi – en teoretisk undersøgelse af stedets og rumlighedens betydning for leg, computerspil og læring, skitserer ikke kun afhandlingens teoretiske dimensionering, men også dens analytiske bliks tematik i forbindelse med undersøgelsen af fænomenerne leg...

  15. Regulatory Anatomy

    DEFF Research Database (Denmark)

    Hoeyer, Klaus

    2015-01-01

    This article proposes the term “safety logics” to understand attempts within the European Union (EU) to harmonize member state legislation to ensure a safe and stable supply of human biological material for transplants and transfusions. With safety logics, I refer to assemblages of discourses, le...... arise. In short, I expose the regulatory anatomy of the policy landscape....

  16. Towards lung EIT image segmentation: automatic classification of lung tissue state from analysis of EIT monitored recruitment manoeuvres

    International Nuclear Information System (INIS)

    There is emerging evidence that the ventilation strategy used in acute lung injury (ALI) makes a significant difference in outcome and that an inappropriate ventilation strategy may produce ventilator-associated lung injury. Most harmful during mechanical ventilation are lung overdistension and lung collapse or atelectasis. Electrical impedance tomography (EIT) as a non-invasive imaging technology may be helpful to identify lung areas at risk. Currently, no automated method is routinely available to identify lung areas that are overdistended, collapsed or ventilated appropriately. We propose a fuzzy logic-based algorithm to analyse EIT images obtained during stepwise changes of mean airway pressures during mechanical ventilation. The algorithm is tested on data from two published studies of stepwise inflation–deflation manoeuvres in an animal model of ALI using conventional and high-frequency oscillatory ventilation. The timing of lung opening and collapsing on segmented images obtained using the algorithm during an inflation–deflation manoeuvre is in agreement with well-known effects of surfactant administration and changes in shunt fraction. While the performance of the algorithm has not been verified against a gold standard, we feel that it presents an important first step in tackling this challenging and important problem

  17. The Anatomy of Learning Anatomy

    Science.gov (United States)

    Wilhelmsson, Niklas; Dahlgren, Lars Owe; Hult, Hakan; Scheja, Max; Lonka, Kirsti; Josephson, Anna

    2010-01-01

    The experience of clinical teachers as well as research results about senior medical students' understanding of basic science concepts has much been debated. To gain a better understanding about how this knowledge-transformation is managed by medical students, this work aims at investigating their ways of setting about learning anatomy.…

  18. Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique.

    Science.gov (United States)

    Nilsson, M; Herlin, A H; Ardö, H; Guzhva, O; Åström, K; Bergsten, C

    2015-11-01

    In this paper the feasibility to extract the proportion of pigs located in different areas of a pig pen by advanced image analysis technique is explored and discussed for possible applications. For example, pigs generally locate themselves in the wet dunging area at high ambient temperatures in order to avoid heat stress, as wetting the body surface is the major path to dissipate the heat by evaporation. Thus, the portion of pigs in the dunging area and resting area, respectively, could be used as an indicator of failure of controlling the climate in the pig environment as pigs are not supposed to rest in the dunging area. The computer vision methodology utilizes a learning based segmentation approach using several features extracted from the image. The learning based approach applied is based on extended state-of-the-art features in combination with a structured prediction framework based on a logistic regression solver using elastic net regularization. In addition, the method is able to produce a probability per pixel rather than form a hard decision. This overcomes some of the limitations found in a setup using grey-scale information only. The pig pen is a difficult imaging environment because of challenging lighting conditions like shadows, poor lighting and poor contrast between pig and background. In order to test practical conditions, a pen containing nine young pigs was filmed from a top view perspective by an Axis M3006 camera with a resolution of 640 × 480 in three, 10-min sessions under different lighting conditions. The results indicate that a learning based method improves, in comparison with greyscale methods, the possibility to reliable identify proportions of pigs in different areas of the pen. Pigs with a changed behaviour (location) in the pen may indicate changed climate conditions. Changed individual behaviour may also indicate inferior health or acute illness. PMID:26189971

  19. Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer

    Directory of Open Access Journals (Sweden)

    La Macchia Mariangela

    2012-09-01

    Full Text Available Abstract Purpose To validate, in the context of adaptive radiotherapy, three commercial software solutions for atlas-based segmentation. Methods and materials Fifteen patients, five for each group, with cancer of the Head&Neck, pleura, and prostate were enrolled in the study. In addition to the treatment planning CT (pCT images, one replanning CT (rCT image set was acquired for each patient during the RT course. Three experienced physicians outlined on the pCT and rCT all the volumes of interest (VOIs. We used three software solutions (VelocityAI 2.6.2 (V, MIM 5.1.1 (M by MIMVista and ABAS 2.0 (A by CMS-Elekta to generate the automatic contouring on the repeated CT. All the VOIs obtained with automatic contouring (AC were successively corrected manually. We recorded the time needed for: 1 ex novo ROIs definition on rCT; 2 generation of AC by the three software solutions; 3 manual correction of AC. To compare the quality of the volumes obtained automatically by the software and manually corrected with those drawn from scratch on rCT, we used the following indexes: overlap coefficient (DICE, sensitivity, inclusiveness index, difference in volume, and displacement differences on three axes (x, y, z from the isocenter. Results The time saved by the three software solutions for all the sites, compared to the manual contouring from scratch, is statistically significant and similar for all the three software solutions. The time saved for each site are as follows: about an hour for Head&Neck, about 40 minutes for prostate, and about 20 minutes for mesothelioma. The best DICE similarity coefficient index was obtained with the manual correction for: A (contours for prostate, A and M (contours for H&N, and M (contours for mesothelioma. Conclusions From a clinical point of view, the automated contouring workflow was shown to be significantly shorter than the manual contouring process, even though manual correction of the VOIs is always needed.

  20. Normal Female Reproductive Anatomy

    Science.gov (United States)

    ... historical Searches are case-insensitive Reproductive System, Female, Anatomy Add to My Pictures View /Download : Small: 720x756 ... Large: 3000x3150 View Download Title: Reproductive System, Female, Anatomy Description: Anatomy of the female reproductive system; drawing ...

  1. 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 ...

  2. 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 ...

  3. Automatic Houses Detection with Color Aerial Images Based on Image Segmentation%彩色分割的航空影像房屋自动检测

    Institute of Scientific and Technical Information of China (English)

    何培培; 万幼川; 蒋朋睿; 高贤君; 秦家鑫

    2014-01-01

    In order to achieve housing automatic detection from high-resolution aerial imagery ,the present paper utilized the color information and spectral characteristics of the roofing material ,with the image segmentation theory ,to study the housing auto-matic detection method .Firstly ,This method proposed in this paper converts the RGB color space to HIS color space ,uses the characteristics of each component of the HIS color space and the spectral characteristics of the roofing material for image segmen-tation to isolate red tiled roofs and gray cement roof areas ,and gets the initial segmentation housing areas by using the marked watershed algorithm .Then ,region growing is conducted in the hue component with the seed segment sample by calculating the average hue in the marked region .Finally through the elimination of small spots and rectangular fitting process to obtain a clear outline of the housing area .Compared with the traditional pixel-based region segmentation algorithm ,the improved method pro-posed in this paper based on segment growing is in a one-dimensional color space to reduce the computation without human inter-vention ,and can cater to the geometry information of the neighborhood pixels so that the speed and accuracy of the algorithm has been significantly improved .A case study was conducted to apply the method proposed in this paper to high resolution aerial ima-ges ,and the experimental results demonstrate that this method has a high precision and rational robustness .%航空影像房屋提取方法的研究中大多基于灰度影像的区域生长算法,此类算法不仅忽略了不同材质的房屋所呈现的光谱特征对提取结果的影响,而且过于依赖种子像素的选取,处理效率不高。为了从高分辨率航空影像中实现房屋的自动检测,综合利用彩色信息与屋顶材料的光谱特征,采用影像分割原理,研究了房屋自动检测的方法。首先对RGB与HIS彩色空间进行

  4. Spinal angiography. Anatomy, technique and indications

    International Nuclear Information System (INIS)

    Spinal angiography is a diagnostic modality requiring detailed knowledge of spinal vascular anatomy. The cervical spinal cord is supplied by the vertebral arteries while segmental arteries which are preserved from fetal anatomy, supply the thoracic and lumbar regions. As spinal angiography carries the risk of paraplegia the indications have to be considered very carefully. Nevertheless, spinal angiography should be performed if there is reason to suspect a spinal vascular malformation from magnetic resonance imaging (MRI). (orig.)

  5. Toward automatic segmentation and quantification of tumor and stroma in whole-slide images of H and E stained rectal carcinomas

    Science.gov (United States)

    Geessink, Oscar G. F.; Baidoshvili, Alexi; Freling, Gerard; Klaase, Joost M.; Slump, Cornelis H.; van der Heijden, Ferdinand

    2015-03-01

    Visual estimation of tumor and stroma proportions in microscopy images yields a strong, Tumor-(lymph)Node- Metastasis (TNM) classification-independent predictor for patient survival in colorectal cancer. Therefore, it is also a potent (contra)indicator for adjuvant chemotherapy. However, quantification of tumor and stroma through visual estimation is highly subject to intra- and inter-observer variability. The aim of this study is to develop and clinically validate a method for objective quantification of tumor and stroma in standard hematoxylin and eosin (H and E) stained microscopy slides of rectal carcinomas. A tissue segmentation algorithm, based on supervised machine learning and pixel classification, was developed, trained and validated using histological slides that were prepared from surgically excised rectal carcinomas in patients who had not received neoadjuvant chemotherapy and/or radiotherapy. Whole-slide scanning was performed at 20× magnification. A total of 40 images (4 million pixels each) were extracted from 20 whole-slide images at sites showing various relative proportions of tumor and stroma. Experienced pathologists provided detailed annotations for every extracted image. The performance of the algorithm was evaluated using cross-validation by testing on 1 image at a time while using the other 39 images for training. The total classification error of the algorithm was 9.4% (SD = 3.2%). Compared to visual estimation by pathologists, the algorithm was 7.3 times (P = 0.033) more accurate in quantifying tissues, also showing 60% less variability. Automatic tissue quantification was shown to be both reliable and practicable. We ultimately intend to facilitate refined prognostic stratification of (colo)rectal cancer patients and enable better personalized treatment.

  6. 基于Gabor小波和SVM的人脸表情识别算法%Facial Expression Recognition Algorithm Based on Gabor Wavelet Automatic Segmentation and SVM

    Institute of Scientific and Technical Information of China (English)

    陈亚雄

    2011-01-01

    针对包含表情信息的静态图像,提出基于Gabor小波和SVM的人脸表情识别算法.根据先验知识,并使用形态学和积分投影相结合定位眉毛眼睛区域,采用模板内计算均值定位嘴巴区域,自动分割出表情子区域.对分割出的表情子区域进行Gabor小波特征提取,在利用Fisher线性判别对特征进行降维,去除冗余和相关.利用支持向量机对人脸表情进行分类.用该算法在日本表情数据库上进行测试,获得了较高的识别准确率.证明了该算法的有效性.%A facial recognition algorithm based on Gabor wavelet and SVM is proposed in allusion to static image containing expression Information. The mathematical morphology combined with projection is adopted to locate the brow and eye region < and the calculating mean value in template is employed to locate the mouth region, which can segment the expression sub-regions automatically. The features of the expression sub-regions are extracted by Gabor wavelet transformation and then effective Gabor expression features are selected by Fisher linear discriminate (FLD) to deduce the dimension and redundancy of the features. The features are sent to support vector machine (SVM) to classify the different expressions. The algorithm was tested in Japanese female expression database. It can get a high precision of recognition. The feasibility of this method was verified by experiments.

  7. Effect of blood vessel segmentation on the outcome of electroporation-based treatments of liver tumors.

    Directory of Open Access Journals (Sweden)

    Marija Marčan

    Full Text Available Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses applied to tissue via electrodes. To ensure that the whole tumor is covered with sufficiently high electric field, accurate numerical models are built based on individual patient anatomy. Extraction of patient's anatomy through segmentation of medical images inevitably produces some errors. In order to ensure the robustness of treatment planning, it is necessary to evaluate the potential effect of such errors on the electric field distribution. In this work we focus on determining the effect of errors in automatic segmentation of hepatic vessels on the electric field distribution in electroporation-based treatments in the liver. First, a numerical analysis was performed on a simple 'sphere and cylinder' model for tumors and vessels of different sizes and relative positions. Second, an analysis of two models extracted from medical images of real patients in which we introduced variations of an error of the automatic vessel segmentation method was performed. The results obtained from a simple model indicate that ignoring the vessels when calculating the electric field distribution can cause insufficient coverage of the tumor with electric fields. Results of this study indicate that this effect happens for small (10 mm and medium-sized (30 mm tumors, especially in the absence of a central electrode inserted in the tumor. The results obtained from the real-case models also show higher negative impact of automatic vessel segmentation errors on the electric field distribution when the central electrode is absent. However, the average error of the automatic vessel segmentation did not have an impact on the electric field distribution if the central electrode was present. This suggests the algorithm is robust enough to be used in creating a model for treatment parameter optimization, but with a central electrode.

  8. Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours

    International Nuclear Information System (INIS)

    Purpose: Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high interobserver variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation of structures in the head-neck area is still rather low. In this project, a new approach for automated segmentation of head-neck CT images that combine the robustness of multiatlas-based segmentation with the flexibility of geodesic active contours and the prior knowledge provided by statistical appearance models is presented. Methods: The presented approach is using an atlas-based segmentation approach in combination with label fusion in order to initialize a segmentation pipeline that is based on using statistical appearance models and geodesic active contours. An anatomically correct approximation of the segmentation result provided by atlas-based segmentation acts as a starting point for an iterative refinement of this approximation. The final segmentation result is based on using model to image registration and geodesic active contours, which are mutually influencing each other. Results: 18 CT images in combination with manually segmented labels of parotid glands and brainstem were used in a leave-one-out cross validation scheme in order to evaluate the presented approach. For this purpose, 50 different statistical appearance models have been created and used for segmentation. Dice coefficient (DC), mean absolute distance and max. Hausdorff distance between the autosegmentation results and expert segmentations were calculated. An average Dice coefficient of DC = 0.81 (right parotid gland), DC = 0.84 (left parotid gland), and DC = 0.86 (brainstem) could be achieved. Conclusions: The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a

  9. Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours

    Energy Technology Data Exchange (ETDEWEB)

    Fritscher, Karl D., E-mail: Karl.Fritscher@umit.at; Sharp, Gregory [Department for Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114 (United States); Peroni, Marta [Paul Scherrer Institut, Villigen 5232 (Switzerland); Zaffino, Paolo; Spadea, Maria Francesca [Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro 88100 (Italy); Schubert, Rainer [Institute for Biomedical Image Analysis, Private University of Health Sciences, Medical Informatics and Technology, Hall in Tirol 6060 (Austria)

    2014-05-15

    Purpose: Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high interobserver variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation of structures in the head-neck area is still rather low. In this project, a new approach for automated segmentation of head-neck CT images that combine the robustness of multiatlas-based segmentation with the flexibility of geodesic active contours and the prior knowledge provided by statistical appearance models is presented. Methods: The presented approach is using an atlas-based segmentation approach in combination with label fusion in order to initialize a segmentation pipeline that is based on using statistical appearance models and geodesic active contours. An anatomically correct approximation of the segmentation result provided by atlas-based segmentation acts as a starting point for an iterative refinement of this approximation. The final segmentation result is based on using model to image registration and geodesic active contours, which are mutually influencing each other. Results: 18 CT images in combination with manually segmented labels of parotid glands and brainstem were used in a leave-one-out cross validation scheme in order to evaluate the presented approach. For this purpose, 50 different statistical appearance models have been created and used for segmentation. Dice coefficient (DC), mean absolute distance and max. Hausdorff distance between the autosegmentation results and expert segmentations were calculated. An average Dice coefficient of DC = 0.81 (right parotid gland), DC = 0.84 (left parotid gland), and DC = 0.86 (brainstem) could be achieved. Conclusions: The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a

  10. Pituitary Adenoma Segmentation

    CERN Document Server

    Egger, Jan; Kuhnt, Daniela; Freisleben, Bernd; Nimsky, Christopher

    2011-01-01

    Sellar tumors are approximately 10-15% among all intracranial neoplasms. The most common sellar lesion is the pituitary adenoma. Manual segmentation is a time-consuming process that can be shortened by using adequate algorithms. In this contribution, we present a segmentation method for pituitary adenoma. The method is based on an algorithm we developed recently in previous work where the novel segmentation scheme was successfully used for segmentation of glioblastoma multiforme and provided an average Dice Similarity Coefficient (DSC) of 77%. This scheme is used for automatic adenoma segmentation. In our experimental evaluation, neurosurgeons with strong experiences in the treatment of pituitary adenoma performed manual slice-by-slice segmentation of 10 magnetic resonance imaging (MRI) cases. Afterwards, the segmentations were compared with the segmentation results of the proposed method via the DSC. The average DSC for all data sets was 77.49% +/- 4.52%. Compared with a manual segmentation that took, on the...

  11. Quick Dissection of the Segmental Bronchi

    Science.gov (United States)

    Nakajima, Yuji

    2010-01-01

    Knowledge of the three-dimensional anatomy of the bronchopulmonary segments is essential for respiratory medicine. This report describes a quick guide for dissecting the segmental bronchi in formaldehyde-fixed human material. All segmental bronchi are easy to dissect, and thus, this exercise will help medical students to better understand the…

  12. Anatomy of the Eye

    Science.gov (United States)

    ... Conditions Frequently Asked Questions Español Condiciones Chinese Conditions Anatomy of the Eye En Español Read in Chinese External (Extraocular) Anatomy Extraocular Muscles: There are six muscles that are ...

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

    International Nuclear Information System (INIS)

    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.)

