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Sample records for automatic anatomy segmentation

  1. 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...

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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...

  7. Anatomy-aware measurement of segmentation accuracy

    Science.gov (United States)

    Tizhoosh, H. R.; Othman, A. A.

    2016-03-01

    Quantifying the accuracy of segmentation and manual delineation of organs, tissue types and tumors in medical images is a necessary measurement that suffers from multiple problems. One major shortcoming of all accuracy measures is that they neglect the anatomical significance or relevance of different zones within a given segment. Hence, existing accuracy metrics measure the overlap of a given segment with a ground-truth without any anatomical discrimination inside the segment. For instance, if we understand the rectal wall or urethral sphincter as anatomical zones, then current accuracy measures ignore their significance when they are applied to assess the quality of the prostate gland segments. In this paper, we propose an anatomy-aware measurement scheme for segmentation accuracy of medical images. The idea is to create a "master gold" based on a consensus shape containing not just the outline of the segment but also the outlines of the internal zones if existent or relevant. To apply this new approach to accuracy measurement, we introduce the anatomy-aware extensions of both Dice coefficient and Jaccard index and investigate their effect using 500 synthetic prostate ultrasound images with 20 different segments for each image. We show that through anatomy-sensitive calculation of segmentation accuracy, namely by considering relevant anatomical zones, not only the measurement of individual users can change but also the ranking of users' segmentation skills may require reordering.

  8. 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 ...

  9. Automatic segmentation of diatom images for classification

    NARCIS (Netherlands)

    Jalba, Andrei C.; Wilkinson, Michael H.F.; Roerdink, Jos B.T.M.

    2004-01-01

    A general framework for automatic segmentation of diatom images is presented. This segmentation is a critical first step in contour-based methods for automatic identification of diatoms by computerized image analysis. We review existing results, adapt popular segmentation methods to this difficult p

  10. 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...

  11. 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.

  12. Automatic Airway Deletion in Pulmonary Segmentation

    Institute of Scientific and Technical Information of China (English)

    WANG Ping; ZHUANG Tian-ge

    2005-01-01

    A method of removing the airway from pulmonary segmentation image was proposed. This method firstly segments the image into several separate regions based on the optimum threshold and morphological operator,and then each region is labeled and noted with its mean grayscale. Therefore, most of the non-lung regions can be removed according to the tissue's Hounsfield units (HU) and the imaging modality. Finally, the airway region is recognized and deleted automatically through using the priori information of its HU and size. This proposed method is tested using several clinical images, yielding satisfying results.

  13. Automatic segmentation of anterior segment optical coherence tomography images

    Science.gov (United States)

    Williams, Dominic; Zheng, Yalin; Bao, Fangjun; Elsheikh, Ahmed

    2013-05-01

    Optical coherence tomography (OCT) images can provide quantitative measurements of the eye's entire anterior segment. A new technique founded on a newly proposed level set-based shape prior segmentation model has been developed for automatic segmentation of the cornea's anterior and posterior boundaries. This technique comprises three major steps: removal of regions containing irrelevant structures and artifacts, estimation of the cornea's location using a thresholding technique, and application of the new level set-based shape prior segmentation model to improve segmentation. The performance of our technique is compared to previously developed methods for analysis of the cornea in 33 OCT images of normal eyes, whereby manual annotations are used as a reference standard. The new technique achieves much improved concordance than previous methods, with a mean Dice's similarity coefficient of >0.92. This demonstrates the technique's potential to provide accurate and reliable measurements of the anterior segment geometry, which is important for many applications, including the construction of representative numerical simulations of the eye's mechanical behavior.

  14. 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

  15. Automatic segmentation of bladder in CT images

    Institute of Scientific and Technical Information of China (English)

    Feng SHI; Jie YANG; Yue-min ZHU

    2009-01-01

    Segmentation of the bladder in computerized tomography (CT) images is an important step in radiation therapy planning of prostate cancer. We present a new segmentation scheme to automatically delineate the bladder contour in CT images with three major steps. First, we use the mean shift algorithm to obtain a clustered image containing the rough contour of the bladder, which is then extracted in the second step by applying a region-growing algorithm with the initial seed point selected from a line-by-line scanning process. The third step is to refine the bladder contour more accurately using the rolling-ball algorithm. These steps are then extended to segment the bladder volume in a slice-by-slice manner. The obtained results were compared to manual segmentation by radiation oncologists. The average values of sensitivity, specificity, positive predictive value, negative predictive value, and Hausdorff distance are 86.5%, 96.3%, 90.5%, 96.5%, and 2.8 pixels, respectively. The results show that the bladder can be accurately segmented.

  16. 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.

  17. 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.

  18. Automatic anatomy recognition on CT images with pathology

    Science.gov (United States)

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

    2016-03-01

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

  19. 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

  20. 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.

  1. 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.

  2. Automatic Image Segmentation based on MRF-MAP

    CERN Document Server

    Qiyang, Zhao

    2012-01-01

    Solving the Maximum a Posteriori on Markov Random Field, MRF-MAP, is a prevailing method in recent interactive image segmentation tools. Although mathematically explicit in its computational targets, and impressive for the segmentation quality, MRF-MAP is hard to accomplish without the interactive information from users. So it is rarely adopted in the automatic style up to today. In this paper, we present an automatic image segmentation algorithm, NegCut, based on the approximation to MRF-MAP. First we prove MRF-MAP is NP-hard when the probabilistic models are unknown, and then present an approximation function in the form of minimum cuts on graphs with negative weights. Finally, the binary segmentation is taken from the largest eigenvector of the target matrix, with a tuned version of the Lanczos eigensolver. It is shown competitive at the segmentation quality in our experiments.

  3. Edge Segment-Based Automatic Video Surveillance

    Directory of Open Access Journals (Sweden)

    Oksam Chae

    2007-12-01

    Full Text Available This paper presents a moving-object segmentation algorithm using edge information as segment. The proposed method is developed to address challenges due to variations in ambient lighting and background contents. We investigated the suitability of the proposed algorithm in comparison with the traditional-intensity-based as well as edge-pixel-based detection methods. In our method, edges are extracted from video frames and are represented as segments using an efficiently designed edge class. This representation helps to obtain the geometric information of edge in the case of edge matching and moving-object segmentation; and facilitates incorporating knowledge into edge segment during background modeling and motion tracking. An efficient approach for background initialization and robust method of edge matching is presented, to effectively reduce the risk of false alarm due to illumination change and camera motion while maintaining the high sensitivity to the presence of moving object. Detected moving edges are utilized along with watershed algorithm for extracting video object plane (VOP with more accurate boundary. Experiment results with real image sequence reflect that the proposed method is suitable for automated video surveillance applications in various monitoring systems.

  4. 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.

  5. 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....... The upper hair region then allows us to learn the hair appearance parameters (color and texture) for the image considered. Finally, those parameters drive a pixel-wise segmentation technique that yields the desired (complete) hair region. We demonstrate the applicability of our method on several real images....

  6. 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.

  7. Automatic scale selection for medical image segmentation

    Science.gov (United States)

    Bayram, Ersin; Wyatt, Christopher L.; Ge, Yaorong

    2001-07-01

    The scale of interesting structures in medical images is space variant because of partial volume effects, spatial dependence of resolution in many imaging modalities, and differences in tissue properties. Existing segmentation methods either apply a single scale to the entire image or try fine-to-coarse/coarse-to-fine tracking of structures over multiple scales. While single scale approaches fail to fully recover the perceptually important structures, multi-scale methods have problems in providing reliable means to select proper scales and integrating information over multiple scales. A recent approach proposed by Elder and Zucker addresses the scale selection problem by computing a minimal reliable scale for each image pixel. The basic premise of this approach is that, while the scale of structures within an image vary spatially, the imaging system is fixed. Hence, sensor noise statistics can be calculated. Based on a model of edges to be detected, and operators to be used for detection, one can locally compute a unique minimal reliable scale at which the likelihood of error due to sensor noise is less than or equal to a predetermined threshold. In this paper, we improve the segmentation method based on the minimal reliable scale selection and evaluate its effectiveness with both simulated and actual medical data.

  8. 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

  9. 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

  10. 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

  11. Automatic labeling and segmentation of vertebrae in CT images

    Science.gov (United States)

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

    2014-03-01

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

  12. 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

  13. 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.

  14. Automatic Compartment Modelling and Segmentation for Dynamical Renal Scintigraphies

    DEFF Research Database (Denmark)

    Ståhl, Daniel; Åström, Kalle; Overgaard, Niels Christian;

    2011-01-01

    for segmentation of pixels into physical compartments, extract their corresponding time-activity curves and then compute the parameters that are relevant for medical assessment. In this paper we present a fully automatic system that incorporates spatial smoothing constraints, compartment modelling and positivity......Time-resolved medical data has important applications in a large variety of medical applications. In this paper we study automatic analysis of dynamical renal scintigraphies. The traditional analysis pipeline for dynamical renal scintigraphies is to use manual or semiautomatic methods...

  15. 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

  16. 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.

  17. 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...

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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...

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. Automatic Segmentation of Abdominal Adipose Tissue in MRI

    DEFF Research Database (Denmark)

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

    2011-01-01

    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...... 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 between our...

  10. Automatic character detection and segmentation in natural scene images

    Institute of Scientific and Technical Information of China (English)

    ZHU Kai-hua; QI Fei-hu; JIANG Ren-jie; XU Li

    2007-01-01

    We present a robust connected-component (CC) based method for automatic detection and segmentation of text in real-scene images. This technique can be applied in robot vision, sign recognition, meeting processing and video indexing. First, a Non-Linear Niblack method (NLNiblack) is proposed to decompose the image into candidate CCs. Then, all these CCs are fed into a cascade of classifiers trained by Adaboost algorithm. Each classifier in the cascade responds to one feature of the CC. Proposed here are 12 novel features which are insensitive to noise, scale, text orientation and text language. The classifier cascade allows non-text CCs of the image to be rapidly discarded while more computation is spent on promising text-like CCs. The CCs passing through the cascade are considered as text components and are used to form the segmentation result. A prototype system was built,with experimental results proving the effectiveness and efficiency of the proposed method.

  11. A Geometric Approach For Fully Automatic Chromosome Segmentation

    CERN Document Server

    Minaee, Shervin; Khalaj, Babak Hossein

    2011-01-01

    Chromosome segmentation is a fundamental task in human chromosome analysis. Most of previous methods for separation between touching chromosomes require human intervention. In this paper, a geometry based method is used for automatic chromosome segmentation. This method can be divided into two phases. In the first phase, chromosome clusters are detected using three geometric criteria and in the second phase chromosome clusters are separated using a proper cut line. However, most earlier methods do not work well with chromosome clusters that contain more than two chromosomes. Our method, on the other hand, has a high efficiency in separation of chromosome clusters in such scenarios. Another advantage of the proposed method is that it can easily apply to any type of images such as binary images. This is due to the fact that the proposed scheme uses the geometric features of chromosomes which are independent of the type of images. The performance of the proposed scheme is demonstrated on a database containing to...

  12. 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.

  13. Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding.

    Science.gov (United States)

    Jati, Arindam; Singh, Garima; Mukherjee, Rashmi; Ghosh, Madhumala; Konar, Amit; Chakraborty, Chandan; Nagar, Atulya K

    2014-03-01

    The paper proposes a robust approach to automatic segmentation of leukocyte's nucleus from microscopic blood smear images under normal as well as noisy environment by employing a new exponential intuitionistic fuzzy divergence based thresholding technique. The algorithm minimizes the divergence between the actual image and the ideally thresholded image to search for the final threshold. A new divergence formula based on exponential intuitionistic fuzzy entropy has been proposed. Further, to increase its noise handling capacity, a neighborhood-based membership function for the image pixels has been designed. The proposed scheme has been applied on 110 normal and 54 leukemia (chronic myelogenous leukemia) affected blood samples. The nucleus segmentation results have been validated by three expert hematologists. The algorithm achieves an average segmentation accuracy of 98.52% in noise-free environment. It beats the competitor algorithms in terms of several other metrics. The proposed scheme with neighborhood based membership function outperforms the competitor algorithms in terms of segmentation accuracy under noisy environment. It achieves 93.90% and 94.93% accuracies for Speckle and Gaussian noises, respectively. The average area under the ROC curves comes out to be 0.9514 in noisy conditions, which proves the robustness of the proposed algorithm.

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

    Science.gov (United States)

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

    2013-12-01

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

  15. 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

  16. 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.

  17. Automatic segmentation of brain images: selection of region extraction methods

    Science.gov (United States)

    Gong, Leiguang; Kulikowski, Casimir A.; Mezrich, Reuben S.

    1991-07-01

    In automatically analyzing brain structures from a MR image, the choice of low level region extraction methods depends on the characteristics of both the target object and the surrounding anatomical structures in the image. The authors have experimented with local thresholding, global thresholding, and other techniques, using various types of MR images for extracting the major brian landmarks and different types of lesions. This paper describes specifically a local- binary thresholding method and a new global-multiple thresholding technique developed for MR image segmentation and analysis. The initial testing results on their segmentation performance are presented, followed by a comparative analysis of the two methods and their ability to extract different types of normal and abnormal brain structures -- the brain matter itself, tumors, regions of edema surrounding lesions, multiple sclerosis lesions, and the ventricles of the brain. The analysis and experimental results show that the global multiple thresholding techniques are more than adequate for extracting regions that correspond to the major brian structures, while local binary thresholding is helpful for more accurate delineation of small lesions such as those produced by MS, and for the precise refinement of lesion boundaries. The detection of other landmarks, such as the interhemispheric fissure, may require other techniques, such as line-fitting. These experiments have led to the formulation of a set of generic computer-based rules for selecting the appropriate segmentation packages for particular types of problems, based on which further development of an innovative knowledge- based, goal directed biomedical image analysis framework is being made. The system will carry out the selection automatically for a given specific analysis task.

  18. 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

  19. Automatic extraction of mandibular bone geometry for anatomy-based synthetization of radiographs.

    Science.gov (United States)

    Antila, Kari; Lilja, Mikko; Kalke, Martti; Lötjönen, Jyrki

    2008-01-01

    We present an automatic method for segmenting Cone-Beam Computerized Tomography (CBCT) volumes and synthetizing orthopantomographic, anatomically aligned views of the mandibular bone. The model-based segmentation method was developed having the characteristics of dental CBCT, severe metal artefacts, relatively high noise and high variability of the mandibular bone shape, in mind. First, we applied the segmentation method to delineate the bone. Second, we aligned a model resembling the geometry of orthopantomographic imaging according to the segmented surface. Third, we estimated the tooth orientations based on the local shape of the segmented surface. These results were used in determining the geometry of the synthetized radiograph. Segmentation was done with excellent results: with 14 samples we reached 0.57+/-0.16 mm mean distance from hand drawn reference. The estimation of tooth orientations was accurate with error of 0.65+/-8.0 degrees. An example of these results used in synthetizing panoramic radiographs is presented.

  20. A dorsolateral prefrontal cortex semi-automatic segmenter

    Science.gov (United States)

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

    2006-03-01

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

  1. 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...

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

    Directory of Open Access Journals (Sweden)

    Hongda Mao

    2013-01-01

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

  3. 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.

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

    Science.gov (United States)

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

    2012-06-01

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

  5. 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.

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

    NARCIS (Netherlands)

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

    2015-01-01

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

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

    NARCIS (Netherlands)

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

    2016-01-01

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

  8. 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.

  9. 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.)

  10. Fully automatic segmentation of complex organ systems: example of trachea, esophagus and heart segmentation in CT images

    Science.gov (United States)

    Meyer, Carsten; Peters, Jochen; Weese, Jürgen

    2011-03-01

    Automatic segmentation is a prerequisite to efficiently analyze the large amount of image data produced by modern imaging modalities. Many algorithms exist to segment individual organs or organ systems. However, new clinical applications and the progress in imaging technology will require the segmentation of more and more complex organ systems composed of a number of substructures, e.g., the heart, the trachea, and the esophagus. The goal of this work is to demonstrate that such complex organ systems can be successfully segmented by integrating the individual organs into a general model-based segmentation framework, without tailoring the core adaptation engine to the individual organs. As an example, we address the fully automatic segmentation of the trachea (around its main bifurcation, including the proximal part of the two main bronchi) and the esophagus in addition to the heart with all chambers and attached major vessels. To this end, we integrate the trachea and the esophagus into a model-based cardiac segmentation framework. Specifically, in a first parametric adaptation step of the segmentation workflow, the trachea and the esophagus share global model transformations with adjacent heart structures. This allows to obtain a robust, approximate segmentation for the trachea even if it is only partly inside the field-of-view, and for the esophagus in spite of limited contrast. The segmentation is then refined in a subsequent deformable adaptation step. We obtained a mean segmentation error of about 0.6mm for the trachea and 2.3mm for the esophagus on a database of 23 volumetric cardiovascular CT images. Furthermore, we show by quantitative evaluation that our integrated framework outperforms individual esophagus segmentation, and individual trachea segmentation if the trachea is only partly inside the field-of-view.