  14. Babel Tower, Paris and Brisbane: a tour around their influences on hepatic segmentation terminology.

    OpenAIRE

    Oscar Claudio Andriani

    2010-01-01

    Liver anatomy has always been the same. However, its interpretation has changed during time according to the development of imaging and surgery. Cantlie introduced a new concept on liver anatomy using the term ―lobe‖ in a different way. Segmental anatomy, introduced in the middle of the XXth Century has a cornerstone in Couinaud’s studies. The fact that anglosaxons followed Cantlie’s concept about right and left lobes even in segmental anatomy after the description by Goldsmith & Woodburne, w...

  15. Dorsal fin anatomy (Cetacean dorsal fin Anatomy)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Cetacean dorsal fin Anatomy for ONR. Comparison within populations to ascertain phenotypic differences. Findings corroborate field observation. dorsal fin description

  16. Automatic detection and segmentation of vascular structures in dermoscopy images using a novel vesselness measure based on pixel redness and tubularness

    Science.gov (United States)

    Kharazmi, Pegah; Lui, Harvey; Stoecker, William V.; Lee, Tim

    2015-03-01

    Vascular structures are one of the most important features in the diagnosis and assessment of skin disorders. The presence and clinical appearance of vascular structures in skin lesions is a discriminating factor among different skin diseases. In this paper, we address the problem of segmentation of vascular patterns in dermoscopy images. Our proposed method is composed of three parts. First, based on biological properties of human skin, we decompose the skin to melanin and hemoglobin component using independent component analysis of skin color images. The relative quantities and pure color densities of each component were then estimated. Subsequently, we obtain three reference vectors of the mean RGB values for normal skin, pigmented skin and blood vessels from the hemoglobin component by averaging over 100000 pixels of each group outlined by an expert. Based on the Euclidean distance thresholding, we generate a mask image that extracts the red regions of the skin. Finally, Frangi measure was applied to the extracted red areas to segment the tubular structures. Finally, Otsu's thresholding was applied to segment the vascular structures and get a binary vessel mask image. The algorithm was implemented on a set of 50 dermoscopy images. In order to evaluate the performance of our method, we have artificially extended some of the existing vessels in our dermoscopy data set and evaluated the performance of the algorithm to segment the newly added vessel pixels. A sensitivity of 95% and specificity of 87% were achieved.

  17. SU-E-J-134: Optimizing Technical Parameters for Using Atlas Based Automatic Segmentation for Evaluation of Contour Accuracy Experience with Cardiac Structures From NRG Oncology/RTOG 0617

    International Nuclear Information System (INIS)

    Purpose: Accurate contour delineation is crucial for radiotherapy. Atlas based automatic segmentation tools can be used to increase the efficiency of contour accuracy evaluation. This study aims to optimize technical parameters utilized in the tool by exploring the impact of library size and atlas number on the accuracy of cardiac contour evaluation. Methods: Patient CT DICOMs from RTOG 0617 were used for this study. Five experienced physicians delineated the cardiac structures including pericardium, atria and ventricles following an atlas guideline. The consistency of cardiac structured delineation using the atlas guideline was verified by a study with four observers and seventeen patients. The CT and cardiac structure DICOM files were then used for the ABAS technique.To study the impact of library size (LS) and atlas number (AN) on automatic contour accuracy, automatic contours were generated with varied technique parameters for five randomly selected patients. Three LS (20, 60, and 100) were studied using commercially available software. The AN was four, recommended by the manufacturer. Using the manual contour as the gold standard, Dice Similarity Coefficient (DSC) was calculated between the manual and automatic contours. Five-patient averaged DSCs were calculated for comparison for each cardiac structure.In order to study the impact of AN, the LS was set 100, and AN was tested from one to five. The five-patient averaged DSCs were also calculated for each cardiac structure. Results: DSC values are highest when LS is 100 and AN is four. The DSC is 0.90±0.02 for pericardium, 0.75±0.06 for atria, and 0.86±0.02 for ventricles. Conclusion: By comparing DSC values, the combination AN=4 and LS=100 gives the best performance. This project was supported by NCI grants U24CA12014, U24CA180803, U10CA180868, U10CA180822, PA CURE grant and Bristol-Myers Squibb and Eli Lilly

  18. SU-E-J-134: Optimizing Technical Parameters for Using Atlas Based Automatic Segmentation for Evaluation of Contour Accuracy Experience with Cardiac Structures From NRG Oncology/RTOG 0617

    Energy Technology Data Exchange (ETDEWEB)

    Yu, J; Gong, Y; Bar-Ad, V; Giaddui, T; Galvin, J; Xiao, Y [Thomas Jefferson University, Philadelphia, PA (United States); Hu, C [NRG oncology, Philadelphia, PA (United States); Gore, E; Wheatley, M [Medical College of Wisconsin, Milwaukee, WI (United States); Witt, J; Robinson, C; Bradley, J [Washington University in St. Louis School of Medicine, St. Louis, MO (United States); Kong, F [Georgia Regents University, Augusta, GA (Georgia)

    2015-06-15

    Purpose: Accurate contour delineation is crucial for radiotherapy. Atlas based automatic segmentation tools can be used to increase the efficiency of contour accuracy evaluation. This study aims to optimize technical parameters utilized in the tool by exploring the impact of library size and atlas number on the accuracy of cardiac contour evaluation. Methods: Patient CT DICOMs from RTOG 0617 were used for this study. Five experienced physicians delineated the cardiac structures including pericardium, atria and ventricles following an atlas guideline. The consistency of cardiac structured delineation using the atlas guideline was verified by a study with four observers and seventeen patients. The CT and cardiac structure DICOM files were then used for the ABAS technique.To study the impact of library size (LS) and atlas number (AN) on automatic contour accuracy, automatic contours were generated with varied technique parameters for five randomly selected patients. Three LS (20, 60, and 100) were studied using commercially available software. The AN was four, recommended by the manufacturer. Using the manual contour as the gold standard, Dice Similarity Coefficient (DSC) was calculated between the manual and automatic contours. Five-patient averaged DSCs were calculated for comparison for each cardiac structure.In order to study the impact of AN, the LS was set 100, and AN was tested from one to five. The five-patient averaged DSCs were also calculated for each cardiac structure. Results: DSC values are highest when LS is 100 and AN is four. The DSC is 0.90±0.02 for pericardium, 0.75±0.06 for atria, and 0.86±0.02 for ventricles. Conclusion: By comparing DSC values, the combination AN=4 and LS=100 gives the best performance. This project was supported by NCI grants U24CA12014, U24CA180803, U10CA180868, U10CA180822, PA CURE grant and Bristol-Myers Squibb and Eli Lilly.

  19. Neuron anatomy structure reconstruction based on a sliding filter

    OpenAIRE

    Luo, Gongning; Sui, Dong; Wang, Kuanquan; Chae, Jinseok

    2015-01-01

    Background Reconstruction of neuron anatomy structure is a challenging and important task in neuroscience. However, few algorithms can automatically reconstruct the full structure well without manual assistance, making it essential to develop new methods for this task. Methods This paper introduces a new pipeline for reconstructing neuron anatomy structure from 3-D microscopy image stacks. This pipeline is initialized with a set of seeds that were detected by our proposed Sliding Volume Filte...

  20. TU-A-9A-06: Semi-Automatic Segmentation of Skin Cancer in High-Frequency Ultrasound Images: Initial Comparison with Histology

    Energy Technology Data Exchange (ETDEWEB)

    Gao, Y [Univ. Alabama at Birmingham, Birmingham, AL (United States); Li, X [Medical College of Wisconsin, Milwaukee, WI (United States); Fishman, K [Sensus Healthcare, Boca Raton, FL (United States); Yang, X [Department of Radiation Oncology and Winship Cancer Institute, Emory Univ., Atlanta, GA (United States); Liu, T [Emory Univ, Atlanta, GA (United States)

    2014-06-15

    Purpose: In skin-cancer radiotherapy, the assessment of skin lesion is challenging, particularly with important features such as the depth and width hard to determine. The aim of this study is to develop interative segmentation method to delineate tumor boundary using high-frequency ultrasound images and to correlate the segmentation results with the histopathological tumor dimensions. Methods: We analyzed 6 patients who comprised a total of 10 skin lesions involving the face, scalp, and hand. The patient’s various skin lesions were scanned using a high-frequency ultrasound system (Episcan, LONGPORT, INC., PA, U.S.A), with a 30-MHz single-element transducer. The lateral resolution was 14.6 micron and the axial resolution was 3.85 micron for the ultrasound image. Semiautomatic image segmentation was performed to extract the cancer region, using a robust statistics driven active contour algorithm. The corresponding histology images were also obtained after tumor resection and served as the reference standards in this study. Results: Eight out of the 10 lesions are successfully segmented. The ultrasound tumor delineation correlates well with the histology assessment, in all the measurements such as depth, size, and shape. The depths measured by the ultrasound have an average of 9.3% difference comparing with that in the histology images. The remaining 2 cases suffered from the situation of mismatching between pathology and ultrasound images. Conclusion: High-frequency ultrasound is a noninvasive, accurate and easy-accessible modality to image skin cancer. Our segmentation method, combined with high-frequency ultrasound technology, provides a promising tool to estimate the extent of the tumor to guide the radiotherapy procedure and monitor treatment response.

  1. TU-A-9A-06: Semi-Automatic Segmentation of Skin Cancer in High-Frequency Ultrasound Images: Initial Comparison with Histology

    International Nuclear Information System (INIS)

    Purpose: In skin-cancer radiotherapy, the assessment of skin lesion is challenging, particularly with important features such as the depth and width hard to determine. The aim of this study is to develop interative segmentation method to delineate tumor boundary using high-frequency ultrasound images and to correlate the segmentation results with the histopathological tumor dimensions. Methods: We analyzed 6 patients who comprised a total of 10 skin lesions involving the face, scalp, and hand. The patient’s various skin lesions were scanned using a high-frequency ultrasound system (Episcan, LONGPORT, INC., PA, U.S.A), with a 30-MHz single-element transducer. The lateral resolution was 14.6 micron and the axial resolution was 3.85 micron for the ultrasound image. Semiautomatic image segmentation was performed to extract the cancer region, using a robust statistics driven active contour algorithm. The corresponding histology images were also obtained after tumor resection and served as the reference standards in this study. Results: Eight out of the 10 lesions are successfully segmented. The ultrasound tumor delineation correlates well with the histology assessment, in all the measurements such as depth, size, and shape. The depths measured by the ultrasound have an average of 9.3% difference comparing with that in the histology images. The remaining 2 cases suffered from the situation of mismatching between pathology and ultrasound images. Conclusion: High-frequency ultrasound is a noninvasive, accurate and easy-accessible modality to image skin cancer. Our segmentation method, combined with high-frequency ultrasound technology, provides a promising tool to estimate the extent of the tumor to guide the radiotherapy procedure and monitor treatment response

  2. AnatomiQuiz

    DEFF Research Database (Denmark)

    Brent, Mikkel Bo; Kristoffersen, Thomas

    2015-01-01

    AnatomiQuiz er en quiz-app udviklet til bevægeapparatets anatomi. Den består af mere end 2300 spørgsmål og over 1000 anatomiske billeder. Alle spørgsmål tager udgangspunkt i lærebogen Bevægeapparatets anatomi af Finn Bojsen-Møller m.fl.......AnatomiQuiz er en quiz-app udviklet til bevægeapparatets anatomi. Den består af mere end 2300 spørgsmål og over 1000 anatomiske billeder. Alle spørgsmål tager udgangspunkt i lærebogen Bevægeapparatets anatomi af Finn Bojsen-Møller m.fl....

  3. Automatic segmentation of lung parenchyma from thoracic CT based on image resampling%基于重采样的胸部CT图像肺实质自动分割

    Institute of Scientific and Technical Information of China (English)

    司广磊; 齐守良; 岳勇; Han J.W.van Triest; 康雁

    2012-01-01

    Automatic lung parenchyma segmentation is one of the most important steps in the computer aided diagnosis (CAD) of the lung. To increase segmentation speed, an algorithm based on resampling of the image data is proposed and implemented. Methods The algorithm firstly resamples and extracts a small part (1/8 ) of the original CT images data. Several steps are implemented to get preliminary segmentation with the resampled data, which include simple threshold segmentation, body region elimination, trachea extraction, removal of interior cavities, left-right lung separation and lung nodule filling. The final results are obtained after projecting the preliminary segmentation to the original dataset and morphology smoothing. The proposed algorithm is applied to 20 patients' data (2556 slices) , and the results are compared to the manual segmentations. Results The algorithm can get accurate results with an average area overlapped ratio 99. 02% to the manual segmentation by the radiologist, and works well for the abnormal cases (right-left connected, with nodules and uncompleted views) . Through resampling, the time consumption of the algorithm is shortened significantly, typically by 50%, and the processing for one slice image is less than 0. 25 s. Conclusions The proposed automatic lung parenchyma segmentation algorithm with excellent robustness and high speed, can get accurate result and satisfy the requirements of current clinical applications.%目的 胸部CT图像的肺实质自动分割是肺部疾病计算机辅助检测的重要基础.为提高分割速度,本文提出并实现了一种基于重采样的分割算法.方法 首先对数据重采样,提取部分(1/8)体数据.再基于重采样体数据,通过阈值分割、胸腔提取、气管剔除、血管填充、左右肺分离和肺壁结节填充等步骤,得到初步分割结果.然后将该结果还原到完整数据体上,形态学平滑后即完成最终分割.最后将算法应用于20

  4. 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.

  5. GesRec3D: a real-time coded gesture-to-speech system with automatic segmentation and recognition thresholding using dissimilarity measures

    OpenAIRE

    Craven, Michael P; Curtis, K. Mervyn

    2004-01-01

    A complete microcomputer system is described, GesRec3D, which facilitates the data acquisition, segmentation, learning, and recognition of 3-Dimensional arm gestures, with application as a Augmentative and Alternative Communication (AAC) aid for people with motor and speech disability. The gesture data is acquired from a Polhemus electro-magnetic tracker system, with sensors attached to the finger, wrist and elbow of one arm. Coded gestures are linked to user-defined text, to be spoken by a t...

  6. Robust optic nerve segmentation on clinically acquired CT

    Science.gov (United States)

    Panda, Swetasudha; Asman, Andrew J.; DeLisi, Michael P.; Mawn, Louise A.; Galloway, Robert L.; Landman, Bennett A.

    2014-03-01

    The optic nerve is a sensitive central nervous system structure, which plays a critical role in many devastating pathological conditions. Several methods have been proposed in recent years to segment the optic nerve automatically, but progress toward full automation has been limited. Multi-atlas methods have been successful for brain segmentation, but their application to smaller anatomies remains relatively unexplored. Herein we evaluate a framework for robust and fully automated segmentation of the optic nerves, eye globes and muscles. We employ a robust registration procedure for accurate registrations, variable voxel resolution and image fieldof- view. We demonstrate the efficacy of an optimal combination of SyN registration and a recently proposed label fusion algorithm (Non-local Spatial STAPLE) that accounts for small-scale errors in registration correspondence. On a dataset containing 30 highly varying computed tomography (CT) images of the human brain, the optimal registration and label fusion pipeline resulted in a median Dice similarity coefficient of 0.77, symmetric mean surface distance error of 0.55 mm, symmetric Hausdorff distance error of 3.33 mm for the optic nerves. Simultaneously, we demonstrate the robustness of the optimal algorithm by segmenting the optic nerve structure in 316 CT scans obtained from 182 subjects from a thyroid eye disease (TED) patient population.

  7. Automatic Left Ventricle Segmentation Based on Improved Coupled Level Set Approach from MSCT Dataset%用改进的耦合水平集方法从MSCT中分割左心室

    Institute of Scientific and Technical Information of China (English)

    王兴家; 董利娜; 李传富; 范亚; 冯焕清

    2011-01-01

    Recently several new multi-slice spiral CT (MSCT) machines have come into the market and they can provide 4D-CT imaging datasets which are useful for the dynamic function analysis of heart and lung. However, the very challenging key step is to find a powerful automatic/semi-automatic method to segment these organs from 4D-CT dataset accurately. A novel approach based on the improved coupled level set ( ICLS) method has been developed to extract both the epicardium and endocardium boundaries of the left ventricle ( LV ) automatically. Based on structure continuity of MSCT slices, the LV cavity coarse region can be extracted from the real datasets by a new automatic localization algorithm, as the initial contour of the improved coupled level set. By incorporating the coarse cavity contours and prior knowledge into the traditional levelset model, the new established coupled level set approach could extract the epicardium and endocardium automatically and accurately. The experimental results of 8 cases of 256-slice 3D cardiac MSCT datasets show that, the average similarity of segmentation results of the LV cavity between ICLS model and manual operation is 95% or more, on average the LV myocardium more than 90%. The segmentation results of 3D surface reconstruction demonstrate the identity and integrity of the LV extracted from real MSCT datasets by ICLS approach.%新型的多层螺旋CT(MSCT)能提供含有时间信息的四维CT成像数据,可用于动态心、肺功能的分析,但如何从中自动或半自动地精确分割出心脏和肺等器官是研究成功的关键.提出一种基于改进的耦合水平集自动分割方法(ICLS),从心脏MSCT数据集中精确提取左心室腔和心肌.根据层片间的结构连续性,自动定位并获取左心室腔的粗轮廓,作为水平集的初始化轮廓,同时,将腔粗轮廓和左心室先验知识融合到耦合水平集中,自动获取左心室内外膜的精确边缘.在8例256层MSCT三维心脏数据集

  8. Automatic generation of digital anthropomorphic phantoms from simulated MRI acquisitions

    Science.gov (United States)

    Lindsay, C.; Gennert, M. A.; KÓ§nik, A.; Dasari, P. K.; King, M. A.

    2013-03-01

    In SPECT imaging, motion from patient respiration and body motion can introduce image artifacts that may reduce the diagnostic quality of the images. Simulation studies using numerical phantoms with precisely known motion can help to develop and evaluate motion correction algorithms. Previous methods for evaluating motion correction algorithms used either manual or semi-automated segmentation of MRI studies to produce patient models in the form of XCAT Phantoms, from which one calculates the transformation and deformation between MRI study and patient model. Both manual and semi-automated methods of XCAT Phantom generation require expertise in human anatomy, with the semiautomated method requiring up to 30 minutes and the manual method requiring up to eight hours. Although faster than manual segmentation, the semi-automated method still requires a significant amount of time, is not replicable, and is subject to errors due to the difficulty of aligning and deforming anatomical shapes in 3D. We propose a new method for matching patient models to MRI that extends the previous semi-automated method by eliminating the manual non-rigid transformation. Our method requires no user supervision and therefore does not require expert knowledge of human anatomy to align the NURBs to anatomical structures in the MR image. Our contribution is employing the SIMRI MRI simulator to convert the XCAT NURBs to a voxel-based representation that is amenable to automatic non-rigid registration. Then registration is used to transform and deform the NURBs to match the anatomy in the MR image. We show that our automated method generates XCAT Phantoms more robustly and significantly faster than the previous semi-automated method.

  9. Anatomy: Spotlight on Africa

    Science.gov (United States)

    Kramer, Beverley; Pather, Nalini; Ihunwo, Amadi O.

    2008-01-01

    Anatomy departments across Africa were surveyed regarding the type of curriculum and method of delivery of their medical courses. While the response rate was low, African anatomy departments appear to be in line with the rest of the world in that many have introduced problem based learning, have hours that are within the range of western medical…

  10. Anatomy comic strips.

    Science.gov (United States)

    Park, Jin Seo; Kim, Dae Hyun; Chung, Min Suk

    2011-01-01

    Comics are powerful visual messages that convey immediate visceral meaning in ways that conventional texts often cannot. This article's authors created comic strips to teach anatomy more interestingly and effectively. Four-frame comic strips were conceptualized from a set of anatomy-related humorous stories gathered from the authors' collective imagination. The comics were drawn on paper and then recreated with digital graphics software. More than 500 comic strips have been drawn and labeled in Korean language, and some of them have been translated into English. All comic strips can be viewed on the Department of Anatomy homepage at the Ajou University School of Medicine, Suwon, Republic of Korea. The comic strips were written and drawn by experienced anatomists, and responses from viewers have generally been favorable. These anatomy comic strips, designed to help students learn the complexities of anatomy in a straightforward and humorous way, are expected to be improved further by the authors and other interested anatomists. PMID:21634024

  11. Spinal Cord Segmentation by One Dimensional Normalized Template Matching: A Novel, Quantitative Technique to Analyze Advanced Magnetic Resonance Imaging Data.

    Science.gov (United States)

    Cadotte, Adam; Cadotte, David W; Livne, Micha; Cohen-Adad, Julien; Fleet, David; Mikulis, David; Fehlings, Michael G

    2015-01-01

    Spinal cord segmentation is a developing area of research intended to aid the processing and interpretation of advanced magnetic resonance imaging (MRI). For example, high resolution three-dimensional volumes can be segmented to provide a measurement of spinal cord atrophy. Spinal cord segmentation is difficult due to the variety of MRI contrasts and the variation in human anatomy. In this study we propose a new method of spinal cord segmentation based on one-dimensional template matching and provide several metrics that can be used to compare with other segmentation methods. A set of ground-truth data from 10 subjects was manually-segmented by two different raters. These ground truth data formed the basis of the segmentation algorithm. A user was required to manually initialize the spinal cord center-line on new images, taking less than one minute. Template matching was used to segment the new cord and a refined center line was calculated based on multiple centroids within the segmentation. Arc distances down the spinal cord and cross-sectional areas were calculated. Inter-rater validation was performed by comparing two manual raters (n = 10). Semi-automatic validation was performed by comparing the two manual raters to the semi-automatic method (n = 10). Comparing the semi-automatic method to one of the raters yielded a Dice coefficient of 0.91 +/- 0.02 for ten subjects, a mean distance between spinal cord center lines of 0.32 +/- 0.08 mm, and a Hausdorff distance of 1.82 +/- 0.33 mm. The absolute variation in cross-sectional area was comparable for the semi-automatic method versus manual segmentation when compared to inter-rater manual segmentation. The results demonstrate that this novel segmentation method performs as well as a manual rater for most segmentation metrics. It offers a new approach to study spinal cord disease and to quantitatively track changes within the spinal cord in an individual case and across cohorts of subjects. PMID:26445367

  12. Segmentation of tongue muscles from super-resolution magnetic resonance images.

    Science.gov (United States)

    Ibragimov, Bulat; Prince, Jerry L; Murano, Emi Z; Woo, Jonghye; Stone, Maureen; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

    2015-02-01

    Imaging and quantification of tongue anatomy is helpful in surgical planning, post-operative rehabilitation of tongue cancer patients, and studying of how humans adapt and learn new strategies for breathing, swallowing and speaking to compensate for changes in function caused by disease, medical interventions or aging. In vivo acquisition of high-resolution three-dimensional (3D) magnetic resonance (MR) images with clearly visible tongue muscles is currently not feasible because of breathing and involuntary swallowing motions that occur over lengthy imaging times. However, recent advances in image reconstruction now allow the generation of super-resolution 3D MR images from sets of orthogonal images, acquired at a high in-plane resolution and combined using super-resolution techniques. This paper presents, to the best of our knowledge, the first attempt towards automatic tongue muscle segmentation from MR images. We devised a database of ten super-resolution 3D MR images, in which the genioglossus and inferior longitudinalis tongue muscles were manually segmented and annotated with landmarks. We demonstrate the feasibility of segmenting the muscles of interest automatically by applying the landmark-based game-theoretic framework (GTF), where a landmark detector based on Haar-like features and an optimal assignment-based shape representation were integrated. The obtained segmentation results were validated against an independent manual segmentation performed by a second observer, as well as against B-splines and demons atlasing approaches. The segmentation performance resulted in mean Dice coefficients of 85.3%, 81.8%, 78.8% and 75.8% for the second observer, GTF, B-splines atlasing and demons atlasing, respectively. The obtained level of segmentation accuracy indicates that computerized tongue muscle segmentation may be used in surgical planning and treatment outcome analysis of tongue cancer patients, and in studies of normal subjects and subjects with speech and

  13. A supervised texton based approach for automatic segmentation and measurement of the fetal head and femur in 2D ultrasound images

    Science.gov (United States)

    Zhang, Lei; Ye, Xujiong; Lambrou, Tryphon; Duan, Wenting; Allinson, Nigel; Dudley, Nicholas J.

    2016-02-01

    This paper presents a supervised texton based approach for the accurate segmentation and measurement of ultrasound fetal head (BPD, OFD, HC) and femur (FL). The method consists of several steps. First, a non-linear diffusion technique is utilized to reduce the speckle noise. Then, based on the assumption that cross sectional intensity profiles of skull and femur can be approximated by Gaussian-like curves, a multi-scale and multi-orientation filter bank is designed to extract texton features specific to ultrasound fetal anatomic structure. The extracted texton cues, together with multi-scale local brightness, are then built into a unified framework for boundary detection of ultrasound fetal head and femur. Finally, for fetal head, a direct least square ellipse fitting method is used to construct a closed head contour, whilst, for fetal femur a closed contour is produced by connecting the detected femur boundaries. The presented method is demonstrated to be promising for clinical applications. Overall the evaluation results of fetal head segmentation and measurement from our method are comparable with the inter-observer difference of experts, with the best average precision of 96.85%, the maximum symmetric contour distance (MSD) of 1.46 mm, average symmetric contour distance (ASD) of 0.53 mm while for fetal femur, the overall performance of our method is better than the inter-observer difference of experts, with the average precision of 84.37%, MSD of 2.72 mm and ASD of 0.31 mm.

  14. Segmentation of the whole breast from low-dose chest CT images

    Science.gov (United States)

    Liu, Shuang; Salvatore, Mary; Yankelevitz, David F.; Henschke, Claudia I.; Reeves, Anthony P.

    2015-03-01

    The segmentation of whole breast serves as the first step towards automated breast lesion detection. It is also necessary for automatically assessing the breast density, which is considered to be an important risk factor for breast cancer. In this paper we present a fully automated algorithm to segment the whole breast in low-dose chest CT images (LDCT), which has been recommended as an annual lung cancer screening test. The automated whole breast segmentation and potential breast density readings as well as lesion detection in LDCT will provide useful information for women who have received LDCT screening, especially the ones who have not undergone mammographic screening, by providing them additional risk indicators for breast cancer with no additional radiation exposure. The two main challenges to be addressed are significant range of variations in terms of the shape and location of the breast in LDCT and the separation of pectoral muscles from the glandular tissues. The presented algorithm achieves robust whole breast segmentation using an anatomy directed rule-based method. The evaluation is performed on 20 LDCT scans by comparing the segmentation with ground truth manually annotated by a radiologist on one axial slice and two sagittal slices for each scan. The resulting average Dice coefficient is 0.880 with a standard deviation of 0.058, demonstrating that the automated segmentation algorithm achieves results consistent with manual annotations of a radiologist.

  15. Applied peritoneal anatomy

    International Nuclear Information System (INIS)

    The peritoneal cavity is a complex anatomical structure with multiple attachments and connections. These are better understood with reference to the embryological development of this region. Armed with this knowledge, the diagnosis and assessment of a wide range of common intra-abdominal diseases becomes straightforward. This article will review and simplify the terminology, complex embryological development, and anatomy of the peritoneum, peritoneal attachments, and the reflections forming the peritoneal boundaries. Normal anatomy will be described using schematic diagrams with corresponding computed tomography (CT) and magnetic resonance imaging (MRI) images, including CT peritoneograms. The relevance of intra- and extra-peritoneal anatomy to common pathological processes will be demonstrated

  16. Anatomy of Sarcocaulon

    Directory of Open Access Journals (Sweden)

    R. L. Verhoeven

    1983-12-01

    Full Text Available The anatomy of the leaf blade, petiole, stem and root of the genus Sarcocaulon (DC. Sweet is discussed. On the basis of the leaf anatomy, the four sections recognized by Moffett (1979 can be identified: section Denticulati (dorsiventral leaves, section Multifidi (isobilateral leaves and adaxial and abaxial palisade continuous at midvein, section Crenati (isobilateral leaves, short curved trichomes and glandular hairs, section Sarcocaulon (isobilateral leaves and glandular hairs only. The anatomy of the stem is typically that of a herbaceous dicotyledon with a thick periderm. The root structure shows that the function of the root is not food storage.

  17. 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…

  18. Anatomy of the Eye

    Science.gov (United States)

    ... Errors Scientists in the Laboratory Visual Acuity Testing Anatomy of the Eye View complete NEI image albums ... the NEI Website Manager . Department of Health and Human Services | The National Institutes of Health | USA.gov ...

  19. Anatomy of The Anatomy of Racial Inequality

    OpenAIRE

    Steven Raphael

    2002-01-01

    In this review, I summarize and offer thoughts about two arguments key to Glenn Loury's analysis of the anatomy of racial inequality. The first concerns the idea that many negative stereotypes held about blacks in the United States are self-fulfilling, despite little evidence of inherent differences between the races in human potential. The second argument concerns the proposition that the racial stigmatization of blacks is deeply embedded in the public consciousness and that such stigma raci...

  20. Facial expression recognition algorithm based on local Gabor wavelet automatic segmentation%基于自动分割的局部Gabor小波人脸表情识别算法

    Institute of Scientific and Technical Information of China (English)

    刘姗姗; 王玲

    2009-01-01

    针对包含表情信息的静态灰度图像,提出基于自动分割的局部Gabor小波人脸表情识别算法.首先使用数学形态学与积分投影相结合定位眉毛眼睛区域,采用模板内计算均值定位嘴巴区域,自动分割出表情子区域.接着,对分割出的表情子区域进行Gabor小波变换提取表情特征,再利用Fisher线性判别分析进行选择,有效地去除了表情特征的冗余性和相关性.最后利用支持向量机实现对人脸表情的分类.用该算法在日本女性表情数据库上进行测试,实现了自动化且易于实现,结果证明了该方法的有效性.%A local Gabor wavelet facial expression recognition algorithm based on automatic segmentation to the still image containing facial expression information was introduced. Firstly, mathematical morphology combined with projection was used to locate the brow and eye region, and the mouth region was located by calculating template average, which can segment the expression sub-regions automatically. Secondly, features of the expression sub-regions were extracted by Gabor wavelet transformation and then effective Gabor expression features were selected by Fisher Linear Discriminant ( FLD) analysis, removing the redundancy and relevance of expression features. Finally the features were sent to Support Vector Machine (SVM) to classify different expressions. The algorithm was tested on Japanese female facial expression database. It is easy to realize automation. The feasibility of this method has been verified by experiments.

  1. Probabilistic Segmentation of Folk Music Recordings

    OpenAIRE

    Ciril Bohak; Matija Marolt

    2016-01-01

    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...

  2. Robust whole-brain segmentation: application to traumatic brain injury.

    Science.gov (United States)

    Ledig, Christian; Heckemann, Rolf A; Hammers, Alexander; Lopez, Juan Carlos; Newcombe, Virginia F J; Makropoulos, Antonios; Lötjönen, Jyrki; Menon, David K; Rueckert, Daniel

    2015-04-01

    We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called "Multi-Atlas Label Propagation with Expectation-Maximisation based refinement" (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to

  3. 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)

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

    International Nuclear Information System (INIS)

    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. LAPAROSCOPIC ANATOMY OF THE EXTRAHEPATIC BILIARY TRACT

    Directory of Open Access Journals (Sweden)

    E. Târcoveanu

    2005-01-01

    Full Text Available Development of mini-invasive surgery determinates a rapid improvement in laparoscopic regional anatomy. As laparoscopy is becoming common in most surgical departments, basic laparoscopic anatomy is mandatory for all residents in general surgery. Successful general surgery starts in the anatomy laboratory. Successfully minim invasive surgery starts in the operative theatre with laparoscopic exploration. The initial laparoscopic view of the right upper quadrant demonstrates primarily the subphrenic spaces, abdominal surface of the diaphragm and diaphragmatic surface of the liver. The falciform ligament is a prominent dividing point between the left subphrenic space and the right subphrenic space. The ligamentum teres hepatis is seen in the free edge of the falciform. Upward traction on the gallbladder exposes the structures of Calot’s triangle and the hepatoduodenal ligament. The liver is divided into anatomic segments based on internal anatomy that is invisible to the laparoscopist. Surface landmarks include the falciform ligament and the gallbladder fossa. The surgical procedures performed laparoscopically currently include liver biopsy, wedge resection, fenestration of hepatic cysts, laparoscopic approach of the hidatid hepatic cyst, and atypical hepatectomy. We present the laparoscopic anatomy of extrahepatic biliary tract. Once the gallbladder is elevated, inspection reveals Hartmann’s pouch and the cystic duct. The typical angular junction of the cystic duct on the common duct actually occurs in a minority of patients and the length and course of the cystic duct are highly variable. The boundaries of Calot’s triangle are often not well seen. The cystic artery is often visible under the peritoneum as it runs along the surface of the gallbladder. The variations of the structures of the hepatoduodenal ligament may occur to injuries during laparoscopic cholecystectomy. Cholangiography increases the safety of dissection of biliary tract by

  6. [Viennese school of anatomy].

    Science.gov (United States)

    Angetter, D C

    1999-10-01

    Anatomical science played a minor role in Vienna for centuries until Gerard van Swieten, in the 18th century, recognized the importance of anatomy for medical education. In the 19th century the anatomical school at the University of Vienna development to its height. A new building and a collection of preparations attracted a large number of students. Finally, a second department of anatomy was established. Political ideologies started to affect this institution in the beginning of the 20th century. Anti-Semitism emerged and caused uproars and fights among the students of the two departments. In 1938 both were united under Eduard Pernkopf, a dedicated Nazi and chairman of the department of anatomy, Decan of the medical faculty (1938-1943) and later on President of the University of Vienna (1943-1945). He was suspected of using cadavers of executed persons for the purpose of research and education. PMID:10546321

  7. Variation in root wood anatomy

    NARCIS (Netherlands)

    Cutler, D.F.

    1976-01-01

    Variability in the anatomy of root wood of selected specimens particularly Fraxinus excelsior L. and Acer pseudoplatanus L. in the Kew reference microscope slide collection is discussed in relation to generalised statements in the literature on root wood anatomy.

  8. An interactive anatomy dissection DVD

    OpenAIRE

    Al-Sabah, Fadel YS

    2013-01-01

    Anatomy remains the cornerstone of medical education. Human anatomy has not changed, yet our understanding of the topic and the methods by which we teach anatomy continue to evolve. At present lectures, tutorials and human cadaveric dissection in the anatomy room remain central to anatomical education in the Republic of Ireland and throughout many parts of the world. With the emergence of new technologies, new teaching methods can be explored. In-house and on-line teaching of Radiology and...

  9. Learning Anatomy Enhances Spatial Ability

    Science.gov (United States)

    Vorstenbosch, Marc A. T. M.; Klaassen, Tim P. F. M.; Donders, A. R. T.; Kooloos, Jan G. M.; Bolhuis, Sanneke M.; Laan, Roland F. J. M.