  11. 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.

  12. Quantitative evaluation of six graph based semi-automatic liver tumor segmentation techniques using multiple sets of reference segmentation

    Science.gov (United States)

    Su, Zihua; Deng, Xiang; Chefd'hotel, Christophe; Grady, Leo; Fei, Jun; Zheng, Dong; Chen, Ning; Xu, Xiaodong

    2011-03-01

    Graph based semi-automatic tumor segmentation techniques have demonstrated great potential in efficiently measuring tumor size from CT images. Comprehensive and quantitative validation is essential to ensure the efficacy of graph based tumor segmentation techniques in clinical applications. In this paper, we present a quantitative validation study of six graph based 3D semi-automatic tumor segmentation techniques using multiple sets of expert segmentation. The six segmentation techniques are Random Walk (RW), Watershed based Random Walk (WRW), LazySnapping (LS), GraphCut (GHC), GrabCut (GBC), and GrowCut (GWC) algorithms. The validation was conducted using clinical CT data of 29 liver tumors and four sets of expert segmentation. The performance of the six algorithms was evaluated using accuracy and reproducibility. The accuracy was quantified using Normalized Probabilistic Rand Index (NPRI), which takes into account of the variation of multiple expert segmentations. The reproducibility was evaluated by the change of the NPRI from 10 different sets of user initializations. Our results from the accuracy test demonstrated that RW (0.63) showed the highest NPRI value, compared to WRW (0.61), GWC (0.60), GHC (0.58), LS (0.57), GBC (0.27). The results from the reproducibility test indicated that GBC is more sensitive to user initialization than the other five algorithms. Compared to previous tumor segmentation validation studies using one set of reference segmentation, our evaluation methods use multiple sets of expert segmentation to address the inter or intra rater variability issue in ground truth annotation, and provide quantitative assessment for comparing different segmentation algorithms.

  13. 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.

  14. 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

  15. 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...

  16. Automatic segmentation of HeLa cell images

    CERN Document Server

    Urban, Jan

    2011-01-01

    In this work, the possibilities for segmentation of cells from their background and each other in digital image were tested, combined and improoved. Lot of images with young, adult and mixture cells were able to prove the quality of described algorithms. Proper segmentation is one of the main task of image analysis and steps order differ from work to work, depending on input images. Reply for biologicaly given question was looking for in this work, including filtration, details emphasizing, segmentation and sphericity computing. Order of algorithms and way to searching for them was also described. Some questions and ideas for further work were mentioned in the conclusion part.

  17. Unsupervised segmentation of cardiac PET transmission images for automatic heart volume extraction.

    Science.gov (United States)

    Juslin, Anu; Tohka, Jussi

    2006-01-01

    In this study, we propose an automatic method to extract the heart volume from the cardiac positron emission tomography (PET) transmission images. The method combines the automatic 3D segmentation of the transmission image using Markov random fields (MRFs) to surface extraction using deformable models. Deformable models were automatically initialized using the MRFs segmentation result. The extraction of the heart region is needed e.g. in independent component analysis (ICA). The volume of the heart can be used to mask the emission image corresponding to the transmission image, so that only the cardiac region is used for the analysis. The masking restricts the number of independent components and reduces the computation time. In addition, the MRF segmentation result could be used for attenuation correction. The method was tested with 25 patient images. The MRF segmentation results were of good quality in all cases and we were able to extract the heart volume from all the images. PMID:17946020

  18. 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

  19. 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.

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

    DEFF Research Database (Denmark)

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

    2011-01-01

    Segmentation of vertebral contours is an essential task in the design of automatic tools for vertebral fracture assessment. In this paper, we propose a novel segmentation technique which does not require operator interaction. The proposed technique solves the segmentation problem in a hierarchical...... manner. In a first phase, a coarse estimate of the overall spine alignment and the vertebra locations is computed using a shape model sampling scheme. These samples are used to initialize a second phase of active shape model search, under a nonlinear model of vertebra appearance. The search...... is constrained by a conditional shape model, based on the variability of the coarse spine location estimates. The technique is evaluated on a data set of manually annotated lumbar radiographs. The results compare favorably to the previous work in automatic vertebra segmentation, in terms of both segmentation...

  1. 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.

  2. 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

  3. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Alirezaie, J.; Jernigan, M.E. [Univ. of Waterloo, Ontario (Canada); Nahmias, C. [McMaster Univ., Hamilton, Ontario (Canada)

    1996-12-31

    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.

  5. Automatic and Manual Segmentation of Hippocampus in Epileptic Patients MRI

    CERN Document Server

    Hosseini, Mohammad-Parsa; Pompili, Dario; Jafari-Khouzani, Kourosh; Elisevich, Kost; Soltanian-Zadeh, Hamid

    2016-01-01

    The hippocampus is a seminal structure in the most common surgically-treated form of epilepsy. Accurate segmentation of the hippocampus aids in establishing asymmetry regarding size and signal characteristics in order to disclose the likely site of epileptogenicity. With sufficient refinement, it may ultimately aid in the avoidance of invasive monitoring with its expense and risk for the patient. To this end, a reliable and consistent method for segmentation of the hippocampus from magnetic resonance imaging (MRI) is needed. In this work, we present a systematic and statistical analysis approach for evaluation of automated segmentation methods in order to establish one that reliably approximates the results achieved by manual tracing of the hippocampus.

  6. 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.

  7. 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.

  8. 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...

  9. AUTOMATIC SEGMENTATION AND STRUCTURAL CHARACTERIZATION OF LOW DENSITY FIBREBOARDS

    Directory of Open Access Journals (Sweden)

    Jérôme Lux

    2013-03-01

    Full Text Available In this paper, a new skeleton-based algorithm for the segmentation of individual fibres in 3D tomographic images is described. The proposed method is designed to deal with low density materials featuring fibres with varied sizes, shapes and tortuosities, like composite fibreboards used for buildings insulation. To this end the paths of the skeleton are first classified according to their connectivity, the connectivity of their adjacent nodes, their orientation, their average radius and the variation of the distance transform along each path. This allows for the identification of spurious paths and paths linking two fibres. Reconstruction of the path of the fibres is done thanks to an optimal pairing algorithm which joins paths that show the most similar orientation and radius at each node/link. The segmented skeleton is finally dilated by means of a growing algorithm ordonned by the average radius of the fibres in order to reconstruct each identified fibres. As an application, the algorithm is used to segment a 3D tomographic image of hemp/polymer fibreboard for buildings insulation. Information such as the number of contacts, tortuosity, length, average radius, orientation of fibres are finally measured on both the segmented skeleton and reconstructed image, which allow for a thorough characterization of the fibre network.

  10. Automatic segmentation of relevant textures in agricultural images

    OpenAIRE

    Guijarro, Maria; Pajares, Gonzalo; Riomoros, I.; Herrera, P.J.; Burgos Artizzu, Xavier; Ribeiro Seijas, Angela

    2011-01-01

    One important issue emerging strongly in agriculture is related with the automatization of tasks, where the optical sensors play an important role. They provide images that must be conveniently processed. The most relevantimage processing procedures require the identification of green plants, in our experiments they come from barley and corn crops including weeds, so that some types of action can be carried out, including site-specific treatments with chemical products or mechanical manipulat...

  11. 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)

  12. 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...

  13. Automatic segmentation of histological structures in mammary gland tissue sections

    Energy Technology Data Exchange (ETDEWEB)

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

    2004-02-17

    Real-time three-dimensional (3D) reconstruction of epithelial structures in human mammary gland tissue blocks mapped with selected markers would be an extremely helpful tool for breast cancer diagnosis and treatment planning. Besides its clear clinical application, this tool could also shed a great deal of light on the molecular basis of breast cancer initiation and progression. In this paper we present a framework for real-time segmentation of epithelial structures in two-dimensional (2D) images of sections of normal and neoplastic mammary gland tissue blocks. Complete 3D rendering of the tissue can then be done by surface rendering of the structures detected in consecutive sections of the blocks. Paraffin embedded or frozen tissue blocks are first sliced, and sections are stained with Hematoxylin and Eosin. The sections are then imaged using conventional bright field microscopy and their background is corrected using a phantom image. We then use the Fast-Marching algorithm to roughly extract the contours of the different morphological structures in the images. The result is then refined with the Level-Set method which converges to an accurate (sub-pixel) solution for the segmentation problem. Finally, our system stacks together the 2D results obtained in order to reconstruct a 3D representation of the entire tissue block under study. Our method is illustrated with results from the segmentation of human and mouse mammary gland tissue samples.

  14. 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.

  15. 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.

  16. Automatic Segmentation of Nature Object Using Salient Edge Points Based Active Contour

    Directory of Open Access Journals (Sweden)

    Shangbing Gao

    2015-01-01

    Full Text Available Natural image segmentation is often a crucial first step for high-level image understanding, significantly reducing the complexity of content analysis of images. LRAC may have some disadvantages. (1 Segmentation results heavily depend on the initial contour selection which is a very skillful task. (2 In some situations, manual interactions are infeasible. To overcome these shortcomings, we propose a novel model for unsupervised segmentation of viewer’s attention object from natural images based on localizing region-based active model (LRAC. With aid of the color boosting Harris detector and the core saliency map, we get the salient object edge points. Then, these points are employed as the seeds of initial convex hull. Finally, this convex hull is improved by the edge-preserving filter to generate the initial contour for our automatic object segmentation system. In contrast with localizing region-based active contours that require considerable user interaction, the proposed method does not require it; that is, the segmentation task is fulfilled in a fully automatic manner. Extensive experiments results on a large variety of natural images demonstrate that our algorithm consistently outperforms the popular existing salient object segmentation methods, yielding higher precision and better recall rates. Our framework can reliably and automatically extract the object contour from the complex background.

  17. Automatic segmentation of seeds and fluoroscope tracking (FTRAC) fiducial in prostate brachytherapy x-ray images

    Science.gov (United States)

    Kuo, Nathanael; Lee, Junghoon; Deguet, Anton; Song, Danny; Burdette, E. Clif; Prince, Jerry

    2010-02-01

    C-arm X-ray fluoroscopy-based radioactive seed localization for intraoperative dosimetry of prostate brachytherapy is an active area of research. The fluoroscopy tracking (FTRAC) fiducial is an image-based tracking device composed of radio-opaque BBs, lines, and ellipses that provides an effective means for pose estimation so that three-dimensional reconstruction of the implanted seeds from multiple X-ray images can be related to the ultrasound-computed prostate volume. Both the FTRAC features and the brachytherapy seeds must be segmented quickly and accurately during the surgery, but current segmentation algorithms are inhibitory in the operating room (OR). The first reason is that current algorithms require operators to manually select a region of interest (ROI), preventing automatic pipelining from image acquisition to seed reconstruction. Secondly, these algorithms fail often, requiring operators to manually correct the errors. We propose a fast and effective ROI-free automatic FTRAC and seed segmentation algorithm to minimize such human intervention. The proposed algorithm exploits recent image processing tools to make seed reconstruction as easy and convenient as possible. Preliminary results on 162 patient images show this algorithm to be fast, effective, and accurate for all features to be segmented. With near perfect success rates and subpixel differences to manual segmentation, our automatic FTRAC and seed segmentation algorithm shows promising results to save crucial time in the OR while reducing errors.

  18. 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

  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. 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

  1. 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.

  2. Automatic segmentation of cerebral white matter hyperintensities using only 3D FLAIR images

    NARCIS (Netherlands)

    Lopes Simoes, A.R.; Monninghoff, C.; Dlugaj, M.; Weimar, C.; Wanke, I.; Cappellen van Walsum, A.; Slump, C.H.

    2013-01-01

    Magnetic Resonance (MR) white matter hyperintensities have been shown to predict an increased risk of developing cognitive decline. However, their actual role in the conversion to dementia is still not fully understood. Automatic segmentation methods can help in the screening and monitoring of Mild

  3. Automatic segmentation of cerebral white matter hyperintensities using only 3D FLAIR images.

    NARCIS (Netherlands)

    Simoes, R.; Monninghoff, C.; Dlugaj, M.; Weimar, C.; Wanke, I.; Cappellen van Walsum, A.M. van; Slump, C.

    2013-01-01

    Magnetic Resonance (MR) white matter hyperintensities have been shown to predict an increased risk of developing cognitive decline. However, their actual role in the conversion to dementia is still not fully understood. Automatic segmentation methods can help in the screening and monitoring of Mild

  4. Ceramography and segmentation of polycristalline ceramics: application to grain size analysis by automatic methods

    Energy Technology Data Exchange (ETDEWEB)

    Arnould, X.; Coster, M.; Chermant, J.L.; Chermant, L. [LERMAT, ISMRA, Caen (France); Chartier, T. [SPCTS, ENSCI, Limoges (France)

    2002-07-01

    The knowledge of the mean grain size of ceramics is a very important problem to solve in the ceramic industry. Some specific methods of segmentation are presented to analyse, by an automatic way, the granulometry and morphological parameters of ceramic materials. Example presented concerns cerine materials. Such investigations lead to important information on the sintering process. (orig.)

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

    DEFF Research Database (Denmark)

    Jørgensen, Peter Stanley; Bowen, Jacob R.

    2008-01-01

    anode in 3D. The technique is based on numerical approximations to partial differential equations to evolve a 3D surface to the desired phase boundary. Vector fields derived from the experimentally acquired data are used as the driving force. The automatic segmentation compared to manual delineation...

  6. 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

  7. 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

  8. 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

  9. Automatic segmentation of canine retinal OCT using adaptive gradient enhancement and region growing

    Science.gov (United States)

    He, Yufan; Sun, Yankui; Chen, Min; Zheng, Yuanjie; Liu, Hui; Leon, Cecilia; Beltran, William; Gee, James C.

    2016-03-01

    In recent years, several studies have shown that the canine retina model offers important insight for our understanding of human retinal diseases. Several therapies developed to treat blindness in such models have already moved onto human clinical trials, with more currently under development [1]. Optical coherence tomography (OCT) offers a high resolution imaging modality for performing in-vivo analysis of the retinal layers. However, existing algorithms for automatically segmenting and analyzing such data have been mostly focused on the human retina. As a result, canine retinal images are often still being analyzed using manual segmentations, which is a slow and laborious task. In this work, we propose a method for automatically segmenting 5 boundaries in canine retinal OCT. The algorithm employs the position relationships between different boundaries to adaptively enhance the gradient map. A region growing algorithm is then used on the enhanced gradient maps to find the five boundaries separately. The automatic segmentation was compared against manual segmentations showing an average absolute error of 5.82 +/- 4.02 microns.

  10. A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images.

    Science.gov (United States)

    Gloger, Oliver; Kühn, Jens; Stanski, Adam; Völzke, Henry; Puls, Ralf

    2010-07-01

    Automatic 3D liver segmentation in magnetic resonance (MR) data sets has proven to be a very challenging task in the domain of medical image analysis. There exist numerous approaches for automatic 3D liver segmentation on computer tomography data sets that have influenced the segmentation of MR images. In contrast to previous approaches to liver segmentation in MR data sets, we use all available MR channel information of different weightings and formulate liver tissue and position probabilities in a probabilistic framework. We apply multiclass linear discriminant analysis as a fast and efficient dimensionality reduction technique and generate probability maps then used for segmentation. We develop a fully automatic three-step 3D segmentation approach based upon a modified region growing approach and a further threshold technique. Finally, we incorporate characteristic prior knowledge to improve the segmentation results. This novel 3D segmentation approach is modularized and can be applied for normal and fat accumulated liver tissue properties.

  11. Automatic segmentation of vertebral arteries in CT angiography using combined circular and cylindrical model fitting

    Science.gov (United States)

    Lee, Min Jin; Hong, Helen; Chung, Jin Wook

    2014-03-01

    We propose an automatic vessel segmentation method of vertebral arteries in CT angiography using combined circular and cylindrical model fitting. First, to generate multi-segmented volumes, whole volume is automatically divided into four segments by anatomical properties of bone structures along z-axis of head and neck. To define an optimal volume circumscribing vertebral arteries, anterior-posterior bounding and side boundaries are defined as initial extracted vessel region. Second, the initial vessel candidates are tracked using circular model fitting. Since boundaries of the vertebral arteries are ambiguous in case the arteries pass through the transverse foramen in the cervical vertebra, the circle model is extended along z-axis to cylinder model for considering additional vessel information of neighboring slices. Finally, the boundaries of the vertebral arteries are detected using graph-cut optimization. From the experiments, the proposed method provides accurate results without bone artifacts and eroded vessels in the cervical vertebra.