    2013-01-01

    Spatial ability is an important factor in learning anatomy. Students with high scores on a mental rotation test (MRT) systematically score higher on anatomy examinations. This study aims to investigate if learning anatomy also oppositely improves the MRT-score. Five hundred first year students of medicine ("n" = 242, intervention) and…

  10. Modeling of Craniofacial Anatomy, Variation, and Growth

    DEFF Research Database (Denmark)

    Thorup, Signe Strann

    the two images. To elaborate further: a computational atlas of the average anatomy was constructed. Using non-rigid registration, image data from a subject is automatically transformed into the coordinate space of the atlas. In this process, all knowledge built into the atlas is transferred to the......The topic of this thesis is automatic analysis of craniofacial images with respect to changes due to growth and surgery, inter-subject variation and intracranial volume estimation. The methods proposed contribute to the knowledge about specific craniofacial anomalies, as well as provide a tool for...... detailed analyses for clinical and research purposes. Most of the applications in this thesis rely on non-rigid image registration by the means of warping one image into the coordinate system of another image. This warping results in a deformation field that describes the anatomical correspondence between...

  11. Automatic digital image registration

    Science.gov (United States)

    Goshtasby, A.; Jain, A. K.; Enslin, W. R.

    1982-01-01

    This paper introduces a general procedure for automatic registration of two images which may have translational, rotational, and scaling differences. This procedure involves (1) segmentation of the images, (2) isolation of dominant objects from the images, (3) determination of corresponding objects in the two images, and (4) estimation of transformation parameters using the center of gravities of objects as control points. An example is given which uses this technique to register two images which have translational, rotational, and scaling differences.

  12. Anatomy of the Brain

    Science.gov (United States)

    ... Dictionary Webinars Anytime Learning About Us Our Founders Board of Directors Staff Leadership Strategic Plan Financials News Careers Brain Tumor Information Brain Anatomy Brain Tumor Symptoms Diagnosis Types of Tumors Tumor Grade Risk Factors Brain Tumor Statistics ABTA Publications Brain Tumor ...

  13. The Anatomy Puzzle Book.

    Science.gov (United States)

    Jacob, Willis H.; Carter, Robert, III

    This document features review questions, crossword puzzles, and word search puzzles on human anatomy. Topics include: (1) Anatomical Terminology; (2) The Skeletal System and Joints; (3) The Muscular System; (4) The Nervous System; (5) The Eye and Ear; (6) The Circulatory System and Blood; (7) The Respiratory System; (8) The Urinary System; (9) The…

  14. Automatic sequences

    CERN Document Server

    Haeseler, Friedrich

    2003-01-01

    Automatic sequences are sequences which are produced by a finite automaton. Although they are not random they may look as being random. They are complicated, in the sense of not being not ultimately periodic, they may look rather complicated, in the sense that it may not be easy to name the rule by which the sequence is generated, however there exists a rule which generates the sequence. The concept automatic sequences has special applications in algebra, number theory, finite automata and formal languages, combinatorics on words. The text deals with different aspects of automatic sequences, in particular:· a general introduction to automatic sequences· the basic (combinatorial) properties of automatic sequences· the algebraic approach to automatic sequences· geometric objects related to automatic sequences.

  15. Towards Perceptually Driven Segmentation Evaluation Metrics

    OpenAIRE

    Drelie Gelasca, E.; Ebrahimi, T.; Farias, M; Carli, M; Mitra, S.

    2004-01-01

    To be reliable, an automatic segmentation evaluation metric has to be validated by subjective tests. In this paper, a formal protocol for subjective tests for segmentation quality assessment is presented. The most common artifacts produced by segmentation algorithms are identified and an extensive analysis of their effects on the perceived quality is performed. A psychophysical experiment was performed to assess the quality of video with segmentation errors. The results show how an objective ...

  16. Speech Segmentation Algorithm Based On Fuzzy Memberships

    OpenAIRE

    Luis D. Huerta; Jose Antonio Huesca; Julio C. Contreras

    2010-01-01

    In this work, an automatic speech segmentation algorithm with text independency was implemented. In the algorithm, the use of fuzzy memberships on each characteristic in different speech sub-bands is proposed. Thus, the segmentation is performed a greater detail. Additionally, we tested with various speech signal frequencies and labeling, and we could observe how they affect the performance of the segmentation process in phonemes. The speech segmentation algorithm used is described. During th...

  17. Automatic Number Plate Recognition System

    OpenAIRE

    Rajshree Dhruw; Dharmendra Roy

    2014-01-01

    Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their license number. The objective is to design an efficient automatic authorized vehicle identification system by using the Indian vehicle number plate. In this paper we discus different methodology for number plate localization, character segmentation & recognition of the number plate. The system is mainly applicable for non standard Indian number plates by recognizing...

  18. 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.

  19. Two-step intensity modulated arc therapy (2-step IMAT) with segment weight and width optimization

    International Nuclear Information System (INIS)

    2-step intensity modulated arc therapy (IMAT) is a simplified IMAT technique which delivers the treatment over typically two continuous gantry rotations. The aim of this work was to implement the technique into a computerized treatment planning system and to develop an approach to optimize the segment weights and widths. 2-step IMAT was implemented into the Prism treatment planning system. A graphical user interface was developed to generate the plan segments automatically based on the anatomy in the beam's-eye-view. The segment weights and widths of 2-step IMAT plans were subsequently determined in Matlab using a dose-volume based optimization process. The implementation was tested on a geometric phantom with a horseshoe shaped target volume and then applied to a clinical paraspinal tumour case. The phantom study verified the correctness of the implementation and showed a considerable improvement over a non-modulated arc. Further improvements in the target dose uniformity after the optimization of 2-step IMAT plans were observed for both the phantom and clinical cases. For the clinical case, optimizing the segment weights and widths reduced the maximum dose from 114% of the prescribed dose to 107% and increased the minimum dose from 87% to 97%. This resulted in an improvement in the homogeneity index of the target dose for the clinical case from 1.31 to 1.11. Additionally, the high dose volume V105 was reduced from 57% to 7% while the maximum dose in the organ-at-risk was decreased by 2%. The intuitive and automatic planning process implemented in this study increases the prospect of the practical use of 2-step IMAT. This work has shown that 2-step IMAT is a viable technique able to achieve highly conformal plans for concave target volumes with the optimization of the segment weights and widths. Future work will include planning comparisons of the 2-step IMAT implementation with fixed gantry intensity modulated radiotherapy (IMRT) and commercial IMAT

  20. GPU-based relative fuzzy connectedness image segmentation

    International Nuclear Information System (INIS)

    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.

  1. Quantitative computed tomography of bone mineral density in the mandible. Imaging the topographical distribution of bone mineral density by automatic segmentation of jaw structures; Quantitative Computertomographie der Knochenmineraldichte des Unterkiefers. Darstellung der topographischen Verteilung der Knochenmineraldichte bei automatisierter Segmentierung der Kieferstrukturen

    Energy Technology Data Exchange (ETDEWEB)

    Hothan, T.; Hidajat, N.; Felix, R. [Humboldt-Universitaet, Berlin (Germany). Klinik fuer Strahlenheilkunde; Nelson, K. [Humboldt-Universitaet, Berlin (Germany). Klinik fuer Mund,- Kiefer und Gesichtschirurgie; Maeurer, J. [Radiologie am Prinzregentenplatz, Muenchen (Germany)

    2001-06-01

    Purpose. Confirmation of a new technique for evaluating bone mineral density (BMD). Colored coded imaging of topographical distribution of bone mineral density in the spongy substance. Method. For 20 patients, dental CT examinations of the mandible were made in axial slices. Spongy substance and cortical bone pixels were automatically segmented at foramina mentalia level by means of threshold fixation. The segments were separated in areas relevant to implantology. For each region, BMD was measured by means of quantitative computed tomography (QCT). Spongiose substance of 20 mandibles was segmented by using three treshold intervals to image topographical BMD distribution. Results. Cortical bone and spongy substance could be automatically segmented for 20 mandibles. BMD could be measured in each region. The results were comparable with those of other techniques. Three threshold intervals were segmented for 20 mandibles in the spongy substance to depict topographical BMD distribution. Conclusions. Areas of low BMD can be detected by imaging topographical BMD distribution. This way, subjective rating by the examiners is eliminated. (orig.) [German] Fragestellung. Verifizierung einer neuen Technik zur Bestimmung der Knochenmineraldichte bei automatisierter Segmentierung der Kieferstrukturen. Farblich differenzierte Darstellung der topographischen Verteilung der Knochenmineraldichte der Spongiosa. Methoden. Fuer 20 Patienten wurden Dental-CT-Untersuchungen des Unterkiefers in Axialschichten durchgefuehrt. Die Bildpunkte der Kortikalis und Spongiosa wurden auf Hoehe der Foramina mentalia ueber einer Schwellenwerteinstellung automatisiert segmentiert. Die Segmente wurden in implantologisch interessierende Regionen unterteilt. Fuer jede Region wurde die Knochenmineraldichte mittels quantitativer Computertomographie gemessen. Die Spongiosa von 20 Unterkiefern wurde durch 3 Schwellenwertintervalle segmentiert, um die topographische Verteilung der Knochenmineraldichte

  2. Microscopic Halftone Image Segmentation

    Institute of Scientific and Technical Information of China (English)

    WANG Yong-gang; YANG Jie; DING Yong-sheng

    2004-01-01

    Microscopic halftone image recognition and analysis can provide quantitative evidence for printing quality control and fault diagnosis of printing devices, while halftone image segmentation is one of the significant steps during the procedure. Automatic segmentation on microscopic dots by the aid of the Fuzzy C-Means (FCM) method that takes account of the fuzziness of halftone image and utilizes its color information adequately is realized. Then some examples show the technique effective and simple with better performance of noise immunity than some usual methods. In addition, the segmentation results obtained by the FCM in different color spaces are compared, which indicates that the method using the FCM in the f1f2f3 color space is superior to the rest.

  3. The anatomy workbook

    International Nuclear Information System (INIS)

    This is an atlas of human anatomy presented in the form of line drawings, many of which correspond to imaging planes used in ultrasound (US), computed tomography (CT), and magnetic resonance (MR). The book is organized into 17 sections, each covering a specific structure or organ system. Large, uncluttered drawings are labeled for identification of structures of interest. Many illustrations include captions consisting of comments explaining major divisions within organs, specific anatomic relationships and landmarks, and pertinent vascular anatomy. Most organs are first depicted in isolation or in relation to important adjacent organs or blood vessels and are rendered as if viewed from anterior, posterior, inferior, or superior perspectives. The organs are demonstrated again in serial transverse, saggital, and coronal sections, each accompanied by a drawing of a body in anatomic position denoting the plane of the section

  4. Penile Embryology and Anatomy

    OpenAIRE

    Yiee, Jenny H.; Baskin, Laurence S

    2010-01-01

    Knowledge of penile embryology and anatomy is essential to any pediatric urologist in order to fully understand and treat congenital anomalies. Sex differentiation of the external genitalia occurs between the 7thand 17th weeks of gestation. The Y chromosome initiates male differentiation through the SRY gene, which triggers testicular development. Under the influence of androgens produced by the testes, external genitalia then develop into the penis and scrotum. Dorsal nerves supply penile sk...

  5. Executions and scientific anatomy.

    Science.gov (United States)

    Dolezal, Antonín; Jelen, Karel; Stajnrtova, Olga

    2015-12-01

    The very word "anatomy" tells us about this branch's connection with dissection. Studies of anatomy have taken place for approximately 2.300 years already. Anatomy's birthplace lies in Greece and Egypt. Knowledge in this specific field of science was necessary during surgical procedures in ophthalmology and obstetrics. Embalming took place without public disapproval just like autopsies and manipulation with relics. Thus, anatomical dissection became part of later forensic sciences. Anatomical studies on humans themselves, which needed to be compared with the knowledge gained through studying procedures performed on animals, elicited public disapprobation and prohibition. When faced with a shortage of cadavers, anatomists resorted to obtaining bodies of the executed and suicide victims - since torture, public display of the mutilated body, (including anatomical autopsy), were perceived as an intensification of the death penalty. Decapitation and hanging were the main execution methods meted out for death sentences. Anatomists preferred intact bodies for dissection; hence, convicts could thus avoid torture. This paper lists examples of how this process was resolved. It concerns the manners of killing, vivisection on people in the antiquity and middle-ages, experiments before the execution and after, vivifying from seeming death, experiments with galvanizing electricity on fresh cadavers, evaluating of sensibility after guillotine execution, and making perfect anatomical preparations and publications during Nazism from fresh bodies of the executed. PMID:26859596

  6. Segmentation: Identification of consumer segments

    DEFF Research Database (Denmark)

    Høg, Esben

    2005-01-01

    It is very common to categorise people, especially in the advertising business. Also traditional marketing theory has taken in consumer segments as a favorite topic. Segmentation is closely related to the broader concept of classification. From a historical point of view, classification has its...... analysed possible segments in the market. Results show that the statistical model used identified two segments - a segment of so-called "fish lovers" and another segment called "traditionalists". The "fish lovers" are very fond of eating fish and they actually prefer fish to other dishes. The...... origin in other sciences as for example biology, anthropology etc. From an economic point of view, it is called segmentation when specific scientific techniques are used to classify consumers to different characteristic groupings. What is the purpose of segmentation? For example, to be able to obtain a...

  7. 股骨近端相关影像解剖学测量研究%The research of relevant image anatomy measurements of the proximal segment of femur

    Institute of Scientific and Technical Information of China (English)

    吴炳华; 尹生江; 戴闽; 徐聪

    2015-01-01

    Objective To obtain the morphological parameters of the proximal segment of the femur in Chinese healthy senior population through three-dimensional reconstruction of CT images of the femur in order to provide the anatomical basis for the design of the proximal femoral internal fixation system. Method The CT scan data of the proximal segment of the femur of 60 Chinese healthy senior personswas obtained and the three-dimensional reconstruction of the femur was performed using the system’s default software. The bony landmarks were determined on the three-dimensional images of the normal femur. Measurement included the anatomic indexes of the lesser trochanter, neck shaft, angle, femoral offset and femoral canal, etc. Results There was no statistically significant difference of all measurement parameters between the left side and the right side (P>0.05), and length of the vertical axis of the lesser trochanter was (26.80 ± 2.53) mm and its the transverse diameter was(16.09±1.72) mm;The height and the volume of the lesser trochanter was (8.41± 1.50) mm and (727.15 ± 165.18) mm3, respectively. The minimum length of the screw used to fix the lesser trochanter of the femur was (46.77 ± 2.70) mm, and the up-dip angle was (16.83 ± 2.06)° , the neck-shaft angle was(131.42 ± 8.03)° , the range of femoral offset was(37.85 ± 7.02)mm. Conclusion It is feasible to accurately identify the anatomical landmarks on the three-dimensional reconstruction of the femur. The measurement data was beneficial to treatment of the elderly patients with intertrochanteric fractures and design of the proximal femoral internal fixation system for fixation of the lesser trochanter.%目的:目的通过CT三维重建测量正常国人老年股骨近端的相关形态参数,为设计股骨近端内固定系统提供解剖学依据。方法对60位老年国人的股骨近端进行CT扫描,利用自带的医学图像处理软件进行三维重建,标定相关解剖标志,测量指

  8. Poster — Thur Eve — 70: Automatic lung bronchial and vessel bifurcations detection algorithm for deformable image registration assessment

    International Nuclear Information System (INIS)

    Purpose: To investigate an automatic bronchial and vessel bifurcations detection algorithm for deformable image registration (DIR) assessment to improve lung cancer radiation treatment. Methods: 4DCT datasets were acquired and exported to Varian treatment planning system (TPS) EclipseTM for contouring. The lungs TPS contour was used as the prior shape for a segmentation algorithm based on hierarchical surface deformation that identifies the deformed lungs volumes of the 10 breathing phases. Hounsfield unit (HU) threshold filter was applied within the segmented lung volumes to identify blood vessels and airways. Segmented blood vessels and airways were skeletonised using a hierarchical curve-skeleton algorithm based on a generalized potential field approach. A graph representation of the computed skeleton was generated to assign one of three labels to each node: the termination node, the continuation node or the branching node. Results: 320 ± 51 bifurcations were detected in the right lung of a patient for the 10 breathing phases. The bifurcations were visually analyzed. 92 ± 10 bifurcations were found in the upper half of the lung and 228 ± 45 bifurcations were found in the lower half of the lung. Discrepancies between ten vessel trees were mainly ascribed to large deformation and in regions where the HU varies. Conclusions: We established an automatic method for DIR assessment using the morphological information of the patient anatomy. This approach allows a description of the lung's internal structure movement, which is needed to validate the DIR deformation fields for accurate 4D cancer treatment planning

  9. Automatic quantification of iris color

    DEFF Research Database (Denmark)

    Christoffersen, S.; Harder, Stine; Andersen, J. D.;

    2012-01-01

    An automatic algorithm to quantify the eye colour and structural information from standard hi-resolution photos of the human iris has been developed. Initially, the major structures in the eye region are identified including the pupil, iris, sclera, and eyelashes. Based on this segmentation, the ...

  10. PET/CT Interpretation: Abdominal Anatomy

    International Nuclear Information System (INIS)

    Detail knowledge of abdominal anatomy is essential for accurate interpretation of oncologic PET/CT. The objective of this lecture is to provide the core knowledge and guidance about, peritoneal cavity, vessels, nodal, internal organ, especially liver segmental anatomy, and retroperitoneal spaces to nuclear medicine physicians in their interpretation of oncologic PET/CT. Peritoneal Cavity: The peritoneal spaces are easiest to recognize when there is ascites. The right subphrenic space communicates with anterior and posterior subhepatic (Morrison's) space. The left subphrenic space freely communicate with the left subhepatic space. The right and left subphrenic spaces are separated by falciform ligament and do not communicate directly. The lesser sac is the isolated peritoneal compartment between the stomach and pancreas. It communicates with the rest of the peritoneal cavity (greater sac) through the Foramen of Winslow. The right subphrenic and subhepatic spaces communicate freely with the pelvic peritoneal cavity thru the right paracolic gutter. The phrenicocolic ligament prevents free communication between the left subphrenic / subhepatic space and left paracolic gutter. Free fluid and peritoneal metastases commonly settles in pelvis as the most dependent portion of the peritoneal cavity. The small mesentery suspends the jejunum and ileum and extends obliquely from the left upper quadrant to right lower quadrant. Disease originating above the ligament is directed towards the right lower quadrant and below the ligament can spread to pelvis. The greater omentum hangs from the greater curvature of the stomach and descends in front of the abdominal viscera and serves as a fertile ground for peritoneal metastases. Vessels: The abdominal aorta descends anterior to the left side of the spine to its bifurcation at the level of the iliac crest. The normal aorta does not exceed 3 cm diameter and tapers progressively as it descends distally. The common iliac arteries

  11. Who Is Repeating Anatomy? Trends in an Undergraduate Anatomy Course

    Science.gov (United States)

    Schutte, Audra F.

    2016-01-01

    Anatomy courses frequently serve as prerequisites or requirements for health sciences programs. Due to the challenging nature of anatomy, each semester there are students remediating the course (enrolled in the course for a second time), attempting to earn a grade competitive for admissions into a program of study. In this retrospective study,…

  12. Who is repeating anatomy? Trends in an undergraduate anatomy course.

    Science.gov (United States)

    Schutte, Audra F

    2016-03-01

    Anatomy courses frequently serve as prerequisites or requirements for health sciences programs. Due to the challenging nature of anatomy, each semester there are students remediating the course (enrolled in the course for a second time), attempting to earn a grade competitive for admissions into a program of study. In this retrospective study, remediation rates and trends in an undergraduate anatomy course with over 400 students enrolled each semester at a large Midwestern university were identified. Demographic data was collected from spring 2004 to spring 2010, including students' age, ethnicity, major of study, class standing, college admission tests (ACT and SAT®) scores, anatomy laboratory and lecture examination scores, and final anatomy grades for each semester. Eleven percent of the students repeated the course at least once. Gender, ethnicity, major of study and SAT scores were all shown to be associated with whether or not a student would need to repeat the course. On average, students who repeated anatomy demonstrated significant improvements in lecture and laboratory scores when comparing first and second enrollments in anatomy, and therefore also saw improved final course grades in their second enrollment. These findings will aid future instructors to identify and assist at-risk students to succeed in anatomy. Instructors from other institutions may also find the results to be useful for identifying students at risk for struggling. Anat Sci Educ 9: 171-178. © 2015 American Association of Anatomists. PMID:26179910

  13. [Pandora's box of anatomy].

    Science.gov (United States)

    Weinberg, Uri; Reis, Shmuel

    2008-05-01

    Physicians in Nazi Germany were among the first to join the Nazi party and the SS, and were considered passionate and active supporters of the regime. Their actions included development and implementation of the racial theory thus legitimizing the development of the Nazi genocide plan, leadership and execution of the sterilization and euthanasia programs as well as atrocious human experimentation. Nazi law allowed the use of humans and their remains in research institutions. One of the physicians whose involvement in the Nazi regime was particularly significant was Eduard Pernkopf. He was the head of the Anatomy Institute at the University of Vienna, and later became the president of the university. Pernkopf was a member of the Nazi party, promoted the idea of "racial hygiene", and in 1938, "purified" the university from all Jews. In Pernkopfs atlas of anatomy, the illustrators expressed their sympathy to Nazism by adding Nazi symbols to their illustrations. In light of the demand stated by the "Yad Vashem" Institute, the sources of the atlas were investigated. The report, which was published in 1998, determined that Pernkopfs Anatomy Institute received almost 1400 corpses from the Gestapo's execution chambers. Copies of Pernkopfs atlas, accidentally exposed at the Rappaport School of Medicine in the Technion, led to dilemmas concerning similar works with a common background. The books initiated a wide debate in Israel and abroad, regarding ethical aspects of using information originated in Nazi crimes. Moreover, these findings are evidence of the evil to which science and medicine can give rise, when they are captured as an unshakable authority. PMID:18770971

  14. 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.

  15. Segmental neurofibromatosis

    OpenAIRE

    Galhotra, Virat; Sheikh, Soheyl; Jindal, Sanjeev; Singla, Anshu

    2014-01-01

    Segmental neurofibromatosis is a rare disorder, characterized by neurofibromas or cafι-au-lait macules limited to one region of the body. Its occurrence on the face is extremely rare and only few cases of segmental neurofibromatosis over the face have been described so far. We present a case of segmental neurofibromatosis involving the buccal mucosa, tongue, cheek, ear, and neck on the right side of the face.

  16. Normal cranial CT anatomy

    International Nuclear Information System (INIS)

    The human brain consists of well-known anatomical components. Some parts of these components have been shown to be concerned with certain functions. A complete cranial CT examination consists of a series of several slices obtained in a sequence usually from the base to the vertex of the cranial vault, in the axial mode. The ultimate goal of this chapter is to pinpoint those slices that depict a given anatomical structure or several structures that deal with a given function. To achieve this goal, the discussion of CT cranial anatomy is presented in three sections

  17. TEACHING ANATOMY TO UNDERGRADUATE STUDENTS

    Directory of Open Access Journals (Sweden)

    Sharadkumar Pralhad Sawant,

    2015-09-01

    Full Text Available Introduction: Anatomy is the base of medical science in India and is taught practically to all disciplines of undergraduate health sciences in the first year. It is an acknowledged fact that a basic knowledge of Anatomy is a prerequisite to learn any other branch of medicine. All medical professionals must have a basic knowledge of Anatomy so as to ensure safe medical practice. Traditionally Anatomy teaching consists of didactic lectures as well as dissections or prosections as per the requirement of the course. Lecture is defined as an oral discourse on a given subject before an audience for purpose of instruction and leaning. In the traditional method lectures were taken via chalk & board, but nowadays power point presentations are increasingly being used. To make Anatomy learning both pleasant and motivating, new methods of teaching gross anatomy are being assessed as medical colleges endeavour to find time in their curricula for new content without fore-going fundamental anatomical knowledge. This paper examines the other teaching methodologies for teaching gross anatomy. Conclusion: Proper utilization of newer technologies along with the traditional teaching methods will certainly lead to enhanced understanding of gross anatomy and will ultimately improve students’ performance.

  18. Program Structure Combines Segmentation and Dynamic Storage

    Science.gov (United States)

    Tiffany, S. H.

    1982-01-01

    Programing techniques incorporate advantages of overlaying into segmented loads while retaining all dynamic load advantages of segmentation, employing those capabilities that best suit mode of operation, whether batch or interactive. User is allowed to load a program automatically in a variable manner, based solely on a single data input to the program, to maintain minimal field lengths for interactive use.

  19. Segmental Neurofibromatosis

    Directory of Open Access Journals (Sweden)

    Yesudian Devakar

    1997-01-01

    Full Text Available Segmental neurofibromatosis is a rare variant of neurofibromatosis in which the lesions are confined to one segment or dermatome of the body. They resemble classical neurofibromas in their morphology, histopathology and electron microscopy. However, systemic associations are usually absent. We report one such case with these classical features.

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

    International Nuclear Information System (INIS)

    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

  1. Revisiting the dose-effect correlations in irradiated head and neck cancer using automatic segmentation tools of the dental structures, mandible and maxilla; Dentalmaps: un outil pratique pour chirurgiens dentistes et radiotherapeutes pour l'estimation de la dose recue aux dents, mandibule et maxillaire et du risque de complications postradiques en cas de soins dentaires

    Energy Technology Data Exchange (ETDEWEB)

    Thariat, J. [Departement de radiotherapie, centre Antoine-Lacassagne, 33, avenue de Valombrose, 06189 Nice cedex 2 (France); IBDC CNRS UMR 6543, Parc Valrose, 06108 Nice cedex 2 (France); Universite de Nice Sophia-Antipolis, 33, avenue de Valombrose, 06189 Nice cedex 2 (France); Ramus, L. [Dosisoft, 45/47, avenue Carnot, 94230 Cachan (France); equipe de recherche Asclepios, Inria, 2004, route des Lucioles, BP 93, 06902 Sophia-Antipolis (France); Odin, G. [Departement d' odontologie, hopital Saint-Roch, CHU de Nice, 5, rue Pierre-Devoluy, 06006 Nice (France); Vincent, S.; Orlanducci, M.H.; Dassonville, O. [Institut universitaire de la face et du cou, 33, avenue de Valombrose, 06189 Nice cedex 2 (France); Departement de chirurgie, centre Antoine-Lacassagne, 33, avenue de Valombrose, 06189 Nice cedex 2 (France); Darcourt, V. [Departement de radiotherapie, centre Antoine-Lacassagne, 33, avenue de Valombrose, 06189 Nice cedex 2 (France); Lacout, A.; Marcy, P.Y. [Departement of radiologie, centre d' imagerie medicale, 83, avenue Charles-de-Gaulle, 15000 Aurillac (France); Cagnol, G. [Departement de chirurgie cervicofaciale, clinique de l' Esperance, 122, avenue du Docteur-Maurice-Donat, BP 1250, 06254 Mougins (France); Malandain, G. [equipe de recherche Asclepios, Inria, 2004, route des Lucioles, BP 93, 06902 Sophia-Antipolis (France)

    2011-12-15

    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)

  2. Generative models for image segmentation and representation

    OpenAIRE

    González Díaz, Iván

    2011-01-01

    This PhD. Thesis consists of two well differentiated parts, each of them focusing on one particular field of Computer Vision. The first part of the document considers the problem of automatically generating image segmentations in video sequences in the absence of any kind of semantic knowledge or labeled data. To that end, a blind spatio-temporal segmentation algorithm is proposed that fuses motion, color and spatial information to produce robust segmentations. The approach follows an iterati...

  3. 基于词频学习和动态词频更新的藏文自动分词系统设计%DESIGN OF AUTOMATIC TIBETAN WORD SEGMENTATION SYSTEM BASED ON WORD FREQUENCY LEARNING AND DYNAMIC WORD FREQUENCY UPDATING

    Institute of Scientific and Technical Information of China (English)

    项炜; 金澎

    2014-01-01

    Automatic Tibetan word segmentation is one of the basic problems in natural language processing of Tibetan.In this paper,we design a new automatic Tibetan word segmentation system in light of the keys and difficulties in it,for example:the technologies of identification of case-auxiliary word,the ambiguity segmentation,and the unknown words recognition.The system uses the techniques of the dynamic word frequency up-date and the ambiguity treatment and unknown words recognition which are based on the word frequency of the context.The presented system has relatively high performance in terms of the recognition accuracy of ambiguities,the recognition rate of unknown word and the segmentation speed.%藏文自动分词问题是藏文自然语言处理的基本问题之一。针对藏文自动分词中的重点难点,例如:格助词的识别、歧义切分、未登录词识别技术设计一个新的藏文自动分词系统。该系统采用动态词频更新和基于上下文词频的歧义处理和未登录词识别技术。在歧义字段分词准确性、未登录词识别率和分词速度上,该系统具有较优的性能。

  4. Segmentation Using Symmetry Deviation

    DEFF Research Database (Denmark)

    Hollensen, Christian; Højgaard, L.; Specht, L.;

    2011-01-01

    the 10 hypopharyngeal cancer patients to find anatomical symmetry and evaluate it against the standard deviation of the normal patients to locate pathologic volumes. Combining the information with an absolute PET threshold of 3 Standard uptake value (SUV) a volume was automatically delineated. The...... overlap of automated segmentations on manual contours was evaluated using concordance index and sensitivity for the hypopharyngeal patients. The resulting concordance index and sensitivity was compared with the result of using a threshold of 3 SUV using a paired t-test. Results: The anatomical and...... overlap concordance index and sensitivity of respectively 0.43±0.15 and 0.56±0.18 was acquired. It was compared to the concordance index of segmentation using absolute threshold of 3 SUV giving respectively 0.41±0.16 and 0.51±0.19 for concordance index and sensitivity yielding p-values of 0.33 and 0...

  5. Anatomy of the Vestibulo-automatic Outflow to the Gut

    Science.gov (United States)

    Torigoe, Y.

    1985-01-01

    Motion sickness can be induced by vestibular effects on the sympathetic portion of the autonomic nervous system. However, the pathways linking the vestibular and autonomic pathways are unknown. As a first step in this analysis, the locations of preganglionic sympathetic neurons (PSN) and dorsal root afferent ganglionic neurons (DRG) which supply sympathetic innervation to major portions of the gastrointestinal tract in rabbits were identified. The objective of a second series of experiments is to determine which of the brainstem nuclei project to the autonomic regions of the spinal cord that control gastrointestinal motility. To achieve this goal, a trans-synaptic retrograde tracer (3H-tetanus toxoid) is applied to the greater splanchnic nerve. This method allows the labeling of neurons within the brainstem that project only to the preganglionic synpathetic neurons. One structure that has been strongly implicated in mediating vestibulo-autonomic control is the cerebellum (i.e., nodulus and uvula). The outflow of these lobules to the autonomic regions of the brainstem is mediated by the fastigial nucleus. To determine the precise projections of the fastigial nucleus to the brainstem nuclei involved in emesis, anterograde tracer (3H-leucine) was injected into the fastigial nucleus in a third series of experiments.

  6. Automatic detection of retinal anatomy to assist diabetic retinopathy screening

    Energy Technology Data Exchange (ETDEWEB)

    Fleming, Alan D [Biomedical Physics, University of Aberdeen, Aberdeen, AB25 2ZD (United Kingdom); Goatman, Keith A [Biomedical Physics, University of Aberdeen, Aberdeen, AB25 2ZD (United Kingdom); Philip, Sam [Grampian Diabetes Retinal Screening Programme, Woolmanhill Hospital, Aberdeen, AB25 1LD (United Kingdom); Olson, John A [Grampian Diabetes Retinal Screening Programme, Woolmanhill Hospital, Aberdeen, AB25 1LD (United Kingdom); Sharp, Peter F [Biomedical Physics, University of Aberdeen, Aberdeen, AB25 2ZD (United Kingdom)

    2007-01-21

    Screening programmes for diabetic retinopathy are being introduced in the United Kingdom and elsewhere. These require large numbers of retinal images to be manually graded for the presence of disease. Automation of image grading would have a number of benefits. However, an important prerequisite for automation is the accurate location of the main anatomical features in the image, notably the optic disc and the fovea. The locations of these features are necessary so that lesion significance, image field of view and image clarity can be assessed. This paper describes methods for the robust location of the optic disc and fovea. The elliptical form of the major retinal blood vessels is used to obtain approximate locations, which are refined based on the circular edge of the optic disc and the local darkening at the fovea. The methods have been tested on 1056 sequential images from a retinal screening programme. Positional accuracy was better than 0.5 of a disc diameter in 98.4% of cases for optic disc location, and in 96.5% of cases for fovea location. The methods are sufficiently accurate to form an important and effective component of an automated image grading system for diabetic retinopathy screening.

  7. Automatic detection of retinal anatomy to assist diabetic retinopathy screening

    International Nuclear Information System (INIS)

    Screening programmes for diabetic retinopathy are being introduced in the United Kingdom and elsewhere. These require large numbers of retinal images to be manually graded for the presence of disease. Automation of image grading would have a number of benefits. However, an important prerequisite for automation is the accurate location of the main anatomical features in the image, notably the optic disc and the fovea. The locations of these features are necessary so that lesion significance, image field of view and image clarity can be assessed. This paper describes methods for the robust location of the optic disc and fovea. The elliptical form of the major retinal blood vessels is used to obtain approximate locations, which are refined based on the circular edge of the optic disc and the local darkening at the fovea. The methods have been tested on 1056 sequential images from a retinal screening programme. Positional accuracy was better than 0.5 of a disc diameter in 98.4% of cases for optic disc location, and in 96.5% of cases for fovea location. The methods are sufficiently accurate to form an important and effective component of an automated image grading system for diabetic retinopathy screening

  8. MARKET SEGMENTATION

    Directory of Open Access Journals (Sweden)

    Munaga Ramakrishna Mohan Rao

    2015-01-01

    Full Text Available Market segmentation is a marketing strategy that involves dividing a broad target market into subsets of consumers, businesses, or countries who have common needs and priorities, and then designing and implementing strategies to target them. Market segmentation strategies may be used to identify the target customers, and provide supporting data for positioning to achieve a marketing plan objective. Businesses may develop product differentiation strategies, or an undifferentiated approach, involving specific products or product lines depending on the specific demand and attributes of the target segment.

  9. [Surgery without anatomy?].