  12. 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.

  13. 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).

  14. A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI

    Science.gov (United States)

    Yu, Ning; Wu, Jia; Weinstein, Susan P.; Gaonkar, Bilwaj; Keller, Brad M.; Ashraf, Ahmed B.; Jiang, YunQing; Davatzikos, Christos; Conant, Emily F.; Kontos, Despina

    2015-03-01

    Accurate and efficient automated tumor segmentation in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is highly desirable for computer-aided tumor diagnosis. We propose a novel automatic segmentation framework which incorporates mean-shift smoothing, superpixel-wise classification, pixel-wise graph-cuts partitioning, and morphological refinement. A set of 15 breast DCE-MR images, obtained from the American College of Radiology Imaging Network (ACRIN) 6657 I-SPY trial, were manually segmented to generate tumor masks (as ground truth) and breast masks (as regions of interest). Four state-of-the-art segmentation approaches based on diverse models were also utilized for comparison. Based on five standard evaluation metrics for segmentation, the proposed framework consistently outperformed all other approaches. The performance of the proposed framework was: 1) 0.83 for Dice similarity coefficient, 2) 0.96 for pixel-wise accuracy, 3) 0.72 for VOC score, 4) 0.79 mm for mean absolute difference, and 5) 11.71 mm for maximum Hausdorff distance, which surpassed the second best method (i.e., adaptive geodesic transformation), a semi-automatic algorithm depending on precise initialization. Our results suggest promising potential applications of our segmentation framework in assisting analysis of breast carcinomas.

  15. 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.

  16. 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.

  17. 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

  18. 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

  19. Semi-automatic segmentation and detection of aorta dissection wall in MDCT angiography.

    Science.gov (United States)

    Krissian, Karl; Carreira, Jose M; Esclarin, Julio; Maynar, Manuel

    2014-01-01

    Aorta dissection is a serious vascular disease produced by a rupture of the tunica intima of the vessel wall that can be lethal to the patient. The related diagnosis is strongly based on images, where the multi-detector CT is the most generally used modality. We aim at developing a semi-automatic segmentation tool for aorta dissections, which will isolate the dissection (or flap) from the rest of the vascular structure. The proposed method is based on different stages, the first one being the semi-automatic extraction of the aorta centerline and its main branches, allowing an subsequent automatic segmentation of the outer wall of the aorta, based on a geodesic level set framework. This segmentation is then followed by an extraction the center of the dissected wall as a 3D mesh using an original algorithm based on the zero crossing of two vector fields. Our method has been applied to five datasets from three patients with chronic aortic dissection. The comparison with manually segmented dissections shows an average absolute distance value of about half a voxel. We believe that the proposed method, which tries to solve a problem that has attracted little attention to the medical image processing community, provides a new and interesting tool to isolate the intimal flap that can provide very useful information to the clinician. PMID:24161795

  20. 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.

  1. 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 ...

  2. 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)

  3. 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.

  4. A fully automatic framework for cell segmentation on non-confocal adaptive optics images

    Science.gov (United States)

    Liu, Jianfei; Dubra, Alfredo; Tam, Johnny

    2016-03-01

    By the time most retinal diseases are diagnosed, macroscopic irreversible cellular loss has already occurred. Earlier detection of subtle structural changes at the single photoreceptor level is now possible, using the adaptive optics scanning light ophthalmoscope (AOSLO). This work aims to develop a fully automatic segmentation framework to extract cell boundaries from non-confocal split-detection AOSLO images of the cone photoreceptor mosaic in the living human eye. Significant challenges include anisotropy, heterogeneous cell regions arising from shading effects, and low contrast between cells and background. To overcome these challenges, we propose the use of: 1) multi-scale Hessian response to detect heterogeneous cell regions, 2) convex hulls to create boundary templates, and 3) circularlyconstrained geodesic active contours to refine cell boundaries. We acquired images from three healthy subjects at eccentric retinal regions and manually contoured cells to generate ground-truth for evaluating segmentation accuracy. Dice coefficient, relative absolute area difference, and average contour distance were 82±2%, 11±6%, and 2.0±0.2 pixels (Mean±SD), respectively. We find that strong shading effects from vessels are a main factor that causes cell oversegmentation and false segmentation of non-cell regions. Our segmentation algorithm can automatically and accurately segment photoreceptor cells on non-confocal AOSLO images, which is the first step in longitudinal tracking of cellular changes in the individual eye over the time course of disease progression.

  5. Correlation analysis-based image segmentation approach for automatic agriculture vehicle

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    It is important to segment image correctly to extract guidance information for automatic agriculture vehicle. If we can make the computer know where the crops are, we can extract the guidance line easily. Images were divided into some rectangle small windows, then a pair of 1-D arrays was constructed in each small windows. The correlation coefficients of every small window constructed the features to segment images. The results showed that correlation analysis is a potential approach for processing complex farmland for guidance system, and more correlation analysis methods must be researched.

  6. 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...

  7. Automatic Liver Segmentation from CT Images Using Single-Block Linear Detection.

    Science.gov (United States)

    Huang, Lianfen; Weng, Minghui; Shuai, Haitao; Huang, Yue; Sun, Jianjun; Gao, Fenglian

    2016-01-01

    Automatic liver segmentation not only plays an important role in the analysis of liver disease, but also reduces the cost and humanity's impact in segmentation. In addition, liver segmentation is a very challenging task due to countless anatomical variations and technical difficulties. Many methods have been designed to overcome these challenges, but these methods still need to be improved to obtain the desired segmentation precision. In this paper, a fast algorithm is proposed for liver extraction from CT images with single-block linear detection. The proposed method does not require iteration; thus, the computational time and complexity are decreased enormously. In addition, the initialization is not crucial in the algorithm, so the algorithm's robustness and specificity are improved. The experimental evaluation of the proposed method revealed effective segmentation in normal and abnormal (liver hemangioma and liver cancer) abdominal CT images. The average sensitivity, accuracy, and specificity for liver cancer are 96.59%, 98.65%, and 99.03%, respectively. The results of image segmentation approximate the manual segmentation results by the technical doctor. Moreover, our method shows superior flexibility to newly published method with comparable performance. The advantage of our method is verified with experimental results, which is described in detail. PMID:27631012

  8. 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.

  9. 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.

  10. 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.

  11. 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...

  12. Automatic Speech Segmentation Based On Audio and Optical Flow Visual Classification

    Directory of Open Access Journals (Sweden)

    Behnam Torabi

    2014-10-01

    Full Text Available Automatic speech segmentation as an important part of speech recognition system (ASR is highly noise dependent. Noise is made by changes in the communication channel, background, level of speaking etc. In recent years, many researchers have proposed noise cancelation techniques and have added visual features from speaker’s face to reduce the effect of noise on ASR systems. Removing noise from audio signals depends on the type of the noise; so it cannot be used as a general solution. Adding visual features improve this lack of efficiency, but advanced methods of this type need manual extraction of visual features. In this paper we propose a completely automatic system which uses optical flow vectors from speaker’s image sequence to obtain visual features. Then, Hidden Markov Models are trained to segment audio signals from image sequences and audio features based on extracted optical flow. The developed segmentation system based on such method acts totally automatic and become more robust to noise.

  13. 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.

  14. Robust semi-automatic segmentation of single- and multichannel MRI volumes through adaptable class-specific representation

    Science.gov (United States)

    Nielsen, Casper F.; Passmore, Peter J.

    2002-05-01

    Segmentation of MRI volumes is complicated by noise, inhomogeneity and partial volume artefacts. Fully or semi-automatic methods often require time consuming or unintuitive initialization. Adaptable Class-Specific Representation (ACSR) is a semi-automatic segmentation framework implemented by the Path Growing Algorithm (PGA), which reduces artefacts near segment boundaries. The user visually defines the desired segment classes through the selection of class templates and the following segmentation process is fully automatic. Good results have previously been achieved with color cryo section segmentation and ACSR has been developed further for the MRI modality. In this paper we present two optimizations for robust ACSR segmentation of MRI volumes. Automatic template creation based on an initial segmentation step using Learning Vector Quantization is applied for higher robustness to noise. Inhomogeneity correction is added as a pre-processing step, comparing the EQ and N3 algorithms. Results based on simulated T1-weighed and multispectral (T1 and T2) MRI data from the BrainWeb database and real data from the Internet Brain Segmentation Repository are presented. We show that ACSR segmentation compares favorably to previously published results on the same volumes and discuss the pros and cons of using quantitative ground truth evaluation compared to qualitative visual assessment.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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%.

  20. 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.

  1. 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.

  2. 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.

  3. Fully Automatic Method for 3D T1-Weighted Brain Magnetic Resonance Images Segmentation

    Directory of Open Access Journals (Sweden)

    Bouchaib Cherradi

    2011-05-01

    Full Text Available Accurate segmentation of brain MR images is of interest for many brain disorders. However, dueto several factors such noise, imaging artefacts, intrinsic tissue variation and partial volumeeffects, brain extraction and tissue segmentation remains a challenging task. So, in this paper, afull automatic method for segmentation of anatomical 3D brain MR images is proposed. Themethod consists of many steps. First, noise reduction by median filtering is done; secondsegmentation of brain/non-brain tissue is performed by using a Threshold Morphologic BrainExtraction method (TMBE. Then initial centroids estimation by gray level histogram analysis isexecuted, this stage yield to a Modified version of Fuzzy C-means Algorithm (MFCM that is usedfor MRI tissue segmentation. Finally 3D visualisation of the three clusters (CSF, GM and WM isperformed. The efficiency of the proposed method is demonstrated by extensive segmentationexperiments using simulated and real MR images. A confrontation of the method with similarmethods of the literature has been undertaken trough different performance measures. TheMFCM for tissue segmentation introduce a gain in rapidity of convergence of about 70%.

  4. 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.

  5. 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%.

  6. 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.

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

    Directory of Open Access Journals (Sweden)

    Yehu Shen

    2014-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, R; Yang, J; Pan, T; Milgrom, S; Pinnix, C; Shi, A; Yang, J; Liu, Y; Nguyen, Q; Gomez, D; Dabaja, B; Balter, P; Court, L; Liao, Z [MD Anderson Cancer Center, Houston, TX (United States)

    2015-06-15

    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

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

    Directory of Open Access Journals (Sweden)

    Thomas Samaille

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

  10. Automatic segmentation of white matter hyperintensities robust to multicentre acquisition and pathological variability

    Science.gov (United States)

    Samaille, T.; Colliot, O.; Cuingnet, R.; Jouvent, E.; Chabriat, H.; Dormont, D.; Chupin, M.

    2012-02-01

    White matter hyperintensities (WMH), commonly seen on FLAIR images in elderly people, are a risk factor for dementia onset and have been associated with motor and cognitive deficits. We present here a method to fully automatically segment WMH from T1 and FLAIR images. Iterative steps of non linear diffusion followed by watershed segmentation were applied on FLAIR images until convergence. Diffusivity function and associated contrast parameter were carefully designed to adapt to WMH segmentation. It resulted in piecewise constant images with enhanced contrast between lesions and surrounding tissues. Selection of WMH areas was based on two characteristics: 1) a threshold automatically computed for intensity selection, 2) main location of areas in white matter. False positive areas were finally removed based on their proximity with cerebrospinal fluid/grey matter interface. Evaluation was performed on 67 patients: 24 with amnestic mild cognitive impairment (MCI), from five different centres, and 43 with Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoaraiosis (CADASIL) acquired in a single centre. Results showed excellent volume agreement with manual delineation (Pearson coefficient: r=0.97, p<0.001) and substantial spatial correspondence (Similarity Index: 72%+/-16%). Our method appeared robust to acquisition differences across the centres as well as to pathological variability.

  11. 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.

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

    Science.gov (United States)

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

    2015-03-01

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

  13. 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

  14. 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

  15. Automatic segmentation and centroid detection of skin sensors for lung interventions

    Science.gov (United States)

    Lu, Kongkuo; Xu, Sheng; Xue, Zhong; Wong, Stephen T.

    2012-02-01

    Electromagnetic (EM) tracking has been recognized as a valuable tool for locating the interventional devices in procedures such as lung and liver biopsy or ablation. The advantage of this technology is its real-time connection to the 3D volumetric roadmap, i.e. CT, of a patient's anatomy while the intervention is performed. EM-based guidance requires tracking of the tip of the interventional device, transforming the location of the device onto pre-operative CT images, and superimposing the device in the 3D images to assist physicians to complete the procedure more effectively. A key requirement of this data integration is to find automatically the mapping between EM and CT coordinate systems. Thus, skin fiducial sensors are attached to patients before acquiring the pre-operative CTs. Then, those sensors can be recognized in both CT and EM coordinate systems and used calculate the transformation matrix. In this paper, to enable the EM-based navigation workflow and reduce procedural preparation time, an automatic fiducial detection method is proposed to obtain the centroids of the sensors from the pre-operative CT. The approach has been applied to 13 rabbit datasets derived from an animal study and eight human images from an observation study. The numerical results show that it is a reliable and efficient method for use in EM-guided application.

  16. A Fast and Fully Automatic Method for Cerebrovascular Segmentation on Time-of-Flight (TOF) MRA Image

    OpenAIRE

    Gao, Xin; Uchiyama, Yoshikazu; Zhou, Xiangrong; HARA, TAKESHI; Asano, Takahiko; Fujita, Hiroshi

    2010-01-01

    The precise three-dimensional (3-D) segmentation of cerebral vessels from magnetic resonance angiography (MRA) images is essential for the detection of cerebrovascular diseases (e.g., occlusion, aneurysm). The complex 3-D structure of cerebral vessels and the low contrast of thin vessels in MRA images make precise segmentation difficult. We present a fast, fully automatic segmentation algorithm based on statistical model analysis and improved curve evolution for extracting the 3-D cerebral ve...

  17. 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 ...

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

    NARCIS (Netherlands)

    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-o

  19. New approach to the automatic segmentation of coronary artery in X-ray angiograms

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    For the segmentation of X-ray angiograms (XRA), the essential feature and the prior knowledge of angiographic image were analyzed, and a multi-feature based fuzzy recognition (MFFR) algorithm was proposed to infer the local vessel structure in this paper. Guided by the prior knowledge of artery vessel, a probability tracking operator (PTO) can rapidly track along the artery tree, and walk across the weak region or gaps because of disturbance or preprocessing to angiographic image. Another, the accurate measurement of the vascular axis-lines and diameters can be synchronously implemented in the tracking process. To correctly evaluate the proposed method, a simulated image of CAT and some clinical XRA images were used in the experimentations. The algorithms performed better than the conventional one: given one start-point, on average 92.7% of the visible segments or branches was automatically delineated; the correctness ratio of vessel structure inference reached to 90.0% on the average.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. ICA based automatic segmentation of dynamic H(2)(15)O cardiac PET images.

    Science.gov (United States)

    Margadán-Méndez, Margarita; Juslin, Anu; Nesterov, Sergey V; Kalliokoski, Kari; Knuuti, Juhani; Ruotsalainen, Ulla

    2010-05-01

    In this study, we applied an iterative independent component analysis (ICA) method for the separation of cardiac tissue components (myocardium, right, and left ventricle) from dynamic positron emission tomography (PET) images. Previous phantom and animal studies have shown that ICA separation extracts the cardiac structures accurately. Our goal in this study was to investigate the methodology with human studies. The ICA separated cardiac structures were used to calculate the myocardial perfusion in two different cases: 1) the regions of interest were drawn manually on the ICA separated component images and 2) the volumes of interest (VOI) were automatically segmented from the component images. For the whole myocardium, the perfusion values of 25 rest and six drug-induced stress studies obtained with these methods were compared to the values from the manually drawn regions of interest on differential images. The separation of the rest and stress studies using ICA-based methods was successful in all cases. The visualization of the cardiac structures from H (2) (15) O PET studies was improved with the ICA separation. Also, the automatic segmentation of the VOI seemed to be feasible. PMID:19273031

  7. 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)

  8. 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.

  9. 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

  10. Automated segmentation of tumors on bone scans using anatomy-specific thresholding

    Science.gov (United States)

    Chu, Gregory H.; Lo, Pechin; Kim, Hyun J.; Lu, Peiyun; Ramakrishna, Bharath; Gjertson, David; Poon, Cheryce; Auerbach, Martin; Goldin, Jonathan; Brown, Matthew S.

    2012-03-01

    Quantification of overall tumor area on bone scans may be a potential biomarker for treatment response assessment and has, to date, not been investigated. Segmentation of bone metastases on bone scans is a fundamental step for this response marker. In this paper, we propose a fully automated computerized method for the segmentation of bone metastases on bone scans, taking into account characteristics of different anatomic regions. A scan is first segmented into anatomic regions via an atlas-based segmentation procedure, which involves non-rigidly registering a labeled atlas scan to the patient scan. Next, an intensity normalization method is applied to account for varying levels of radiotracer dosing levels and scan timing. Lastly, lesions are segmented via anatomic regionspecific intensity thresholding. Thresholds are chosen by receiver operating characteristic (ROC) curve analysis against manual contouring by board certified nuclear medicine physicians. A leave-one-out cross validation of our method on a set of 39 bone scans with metastases marked by 2 board-certified nuclear medicine physicians yielded a median sensitivity of 95.5%, and specificity of 93.9%. Our method was compared with a global intensity thresholding method. The results show a comparable sensitivity and significantly improved overall specificity, with a p-value of 0.0069.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-12-15

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

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

    Directory of Open Access Journals (Sweden)

    Shoujun Zhou

    2010-08-01

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

  13. A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks.

    Science.gov (United States)

    Wang, Changhan; Yan, Xinchen; Smith, Max; Kochhar, Kanika; Rubin, Marcie; Warren, Stephen M; Wrobel, James; Lee, Honglak

    2015-08-01

    Wound surface area changes over multiple weeks are highly predictive of the wound healing process. Furthermore, the quality and quantity of the tissue in the wound bed also offer important prognostic information. Unfortunately, accurate measurements of wound surface area changes are out of reach in the busy wound practice setting. Currently, clinicians estimate wound size by estimating wound width and length using a scalpel after wound treatment, which is highly inaccurate. To address this problem, we propose an integrated system to automatically segment wound regions and analyze wound conditions in wound images. Different from previous segmentation techniques which rely on handcrafted features or unsupervised approaches, our proposed deep learning method jointly learns task-relevant visual features and performs wound segmentation. Moreover, learned features are applied to further analysis of wounds in two ways: infection detection and healing progress prediction. To the best of our knowledge, this is the first attempt to automate long-term predictions of general wound healing progress. Our method is computationally efficient and takes less than 5 seconds per wound image (480 by 640 pixels) on a typical laptop computer. Our evaluations on a large-scale wound database demonstrate the effectiveness and reliability of the proposed system. PMID:26736781

  14. X-ray CT image segmentation: automatic sandwich structure layer separation using reduced dimension Hough transformation

    Science.gov (United States)

    Xu, J.; Liu, T.; Kakarala, R.; Yin, X. M.

    2010-03-01

    Many structures in aerospace, semiconductor and precision engineering are multi-layer in nature. Examples include Low Temperature Co-Fire Ceramic (LTCC), PCBA, stacked IC, Through-Silicon-Via and composite materials for aircraft wings. Segmentation of each internal layer in any orientation is essential for layer alignment as well as delamination, disbond and warpage analysis. In this paper we propose a RDHT (Reduced Dimension Hough Transformation) for automatic layer detection. Instead of segmenting internal surfaces at voxel level, correlation based edge operator is applied to extract features in 3D space whereby the likelihood of any planar structure is associated with the number of features on a specific plane. We use Randomized Hough Transform to map 3D features in three one dimensional accumulators plus one verification accumulator to reduce Hough space dimension. The RDHT has been successfully applied to various objects to reveal internal planar structures. For a CT result with a 512×512×512 cube, the feature detection takes 30 seconds and the subsequent layer separation takes 12 seconds (laptop with Intel dual core 1.6G). We demonstrate that the algorithm can segment all 16 layers of a stacked IC with an accuracy of 0.5 voxel.

  15. Automatic Segmentation of Moving Objects in Video Sequences for Indoor and Outdoor Applications

    Institute of Scientific and Technical Information of China (English)

    FALAH E. ALSAQRE; YUAN Bao-zong

    2003-01-01

    Computer vision systems have an impressive spread both for their practical application and for theoretical research. The common approach used in such systems consists of a good segmentation of moving objects from video sequences. This paper presents an automatic algorithm for segmenting and evxtracting moving objects suitable for indoor and outdoor video applications, where the background scene can be captured beforehand. Since edge detection is often used to extract accurate boundaries of the image's objects, the first step in our algorithm is accomplished by combining two edge maps which are detected from the frame difference in two consecutive frames and the background subtraction. After removing edge points that belong to the background, the resulting moving edge map is fed to the object extraction step. A fundamental task in this step is to declare the candidates of the moving object, followed by applying morphological operations. The algorithm is implemented on a real video sequence as well as MPEG-4 sequence and good segmentation results are achieved.

  16. Fully automatic lung segmentation and rib suppression methods to improve nodule detection in chest radiographs.

    Science.gov (United States)

    Soleymanpour, Elaheh; Pourreza, Hamid Reza; Ansaripour, Emad; Yazdi, Mehri Sadooghi

    2011-07-01

    Computer-aided Diagnosis (CAD) systems can assist radiologists in several diagnostic tasks. Lung segmentation is one of the mandatory steps for initial detection of lung cancer in Posterior-Anterior chest radiographs. On the other hand, many CAD schemes in projection chest radiography may benefit from the suppression of the bony structures that overlay the lung fields, e.g. ribs. The original images are enhanced by an adaptive contrast equalization and non-linear filtering. Then an initial estimation of lung area is obtained based on morphological operations and then it is improved by growing this region to find the accurate final contour, then for rib suppression, we use oriented spatial Gabor filter. The proposed method was tested on a publicly available database of 247 chest radiographs. Results show that this method outperformed greatly with accuracy of 96.25% for lung segmentation, also we will show improving the conspicuity of lung nodules by rib suppression with local nodule contrast measures. Because there is no additional radiation exposure or specialized equipment required, it could also be applied to bedside portable chest x-rays. In addition to simplicity of these fully automatic methods, lung segmentation and rib suppression algorithms are performed accurately with low computation time and robustness to noise because of the suitable enhancement procedure.

  17. 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)

  18. 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

  19. Robust Automatic Pectoral Muscle Segmentation from Mammograms Using Texture Gradient and Euclidean Distance Regression.

    Science.gov (United States)

    Bora, Vibha Bafna; Kothari, Ashwin G; Keskar, Avinash G

    2016-02-01

    In computer-aided diagnosis (CAD) of mediolateral oblique (MLO) view of mammogram, the accuracy of tissue segmentation highly depends on the exclusion of pectoral muscle. Robust methods for such exclusions are essential as the normal presence of pectoral muscle can bias the decision of CAD. In this paper, a novel texture gradient-based approach for automatic segmentation of pectoral muscle is proposed. The pectoral edge is initially approximated to a straight line by applying Hough transform on Probable Texture Gradient (PTG) map of the mammogram followed by block averaging with the aid of approximated line. Furthermore, a smooth pectoral muscle curve is achieved with proposed Euclidean Distance Regression (EDR) technique and polynomial modeling. The algorithm is robust to texture and overlapping fibro glandular tissues. The method is validated with 340 MLO views from three databases-including 200 randomly selected scanned film images from miniMIAS, 100 computed radiography images and 40 full-field digital mammogram images. Qualitatively, 96.75 % of the pectoral muscles are segmented with an acceptable pectoral score index. The proposed method not only outperforms state-of-the-art approaches but also accurately quantifies the pectoral edge. Thus, its high accuracy and relatively quick processing time clearly justify its suitability for CAD.

  20. Automatic iterative segmentation of multiple sclerosis lesions using Student's t mixture models and probabilistic anatomical atlases in FLAIR images.

    Science.gov (United States)

    Freire, Paulo G L; Ferrari, Ricardo J

    2016-06-01

    Multiple sclerosis (MS) is a demyelinating autoimmune disease that attacks the central nervous system (CNS) and affects more than 2 million people worldwide. The segmentation of MS lesions in magnetic resonance imaging (MRI) is a very important task to assess how a patient is responding to treatment and how the disease is progressing. Computational approaches have been proposed over the years to segment MS lesions and reduce the amount of time spent on manual delineation and inter- and intra-rater variability and bias. However, fully-automatic segmentation of MS lesions still remains an open problem. In this work, we propose an iterative approach using Student's t mixture models and probabilistic anatomical atlases to automatically segment MS lesions in Fluid Attenuated Inversion Recovery (FLAIR) images. Our technique resembles a refinement approach by iteratively segmenting brain tissues into smaller classes until MS lesions are grouped as the most hyperintense one. To validate our technique we used 21 clinical images from the 2015 Longitudinal Multiple Sclerosis Lesion Segmentation Challenge dataset. Evaluation using Dice Similarity Coefficient (DSC), True Positive Ratio (TPR), False Positive Ratio (FPR), Volume Difference (VD) and Pearson's r coefficient shows that our technique has a good spatial and volumetric agreement with raters' manual delineations. Also, a comparison between our proposal and the state-of-the-art shows that our technique is comparable and, in some cases, better than some approaches, thus being a viable alternative for automatic MS lesion segmentation in MRI.

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

    Science.gov (United States)

    Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K.; Ourselin, Sébastien

    2007-03-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Schoot, A. J. A. J. van de, E-mail: a.j.schootvande@amc.uva.nl; Schooneveldt, G.; Wognum, S.; Stalpers, L. J. A.; Rasch, C. R. N.; Bel, A. [Department of Radiation Oncology, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam (Netherlands); Hoogeman, M. S. [Department of Radiation Oncology, Daniel den Hoed Cancer Center, Erasmus Medical Center, Groene Hilledijk 301, 3075 EA Rotterdam (Netherlands); Chai, X. [Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Palo Alto, California 94305 (United States)

    2014-03-15

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

  3. 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

  4. 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

  5. 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

  6. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Mazzurana, M [ITC-irst - Bioelectromagnetism Laboratory, FCS Department, 38050 Povo, Trento (Italy); Sandrini, L [ITC-irst - Bioelectromagnetism Laboratory, FCS Department, 38050 Povo, Trento (Italy); Vaccari, A [ITC-irst - Bioelectromagnetism Laboratory, FCS Department, 38050 Povo, Trento (Italy); Malacarne, C [ITC-irst - Bioelectromagnetism Laboratory, FCS Department, 38050 Povo, Trento (Italy); Cristoforetti, L [ITC-irst - Bioelectromagnetism Laboratory, FCS Department, 38050 Povo, Trento (Italy); Pontalti, R [ITC-irst - Bioelectromagnetism Laboratory, FCS Department, 38050 Povo, Trento (Italy)

    2003-10-07

    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.

  8. 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

  9. 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.

  10. A framework for automatic construction of 3D PDM from segmented volumetric neuroradiological data sets.

    Science.gov (United States)

    Fu, Yili; Gao, Wenpeng; Xiao, Yongfei; Liu, Jimin

    2010-03-01

    3D point distribution model (PDM) of subcortical structures can be applied in medical image analysis by providing priori-knowledge. However, accurate shape representation and point correspondence are still challenging for building 3D PDM. This paper presents a novel framework for the automated construction of 3D PDMs from a set of segmented volumetric images. First, a template shape is generated according to the spatial overlap. Then the corresponding landmarks among shapes are automatically identified by a novel hierarchical global-to-local approach, which combines iterative closest point based global registration and active surface model based local deformation to transform the template shape to all other shapes. Finally, a 3D PDM is constructed. Experiment results on four subcortical structures show that the proposed method is able to construct 3D PDMs with a high quality in compactness, generalization and specificity, and more efficient and effective than the state-of-art methods such as MDL and SPHARM. PMID:19631401

  11. Integration of Morphology and Graph-based Techniques for Fully Automatic Liver Segmentation

    Directory of Open Access Journals (Sweden)

    Hans Burkhardt

    2010-09-01

    Full Text Available Here a fully 3D algorithm for automatic liver segmentation from CT volumetric datasets is presented. The algorithm starts by smoothing the original volume using anisotropic diffusion. The coarse liver region is obtained from the threshold process that is based on a priori knowledge. Then, several morphological operations is performed such as operating the liver to detach the unwanted region connected to the liver and finding the largest component using the connected component labeling (CCL algorithm. At this stage, both 3D and 2D CCL is done subsequently. However, in 2D CCL, the adjacent slices are also affected from current slice changes. Finally, the boundary of the liver is refined using graph-cuts solver. Our algorithm does not require any user interaction or training datasets to be used. The algorithm has been evaluated on 10 CT scans of the liver and the results are encouraging to poor quality of images.

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. 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

  17. Applied anatomy of the atlantal segment of the vertebral artery%椎动脉寰椎部的应用解剖

    Institute of Scientific and Technical Information of China (English)

    郭徐华; 高宜录; 金国华

    2012-01-01

    Objective: To study the anatomy of the atlantal segment of the vertebral artery and to provide the basis of micro-vascular anatomy for clinical treatment. Methods: Adult cadaver heads were dissected in the study, and the morphology of the atlantal segment of the vertebral arteries were observed; The dimensions of the atlantal segment of vertebral arteries and their adjacent structures were measured. Results: The atlantal segments of the vertebral artery was divided into occipo-atlan-tal segment and atlanto-axoidean segment. The atlantal segment of vertebral artery had four permanent and continuous blood vessel loops. There were abundant paravertebral venous plexus around the atlantal segment of the vertebral artery. The anterior root of C2 permanent sticking to dorsal of occipo-atlantal segment of atlantal segment of the vertebral artery. Conclusion: The anatomy of the atlantal segment of vertebral artery and their adjacent structures have great clinical significance to avoid damaging the vertebral artery in a far lateral operative approach.%目的:研究椎动脉寰椎部的解剖,为临床应用提供解剖学依据.方法:成人尸头标本,解剖观察椎动脉寰椎部的形态结构,测量椎动脉寰椎部及其毗邻结构.结果:椎动脉寰椎部可分为寰枕段及寰枢椎段2部分;椎动脉寰椎部有恒定而连续的4个血管襻;周围有丰富的椎旁静脉丛,第2颈椎神经前根恒定地紧贴于椎动脉寰椎部寰枕段背侧.结论:对椎动脉寰椎部的解剖及其毗邻结构的研究,对于远外侧手术入路中避免椎动脉的损伤有重大临床意义.

  18. Auxiliary anatomical labels for joint segmentation and atlas registration

    Science.gov (United States)

    Gass, Tobias; Szekely, Gabor; Goksel, Orcun

    2014-03-01

    This paper studies improving joint segmentation and registration by introducing auxiliary labels for anatomy that has similar appearance to the target anatomy while not being part of that target. Such auxiliary labels help avoid false positive labelling of non-target anatomy by resolving ambiguity. A known registration of a segmented atlas can help identify where a target segmentation should lie. Conversely, segmentations of anatomy in two images can help them be better registered. Joint segmentation and registration is then a method that can leverage information from both registration and segmentation to help one another. It has received increasing attention recently in the literature. Often, merely a single organ of interest is labelled in the atlas. In the presense of other anatomical structures with similar appearance, this leads to ambiguity in intensity based segmentation; for example, when segmenting individual bones in CT images where other bones share the same intensity profile. To alleviate this problem, we introduce automatic generation of additional labels in atlas segmentations, by marking similar-appearance non-target anatomy with an auxiliary label. Information from the auxiliary-labeled atlas segmentation is then incorporated by using a novel coherence potential, which penalizes differences between the deformed atlas segmentation and the target segmentation estimate. We validated this on a joint segmentation-registration approach that iteratively alternates between registering an atlas and segmenting the target image to find a final anatomical segmentation. The results show that automatic auxiliary labelling outperforms the same approach using a single label atlasses, for both mandibular bone segmentation in 3D-CT and corpus callosum segmentation in 2D-MRI.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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...

  5. 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

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

    Directory of Open Access Journals (Sweden)

    Liansheng Wang

    Full Text Available 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.

  7. 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

  8. 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...

  9. 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)

  10. 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...

  11. 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.

  12. 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.

  13. Semi-automatic segmentation of vertebral bodies in volumetric MR images using a statistical shape+pose model

    Science.gov (United States)

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

    2014-03-01

    Segmentation of vertebral structures in magnetic resonance (MR) images is challenging because of poor con­trast between bone surfaces and surrounding soft tissue. This paper describes a semi-automatic method for segmenting vertebral bodies in multi-slice MR images. In order to achieve a fast and reliable segmentation, the method takes advantage of the correlation between shape and pose of different vertebrae in the same patient by using a statistical multi-vertebrae anatomical shape+pose model. Given a set of MR images of the spine, we initially reduce the intensity inhomogeneity in the images by using an intensity-correction algorithm. Then a 3D anisotropic diffusion filter smooths the images. Afterwards, we extract edges from a relatively small region of the pre-processed image with a simple user interaction. Subsequently, an iterative Expectation Maximization tech­nique is used to register the statistical multi-vertebrae anatomical model to the extracted edge points in order to achieve a fast and reliable segmentation for lumbar vertebral bodies. We evaluate our method in terms of speed and accuracy by applying it to volumetric MR images of the spine acquired from nine patients. Quantitative and visual results demonstrate that the method is promising for segmentation of vertebral bodies in volumetric MR images.