    Science.gov (United States)

    Stelzner, F

    2016-08-01

    Anatomy is the basis of all operative medicine. While this branch of scientific medicine is frequently not explicitly mentioned in surgical publications, it is nonetheless quintessential to medical education. In the era of video sequences and digitized images, surgical methods are frequently communicated in the form of cinematic documentation of surgical procedures; however, this occurs without the help of explanatory drawings or subtexts that would illustrate the underlying anatomical nomenclature, comment on fine functionally important details or even without making any mention of the surgeon. In scientific manuscripts color illustrations frequently appear in such overwhelming quantities that they resemble long arrays of trophies but fail to give detailed explanations that would aid the therapeutic translation of the novel datasets. In a similar fashion, many anatomy textbooks prefer to place emphasis on illustrations and photographs while supplying only a paucity of explanations that would foster the understanding of functional contexts and thus confuse students and practitioners alike. There is great temptation to repeat existing data and facts over and over again, while it is proportionally rare to make reference to truly original scientific discoveries. A number of examples are given in this article to illustrate how discoveries that were made even a long time ago can still contribute to scientific progress in current times. This includes the NO signaling molecules, which were first described in 1775 but were only discovered to have a pivotal role as neurotransmitters in the function of human paradoxical sphincter muscles in 2012 and 2015. Readers of scientific manuscripts often long for explanations by the numerous silent coauthors of a publication who could contribute to the main topic by adding in-depth illustrations (e. g. malignograms, evolution and involution of lymph node structures). PMID:27251482

  10. Carpal Ligament Anatomy and Biomechanics.

    Science.gov (United States)

    Pulos, Nicholas; Bozentka, David J

    2015-08-01

    A fundamental understanding of the ligamentous anatomy of the wrist is critical for any physician attempting to treat carpal instability. The anatomy of the wrist is complex, not only because of the number of named structures and their geometry but also because of the inconsistencies in describing these ligaments. The complex anatomy of the wrist is described through a review of the carpal ligaments and their effect on normal carpal motion. Mastery of this topic facilitates the physician's understanding of the patterns of instability that are seen clinically. PMID:26205699

  11. MARKET SEGMENTATION

    OpenAIRE

    Munaga Ramakrishna Mohan Rao

    2015-01-01

    Market segmentation is a marketing strategy that involves dividing a broad target market into subsets of consumers, businesses, or countries who have common needs and priorities, and then designing and implementing strategies to target them. Market segmentation strategies may be used to identify the target customers, and provide supporting data for positioning to achieve a marketing plan objective. Businesses may develop product differentiation strategies, or an undifferentiated approach, inv...

  12. Fingerprint Segmentation

    OpenAIRE

    Jomaa, Diala

    2009-01-01

    In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve th...

  13. Genus Zero Graph Segmentation: Estimation of Intracranial Volume

    DEFF Research Database (Denmark)

    Jensen, Rasmus Ramsbøl; Thorup, Signe Strann; Paulsen, Rasmus Reinhold; Darvann, Tron A.; Hermann, Nuno V.; Larsen, Per; Kreiborg, Sven; Larsen, Rasmus

    present a fully automatic 3D graph-based method for segmentation of the ICV in non-contrast CT scans. We reformulate the ICV segmentation problem as an optimal genus 0 segmentation problem in a volumetric graph. The graph is the result of a volumetric spherical subsample from the data connected using...

  14. Olfaction: anatomy, physiology and behavior

    OpenAIRE

    Benignus, Vernon A.; Prah, James D.

    1982-01-01

    The anatomy, physiology and function of the olfactory system are reviewed, as are the normal effects of olfactory stimulation. It is speculated that olfaction may have important but unobtrusive effects on human behavior.

  15. OLFACTION: ANATOMY, PHYSIOLOGY AND BEHAVIOR

    Science.gov (United States)

    The anatomy, physiology and function of the olfactory system are reviewed, as are the normal effects of olfactory stimulation. It is speculated that olfaction may have important but unobtrusive effects on human behavior.

  16. Microsurgical anatomy of the posterior circulation

    Directory of Open Access Journals (Sweden)

    Pai Balaji

    2007-01-01

    Full Text Available Context: The microsurgical anatomy of the posterior circulation is very complex and variable. Surgical approaches to this area are considered risky due to the presence of the various important blood vessels and neural structures. Aims: To document the microsurgical anatomy of the posterior circulation along with variations in the Indian population. Materials and Methods: The authors studied 25 cadaveric brain specimens. Microsurgical dissection was carried out from the vertebral arteries to the basilar artery and its branches, the basilar artery bifurcation, posterior cerebral artery and its various branches. Measurements of the outer diameters of the vertebral artery, basilar artery and posterior cerebral artery and their lengths were taken. Results: The mean diameter of the vertebral artery was 3.4 mm on the left and 2.9 mm on the right. The diameter of the basilar artery varied from 3-7 mm (mean of 4.3 mm. The length varied from 24-35 mm (mean of 24.9 mm. The basilar artery gave off paramedian and circumferential perforating arteries. The origin of the anterior inferior cerebellar artery (AICA varied from 0-21 mm (mean 10.0 mm from the vertebrobasilar junction. The diameter of the AICA varied from being hypoplastic i.e., < 0.5 mm to 2 mm (mean 1.0 mm. The superior cerebellar artery (SCA arises very close to the basilar bifurcation, in our series (1-3 mm from the basilar artery bifurcation. The diameter of the SCA varied from 0.5-2.5 mm on both sides. The posterior cerebral artery (PCA is divided into four segments. The PCA gave rise to perforators (thalamoperforators, thalamogeniculate arteries, circumflex arteries and peduncular arteries, medial posterior choroidal artery, lateral posterior choroidal artery and cortical branches. In 39 specimens the P1 segment was found to be larger than the posterior communicating artery, in six specimens it was found to be equal to the diameter of the posterior communicating artery and in five specimens it

  17. Surgical Anatomy of the Eyelids.

    Science.gov (United States)

    Sand, Jordan P; Zhu, Bovey Z; Desai, Shaun C

    2016-05-01

    Slight alterations in the intricate anatomy of the upper and lower eyelid or their underlying structures can have pronounced consequences for ocular esthetics and function. The understanding of periorbital structures and their interrelationships continues to evolve and requires consideration when performing complex eyelid interventions. Maintaining a detailed appreciation of this region is critical to successful cosmetic or reconstructive surgery. This article presents a current review of the anatomy of the upper and lower eyelid with a focus on surgical implications. PMID:27105794

  18. Anatomie et identification des bois

    OpenAIRE

    Jourez, Benoît

    2010-01-01

    Anatomie des bois Structure anatomique des résineux et des feuillus Structure de la membrane cellulaire structure submicroscopique Anatomie du bois des essences feuillues tropicales Caractères anatomiques servant à l'identification des essences Reconnaissance microscopique du bois des essences résineuses et feuillues Duramen et duraminisation Formations anormales ( bois de compression et bois de tension) Chimie du bois Composition générale Cellulose, hé...

  19. 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 are...

  20. 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.

  1. Penile Embryology and Anatomy

    Directory of Open Access Journals (Sweden)

    Jenny H. Yiee

    2010-01-01

    Full Text Available Knowledge of penile embryology and anatomy is essential to any pediatric urologist in order to fully understand and treat congenital anomalies. Sex differentiation of the external genitalia occurs between the 7thand 17th weeks of gestation. The Y chromosome initiates male differentiation through the SRY gene, which triggers testicular development. Under the influence of androgens produced by the testes, external genitalia then develop into the penis and scrotum. Dorsal nerves supply penile skin sensation and lie within Buck's fascia. These nerves are notably absent at the 12 o'clock position. Perineal nerves supply skin sensation to the ventral shaft skin and frenulum. Cavernosal nerves lie within the corpora cavernosa and are responsible for sexual function. Paired cavernosal, dorsal, and bulbourethral arteries have extensive anastomotic connections. During erection, the cavernosal artery causes engorgement of the cavernosa, while the deep dorsal artery leads to glans enlargement. The majority of venous drainage occurs through a single, deep dorsal vein into which multiple emissary veins from the corpora and circumflex veins from the spongiosum drain. The corpora cavernosa and spongiosum are all made of spongy erectile tissue. Buck's fascia circumferentially envelops all three structures, splitting into two leaves ventrally at the spongiosum. The male urethra is composed of six parts: bladder neck, prostatic, membranous, bulbous, penile, and fossa navicularis. The urethra receives its blood supply from both proximal and distal directions.

  2. Penile embryology and anatomy.

    Science.gov (United States)

    Yiee, Jenny H; Baskin, Laurence S

    2010-01-01

    Knowledge of penile embryology and anatomy is essential to any pediatric urologist in order to fully understand and treat congenital anomalies. Sex differentiation of the external genitalia occurs between the 7th and 17th weeks of gestation. The Y chromosome initiates male differentiation through the SRY gene, which triggers testicular development. Under the influence of androgens produced by the testes, external genitalia then develop into the penis and scrotum. Dorsal nerves supply penile skin sensation and lie within Buck's fascia. These nerves are notably absent at the 12 o'clock position. Perineal nerves supply skin sensation to the ventral shaft skin and frenulum. Cavernosal nerves lie within the corpora cavernosa and are responsible for sexual function. Paired cavernosal, dorsal, and bulbourethral arteries have extensive anastomotic connections. During erection, the cavernosal artery causes engorgement of the cavernosa, while the deep dorsal artery leads to glans enlargement. The majority of venous drainage occurs through a single, deep dorsal vein into which multiple emissary veins from the corpora and circumflex veins from the spongiosum drain. The corpora cavernosa and spongiosum are all made of spongy erectile tissue. Buck's fascia circumferentially envelops all three structures, splitting into two leaves ventrally at the spongiosum. The male urethra is composed of six parts: bladder neck, prostatic, membranous, bulbous, penile, and fossa navicularis. The urethra receives its blood supply from both proximal and distal directions. PMID:20602076

  3. Automatic spikes detection in seismogram

    Institute of Scientific and Technical Information of China (English)

    王海军; 靳平; 刘贵忠

    2003-01-01

    @@ Data processing for seismic network is very complex and fussy, because a lot of data is recorded in seismic network every day, which make it impossible to process these data all by manual work. Therefore, seismic data should be processed automatically to produce a initial results about events detection and location. Afterwards, these results are reviewed and modified by analyst. In automatic processing data quality checking is important. There are three main problem data thatexist in real seismic records, which include: spike, repeated data and dropouts. Spike is defined as isolated large amplitude point; the other two problem datahave the same features that amplitude of sample points are uniform in a interval. In data quality checking, the first step is to detect and statistic problem data in a data segment, if percent of problem data exceed a threshold, then the whole data segment is masked and not be processed in the later process.

  4. Binarization of MRI with Intensity Inhomogeneity using K- Means Clustering for Segmenting Hippocampus

    OpenAIRE

    T. Genish; Somasundaram, K

    2013-01-01

    Medical image segmentation plays a crucial role in identifying the shape and structure of human anatomy. The most widely used image segmentation algorithms are edge-based and typically rely on the intensity inhomogeneity of the image at the edges, which often fail to provide accurate segmentation results. This paper proposes a boundary detection technique for segmenting the hippocampus (the subcortical structure in medial temporal lobe) from MRI with intensity inhomogeneity without ruining it...

  5. An anatomy precourse enhances student learning in veterinary anatomy.

    Science.gov (United States)

    McNulty, Margaret A; Stevens-Sparks, Cathryn; Taboada, Joseph; Daniel, Annie; Lazarus, Michelle D

    2016-07-01

    Veterinary anatomy is often a source of trepidation for many students. Currently professional veterinary programs, similar to medical curricula, within the United States have no admission requirements for anatomy as a prerequisite course. The purpose of the current study was to evaluate the impact of a week-long precourse in veterinary anatomy on both objective student performance and subjective student perceptions of the precourse educational methods. Incoming first year veterinary students in the Louisiana State University School of Veterinary Medicine professional curriculum were asked to participate in a free precourse before the start of the semester, covering the musculoskeletal structures of the canine thoracic limb. Students learned the material either via dissection only, instructor-led demonstrations only, or a combination of both techniques. Outcome measures included student performance on examinations throughout the first anatomy course of the professional curriculum as compared with those who did not participate in the precourse. This study found that those who participated in the precourse did significantly better on examinations within the professional anatomy course compared with those who did not participate. Notably, this significant improvement was also identified on the examination where both groups were exposed to the material for the first time together, indicating that exposure to a small portion of veterinary anatomy can impact learning of anatomical structures beyond the immediate scope of the material previously learned. Subjective data evaluation indicated that the precourse was well received and students preferred guided learning via demonstrations in addition to dissection as opposed to either method alone. Anat Sci Educ 9: 344-356. © 2015 American Association of Anatomists. PMID:26669269

  6. Automatic Morphometry of Nerve Histological Sections

    OpenAIRE

    Romero, E.; Cuisenaire, O.; Denef, J.; Delbeke, J.; Macq, B.; Veraart, C.

    2000-01-01

    A method for the automatic segmentation, recognition and measurement of neuronal myelinated fibers in nerve histological sections is presented. In this method, the fiber parameters i.e. perimeter, area, position of the fiber and myelin sheath thickness are automatically computed. Obliquity of the sections may be taken into account. First, the image is thresholded to provide a coarse classification between myelin and non-myelin pixels. Next, the resulting binary image is further simplified usi...

  7. 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.

  8. Automatic brain cropping enhancement using active contours initialized by a PCNN

    Science.gov (United States)

    Swathanthira Kumar, Murali Murugavel; Sullivan, John M., Jr.

    2009-02-01

    Active contours are a popular medical image segmentation strategy. However in practice, its accuracy is dependent on the initialization of the process. The PCNN (Pulse Coupled Neural Network) algorithm developed by Eckhorn to model the observed synchronization of neural assemblies in small mammals such as cats allows for segmenting regions of similar intensity but it lacks a convergence criterion. In this paper we report a novel PCNN based strategy to initialize the zero level contour for automatic brain cropping of T2 weighted MRI image volumes of Long-Evans rats. Individual 2D anatomy slices of the rat brain volume were processed by means of a PCNN and a surrogate image 'signature' was constructed for each slice. By employing a previously trained artificial neural network (ANN) an approximate PCNN iteration (binary mask) was selected. This mask was then used to initialize a region based active contour model to crop the brain region. We tested this hybrid algorithm on 30 rat brain (256*256*12) volumes and compared the results against manually cropped gold standard. The Dice and Jaccard similarity indices were used for numerical evaluation of the proposed hybrid model. The highly successful system yielded an average of 0.97 and 0.94 respectively.

  9. A Survey of Image Segmentation Algorithms Based On Fuzzy Clustering

    OpenAIRE

    R. Ravindraiah; K. Tejaswini

    2013-01-01

    Medical image segmentation plays a vital role in one of the most challenging fields ofengineering. Imaging modality provides detailed information about anatomy. It is also helpful in the findingof the disease and its progressive treatment. More research and work on it has enhanced more effectivenessas far as the subject is concerned. Different methods are used for medical image segmentation such asClustering methods, Thresholding method, Classifier, Region Growing, Deformable Model, Markov Ra...

  10. Segmental neurofibromatosis

    OpenAIRE

    Yesudian Devakar; Krishnan S. G. S; Jayaraman M; Janaki V R; Yesudian Patrick

    2014-01-01

    Segmental neurofibromatosis or type V neurofibromatosis is a rare genodermatosis characterized by neurofibromas, café-au-lait spots and neurofibromas limited to a circumscribed body region. The disease may be associated with systemic involvement and malignancies. The disorder has not been reported yet in the Polish medical literature. A 63-year-old Caucasian woman presented with a 20-year history of multiple, flesh colored, dome-shaped, soft to firm nodules situated in the right lumbar region...

  11. Language Segmentation

    OpenAIRE

    Alfter, David

    2015-01-01

    Language segmentation consists in finding the boundaries where one language ends and another language begins in a text written in more than one language. This is important for all natural language processing tasks. The problem can be solved by training language models on language data. However, in the case of low- or no-resource languages, this is problematic. I therefore investigate whether unsupervised methods perform better than supervised methods when it is difficult or impossible to trai...

  12. The Anatomy of Anatomy: A Review for Its Modernization

    Science.gov (United States)

    Sugand, Kapil; Abrahams, Peter; Khurana, Ashish

    2010-01-01

    Anatomy has historically been a cornerstone in medical education regardless of nation or specialty. Until recently, dissection and didactic lectures were its sole pedagogy. Teaching methodology has been revolutionized with more reliance on models, imaging, simulation, and the Internet to further consolidate and enhance the learning experience.…

  13. Anatomy Adventure: A Board Game for Enhancing Understanding of Anatomy

    Science.gov (United States)

    Anyanwu, Emeka G.

    2014-01-01

    Certain negative factors such as fear, loss of concentration and interest in the course, lack of confidence, and undue stress have been associated with the study of anatomy. These are factors most often provoked by the unusually large curriculum, nature of the course, and the psychosocial impact of dissection. As a palliative measure, Anatomy…

  14. Anatomy adventure: a board game for enhancing understanding of anatomy.

    Science.gov (United States)

    Anyanwu, Emeka G

    2014-01-01

    Certain negative factors such as fear, loss of concentration and interest in the course, lack of confidence, and undue stress have been associated with the study of anatomy. These are factors most often provoked by the unusually large curriculum, nature of the course, and the psychosocial impact of dissection. As a palliative measure, Anatomy Adventure, a board game on anatomy was designed to reduce some of these pressures, emphasize student centered and collaborative learning styles, and add fun to the process of learning while promoting understanding and retention of the subject. To assess these objectives, 95 out of over 150 medical and dental students who expressed willingness to be part of the study were recruited and divided into a Game group and a Non-game group. A pretest written examination was given to both groups, participants in the Game group were allowed to play the game for ten days, after which a post-test examination was also given. A 20-item questionnaire rated on a three-point scale to access student's perception of the game was given to the game group. The post-test scores of the game group were significantly higher (P game counterparts. Also the post-test score of the game based group was significantly better (P game was interesting, highly informative, encouraged team work, improved their attitude, and perception to gross anatomy. PMID:23878076

  15. 3D virtual table in anatomy education

    DEFF Research Database (Denmark)

    Dahl, Mads Ronald; Simonsen, Eivind Ortind

    The ‘Anatomage’ is a 3D virtual human anatomy table, with touchscreen functionality, where it is possible to upload CT-scans and digital. Learning the human anatomy terminology requires time, a very good memory, anatomy atlas, books and lectures. Learning the 3 dimensional structure, connections...

  16. Toward consistent cell segmentation: quality assessment of cell segments via appearance and geometry features

    Science.gov (United States)

    Brinker, Andrew; Fredrikson, Annika; Zhang, Xiaofan; Sourvenir, Richard; Zhang, Shaoting

    2015-03-01

    Computer-Aided Diagnosis (CAD) systems based on histopathological images rely on quality low-level image processing, including cell segmentation. Many methods for cell segmentation lack in generality and struggle with the wide variety of cell appearance and inter-cell structure present in histopathological images. We present a computationally efficient system to classify segmentation results as the first step toward automatic segment correction. This general method can applied to existing or future cell segmentation methods to provide corrections for low-quality results. Specifically, with a small collection of easy-to-compute features, we can identify incorrect segments with a high degree of accuracy, which then can be used to determine the needed corrections based on the type of segmentation failure present.

  17. Automatic segmentation of T2 weighted brain MRI based on histogram gradient calculation%基于直方图梯度计算的 T2加权脑部 MR 图像自动分割

    Institute of Scientific and Technical Information of China (English)

    齐兴斌; 赵丽; 李雪梅; 田涛

    2015-01-01

    针对脑部核磁共振图像分割问题,提出了一种直方图梯度计算方法。首先,对 MR 图像的直方图进行平滑处理,从而去除三个体素中出现的最低灰度级;然后,在预处理后的直方图上计算梯度;最后,计算对象数和其所在位置的梯度,并对图像进行自动分割。基于直方图处理进行梯度计算,大大降低了计算复杂度。在 T2加权脑部 MR 图像上的实验结果表明,该方法可以有效地从二维和三维图像中提取出主要脑部区域,并在临床环境中获得的人类脑部 MR 图像上成功实施,分割效果优于其他几种现有分割算法。%For the issues of magnetic resonance images segmentation,this paper proposed a method based on histogram gra-dient calculation.Firstly,it smoothed the histogram of MR image so as to remove the lowest grayscale of three individuals. Then,it calculated the gradient on the preprocessed histogram.Finally,it segmented the image after calculating successfully number of objects and their gradients in which they lay.The proposed method was purely based on histogram processing for gra-dient calculation,so the computational complexity was reduced greatly.Experimental results on T2 weighted MR brain images show that the primary brain areas are extracted out efficiently from 2D and 3D images by the proposed method which has been successfully implemented on human brain MR images obtained in clinical environment.It has better segmentation efficiency than the several existing segmentation algorithms.

  18. DAGAL: Detailed Anatomy of Galaxies

    CERN Document Server

    Knapen, Johan H

    2016-01-01

    The current IAU Symposium is closely connected to the EU-funded network DAGAL (Detailed Anatomy of Galaxies), with the final annual network meeting of DAGAL being at the core of this international symposium. In this short paper, we give an overview of DAGAL, its training activities, and some of the scientific advances that have been made under its umbrella.

  19. Soul Anatomy: A virtual cadaver

    Directory of Open Access Journals (Sweden)

    Moaz Bambi

    2014-01-01

    Full Text Available In the traditional science of medicine and medical education, teaching human anatomy in the class has always been done using human cadavers. Not only does this violate human sanctity, but according to our research, it is not adequate to provide students with the alleged educational value that it is supposed to deliver. It is very cumbersome to organise all the aspects of cadaver care. Cadavers are also very limited when it comes to controlling their structures and any benefit is almost completely altered the first time the cadaver is used (dissected, and ironically, it is very weak at delivering actual real-life scenarios of a human body to students. Virtual anatomy has been a promising solution that many are counting on. But even today, we have not found a complete solution that combines all the benefits of using human cadavers and those introduced by its technical counterparts. "Soul Anatomy" aims to do just that. It brings the best of all worlds, from a natural intuitive control system, life-like feel of organs, precise accuracy in moving and controlling bodily structures, to the smallest details of being able to show medical information overlays from various medical databases connected to the internet; thus making use of technology in teaching human anatomy by providing a modern learning experience.

  20. Segmental neurofibromatosis.

    Science.gov (United States)

    Sobjanek, Michał; Dobosz-Kawałko, Magdalena; Michajłowski, Igor; Pęksa, Rafał; Nowicki, Roman

    2014-12-01

    Segmental neurofibromatosis or type V neurofibromatosis is a rare genodermatosis characterized by neurofibromas, café-au-lait spots and neurofibromas limited to a circumscribed body region. The disease may be associated with systemic involvement and malignancies. The disorder has not been reported yet in the Polish medical literature. A 63-year-old Caucasian woman presented with a 20-year history of multiple, flesh colored, dome-shaped, soft to firm nodules situated in the right lumbar region. A histopathologic evaluation of three excised tumors revealed neurofibromas. No neurological and ophthalmologic symptoms of neurofibromatosis were diagnosed. PMID:25610358

  1. Brookhaven segment interconnect

    International Nuclear Information System (INIS)

    We have performed a high energy physics experiment using a multisegment Brookhaven FASTBUS system. The system was composed of three crate segments and two cable segments. We discuss the segment interconnect module which permits communication between the various segments

  2. Virtual Temporal Bone Anatomy

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Background The Visible Human Project(VHP) initiated by the U.S. National Library of Medicine has drawn much attention and interests from around the world. The Visible Chinese Human (VCH) project has started in China. The current study aims at acquiring a feasible virtual methodology for reconstructing the temporal bone of the Chinese population, which may provide an accurate 3-D model of important temporal bone structures that can be used in teaching and patient care for medical scientists and clinicians. Methods A series of sectional images of the temporal bone were generated from section slices of a female cadaver head. On each sectional image, SOIs (structures of interest) were segmented by carefully defining their contours and filling their areas with certain gray scale values. The processed volume data were then inducted into the 3D Slicer software(developed by the Surgical Planning Lab at Brigham and Women's Hospital and the MIT AI Lab) for resegmentation and generation of a set of tagged images of the SOIs. 3D surface models of SOIs were then reconstructed from these images. Results The temporal bone and structures in the temporal bone, including the tympanic cavity, mastoid cells, sigmoid sinus and internal carotid artery, were successfully reconstructed. The orientation of and spatial relationship among these structures were easily visualized in the reconstructed surface models. Conclusion The 3D Slicer software can be used for 3-dimensional visualization of anatomic structures in the temporal bone, which will greatly facilitate the advance of knowledge and techniques critical for studying and treating disorders involving the temporal bone.

  3. Anatomy of a Bird

    Science.gov (United States)

    2007-12-01

    Using ESO's Very Large Telescope, an international team of astronomers [1] has discovered a stunning rare case of a triple merger of galaxies. This system, which astronomers have dubbed 'The Bird' - albeit it also bears resemblance with a cosmic Tinker Bell - is composed of two massive spiral galaxies and a third irregular galaxy. ESO PR Photo 55a/07 ESO PR Photo 55a/07 The Tinker Bell Triplet The galaxy ESO 593-IG 008, or IRAS 19115-2124, was previously merely known as an interacting pair of galaxies at a distance of 650 million light-years. But surprises were revealed by observations made with the NACO instrument attached to ESO's VLT, which peered through the all-pervasive dust clouds, using adaptive optics to resolve the finest details [2]. Underneath the chaotic appearance of the optical Hubble images - retrieved from the Hubble Space Telescope archive - the NACO images show two unmistakable galaxies, one a barred spiral while the other is more irregular. The surprise lay in the clear identification of a third, clearly separate component, an irregular, yet fairly massive galaxy that seems to be forming stars at a frantic rate. "Examples of mergers of three galaxies of roughly similar sizes are rare," says Petri Väisänen, lead author of the paper reporting the results. "Only the near-infrared VLT observations made it possible to identify the triple merger nature of the system in this case." Because of the resemblance of the system to a bird, the object was dubbed as such, with the 'head' being the third component, and the 'heart' and 'body' making the two major galaxy nuclei in-between of tidal tails, the 'wings'. The latter extend more than 100,000 light-years, or the size of our own Milky Way. ESO PR Photo 55b/07 ESO PR Photo 55b/07 Anatomy of a Bird Subsequent optical spectroscopy with the new Southern African Large Telescope, and archive mid-infrared data from the NASA Spitzer space observatory, confirmed the separate nature of the 'head', but also added

  4. Body-wide anatomy recognition in PET/CT images

    Science.gov (United States)

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

    2015-03-01

    With the rapid growth of positron emission tomography/computed tomography (PET/CT)-based medical applications, body-wide anatomy recognition on whole-body PET/CT images becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem and seldom studied due to unclear anatomy reference frame and low spatial resolution of PET images as well as low contrast and spatial resolution of the associated low-dose CT images. We previously developed an automatic anatomy recognition (AAR) system [15] whose applicability was demonstrated on diagnostic computed tomography (CT) and magnetic resonance (MR) images in different body regions on 35 objects. The aim of the present work is to investigate strategies for adapting the previous AAR system to low-dose CT and PET images toward automated body-wide disease quantification. Our adaptation of the previous AAR methodology to PET/CT images in this paper focuses on 16 objects in three body regions - thorax, abdomen, and pelvis - and consists of the following steps: collecting whole-body PET/CT images from existing patient image databases, delineating all objects in these images, modifying the previous hierarchical models built from diagnostic CT images to account for differences in appearance in low-dose CT and PET images, automatically locating objects in these images following object hierarchy, and evaluating performance. Our preliminary evaluations indicate that the performance of the AAR approach on low-dose CT images achieves object localization accuracy within about 2 voxels, which is comparable to the accuracies achieved on diagnostic contrast-enhanced CT images. Object recognition on low-dose CT images from PET/CT examinations without requiring diagnostic contrast-enhanced CT seems feasible.

  5. 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)

    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

  6. Image segmentation based on competitive learning

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jing; LIU Qun; Baikunth Nath

    2004-01-01

    Image segment is a primary step in image analysis of unexploded ordnance (UXO) detection by ground p enetrating radar (GPR) sensor which is accompanied with a lot of noises and other elements that affect the recognition of real target size. In this paper we bring forward a new theory, that is, we look the weight sets as target vector sets which is the new cues in semi-automatic segmentation to form the final image segmentation. The experiment results show that the measure size of target with our method is much smaller than the size with other methods and close to the real size of target.

  7. 水平集分层分割遥感图像中的建筑物%Automatic building segmentation from remote sensing images using multi-layer level set framework

    Institute of Scientific and Technical Information of China (English)

    郭靖; 江洁; 曹世翔

    2014-01-01

    Towards high resolution remote sensing images, combining with features of buildings, a novel method to extract buildings based on multi-layer level set framework was proposed. Firstly, as far as the impact of shadow and vegetation was concerned, it should be removed on the basis of the separation of gray value thresh and the joint distribution of hue and saturation. Then, an improved C-V level set segmentation algorithm combining with building features of roof′s gray and obvious boundaries was applied to extract building regions of similar gray-scales on each gray layer, and thus all building regions of different gray-scales could be extracted layer by layer, followed by layers of segmented regions integration. Finally, the non-building regions were excluded by using normal areas of buildings and related position between buildings and shadows. The experiment results demonstrate that, compared with the traditional level set methods, this one can detect each single building of gray heterogeneity and buildings of multiple shapes and different gray-scales. Meanwhile, compared to the traditional C-V method, it largely reduces the leakage segmentation ratio by 25% and over-segmentation by 22%.%针对高分辨率遥感图像,结合建筑物特征,提出水平集分层模型分割图像中的建筑物。首先,学习植被样本得到其在HSV空间中色调与饱和度的联合分布函数,利用阴影灰度方差通常小于非阴影区域的特点,将植被和阴影剔除以简化背景利于后续分割。然后,根据灰度级高低将一幅图像看作多层图像层,把建筑物的屋顶灰度特征和边缘特征融合到传统Chan-Vese(C-V)水平集算法中,分割出每层中灰度级相似的建筑物候选区域,从而将不同灰度级建筑物候选区域分层分割出来再整合。最后利用建筑物面积、建筑物与阴影位置关系等先验知识排除误分割,得到最终结果。实验表明:该方法能更好地分割

  8. Efficient segmentation of skin epidermis in whole slide histopathological images.

    Science.gov (United States)

    Xu, Hongming; Mandal, Mrinal

    2015-08-01

    Segmentation of epidermis areas is an important step towards automatic analysis of skin histopathological images. This paper presents a robust technique for epidermis segmentation in whole slide skin histopathological images. The proposed technique first performs a coarse epidermis segmentation using global thresholding and shape analysis. The epidermis thickness is then estimated by a series of line segments perpendicular to the main axis of the initially segmented epidermis mask. If the segmented epidermis mask has a thickness greater than a predefined threshold, the segmentation is suspected to be inaccurate. A second pass of fine segmentation using k-means algorithm is then carried out over these coarsely segmented result to enhance the performance. Experimental results on 64 different skin histopathological images show that the proposed technique provides a superior performance compared to the existing techniques. PMID:26737135

  9. Blood vessel segmentation for neck and head computed tomography angiography

    OpenAIRE

    Hedblom, Anders

    2013-01-01

    This thesis presents tests and discussions evaluating different methods for doing automatic or semi automatic blood vessel segmentation on single CT data volumes of the head and neck. The two approaches being closest to accomplish this are a bone subtracting registration process, and a more advanced region growing combined with morphology.

  10. Natural Language Processing: Word Recognition without Segmentation.

    Science.gov (United States)

    Saeed, Khalid; Dardzinska, Agnieszka

    2001-01-01

    Discussion of automatic recognition of hand and machine-written cursive text using the Arabic alphabet focuses on an algorithm for word recognition. Describes results of testing words for recognition without segmentation and considers the algorithms' use for words of different fonts and for processing whole sentences. (Author/LRW)

  11. Hierarchical Image Segmentation Using Correlation Clustering.

    Science.gov (United States)

    Alush, Amir; Goldberger, Jacob

    2016-06-01

    In this paper, we apply efficient implementations of integer linear programming to the problem of image segmentation. The image is first grouped into superpixels and then local information is extracted for each pair of spatially adjacent superpixels. Given local scores on a map of several hundred superpixels, we use correlation clustering to find the global segmentation that is most consistent with the local evidence. We show that, although correlation clustering is known to be NP-hard, finding the exact global solution is still feasible by breaking the segmentation problem down into subproblems. Each such sub-problem can be viewed as an automatically detected image part. We can further accelerate the process by using the cutting-plane method, which provides a hierarchical structure of the segmentations. The efficiency and improved performance of the proposed method is compared to several state-of-the-art methods and demonstrated on several standard segmentation data sets. PMID:26701901

  12. Segmenting images analytically in shape space

    Science.gov (United States)

    Rathi, Yogesh; Dambreville, Samuel; Niethammer, Marc; Malcolm, James; Levitt, James; Shenton, Martha E.; Tannenbaum, Allen

    2008-03-01

    This paper presents a novel analytic technique to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert a given test volume into a binary map representation, and a novel energy functional is proposed whose minimum can be analytically computed to obtain the desired segmentation in the shape space. We compare the proposed method with the log-likelihood based energy to elucidate some key differences. Our algorithm is applied to the segmentation of brain caudate nucleus and hippocampus from MRI data, which is of interest in the study of schizophrenia and Alzheimer's disease. Our validation (we compute the Hausdorff distance and the DICE coefficient between the automatic segmentation and ground-truth) shows that the proposed algorithm is very fast, requires no initialization and outperforms the log-likelihood based energy.

  13. Evaluation of internal carotid artery segmentation by InsightSNAP

    Science.gov (United States)

    Spangler, Emily L.; Brown, Christopher; Roberts, John A.; Chapman, Brian E.

    2007-03-01

    Quantification of cervical carotid geometry may facilitate improved clinical decision making and scientific discovery. We set out to evaluate the ability of InsightSNAP (ITK-SNAP), an open-source segmentation program for 3D medical images (http://www.itksnap.org, version 1.4), to semi-automatically segment internal carotid arteries. A sample of five individuals (three normal volunteers, and two diseased patients) were imaged with an MR exam consisting of a MOTSA TOF MRA image volume and multiple black blood images acquired with different contrast weightings. Comparisons were made to a manual segmentation created during simultaneous evaluation of the MOTSA image and the various black blood images (typically PD-weighted, T1-weighted, and T2-weighted). These individuals were selected as a training set to determine acceptable parameters for ITK-SNAP's semi-automatic level sets segmentation method. The conclusion from this training set was that the initial thresholding (assigning probabilities to the intensities of image pixels) in the image pre-processing step was most important to obtaining an acceptable segmentation. Unfortunately no consistent trends emerged in how this threshold should be chosen. Figures of percent over- and under-segmentation were computed as a means of comparing the hand segmented and semi-automatically segmented internal carotids. Overall the under-segmentation by ITK-SNAP (voxels included in the manual segmentation but not in the semiautomated segmentation) was 10.94% +/- 6.35% while the over-segmentation (voxels excluded in the manual segmentation but included in the semi-automated segmentation) was 8.16% +/- 4.40% defined by reference to the total number of voxels included in the manual segmentation.

  14. Automated image segmentation of haematoxylin and eosin stained skeletal muscle cross-sections

    DEFF Research Database (Denmark)

    Liu, F; Mackey, AL; Srikuea, R;

    2013-01-01

    . This procedure is labour-intensive and time-consuming. In this paper, we have developed and validated an automatic image segmentation algorithm that is not only efficient but also accurate. Our proposed automatic segmentation algorithm for haematoxylin and eosin stained skeletal muscle cross-sections consists...

  15. Soul Anatomy: A virtual cadaver

    OpenAIRE

    Moaz Bambi

    2014-01-01

    In the traditional science of medicine and medical education, teaching human anatomy in the class has always been done using human cadavers. Not only does this violate human sanctity, but according to our research, it is not adequate to provide students with the alleged educational value that it is supposed to deliver. It is very cumbersome to organise all the aspects of cadaver care. Cadavers are also very limited when it comes to controlling their structures and any benefit is almost comple...

  16. Ecological anatomy of ferns fronds

    Directory of Open Access Journals (Sweden)

    Nina M. Derzhavina

    2014-04-01

    Full Text Available Structural types of frond anatomy are distinguished on the basis of investigation of 30 species of homosporous ferns and with regard for literature: hydromorphic, hygromorphic, mesomorphic, subxeromorphic, and subsucculent (cryptic succulent. Following frond traits are of highest adaptive value: their area and thickness, type of mesophyll, dry weight of an area unit – specific superficial density, cellular volume, and number of cells per unit of frond area.