  14. 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)

  15. 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.

  16. 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.

  17. 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...

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

    DEFF Research Database (Denmark)

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

    2010-01-01

    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 segmentation. Two...... applications of the framework are illustrated using solid oxide cell materials as examples....

  19. 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.

  20. 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.

  1. 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.

  2. Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection.

    NARCIS (Netherlands)

    Rikxoort, E.M. van; Hoop, B. de; Viergever, M.A.; Prokop, M.; Ginneken, B. van

    2009-01-01

    Lung segmentation is a prerequisite for automated analysis of chest CT scans. Conventional lung segmentation methods rely on large attenuation differences between lung parenchyma and surrounding tissue. These methods fail in scans where dense abnormalities are present, which often occurs in clinical

  3. 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.

  4. 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.

  5. 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.

  6. Automatic choroid cells segmentation and counting based on approximate convexity and concavity of chain code in fluorescence microscopic image

    Science.gov (United States)

    Lu, Weihua; Chen, Xinjian; Zhu, Weifang; Yang, Lei; Cao, Zhaoyuan; Chen, Haoyu

    2015-03-01

    In this paper, we proposed a method based on the Freeman chain code to segment and count rhesus choroid-retinal vascular endothelial cells (RF/6A) automatically for fluorescence microscopy images. The proposed method consists of four main steps. First, a threshold filter and morphological transform were applied to reduce the noise. Second, the boundary information was used to generate the Freeman chain codes. Third, the concave points were found based on the relationship between the difference of the chain code and the curvature. Finally, cells segmentation and counting were completed based on the characteristics of the number of the concave points, the area and shape of the cells. The proposed method was tested on 100 fluorescence microscopic cell images, and the average true positive rate (TPR) is 98.13% and the average false positive rate (FPR) is 4.47%, respectively. The preliminary results showed the feasibility and efficiency of the proposed method.

  7. Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation.

    Science.gov (United States)

    Häme, Yrjö; Pollari, Mika

    2012-01-01

    A novel liver tumor segmentation method for CT images is presented. The aim of this work was to reduce the manual labor and time required in the treatment planning of radiofrequency ablation (RFA), by providing accurate and automated tumor segmentations reliably. The developed method is semi-automatic, requiring only minimal user interaction. The segmentation is based on non-parametric intensity distribution estimation and a hidden Markov measure field model, with application of a spherical shape prior. A post-processing operation is also presented to remove the overflow to adjacent tissue. In addition to the conventional approach of using a single image as input data, an approach using images from multiple contrast phases was developed. The accuracy of the method was validated with two sets of patient data, and artificially generated samples. The patient data included preoperative RFA images and a public data set from "3D Liver Tumor Segmentation Challenge 2008". The method achieved very high accuracy with the RFA data, and outperformed other methods evaluated with the public data set, receiving an average overlap error of 30.3% which represents an improvement of 2.3% points to the previously best performing semi-automatic method. The average volume difference was 23.5%, and the average, the RMS, and the maximum surface distance errors were 1.87, 2.43, and 8.09 mm, respectively. The method produced good results even for tumors with very low contrast and ambiguous borders, and the performance remained high with noisy image data.

  8. 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.

  9. 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

  10. Automatic segmentation of coronary artery tree based on multiscale Gabor filtering and transition region extraction

    Science.gov (United States)

    Wang, Fang; Wang, Guozhu; Kang, Lie; Wang, Juan

    2011-11-01

    This paper presents a novel segmentation method for extracting coronary artery tree from angiogram, which is based on multiscale Gabor filtering and transition region extraction. Firstly the enhanced image is obtained after multiscale Gabor filtering, then the transition region of the enhanced image is extracted using the local complexity algorithm, and the final segmentation threshold is calculated, finally the image segmentation is achieved. To evaluate the performance of the proposed approach, we carried out experiments on various sets of angiographic images, and compared its effects with those of the improved top-hat segmentation method. The experiments indicate that the proposed method outperforms the latter method about better extraction of small vessels, more background elimination, better visualized coronary artery tree and continuity of the vessels.

  11. 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.

  12. 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

  13. 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.

  14. 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

  15. 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

  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. An automatic segmentation system of acetabulum in sequential CT images for the personalized artificial femoral head design.

    Science.gov (United States)

    Liu, Bin; Zhang, Hui; Hua, Shungang; Jiang, Qianfeng; Huang, Rui; Liu, Wenpeng; Zhang, Shuo; Zhang, Bingbing; Yue, Zongge

    2016-04-01

    This paper describes an automatic and accurate segmentation method to extract the acetabulum tissue from sequential CT images. The hip joint consists of acetabulum and femoral head. In the personalized femoral head prosthesis designing by reverse engineering technology, obtaining the accurate acetabulum shape is the most important task. However, due to the necrotic femoral head's complex shape and the extremely narrow inter-bone region, obtaining the accurate acetabulum shape remains a challenging work. In this paper, we overcame these difficulties and developed an automatic segmentation method. First, we obtain the rough contour of the femoral head by utilizing the constraints of the great trochanter and the shape of femoral head in the initial slice. Second, we refine the rough contour by an orthogonal line edge detection approach and obtain a refined contour which will be used as the initial contour of the snake algorithm. Then, the snake algorithm is performed slice by slice upwards and downwards to generate the adjacent contours. During this process, the contour of the femoral head in a segmented slice is used as the initial contour of the next unsegmented slice. Finally, we can obtain the accurate sequential contours of the acetabulum by removing the femoral head and the femoral regions. And the 3D models of the acetabulum can be obtained correspondingly. The experimental result shows that the 3D models obtained by the proposed method are accurate and satisfactory. On this condition, we can reconstruct the personalized femoral head 3D models and design the personalized femoral head prosthesis. PMID:26803563

  18. Automatic Building Extraction and Roof Reconstruction in 3k Imagery Based on Line Segments

    Science.gov (United States)

    Köhn, A.; Tian, J.; Kurz, F.

    2016-06-01

    We propose an image processing workflow to extract rectangular building footprints using georeferenced stereo-imagery and a derivative digital surface model (DSM) product. The approach applies a line segment detection procedure to the imagery and subsequently verifies identified line segments individually to create a footprint on the basis of the DSM. The footprint is further optimized by morphological filtering. Towards the realization of 3D models, we decompose the produced footprint and generate a 3D point cloud from DSM height information. By utilizing the robust RANSAC plane fitting algorithm, the roof structure can be correctly reconstructed. In an experimental part, the proposed approach has been performed on 3K aerial imagery.

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

    Science.gov (United States)

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

    2016-03-01

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

  20. 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...

  1. 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.

  2. 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)

  3. Automatic segmentation and classification of gestational sac based on mean sac diameter using medical ultrasound image

    Science.gov (United States)

    Khazendar, Shan; Farren, Jessica; Al-Assam, Hisham; Sayasneh, Ahmed; Du, Hongbo; Bourne, Tom; Jassim, Sabah A.

    2014-05-01

    Ultrasound is an effective multipurpose imaging modality that has been widely used for monitoring and diagnosing early pregnancy events. Technology developments coupled with wide public acceptance has made ultrasound an ideal tool for better understanding and diagnosing of early pregnancy. The first measurable signs of an early pregnancy are the geometric characteristics of the Gestational Sac (GS). Currently, the size of the GS is manually estimated from ultrasound images. The manual measurement involves multiple subjective decisions, in which dimensions are taken in three planes to establish what is known as Mean Sac Diameter (MSD). The manual measurement results in inter- and intra-observer variations, which may lead to difficulties in diagnosis. This paper proposes a fully automated diagnosis solution to accurately identify miscarriage cases in the first trimester of pregnancy based on automatic quantification of the MSD. Our study shows a strong positive correlation between the manual and the automatic MSD estimations. Our experimental results based on a dataset of 68 ultrasound images illustrate the effectiveness of the proposed scheme in identifying early miscarriage cases with classification accuracies comparable with those of domain experts using K nearest neighbor classifier on automatically estimated MSDs.

  4. 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.

  5. Microsurgical anatomy of the third segment of vertebral artery%椎动脉第三段的显微解剖研究

    Institute of Scientific and Technical Information of China (English)

    杨帆; 佟小光; 洪健; 靳峥

    2009-01-01

    Objective To accumulate the morphological data of the third segment of vertebral artery and to provide the basis of microvascular anatomy for clinical treatment. Method Ten adult cadaver heads (20 sides)were used in the study. The morphology of the third segment of vertebral arteries was observed and their dimensions were measured with the aid of an operating microscope. Results The third segments of the vertebral arteries were tortuous. The external diameters of left vertebral arteries(4. 01 ±1.12)mm were larger than that of the right ones (3.45± 0. 32) mm. The length of the third segment of the vertebral artery was(50. 93 ±8. 23)ram. There was no significant anatomic variation. Conclusions The third segment of vertebral artery is tortuous to enable the movements of neck. The far-lateral approach is often used to expose the third segment of vertebral artery. The suboccipital triangle may be an anatomic marker for the third segment of vertebral artery.%目的 研究椎动脉第三段(V3)的形态特点,为临床应用提供解剖学依据.方法 10例(20侧)成人尸头标本,解剖观察V3段形态结构,测量椎动脉和枕动脉长度、外径等解剖数据.结果 V3段有明显而连续的多个弯曲,V3段全长(50.93±8.23)mm,未见明显解剖变异.结论 V3段的连续弯曲可适应头颈部复杂的运动,V3段的显露常采用枕下远外侧入路,枕下三角是术中暴露识别V3段的重要标志.

  6. 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.;

    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...... differences. Results: The segmentation method has been used on 9711 low dose CT images from the Danish Lung Cancer Screening Trial (DLCST). Manual inspection of thumbnail images revealed gross errors in a total of 44 images. 29 were missing branches at the lobar level and only 15 had obvious false positives...... extracted perpendicularly to and in random positions of the centerline in 7 subjects. Results show an average Dice's coefficient of 89%. The COPD gene phantom was scanned with the DLCST protocol and all interior and exterior diameters were estimated within 0.3 mm of their actual values. Limiting...

  7. 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...

  8. 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.

  9. Automatic segmentation of blood vessels from MR angiography volume data by using fuzzy logic technique

    Science.gov (United States)

    Kobashi, Syoji; Hata, Yutaka; Tokimoto, Yasuhiro; Ishikawa, Makato

    1999-05-01

    This paper shows a novel medical image segmentation method applied to blood vessel segmentation from magnetic resonance angiography volume data. The principle idea of the method is fuzzy information granulation concept. The method consists of 2 parts: (1) quantization and feature extraction, (2) iterative fuzzy synthesis. In the first part, volume quantization is performed with watershed segmentation technique. Each quantum is represented by three features, vascularity, narrowness and histogram consistency. Using these features, we estimate the fuzzy degrees of each quantum for knowledge models about MRA volume data. In the second part, the method increases the fuzzy degrees by selectively synthesizing neighboring quantums. As a result, we obtain some synthesized quantums. We regard them as fuzzy granules and classify them into blood vessel or fat by evaluating the fuzzy degrees. In the experimental result, three dimensional images are generated using target maximum intensity projection (MIP) and surface shaded display. The comparison with conventional MIP images shows that the unclarity region in conventional images are clearly depict in our images. The qualitative evaluation done by a physician shows that our method can extract blood vessel region and that the results are useful to diagnose the cerebral diseases.

  10. Spatial context learning approach to automatic segmentation of pleural effusion in chest computed tomography images

    Science.gov (United States)

    Mansoor, Awais; Casas, Rafael; Linguraru, Marius G.

    2016-03-01

    Pleural effusion is an abnormal collection of fluid within the pleural cavity. Excessive accumulation of pleural fluid is an important bio-marker for various illnesses, including congestive heart failure, pneumonia, metastatic cancer, and pulmonary embolism. Quantification of pleural effusion can be indicative of the progression of disease as well as the effectiveness of any treatment being administered. Quantification, however, is challenging due to unpredictable amounts and density of fluid, complex topology of the pleural cavity, and the similarity in texture and intensity of pleural fluid to the surrounding tissues in computed tomography (CT) scans. Herein, we present an automated method for the segmentation of pleural effusion in CT scans based on spatial context information. The method consists of two stages: first, a probabilistic pleural effusion map is created using multi-atlas segmentation. The probabilistic map assigns a priori probabilities to the presence of pleural uid at every location in the CT scan. Second, a statistical pattern classification approach is designed to annotate pleural regions using local descriptors based on a priori probabilities, geometrical, and spatial features. Thirty seven CT scans from a diverse patient population containing confirmed cases of minimal to severe amounts of pleural effusion were used to validate the proposed segmentation method. An average Dice coefficient of 0.82685 and Hausdorff distance of 16.2155 mm was obtained.

  11. 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

  12. 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

  13. Automatic segmentation of different-sized leukoaraiosis regions in brain MR images

    Science.gov (United States)

    Uchiyama, Yoshikazu; Kunieda, Takuya; Hara, Takeshi; Fujita, Hiroshi; Ando, Hiromichi; Yamakawa, Hiroyasu; Asano, Takahiko; Kato, Hiroki; Iwama, Toru; Kanematsu, Masayuki; Hoshi, Hiroaki

    2008-03-01

    Cerebrovascular diseases are the third leading cause of death in Japan. Therefore, a screening system for the early detection of asymptomatic brain diseases is widely used. In this screening system, leukoaraiosis is often detected in magnetic resonance (MR) images. The quantitative analysis of leukoaraiosis is important because its presence and extension is associated with an increased risk of severe stroke. However, thus far, the diagnosis of leukoaraiosis has generally been limited to subjective judgments by radiologists. Therefore, the purpose of this study was to develop a computerized method for the segmentation of leukoaraiosis, and provide an objective measurement of the lesion volume. Our database comprised of T1- and T2-weighted images obtained from 73 patients. The locations of leukoaraiosis regions were determined by an experienced neuroradiologist. We first segment cerebral parenchymal regions in T1-weighted images by using a region growing technique. For determining the initial candidate regions for leukoaraiosis, the k-means clustering of pixel values in the T1- and T2-weighted images was applied to the segmented cerebral region. For the elimination of false positives (FPs), we determined features such as the location, size, and circularity from each of the initial candidates. Finally, rule-based schemes and a quadratic discriminant analysis with these features were employed for distinguishing between the leukoaraiosis regions and the FPs. The results indicated that the sensitivity for the detection of leukoaraiosis was 100% with 5.84 FPs per image. Our computerized scheme can be useful in assisting radiologists for the quantitative analysis of leukoaraiosis in T1- and T2-weighted images.

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

    Energy Technology Data Exchange (ETDEWEB)

    Martin, Spencer [Department of Medical Biophysics, University of Western Ontario, London (Canada); Rodrigues, George, E-mail: george.rodrigues@lhsc.on.ca [Department of Radiation Oncology, London Regional Cancer Program, London (Canada); Department of Epidemiology/Biostatistics, University of Western Ontario, London (Canada); Patil, Nikhilesh [Department of Radiation Oncology, London Regional Cancer Program, London (Canada); Bauman, Glenn [Department of Medical Biophysics, University of Western Ontario, London (Canada); Department of Radiation Oncology, London Regional Cancer Program, London (Canada); D' Souza, David; Sexton, Tracy; Palma, David; Louie, Alexander V. [Department of Radiation Oncology, London Regional Cancer Program, London (Canada); Khalvati, Farzad; Tizhoosh, Hamid R. [Department of Systems Design Engineering, University of Waterloo, Waterloo (Canada); Segasist Technologies, Toronto, Ontario (Canada); Gaede, Stewart [Department of Medical Biophysics, University of Western Ontario, London (Canada)

    2013-01-01

    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

  15. Automatic Vehicle Detection during Nighttime Using Bright Pixel Segment with Spatial Temporal Technique

    Directory of Open Access Journals (Sweden)

    S.Nandhini

    2012-06-01

    Full Text Available The paper proposes an effective Traffic surveillance system for detecting and tracking moving vehicles in nighttime traffic. It identifies vehicles by detecting and locating vehicle headlights and taillights using image segmentation and pattern analysis technique. By preprocessing noise is removed using median filter. Morphological operation is used to extract candidate headlight objects and then perform shape analysis. Template matching or pattern classification to find the paired headlight of moving vehicles. Salient points are used to represent local properties of image classification. It carries information about image content. Gabor derivation is used for edge detection and feature extraction.

  16. Automatic quantification of mammary glands on non-contrast x-ray CT by using a novel segmentation approach

    Science.gov (United States)

    Zhou, Xiangrong; Kano, Takuya; Cai, Yunliang; Li, Shuo; Zhou, Xinxin; Hara, Takeshi; Yokoyama, Ryujiro; Fujita, Hiroshi

    2016-03-01

    This paper describes a brand new automatic segmentation method for quantifying volume and density of mammary gland regions on non-contrast CT images. The proposed method uses two processing steps: (1) breast region localization, and (2) breast region decomposition to accomplish a robust mammary gland segmentation task on CT images. The first step detects two minimum bounding boxes of left and right breast regions, respectively, based on a machine-learning approach that adapts to a large variance of the breast appearances on different age levels. The second step divides the whole breast region in each side into mammary gland, fat tissue, and other regions by using spectral clustering technique that focuses on intra-region similarities of each patient and aims to overcome the image variance caused by different scan-parameters. The whole approach is designed as a simple structure with very minimum number of parameters to gain a superior robustness and computational efficiency for real clinical setting. We applied this approach to a dataset of 300 CT scans, which are sampled with the equal number from 30 to 50 years-old-women. Comparing to human annotations, the proposed approach can measure volume and quantify distributions of the CT numbers of mammary gland regions successfully. The experimental results demonstrated that the proposed approach achieves results consistent with manual annotations. Through our proposed framework, an efficient and effective low cost clinical screening scheme may be easily implemented to predict breast cancer risk, especially on those already acquired scans.

  17. 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.

  18. Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image.

    Science.gov (United States)

    Singh, Anushikha; Dutta, Malay Kishore; ParthaSarathi, M; Uher, Vaclav; Burget, Radim

    2016-02-01

    Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification.

  19. Model Based Automatic Segmentation Of Tree Stems From Single Scan Data

    Science.gov (United States)

    Boesch, R.

    2013-10-01

    Forest inventories collect feature data manually on terrestrial field plots. Measuring large amounts of breast height diameters and tree positions is time consuming. Terrestrial laser scanning could be an additional instrument to collect precise and full inventory data in the 3D space. As a preliminary assumption single scan data is used to evaluate a minimal data acquisition scheme. To extract features like trees and diameter from the scanned point cloud, a simple geometric model world is defined in 3D. Trees are cylinder shapes vertically located on a plane. Using a RANSAC-based segmentation approach, cylinders are fitted iteratively in the point cloud. Several threshold parameters increase the robustness of the segmentation model and extract point clouds of single trees, which still contain branches and the tree crown. Fitting circles along the stem using point cloud slices allows to refine the effective diameter for customized heights. The cross section of a single tree point cloud covers only the semi circle towards the scan location, but is still contiguous enough to estimate diameters by using a robust circle fitting method.

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. Automatic segmentation of lung nodules with growing neural gas and support vector machine.

    Science.gov (United States)

    Magalhães Barros Netto, Stelmo; Corrêa Silva, Aristófanes; Acatauassú Nunes, Rodolfo; Gattass, Marcelo

    2012-11-01

    Lung cancer is distinguished by presenting one of the highest incidences and one of the highest rates of mortality among all other types of cancer. Unfortunately, this disease is often diagnosed late, affecting the treatment outcome. In order to help specialists in the search and identification of lung nodules in tomographic images, many research centers have developed computer-aided detection systems (CAD systems) to automate procedures. This work seeks to develop a methodology for automatic detection of lung nodules. The proposed method consists of the acquisition of computerized tomography images of the lung, the reduction of the volume of interest through techniques for the extraction of the thorax, extraction of the lung, and reconstruction of the original shape of the parenchyma. After that, growing neural gas (GNG) is applied to constrain even more the structures that are denser than the pulmonary parenchyma (nodules, blood vessels, bronchi, etc.). The next stage is the separation of the structures resembling lung nodules from other structures, such as vessels and bronchi. Finally, the structures are classified as either nodule or non-nodule, through shape and texture measurements together with support vector machine. The methodology ensures that nodules of reasonable size be found with 86% sensitivity and 91% specificity. This results in a mean accuracy of 91% for 10 experiments of training and testing in a sample of 48 nodules occurring in 29 exams. The rate of false positives per exam was of 0.138, for the 29 exams analyzed. PMID:23021776

  5. 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

  6. An Automatic Traffic Sign Detection and Recognition System Based on Colour Segmentation, Shape Matching, and SVM

    Directory of Open Access Journals (Sweden)

    Safat B. Wali

    2015-01-01

    Full Text Available The main objective of this study is to develop an efficient TSDR system which contains an enriched dataset of Malaysian traffic signs. The developed technique is invariant in variable lighting, rotation, translation, and viewing angle and has a low computational time with low false positive rate. The development of the system has three working stages: image preprocessing, detection, and recognition. The system demonstration using a RGB colour segmentation and shape matching followed by support vector machine (SVM classifier led to promising results with respect to the accuracy of 95.71%, false positive rate (0.9%, and processing time (0.43 s. The area under the receiver operating characteristic (ROC curves was introduced to statistically evaluate the recognition performance. The accuracy of the developed system is relatively high and the computational time is relatively low which will be helpful for classifying traffic signs especially on high ways around Malaysia. The low false positive rate will increase the system stability and reliability on real-time application.

  7. Pharynx Anatomy

    Science.gov (United States)

    ... e.g. -historical Searches are case-insensitive Pharynx Anatomy Add to My Pictures View /Download : Small: 720x576 ... View Download Large: 3000x2400 View Download Title: Pharynx Anatomy Description: Anatomy of the pharynx; drawing shows the ...

  8. Vulva Anatomy

    Science.gov (United States)

    ... e.g. -historical Searches are case-insensitive Vulva Anatomy Add to My Pictures View /Download : Small: 720x634 ... View Download Large: 3000x2640 View Download Title: Vulva Anatomy Description: Anatomy of the vulva; drawing shows the ...

  9. 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 ...

  10. 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.

  11. 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

  12. 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

  13. 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

  14. 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%

  15. A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points.

    Science.gov (United States)

    Yang, Xiaopeng; Yu, Hee Chul; Choi, Younggeun; Lee, Wonsup; Wang, Baojian; Yang, Jaedo; Hwang, Hongpil; Kim, Ji Hyun; Song, Jisoo; Cho, Baik Hwan; You, Heecheon

    2014-01-01

    The present study developed a hybrid semi-automatic method to extract the liver from abdominal computerized tomography (CT) images. The proposed hybrid method consists of a customized fast-marching level-set method for detection of an optimal initial liver region from multiple seed points selected by the user and a threshold-based level-set method for extraction of the actual liver region based on the initial liver region. The performance of the hybrid method was compared with those of the 2D region growing method implemented in OsiriX using abdominal CT datasets of 15 patients. The hybrid method showed a significantly higher accuracy in liver extraction (similarity index, SI=97.6 ± 0.5%; false positive error, FPE = 2.2 ± 0.7%; false negative error, FNE=2.5 ± 0.8%; average symmetric surface distance, ASD=1.4 ± 0.5mm) than the 2D (SI=94.0 ± 1.9%; FPE = 5.3 ± 1.1%; FNE=6.5 ± 3.7%; ASD=6.7 ± 3.8mm) region growing method. The total liver extraction time per CT dataset of the hybrid method (77 ± 10 s) is significantly less than the 2D region growing method (575 ± 136 s). The interaction time per CT dataset between the user and a computer of the hybrid method (28 ± 4 s) is significantly shorter than the 2D region growing method (484 ± 126 s). The proposed hybrid method was found preferred for liver segmentation in preoperative virtual liver surgery planning.

  16. 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

  17. 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.

  18. Semi-automatic lung segmentation of DCE-MRI data sets of 2-year old children after congenital diaphragmatic hernia repair: Initial results.

    Science.gov (United States)

    Zöllner, Frank G; Daab, Markus; Weidner, Meike; Sommer, Verena; Zahn, Katrin; Schaible, Thomas; Weisser, Gerald; Schoenberg, Stefan O; Neff, K Wolfgang; Schad, Lothar R

    2015-12-01

    In congenital diaphragmatic hernia (CDH), lung hypoplasia and secondary pulmonary hypertension are the major causes of death and severe disability. Based on new therapeutic strategies survival rates could be improved to up to 80%. However, after surgical repair of CDH, long-term follow-up of these pediatric patients is necessary. In this, dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides insights into the pulmonary microcirculation and might become a tool within the routine follow-up program of CDH patients. However, whole lung segmentation from DCE-MRI scans is tedious and automated procedures are warranted. Therefore, in this study, an approach to semi-automated lung segmentation is presented. Segmentation of the lung is obtained by calculating the cross correlation and the area under curve between all voxels in the data set and a reference region-of-interest (ROI), here the arterial input function (AIF). By applying an upper and lower threshold to the obtained maps and intersecting these, a final segmentation is reached. This approach was tested on twelve DCE-MRI data sets of 2-year old children after CDH repair. Segmentation accuracy was evaluated by comparing obtained automatic segmentations to manual delineations using the Dice overlap measure. Optimal thresholds for the cross correlation were 0.5/0.95 and 0.1/0.5 for the area under curve, respectively. The ipsilateral (left) lung showed reduced segmentation accuracy compared to the contralateral (right) lung. Average processing time was about 1.4s per data set. Average Dice score was 0.7±0.1 for the whole lung. In conclusion, initial results are promising. By our approach, whole lung segmentation is possible and a rapid evaluation of whole lung perfusion becomes possible. This might allow for a more detailed analysis of lung hypoplasia of children after CDH.

  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. 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

  6. 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

  7. 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)

  8. 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

  9. 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)

  10. 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

  11. 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.

  12. 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.

  13. Hepatic surgical anatomy.

    Science.gov (United States)

    Skandalakis, John E; Skandalakis, Lee J; Skandalakis, Panajiotis N; Mirilas, Petros

    2004-04-01

    The liver, the largest organ in the body, has been misunderstood at nearly all levels of organization, and there is a tendency to ignore details that do not fit the preconception. A complete presentation of the surgical anatomy of the liver includes the study of hepatic surfaces, margins, and fissures; the various classifications of lobes and segments; and the vasculature and lymphatics. A brief overview of the intrahepatic biliary tract is also presented.

  14. Performance evaluation of an automatic segmentation method of cerebral arteries in MRA images by use of a large image database

    Science.gov (United States)

    Uchiyama, Yoshikazu; Asano, Tatsunori; Hara, Takeshi; Fujita, Hiroshi; Kinosada, Yasutomi; Asano, Takahiko; Kato, Hiroki; Kanematsu, Masayuki; Hoshi, Hiroaki; Iwama, Toru

    2009-02-01

    The detection of cerebrovascular diseases such as unruptured aneurysm, stenosis, and occlusion is a major application of magnetic resonance angiography (MRA). However, their accurate detection is often difficult for radiologists. Therefore, several computer-aided diagnosis (CAD) schemes have been developed in order to assist radiologists with image interpretation. The purpose of this study was to develop a computerized method for segmenting cerebral arteries, which is an essential component of CAD schemes. For the segmentation of vessel regions, we first used a gray level transformation to calibrate voxel values. To adjust for variations in the positioning of patients, registration was subsequently employed to maximize the overlapping of the vessel regions in the target image and reference image. The vessel regions were then segmented from the background using gray-level thresholding and region growing techniques. Finally, rule-based schemes with features such as size, shape, and anatomical location were employed to distinguish between vessel regions and false positives. Our method was applied to 854 clinical cases obtained from two different hospitals. The segmentation of cerebral arteries in 97.1%(829/854) of the MRA studies was attained as an acceptable result. Therefore, our computerized method would be useful in CAD schemes for the detection of cerebrovascular diseases in MRA images.

  15. 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

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

    Science.gov (United States)

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

    2009-04-01

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

  17. 快速准确的自动音乐/语音分段方法%Fast and precise automatic music/speech segmentation

    Institute of Scientific and Technical Information of China (English)

    万玉龙; 周若华; 颜永红

    2013-01-01

    自动音乐/语音分段是语音识别技术的重要部分.该文采用回声器时频分析计算平均能量谱及定长片段的优化短时低能量比,用Bayes分类器判定类型,并根据内容连续性对分段结果修正;最后采用振幅包络匹配滤波器求所有起始点,对分段结果进一步优化.实验基于多语种电视电台录音和国内电话录音数据展开,结果显示该方法的性能FMeasure可达0.987,较已有分类系统性能有大幅提升,同时处理速度也有大幅度改进.%This article describes a fast and robust method for automatic music/speech classification and segmentation.A resonator time-frequency image (RTFI) is used to represent the average energy spectrum of the input data with the modified short-time low energy ratios then extracted for each constant length segment.Then,the system uses the Bayesian maximum-a-posteriori (MAP)classifier to decide the audio class of each segment and refine the classification results based on the fact that the audio types are continuous over a short time.An onset detection method is then used to rectify the beginnings and ends of each segment.The system is evaluated using recordings from multi-language radio and television shows and Chinese telephone calls.Tests show that the system outperforms the state-of-art methods with an FMeasure of up to 0.987 and much faster processing speed.

  18. 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.

  19. 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.

  20. 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

  1. Label-free optical lymphangiography: development of an automatic segmentation method applied to optical coherence tomography to visualize lymphatic vessels using Hessian filters

    Science.gov (United States)

    Yousefi, Siavash; Qin, Jia; Zhi, Zhongwei; Wang, Ruikang K.

    2013-08-01

    Lymphatic vessels are a part of the circulatory system that collect plasma and other substances that have leaked from the capillaries into interstitial fluid (lymph) and transport lymph back to the circulatory system. Since lymph is transparent, lymphatic vessels appear as dark hallow vessel-like regions in optical coherence tomography (OCT) cross sectional images. We propose an automatic method to segment lymphatic vessel lumen from OCT structural cross sections using eigenvalues of Hessian filters. Compared to the existing method based on intensity threshold, Hessian filters are more selective on vessel shape and less sensitive to intensity variations and noise. Using this segmentation technique along with optical micro-angiography allows label-free noninvasive simultaneous visualization of blood and lymphatic vessels in vivo. Lymphatic vessels play an important role in cancer, immune system response, inflammatory disease, wound healing and tissue regeneration. Development of imaging techniques and visualization tools for lymphatic vessels is valuable in understanding the mechanisms and studying therapeutic methods in related disease and tissue response.

  2. 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.

  3. 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

  4. 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

  5. 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....

  6. Validation of a method of automatic segmentation for delineation of volumes in PET imaging for radiotherapy; Validacion de un metodo de segmentacion automatica para delineacion de volumenes en imagenes PET para radioterapia

    Energy Technology Data Exchange (ETDEWEB)

    Latorre Musoll, A.; Eudaldo Puell, T.; Ruiz Martinez, A.; Fernandez Leon, A.; Carrasco de Fez, P.; Jornet Sala, N.; Ribas Morales, M.

    2011-07-01

    Prior to clinical use of PET imaging for the delineation of BTV, has made a preliminary study on model, to validate the automatic segmentation tools based on thresholds of activity concentration, which implement both PET-CT equipment as the Eclipse planning system.

  7. 尖锐特征诱导的点云自动分片算法%Automatic Sharp Feature Based Segmentation of Point Clouds

    Institute of Scientific and Technical Information of China (English)

    邹冬; 庞明勇

    2012-01-01

    点云模型的分片技术是数字几何处理领域的基础技术之一.提出一种尖锐特征诱导的点云模型自动分片算法.算法首先计算点云模型的局部微分属性,并以此来识别模型上的尖锐特征点;然后采用改进的折线生长算法生成并完善特征折线,并基于特征折线采用三次B样条曲线来逼近的尖锐特征点;最后采用区域生长方法将点云模型分割成多个几何特征单一、边界整齐的点云数据面片.实验表明,本文算法运行稳定,可以准确地分割点云模型.该算法可用于点云模型的形状匹配、纹理映射、CAD建模、以及逆向工程等应用中.%Segmentation of point clouds is one of basic and key technologies in digital geometry processing. In this paper, based on extracted sharp features, we present a method for automatic ally segmenting point clouds. Our algorithm first calculates local surface differentials features and uses them to identify sharp feature points. And an improved feature-ployline propagation technique is employed to approximate the feature points by a set of polylines and optimize the feature curves. Then, based on feature ploy lines, we approximate the sharp feature points by cubic B-spline curve. Subsequently, based on the extracted feature curves, region growing algorithm was applied to segment the point clouds into multiple regions, the geometric feature of the region is consistent and the boundary of the patch is neat. Experiments show that the algorithm can segment the point clouds precisely and efficiently. Our algorithm can be used in shape matching, texture mapping, CAD modeling and reverse engineering.