  17. 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…

  18. Medical discourse in pathological anatomy.

    Science.gov (United States)

    Moskalenko, R; Tatsenko, N; Romanyuk, A; Perelomova, O; Moskalenko, Yu

    2012-05-01

    The paper is devoted to the peculiarities of medical discourse in pathological anatomy as coherent speech and as a linguistic correlate of medical practice taking into account the analysis of its strategies and tactics. The purpose of the paper is to provide a multifaceted analysis of the speech strategies and tactics of pathological anatomy discourse and ways of their implementation. The main strategies of medical discourse in pathological anatomy are an anticipating strategy, a diagnosing strategy and an explaining one. The supporting strategies are pragmatic, conversational and a rhetorical one. The pragmatic strategy is implemented through contact establishing tactics, the conversational one - with the help of control tactics, the rhetorical one - with the help of attention correction tactics. The above mentioned tactics and strategies are used in the distinguishing of major, closely interrelated strategies: "the contact strategy" (to establish contact with a patient's relatives - phatic replicas of greeting and addressing) and "the strategy of explanation" (used in the practice of a pathologist for a detailed explanation of the reasons of a patient's death). The ethic aspect of speech conduct of a doctor-pathologist is analyzed. PMID:22870841

  19. Anatomy of the infant head

    International Nuclear Information System (INIS)

    This text is mainly an atlas of illustration representing the dissection of the head and upper neck of the infant. It was prepared by the author over a 20-year period. The commentary compares the anatomy of the near-term infant with that of a younger fetus, child, and adult. As the author indicates, the dearth of anatomic information about postnatal anatomic changes represents a considerable handicap to those imaging infants. In part 1 of the book, anatomy is related to physiologic performance involving the pharynx, larynx, and mouth. Sequential topics involve the regional anatomy of the head (excluding the brain), the skeleton of the cranium, the nose, orbit, mouth, larynx, pharynx, and ear. To facilitate use of this text as a reference, the illustrations and text on individual organs are considered separately (i.e., the nose, the orbit, the eye, the mouth, the larynx, the pharynx, and the ear). Each part concerned with a separate organ includes materials from the regional illustrations contained in part 2 and from the skeleton, which is treated in part 3. Also included in a summary of the embryologic and fetal development of the organ

  20. Video-assisted segmentation of speech and audio track

    Science.gov (United States)

    Pandit, Medha; Yusoff, Yusseri; Kittler, Josef; Christmas, William J.; Chilton, E. H. S.

    1999-08-01

    Video database research is commonly concerned with the storage and retrieval of visual information invovling sequence segmentation, shot representation and video clip retrieval. In multimedia applications, video sequences are usually accompanied by a sound track. The sound track contains potential cues to aid shot segmentation such as different speakers, background music, singing and distinctive sounds. These different acoustic categories can be modeled to allow for an effective database retrieval. In this paper, we address the problem of automatic segmentation of audio track of multimedia material. This audio based segmentation can be combined with video scene shot detection in order to achieve partitioning of the multimedia material into semantically significant segments.

  1. Design of Automatic Recognition of Cucumber Disease Image

    OpenAIRE

    Peng Guo; Tonghai Liu; Naixiang Li

    2014-01-01

    An automatic recognition method for cucumber disease images is presented. Threshold for image segmentation was generated with 2 dimensional maximum entropy principle and optimized with differential evolution algorithm. With threshold values generated, we segmented cucumber disease images and picked up the lesion with maximum area from segmentation results as representative lesion. Then we analyzed representative lesions of disease images and extracted theirs color features and texture feature...

  2. Automatic analysis of multiparty meetings

    Indian Academy of Sciences (India)

    Steve Renals

    2011-10-01

    This paper is about the recognition and interpretation of multiparty meetings captured as audio, video and other signals. This is a challenging task since the meetings consist of spontaneous and conversational interactions between a number of participants: it is a multimodal, multiparty, multistream problem. We discuss the capture and annotation of the Augmented Multiparty Interaction (AMI) meeting corpus, the development of a meeting speech recognition system, and systems for the automatic segmentation, summarization and social processing of meetings, together with some example applications based on these systems.

  3. 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.

  4. Track segment synthesis method for NTA film

    International Nuclear Information System (INIS)

    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)

  5. A new framework for interactive segmentation of point clouds

    OpenAIRE

    Liu, K.; J. Boehm

    2014-01-01

    Point cloud segmentation is a fundamental problem in point processing. Segmenting a point cloud fully automatically is very challenging due to the property of point cloud as well as different requirements of distinct users. In this paper, an interactive segmentation method for point clouds is proposed. Only two strokes need to be drawn intuitively to indicate the target object and the background respectively. The draw strokes are sparse and don't necessarily cover the whole object. Given the ...

  6. The White Matter Query Language: A Novel Approach for Describing Human White Matter Anatomy

    OpenAIRE

    Wassermann, Demian; Makris, Nikos; Rathi, Yogesh; Shenton, Martha; Kikinis, Ron; Kubicki, Marek; Westin, Carl-Fredrik

    2015-01-01

    International audience We have developed a novel method to describe human white matter anatomy using an approach that is both intuitive and simple to use, and which automatically extracts white matter tracts from diffusion MRI vol¬umes. Further, our method simplifies the quantification and statistical analysis of white matter tracts on large diffusion MRI databases. This work reflects the careful syntactical definition of major white matter fiber tracts in the human brain based on a neuroa...

  7. Accessory left gastric artery: angiographic anatomy

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Kang Soo; Lim, Hyung Guhn; Kim, Hong Soo; Jeon, Doo Sung [Presbyterian Medical Center, Chunju (Korea, Republic of); Chung, Jin Wook; Park, Jae Hyung [College of Medicine and the Institute of Radiation Medicine, Seoul National University, Seoul (Korea, Republic of); Song, Soon Young [Myongji Hospital, College of Medicine, Kwandong University, Seoul (Korea, Republic of)

    2000-09-01

    To evaluate the angiographic anatomy of the accessory left gastric artery (accLGA). We evaluated the angiographic findings of the accLGA in 50 patients (Angiostar; Siemens, Erlangen, Germany). Performing celiac and selective angiography in 50 and 34 patients, respectively. By means of celiac angiography, (1) site of origin, (2) anatomical course, (3) diameter, (4) degree of tortuosity, and (5) distal tapering were evaluated, while selective angiography was used to determine (1) arterial branching, (2) area of blood supply, and (3) patterns of gastric wall stain. Celiac angiography showed that the accLGA arose from the left hepatic artery (LHA) in 45 cases (90%) and from the proper hepatic artery in five (10%). If the accLGA arose from the LHA, its origin entirely depended on the branching pattern of the latter. It always arose from the lateral branch of the LHA furthest to the left and uppermost, and proximal to its umbilical point. The most common anatomical course of the accLGA, seen in 27 cases (54%), was between the S2 and S3 segmental branch. The diameter and degree of tortuosity of the accLGA were similar to those of adjacent intrahepatic branches in 21 (42%) and 33 cases (66%), respectively. The degree of tapering was less than that of adjacent intrahepatic vessel in 28 (56%). Selective angiography demonstrated esophageal branching of the acc LGA in 27 cases (79%), inferior phrenic arterial branching in three (9%), a mediastinal branch in one (3%), and hypervascularity of the lung in one (3%). In 15 cases (44%), bifurcation of the accLGA was recognized. The vascular territory of the accLGA was the gastric fundus together with the distal esophagus in 21 cases (62%), mainly the gastric fundus in six (18%), and mainly the distal esophagus in four (12%). The pattern of gastric mucosal stain was curvilinear wall in 31 cases (91%) and nodular in three (9%). A knowledge of the angiographic anatomy of the accLGA facilitates accurate recognition of this artery on

  8. Anatomy Live : Performance and the Operating Theatre

    OpenAIRE

    2008-01-01

    Gross anatomy, the study of anatomical structures that can be seen by unassisted vision, has long been a subject of fascination for artists. For most modern viewers, however, the anatomy lesson hardly seems the proper breeding ground for the hybrid workings of art and theory. We forget that, in its early stages, anatomy pursued the highly theatrical spirit of Renaissance science, as painters such as Rembrandt and Da Vinci and medical instructors shared audiences devoted to the workings of the...

  9. Brachial Plexus Anatomy: Normal and Variant

    OpenAIRE

    Orebaugh, Steven L.; Williams, Brian A.

    2009-01-01

    Effective brachial plexus blockade requires a thorough understanding of the anatomy of the plexus, as well as an appreciation of anatomic variations that may occur. This review summarizes relevant anatomy of the plexus, along with variations and anomalies that may affect nerve blocks conducted at these levels. The Medline, Cochrane Library, and PubMed electronic databases were searched in order to compile reports related to the anatomy of the brachial plexus using the following free terms: "b...

  10. A contrario line segment detection

    CERN Document Server

    von Gioi, Rafael Grompone

    2014-01-01

    The reliable detection of low-level image structures is an old and still challenging problem in computer vision. This?book leads a detailed tour through the LSD algorithm, a line segment detector designed to be fully automatic. Based on the a contrario framework, the algorithm works efficiently without the need of any parameter tuning. The design criteria are thoroughly explained and the algorithm's good and bad results are illustrated on real and synthetic images. The issues involved, as well as the strategies used, are common to many geometrical structure detection problems and some possible

  11. Polyp Segmentation in NBI Colonoscopy

    Science.gov (United States)

    Gross, Sebastian; Kennel, Manuel; Stehle, Thomas; Wulff, Jonas; Tischendorf, Jens; Trautwein, Christian; Aach, Til

    Endoscopic screening of the colon (colonoscopy) is performed to prevent cancer and to support therapy. During intervention colon polyps are located, inspected and, if need be, removed by the investigator. We propose a segmentation algorithm as a part of an automatic polyp classification system for colonoscopic Narrow-Band images. Our approach includes multi-scale filtering for noise reduction, suppression of small blood vessels, and enhancement of major edges. Results of the subsequent edge detection are compared to a set of elliptic templates and evaluated. We validated our algorithm on our polyp database with images acquired during routine colonoscopic examinations. The presented results show the reliable segmentation performance of our method and its robustness to image variations.

  12. ZBrush Digital Sculpting Human Anatomy

    CERN Document Server

    Spencer, Scott

    2010-01-01

    Taking into account that many of today?s digital artists?particularly 3D character animators?lack foundational artistic instruction, this book teaches anatomy in a coherent and succinct style. A clear writing style explains how to sculpt an accurate human figure, starting with the skeleton and working out to muscle, fat, and skin. Insightful explanations enable you to quickly and easily create and design characters that can be used in film, game, or print, and allows you to gain a strong understanding of the foundational artistic concepts.

  13. High precision anatomy for MEG

    OpenAIRE

    Troebinger, Luzia; López, José David; Lutti, Antoine; Bradbury, David; Bestmann, Sven; Barnes, Gareth

    2013-01-01

    Precise MEG estimates of neuronal current flow are undermined by uncertain knowledge of the head location with respect to the MEG sensors. This is either due to head movements within the scanning session or systematic errors in co-registration to anatomy. Here we show how such errors can be minimized using subject-specific head-casts produced using 3D printing technology. The casts fit the scalp of the subject internally and the inside of the MEG dewar externally, reducing within session and ...

  14. Gross anatomy of network security

    Science.gov (United States)

    Siu, Thomas J.

    2002-01-01

    Information security involves many branches of effort, including information assurance, host level security, physical security, and network security. Computer network security methods and implementations are given a top-down description to permit a medically focused audience to anchor this information to their daily practice. The depth of detail of network functionality and security measures, like that of the study of human anatomy, can be highly involved. Presented at the level of major gross anatomical systems, this paper will focus on network backbone implementation and perimeter defenses, then diagnostic tools, and finally the user practices (the human element). Physical security measures, though significant, have been defined as beyond the scope of this presentation.

  15. Contour-Driven Atlas-Based Segmentation.

    Science.gov (United States)

    Wachinger, Christian; Fritscher, Karl; Sharp, Greg; Golland, Polina

    2015-12-01

    We propose new methods for automatic segmentation of images based on an atlas of manually labeled scans and contours in the image. First, we introduce a Bayesian framework for creating initial label maps from manually annotated training images. Within this framework, we model various registration- and patch-based segmentation techniques by changing the deformation field prior. Second, we perform contour-driven regression on the created label maps to refine the segmentation. Image contours and image parcellations give rise to non-stationary kernel functions that model the relationship between image locations. Setting the kernel to the covariance function in a Gaussian process establishes a distribution over label maps supported by image structures. Maximum a posteriori estimation of the distribution over label maps conditioned on the outcome of the atlas-based segmentation yields the refined segmentation. We evaluate the segmentation in two clinical applications: the segmentation of parotid glands in head and neck CT scans and the segmentation of the left atrium in cardiac MR angiography images. PMID:26068202

  16. Hyperspectral image segmentation using active contours

    Science.gov (United States)

    Lee, Cheolha P.; Snyder, Wesley E.

    2004-08-01

    Multispectral or hyperspectral image processing has been studied as a possible approach to automatic target recognition (ATR). Hundreds of spectral bands may provide high data redundancy, compensating the low contrast in medium wavelength infrared (MWIR) and long wavelength infrared (LWIR) images. Thus, the combination of spectral (image intensity) and spatial (geometric feature) information analysis could produce a substantial improvement. Active contours provide segments with continuous boundaries, while edge detectors based on local filtering often provide discontinuous boundaries. The segmentation by active contours depends on geometric feature of the object as well as image intensity. However, the application of active contours to multispectral images has been limited to the cases of simply textured images with low number of frames. This paper presents a supervised active contour model, which is applicable to vector-valued images with non-homogeneous regions and high number of frames. In the training stage, histogram models of target classes are estimated from sample vector-pixels. In the test stage, contours are evolved based on two different metrics: the histogram models of the corresponding segments and the histogram models estimated from sample target vector-pixels. The proposed segmentation method integrates segmentation and model-based pattern matching using supervised segmentation and multi-phase active contour model, while traditional methods apply pattern matching only after the segmentation. The proposed algorithm is implemented with both synthetic and real multispectral images, and shows desirable segmentation and classification results even in images with non-homogeneous regions.

  17. Real-time automatic registration in optical surgical navigation

    Science.gov (United States)

    Lin, Qinyong; Yang, Rongqian; Cai, Ken; Si, Xuan; Chen, Xiuwen; Wu, Xiaoming

    2016-05-01

    An image-guided surgical navigation system requires the improvement of the patient-to-image registration time to enhance the convenience of the registration procedure. A critical step in achieving this aim is performing a fully automatic patient-to-image registration. This study reports on a design of custom fiducial markers and the performance of a real-time automatic patient-to-image registration method using these markers on the basis of an optical tracking system for rigid anatomy. The custom fiducial markers are designed to be automatically localized in both patient and image spaces. An automatic localization method is performed by registering a point cloud sampled from the three dimensional (3D) pedestal model surface of a fiducial marker to each pedestal of fiducial markers searched in image space. A head phantom is constructed to estimate the performance of the real-time automatic registration method under four fiducial configurations. The head phantom experimental results demonstrate that the real-time automatic registration method is more convenient, rapid, and accurate than the manual method. The time required for each registration is approximately 0.1 s. The automatic localization method precisely localizes the fiducial markers in image space. The averaged target registration error for the four configurations is approximately 0.7 mm. The automatic registration performance is independent of the positions relative to the tracking system and the movement of the patient during the operation.

  18. A comprehensive segmentation analysis of crude oil market based on time irreversibility

    Science.gov (United States)

    Xia, Jianan; Shang, Pengjian; Lu, Dan; Yin, Yi

    2016-05-01

    In this paper, we perform a comprehensive entropic segmentation analysis of crude oil future prices from 1983 to 2014 which used the Jensen-Shannon divergence as the statistical distance between segments, and analyze the results from original series S and series begin at 1986 (marked as S∗) to find common segments which have same boundaries. Then we apply time irreversibility analysis of each segment to divide all segments into two groups according to their asymmetry degree. Based on the temporal distribution of the common segments and high asymmetry segments, we figure out that these two types of segments appear alternately and do not overlap basically in daily group, while the common portions are also high asymmetry segments in weekly group. In addition, the temporal distribution of the common segments is fairly close to the time of crises, wars or other events, because the hit from severe events to oil price makes these common segments quite different from their adjacent segments. The common segments can be confirmed in daily group series, or weekly group series due to the large divergence between common segments and their neighbors. While the identification of high asymmetry segments is helpful to know the segments which are not affected badly by the events and can recover to steady states automatically. Finally, we rearrange the segments by merging the connected common segments or high asymmetry segments into a segment, and conjoin the connected segments which are neither common nor high asymmetric.

  19. THE VEGETATIVE ANATOMY OF KOSTERMANSIA MALAYANA SOEGENG

    Directory of Open Access Journals (Sweden)

    P. Baas

    2014-01-01

    Full Text Available The anatomy of leaves, twigs, wood and seedling of Kostermansia Soegeng is described. A comparison with some species of Coelostegia and Durio indicates the close affinities between the three taxa, but also shows some differences in leaf anatomy, probably valuable for diagnostic purposes. The stomata in Kostermansia show a very remarkable arrangement in circles around the insertions of the scales.

  20. Anatomy of a Cancer Treatment Scam

    Medline Plus

    Full Text Available ... Anatomy of a Cancer Treatment Scam Anatomy of a Cancer Treatment Scam January 19, 2012 Curious about a product that claims to treat or cure cancer? ... Center Competition Guidance I Would Like To... Submit a Consumer Complaint to the FTC Apply for a ...