  8. 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

  9. 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

  10. Automatic Motion Segmentation of Sparse Feature Points with Mean Shift%一种基于均值偏移的自动运动分割方法

    Institute of Scientific and Technical Information of China (English)

    蒋鹏; 秦娜; 周艳; 唐鹏; 金炜东

    2013-01-01

    We proposed an automatic motion segmentation operating on sparse feature points.Feature points are detected and tracked throughout an image sequence,and feature points are grouped using a mean shift algorithm.The motion segmentation is driven by the density of the motion vector in feature space.The kernel density estimation is performed on the mean-shifted motion vector and the number of motion present is estimated by the number of peaks in the kernel density curve.Experimental results on a number of challenging image sequences demonstrate the effectiveness and robustness of the technique.%运动分割是计算机视觉领域研究的重要内容.提出一种基于均值偏移的自动运动分割算法.该方法首先用特征点匹配关系获得特征点的运动轨迹,并以轨迹的运动向量作为特征,再用均值偏移算法对轨迹的运动向量进行聚类.均值偏移缩小相似的运动向量之间的差别,同时扩大不同运动的运动向量之间的差距.为了自动获得运动分类数,还提出了一种基于非参数核密度的自动分类方法,该方法通过估计运动向量的密度分布,用核密度图自动确定运动分类数.实验结果表明,该算法分割精度高、鲁棒性好,能够自动确定运动分类数.

  11. 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.

  12. 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...

  13. 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...... they arise. In short, I expose the regulatory anatomy of the policy landscape....

  14. 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

  15. 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.…

  16. 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.

  17. 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.

  18. Microsurgical anatomy related to craniocervical junction segment of the vertebral artery in far lateral approach%寰枢段椎动脉在远外侧入路中的应用显微解剖研究

    Institute of Scientific and Technical Information of China (English)

    贾旺; 毕智勇; 鲁润春; 于春江

    2013-01-01

    Objective Microsurgical anatomy of craniocervical junction (CCJ) segment of the vertebral artery (VA) were studied to provide an applied anatomic basis for the far lateral approach.Methods Simulated operation of far lateral approach was performed on 10 cadaveric heads specimens and 10 dry skulls for measurment of the osseous relationships in the region.Results Craniocervical junction segment of the vertebral artery has five curvatures in most of the specimens,and compensatory vascular expansion in the curvatures was found.The average diameter is (4.3 ± 0.5) mm with changeful direction.The average half length of posterior arch of atlas is (19.3 ±4.7) mm,also the safe extent for exposing vertebral artery.Conclusions The key points to successfully preserve vertebral artery in far lateral approach are familiarity with the microanatomical relationship of craniocervical junction segment of the vertebral artery,especially the five curvatures.%目的 为颅颈交界区手术入路提供解剖学参数,帮助神经外科医生安全、准确地暴露手术靶区.方法 应用10%甲醛固定的汉族成人尸头标本10例20侧;漂白干颅骨及寰枢椎10例20侧.模拟手术入路逐层解剖,并对解剖结构进行精确测量和拍照.结果 寰枢段椎动脉在颅颈交界区形成比较恒定的五个生理弯曲,平均直径(4.3±0.5) mm,角度多变.寰椎后弓外侧半距(19.3±4.7)mm.结论 熟悉寰枢段椎动脉五个生理弯曲的定位方法,有助于提高颅颈交界区手术入路的安全性.

  19. 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.)

  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. 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.

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

    Science.gov (United States)

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

    2015-03-01

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

  5. 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

  6. 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...

  7. Automatic Representation and Segmentation of Video Sequences via a Novel Framework Based on the nD-EVM and Kohonen Networks

    Directory of Open Access Journals (Sweden)

    José-Yovany Luis-García

    2016-01-01

    Full Text Available Recently in the Computer Vision field, a subject of interest, at least in almost every video application based on scene content, is video segmentation. Some of these applications are indexing, surveillance, medical imaging, event analysis, and computer-guided surgery, for naming some of them. To achieve their goals, these applications need meaningful information about a video sequence, in order to understand the events in its corresponding scene. Therefore, we need semantic information which can be obtained from objects of interest that are present in the scene. In order to recognize objects we need to compute features which aid the finding of similarities and dissimilarities, among other characteristics. For this reason, one of the most important tasks for video and image processing is segmentation. The segmentation process consists in separating data into groups that share similar features. Based on this, in this work we propose a novel framework for video representation and segmentation. The main workflow of this framework is given by the processing of an input frame sequence in order to obtain, as output, a segmented version. For video representation we use the Extreme Vertices Model in the n-Dimensional Space while we use the Discrete Compactness descriptor as feature and Kohonen Self-Organizing Maps for segmentation purposes.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-09-15

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

  9. 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 ...

  10. 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.

  11. 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.

  12. 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

  13. 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...

  14. 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.

  15. 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...

  16. 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

  17. 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.

  18. 医学图像自动分割的混合聚类方法%A HYBRID CLUSTERING METHOD FOR AUTOMATIC MEDICAL IMAGE SEGMENTATION

    Institute of Scientific and Technical Information of China (English)

    迟冬祥; 黎明; 赵莹

    2011-01-01

    Segmentation result and automation level of medical images impact significantly on those aspects including computer-aided diagnosis and visualisation. In light to the features of medical images with low-contrast and high noisy influences, a hybrid clustering method is proposed. After the pre-processing step, the neighbouring eigenvectors of each pixel are fed into a self-organising map (SOM) for training. The output prototype vectors of SOM, as the preliminary clustering outcomes, are then filtered with hits-map, and followed by further processing with a hierarchical agglomerative clustering method. A quantitative index of image segmentation is selected to determine the best sort number of the clustering after comparing the Davies-Bouldin ( DB) clustering evaluation index with two other image segmentation evaluation indices. The final segmentation results are obtained from the post-processing. Analyses show that the proposed method is effective. Meanwhile, its limitations and the research work in the future are also discussed.%医学图像的分割效果和自动化程度对计算机辅助诊断和可视化等方面有重要影响.针对医学图像低对比度、噪声影响大的特点,提出一种混合聚类方法:在预处理图像之后,将每个像素的邻域特征向量送入自组织特征映射网络SOM(self-organizing map)中进行训练;作为初步聚类的结果,SOM的输出典型向量根据命中图(Hits-Map)过滤,再由层次合并聚类方法进一步处理.在比较了一种聚类评价指数和两种图像分割评价指数之后,采用图像分割量化指数来确定聚类的最佳类别数;再通过后处理得到最后分割结果,分析表明这个方法是有效的.同时,也指出其不足之处和进一步研究的方向.

  19. 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....

  20. Anatomy of the lymphatics.

    Science.gov (United States)

    Skandalakis, John E; Skandalakis, Lee J; Skandalakis, Panagiotis N

    2007-01-01

    The lymphatic system is perhaps the most complicated system of Homo sapiens. An introduction to the anatomy, embryology, and anomalies of the lymphatics is presented. The overall anatomy and drainage of the lymphatic vessels in outlined. The topographic anatomy, relations, and variations of the principle vessels of the lymphatic system (the right lymphatic duct, the thoracic duct, and the cisterna chyli) are presented in detail.

  1. 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

  2. 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.

  3. 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.

  4. SEGMENTACIÓN SEMIAUTOMÁTICA DEL VOLUMEN DEL HÍGADO EN IMÁGENES DE TOMOGRAFÍA COMPUTARIZADA Semi-Automatic Liver Volume Segmentation in Computed Tomography Images

    Directory of Open Access Journals (Sweden)

    GERARDO TIBAMOSO

    Full Text Available El volumen del hígado es un parámetro determinante en cirugía para la extracción de tumores, trasplantes, y en tratamientos de regeneración. Generalmente, la estimación de este volumen se calcula a partir de segmentaciones manuales realizadas por especialistas, siendo éste un proceso tedioso y con poca reproducibilidad de sus resultados. En este trabajo se presenta un método semiautomático para la segmentación del volumen del hígado en imágenes de TAC. El método consiste en superponer manualmente una superficie de triángulos en las imágenes, y deformarla por medio de una ecuación de movimiento asociada a cada uno de sus vértices, para delimitar las fronteras del hígado. La dinámica de la superficie depende de información de intensidades y gradientes, y de relaciones de vecindad entre los vértices, hasta cumplir un número de iteraciones. Comparaciones entre las segmentaciones del método con las segmentaciones de referencia en 20 estudios de TAC, muestran la adaptabilidad de la superficie a la forma y fronteras difusas del hígado, dos de los principales problemas de la segmentación.Liver volume is a significant parameter in surgery for tumor extraction, transplants, and regeneration treatments. Generally, the volume estimation is obtained from manual segmentations 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 segmentations in 20 CT scans, show the surface adaptability to the shape and the diffuse

  5. 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…

  6. 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…

  7. 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

  8. 基于自适应图像分割自动提取道路中心线%The automatic road centerline extraction based on adaptive image segmentation

    Institute of Scientific and Technical Information of China (English)

    汤瑞华

    2015-01-01

    道路是城区地理空间信息中最重要的基础设施之一,从高分辨影像中自动、快速的提取道路特征,是快速更新城市道路网信息的重要途径。文中在分析道路基本特征的基础上,选择基于自适应结构元素的形态分析算法提取初始道路区域;引入面积和长宽比等形状指数,得到较精确的道路信息;最后,采用Hilditch细化算法,并进行优化处理。实验证明,该道路提取过程中无需人工设置参数,且能够得到具有较高完整性和正确性的道路中心线。%The road is one of the most important infrastructures in urban geospatial information .Automatic and fast road extraction from high resolution image is an important way of updating the urban road network fast .Thus a new automatic method is proposed to extract the road feature .First ,the adaptive structure element is adopted in the image segmentation .And then ,the precise road features can be extracted under the constrains of area and length‐breadth ratio (referred to as L/B) .Final ,the road line is obtained by using Hilditch algorithm and optimizing process .The experiments indicate that the road extraction without artificial set parameters in the process can get the centerline of the road with high completeness and correctness .

  9. 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.

  10. 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...

  11. Skull Base Anatomy.

    Science.gov (United States)

    Patel, Chirag R; Fernandez-Miranda, Juan C; Wang, Wei-Hsin; Wang, Eric W

    2016-02-01

    The anatomy of the skull base is complex with multiple neurovascular structures in a small space. Understanding all of the intricate relationships begins with understanding the anatomy of the sphenoid bone. The cavernous sinus contains the carotid artery and some of its branches; cranial nerves III, IV, VI, and V1; and transmits venous blood from multiple sources. The anterior skull base extends to the frontal sinus and is important to understand for sinus surgery and sinonasal malignancies. The clivus protects the brainstem and posterior cranial fossa. A thorough appreciation of the anatomy of these various areas allows for endoscopic endonasal approaches to the skull base.

  12. 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.

  13. 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…

  14. 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)

  15. 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)

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

    Science.gov (United States)

    Park, Eunbi; Kim, Taeho; Park, Jinah

    2016-03-01

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

  17. 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.

  18. 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...

  19. Research on automatic segmentation of agricultural land and artificial buildings in remote sensing image%遥感图像中农用地与人工建筑自动分割技术研究

    Institute of Scientific and Technical Information of China (English)

    王敏彦; 赵坤

    2015-01-01

    以农业用地与人工建筑的分割为研究目的,提出一种可在卫星或航拍遥感图像中自动分割农用田地与人工建筑的图像处理算法。该算法使用图像区域合成、滤波、灰度统计、背景分割等处理技术,可实现大面积农用田地中人工建筑及道路的分割,并给出面积的估算。在此给出了解决思路,并给出试验效果。该技术对提高农地统计自动化水平、农业用地侵占普查效率有一定工程意义。%An image processing algorithm which can automatically divide up agricultural fields and artificial buildings in re⁃mote sensing images from satellite or aerial photo is proposed. With the technologies of image area synthesis,filtering,histo⁃gram statistics and background segmentation,the artificial buildings and roads in large⁃scale agricultural lands can be segment⁃ed by the algorithm,and the area calculation can be realized. Some solutions and test results are given in this paper. It is signifi⁃cant for this algorithm to improve the automation statistic level of agricultural land.

  20. 基于分块采样和遗传算法的自动多阈值图像分割%Automatic Multilevel Thresholding for Image Segmentation Based on Block Sampling and Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    姜允志; 郝志峰; 林智勇; 袁淦钊

    2011-01-01

    图像多阈值分割在图像压缩、图像分析和模式识别等很多领域具有重要应用,但是阈值数的自动选择一直是至今未解决的难题.为此,基于分块采样和遗传算法提出一种自动多阈值图像分割算法.首先将一幅图像看成是由像素值组成的总体,运用分块采样得到若干子样本;其次在每一个子样本中运用遗传算法来使样本的均值与方差比极大化;再基于获得的样本信息对阈值数目和阈值进行自动预测;最后利用一种确定性的算法对阈值数和阈值做进一步的优化.该算法无需事先考虑图像的纹理和分割数等先验信息,具有较高的易用性,其计算复杂性对图像阈值个数敏感性较低,且无需进行灰度直方图分析.在Berkeley图像分割数据集上的大量仿真实验结果表明,文中算法能获得较准确、快速和稳定的图像分割.%Multilevel thresholding is an important technique for image compression, image analysis and pattern recognition. However, it is a hard problem to determine the number of thresholds automatically. In this paper, a new multilevel thresholding method called as automatic multilevel thresholding algorithm for image segmentation based on block sampling and genetic algorithm (AMT-BSGA) is proposed on the basis of block sampling and genetic algorithm. The proposed method can automatically determine the appropriate number of thresholds and the proper threshold values. In AMT-BSGA, an image is treated as a group of individual pixels with the gray values. First, an image is evenly divided into several blocks, and a sample is drawn from each block. Then, genetic algorithm based optimization is applied to each sample to maximize the ratio of mean and variance of the sample. Based on the optimized samples, the number of thresholds and threshold values are preliminarily determined. Finally, a deterministic method is implemented to further optimize the number of thresholds and

  1. 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

  2. Fat segmentation on chest CT images via fuzzy models

    Science.gov (United States)

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

    2016-03-01

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

  3. 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 ...

  4. 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...

  5. 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.

  6. Anatomy relevant to conservative mastectomy

    OpenAIRE

    O’Connell, Rachel L.; Rusby, Jennifer E

    2015-01-01

    Knowledge of the anatomy of the nipple and breast skin is fundamental to any surgeon practicing conservative mastectomies. In this paper, the relevant clinical anatomy will be described, mainly focusing on the anatomy of the “oncoplastic plane”, the ducts and the vasculature. We will also cover more briefly the nerve supply and the arrangement of smooth muscle of the nipple. Finally the lymphatic drainage of the nipple and areola will be described. An appreciation of the relevant anatomy, tog...

  7. 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…

  8. 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.

  9. GPU-based relative fuzzy connectedness image segmentation

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-01-15

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

  10. Cortical surface segmentation and mapping

    OpenAIRE

    Tosun, Duygu; Rettmann, Maryam E.; Han, Xiao; Tao, Xiaodong; Xu, Chenyang; Resnick, Susan M.; Pham, Dzung L.; Prince, Jerry L.

    2004-01-01

    Segmentation and mapping of the human cerebral cortex from magnetic resonance (MR) images plays an important role in neuro-science and medicine. This paper describes a comprehensive approach for cortical reconstruction, flattening, and sulcal segmentation. Robustness to imaging artifacts and anatomical consistency are key achievements in an overall approach that is nearly fully automatic and computationally fast. Results demonstrating the application of this approach to a study of cortical th...

  11. 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.

  12. Anatomy for Biomedical Engineers

    Science.gov (United States)

    Carmichael, Stephen W.; Robb, Richard A.

    2008-01-01

    There is a perceived need for anatomy instruction for graduate students enrolled in a biomedical engineering program. This appeared especially important for students interested in and using medical images. These students typically did not have a strong background in biology. The authors arranged for students to dissect regions of the body that…

  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. Leaf anatomy and photosynthesis

    NARCIS (Netherlands)

    Berghuijs, H.N.C.

    2016-01-01

    Keywords: CO2 diffusion, C3 photosynthesis, mesophyll conductance, mesophyll resistance, re-assimilation, photorespiration, respiration, tomato Herman Nicolaas Cornelis Berghuijs (2016). Leaf anatomy and photosynthesis; unravelling the CO2 diffusion pathway in C3 leaves. PhD thesis. Wageningen Unive

  15. 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 ...

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

    Energy Technology Data Exchange (ETDEWEB)

    Labine, Alexandre; Carrier, Jean-François; Bedwani, Stéphane [Centre hospitalier de l' Université de Montréal (Canada); Chav, Ramnada; De Guise, Jacques [Laboratoire de recherche en imagerie et d' orthopédie-CRCHUM, École de technologie supérieure (Canada)

    2014-08-15

    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.

  17. Segmentation of moving images by the human visual system.

    Science.gov (United States)

    Chantelau, K

    1997-08-01

    New segments appearing in an image sequence or spontaneously accelerated segments are band limited by the visual system due to a nonperfect tracking of these segments by eye movements. In spite of this band limitation and acceleration of segments, a coarse segmentation (initial segmentation phase) can be performed by the visual system. This is interesting for the development of purely automatic segmentation algorithms for multimedia applications. In this paper the segmentation of the visual system is modelled and used in an automatic coarse initial segmentation. A suitable model for motion processing based on a spectral representation is presented and applied to the segmentation of synthetic and real image sequences with band limited and accelerated moving foreground and background segments.

  18. Segmentation of internal brain structures in three-dimensional nuclear magnetic resonance imaging; Segmentation des structures internes du cerveau en imagerie par resonance magnetique tridimensionnelle

    Energy Technology Data Exchange (ETDEWEB)

    Geraud, Th

    1998-06-06

    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

  19. 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

  20. 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

  1. 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...

  2. 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.

  3. 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

  4. 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)

  5. 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.

  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...... a basic understanding of grouping people. Advertising agencies may use segmentation totarget advertisements, while food companies may usesegmentation to develop products to various groups of consumers. MAPP has for example investigated the positioning of fish in relation to other food products...... their evaluation of fish - for example by focusing on quality. Therefore, taking heterogeneity between segments into account makes a lot of difference, quantitatively as well as qualitatively....

  7. 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。

  8. 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...

  9. Neural network based temporal video segmentation.

    Science.gov (United States)

    Cao, X; Suganthan, P N

    2002-01-01

    The organization of video information in video databases requires automatic temporal segmentation with minimal user interaction. As neural networks are capable of learning the characteristics of various video segments and clustering them accordingly, in this paper, a neural network based technique is developed to segment the video sequence into shots automatically and with a minimum number of user-defined parameters. We propose to employ growing neural gas (GNG) networks and integrate multiple frame difference features to efficiently detect shot boundaries in the video. Experimental results are presented to illustrate the good performance of the proposed scheme on real video sequences. PMID:12370954

  10. 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.

  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. [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

  13. 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

  14. 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.

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

    Science.gov (United States)

    Fleming, Alan D; Goatman, Keith A; Philip, Sam; Olson, John A; Sharp, Peter F

    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.

  16. 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.

  17. 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.

  18. 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

  19. 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...

  20. 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...

  1. Multiple scale music segmentation using rhythm, timbre and harmony

    DEFF Research Database (Denmark)

    Jensen, Karl Kristoffer

    2007-01-01

    The segmentation of music into intro-chorus-verse-outro, and similar segments, is a difficult topic. A method for performing automatic segmentation based on features related to rhythm, timbre, and harmony is presented, and compared, between the features and between the features and manual...

  2. Segmentation of anatomical structures in chest CT scans

    NARCIS (Netherlands)

    van Rikxoort, E.M.

    2009-01-01

    In this thesis, methods are described for the automatic segmentation of anatomical structures from chest CT scans. First, a method to segment the lungs from chest CT scans is presented. Standard lung segmentation algorithms rely on large attenuation differences between the lungs and the surrounding

  3. Segmenting anatomy in chest x-rays for tuberculosis screening.

    Science.gov (United States)

    Karargyris, Alexandros; Antani, Sameer; Thoma, George

    2011-01-01

    In this paper we describe the development of a screening system for pulmonary pathologies (i.e. pneumonia, tuberculosis) application in global healthcare settings. As a first step toward this goal, the paper presents a novel approach for detecting lungs and ribs in chest radiographs. The approach is a unified method combining two detection schemes resulting in reduced cost. The novelty of our approach lies on the fact that instead of using pixel-wise techniques exclusively we used region-based features computed as wavelet features that take into consideration the orientation of anatomic structures. Initial results are described. Next steps include classification of non-rib lung regions for radiographic patterns suggesting tuberculosis infection. PMID:22256142

  4. 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...

  5. 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.

  6. Automatic Reading

    Institute of Scientific and Technical Information of China (English)

    胡迪

    2007-01-01

    <正>Reading is the key to school success and,like any skill,it takes practice.A child learns to walk by practising until he no longer has to think about how to put one foot in front of the other.The great athlete practises until he can play quickly,accurately and without thinking.Ed- ucators call it automaticity.

  7. 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.

  8. [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

  9. VISUALIZATION OF REGISTERED SUBSURFACE ANATOMY

    DEFF Research Database (Denmark)

    2010-01-01

    A system and method for visualization of subsurface anatomy includes obtaining a first image from a first camera and a second image from a second camera or a second channel of the first camera, where the first and second images contain shared anatomical structures. The second camera and the second...... channel of the first camera are capable of imaging anatomy beneath the surface in ultra-violet, visual, or infra-red spectrum. A data processor is configured for computing registration of the first image to the second image to provide visualization of subsurface anatomy during surgical procedures...

  10. The anatomy of teaching and the teaching of anatomy.

    Science.gov (United States)

    Peck, David; Skandalakis, John E

    2004-04-01

    Professional education is one of the greatest problems currently confronting the healing professions. The incorporation of basic science departments into colleges of medicine has affected curriculum design, research, admissions criteria, and licensure. Those who are not practicing members of a particular health care profession wield undue influence in medical schools. Ideally, gross anatomy teachers should be health care professionals who use anatomy in their practices. Reorganization of medical education will heal the rift between research and clinical medicine.

  11. [Segmentation Method for Liver Organ Based on Image Sequence Context].

    Science.gov (United States)

    Zhang, Meiyun; Fang, Bin; Wang, Yi; Zhong, Nanchang

    2015-10-01

    In view of the problems of more artificial interventions and segmentation defects in existing two-dimensional segmentation methods and abnormal liver segmentation errors in three-dimensional segmentation methods, this paper presents a semi-automatic liver organ segmentation method based on the image sequence context. The method takes advantage of the existing similarity between the image sequence contexts of the prior knowledge of liver organs, and combines region growing and level set method to carry out semi-automatic segmentation of livers, along with the aid of a small amount of manual intervention to deal with liver mutation situations. The experiment results showed that the liver segmentation algorithm presented in this paper had a high precision, and a good segmentation effect on livers which have greater variability, and can meet clinical application demands quite well.

  12. 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...

  13. 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

  14. An improved automatic computer aided tube detection and labeling system on chest radiographs

    Science.gov (United States)

    Ramakrishna, Bharath; Brown, Matthew; Goldin, Jonathan; Cagnon, Christopher; Enzmann, Dieter

    2012-03-01

    Tubes like Endotracheal (ET) tube used to maintain patient's airway and the Nasogastric (NG) tube used to feed the patient and drain contents of the stomach are very commonly used in Intensive Care Units (ICU). The placement of these tubes is critical for their proper functioning and improper tube placement can even be fatal. Bedside chest radiographs are considered the quickest and safest method to check the placement of these tubes. Tertiary ICU's typically generate over 250 chest radiographs per day to confirm tube placement. This paper develops a new fully automatic prototype computer-aided detection (CAD) system for tube detection on bedside chest radiographs. The core of the CAD system is the randomized algorithm which selects tubes based on their average repeatability from seed points. The CAD algorithm is designed as a 5 stage process: Preprocessing (removing borders, histogram equalization, anisotropic filtering), Anatomy Segmentation (to identify neck, esophagus, abdomen ROI's), Seed Generation, Region Growing and Tube Selection. The preliminary evaluation was carried out on 64 cases. The prototype CAD system was able to detect ET tubes with a True Positive Rate of 0.93 and False Positive Rate of 0.02/image and NG tubes with a True Positive Rate of 0.84 and False Positive Rate of 0.02/image respectively. The results from the prototype system show that it is feasible to automatically detect both tubes on chest radiographs, with the potential to significantly speed the delivery of imaging services while maintaining high accuracy.

  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. 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...

  17. A geometric flow for segmenting vasculature in proton-density weighted MRI.

    Science.gov (United States)

    Descoteaux, Maxime; Collins, D Louis; Siddiqi, Kaleem

    2008-08-01

    Modern neurosurgery takes advantage of magnetic resonance images (MRI) of a patient's cerebral anatomy and vasculature for planning before surgery and guidance during the procedure. Dual echo acquisitions are often performed that yield proton-density (PD) and T2-weighted images to evaluate edema near a tumor or lesion. In this paper we develop a novel geometric flow for segmenting vasculature in PD images, which can also be applied to the easier cases of MR angiography data or Gadolinium enhanced MRI. Obtaining vasculature from PD data is of clinical interest since the acquisition of such images is widespread, the scanning process is non-invasive, and the availability of vessel segmentation methods could obviate the need for an additional angiographic or contrast-based sequence during preoperative imaging. The key idea is to first apply Frangi's vesselness measure [Frangi, A., Niessen, W., Vincken, K.L., Viergever, M.A., 1998. Multiscale vessel enhancement filtering. In: International Conference on Medical Image Computing and Computer Assisted Intervention, vol. 1496 of Lecture Notes in Computer Science, pp. 130-137] to find putative centerlines of tubular structures along with their estimated radii. This measure is then distributed to create a vector field which allows the flux maximizing flow algorithm of Vasilevskiy and Siddiqi [Vasilevskiy, A., Siddiqi, K., 2002. Flux maximizing geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (12), 1565-1578] to be applied to recover vessel boundaries. We carry out a qualitative validation of the approach on PD, MR angiography and Gadolinium enhanced MRI volumes and suggest a new way to visualize the segmentations in 2D with masked projections. We validate the approach quantitatively on a single-subject data set consisting of PD, phase contrast (PC) angiography and time of flight (TOF) angiography volumes, with an expert segmented version of the TOF volume viewed as the ground truth. We then

  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. The Automatic Image Segmentation Method Based on Fast FCM and Random Walk Algorithm%基于快速FCM与随机游走算法的图像自动分割方法

    Institute of Scientific and Technical Information of China (English)

    许健才; 张良均; 余燕团

    2016-01-01

    在图像分割中,针对 FCM 算法存在聚类数目需要预先给定、收敛速度慢等缺点,本文把快速模糊 C 均值聚类算法和随机游走算法相结合,具体方法为先采用快速模糊 C 均值聚类算法对图像进行预分割,以便获得聚类中心的位置,然后将该中心作为随机游走的种子点,再进行图像分割,实验结果得到了较为满意的预期效果,证明该方法是可行的。本文的研究为快速 FCM 实现自适应性和开发图形图像预处理系统提供了技术支持与理论依据。%As far as image segmentation, the defeat of the number of clusters for FCM algorithm is reeded to be improued. In this paper, the fast fuzzy C-means clustering and random walk algorithm are combined to solve the problem of image segmentation. Firstly, the fast FCM for image pre-segmentation to obtain the number of clusters and cluster central location as the seed points of random walk firstly. Then, for image segmentation, experimental results show that this method is feasible, and get a more satisfactory desired purpose. Results of this study achieve self-adaptive and fast FCM develop graphical image preprocessing system provides technical support and theoretical basis.

  20. 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.

  1. [Dental anatomy of dogs].

    Science.gov (United States)

    Sarkisian, E G

    2014-12-01

    The aim of the research was to investigate dog teeth anatomy as animal model for study of etiopathogenesis of caries disease and physiological tooth wear in human. After examining the dog's dental system, following conclusions were drawn: the dog has 42 permanent teeth, which are distributed over the dental arches not equally, and so the upper dentition consists of 20, and the lower of 22 teeth. The largest are considered upper fourth premolar and lower first molars, which are called discordant teeth. Between discordant teeth and fangs a dog has an open bite, which is limited to the top and bottom conical crown premolar teeth. Thus, in the closed position of the jaws, behind this occlusion is limited by discordant teeth, just in contact are smaller in size two molars. Only large dog's molars in a valid comparison can be likened to human molars, which allows us to use them in an analog comparison between them with further study of the morphological features ensure durability short-crown teeth and their predisposition to caries.

  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. 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...

  4. 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...

  5. 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.

  6. New auto-segment method of cerebral hemorrhage

    Science.gov (United States)

    Wang, Weijiang; Shen, Tingzhi; Dang, Hua

    2007-12-01

    A novel method for Computerized tomography (CT) cerebral hemorrhage (CH) image automatic segmentation is presented in the paper, which uses expert system that models human knowledge about the CH automatic segmentation problem. The algorithm adopts a series of special steps and extracts some easy ignored CH features which can be found by statistic results of mass real CH images, such as region area, region CT number, region smoothness and some statistic CH region relationship. And a seven steps' extracting mechanism will ensure these CH features can be got correctly and efficiently. By using these CH features, a decision tree which models the human knowledge about the CH automatic segmentation problem has been built and it will ensure the rationality and accuracy of the algorithm. Finally some experiments has been taken to verify the correctness and reasonable of the automatic segmentation, and the good correct ratio and fast speed make it possible to be widely applied into practice.

  7. 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

  8. 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.

  9. Robust system for human airway-tree segmentation

    Science.gov (United States)

    Graham, Michael W.; Gibbs, Jason D.; Higgins, William E.

    2008-03-01

    Robust and accurate segmentation of the human airway tree from multi-detector computed-tomography (MDCT) chest scans is vital for many pulmonary-imaging applications. As modern MDCT scanners can detect hundreds of airway tree branches, manual segmentation and semi-automatic segmentation requiring significant user intervention are impractical for producing a full global segmentation. Fully-automated methods, however, may fail to extract small peripheral airways. We propose an automatic algorithm that searches the entire lung volume for airway branches and poses segmentation as a global graph-theoretic optimization problem. The algorithm has shown strong performance on 23 human MDCT chest scans acquired by a variety of scanners and reconstruction kernels. Visual comparisons with adaptive region-growing results and quantitative comparisons with manually-defined trees indicate a high sensitivity to peripheral airways and a low false-positive rate. In addition, we propose a suite of interactive segmentation tools for cleaning and extending critical areas of the automatically segmented result. These interactive tools have potential application for image-based guidance of bronchoscopy to the periphery, where small, terminal branches can be important visual landmarks. Together, the automatic segmentation algorithm and interactive tool suite comprise a robust system for human airway-tree segmentation.

  10. 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

  11. Segmentation Using Symmetry Deviation

    DEFF Research Database (Denmark)

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

    2011-01-01

    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 symmetrical atlas was constructed...... 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 0.01 for a paired t-test respectively....

  12. Anal anatomy and normal histology.

    Science.gov (United States)

    Pandey, Priti

    2012-12-01

    The focus of this article is the anatomy and histology of the anal canal, and its clinical relevance to anal cancers. The article also highlights the recent histological and anatomical changes to the traditional terminology of the anal canal. The terminology has been adopted by the American Joint Committee on Cancer, separating the anal region into the anal canal, the perianal region and the skin. This paper describes the gross anatomy of the anal canal, along with its associated blood supply, venous and lymphatic drainage, and nerve supply. The new terminology referred to in this article may assist clinicians and health care providers to identify lesions more precisely through naked eye observation and without the need for instrumentation. Knowledge of the regional anatomy of the anus will also assist in management decisions.

  13. 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

  14. 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.

  15. 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.…

  16. 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

  17. 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

  18. 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...

  19. RGB-D Segmentation of Poultry Entrails

    DEFF Research Database (Denmark)

    Philipsen, Mark Philip; Jørgensen, Anders; Moeslund, Thomas B.;

    2016-01-01

    This paper presents an approach for automatic visual inspection of chicken entrails in RGB-D data. The point cloud is first over-segmented into supervoxels based on color, spatial and geometric information. Color, position and texture features are extracted from each of the resulting supervoxels ...

  20. Segmentation of elongated structures in medical images

    NARCIS (Netherlands)

    Staal, Jozef Johannes

    2004-01-01

    The research described in this thesis concerns the automatic detection, recognition and segmentation of elongated structures in medical images. For this purpose techniques have been developed to detect subdimensional pointsets (e.g. ridges, edges) in images of arbitrary dimension. These pointsets ar

  1. Skeleton-based Hierarchical Shape Segmentation

    NARCIS (Netherlands)

    Reniers, Dennie; Telea, Alexandru

    2007-01-01

    We present an effective framework for segmenting 3D shapes into meaningful components using the curve skeleton. Our algorithm identifies a number of critical points on the curve skeleton, either fully automatically as the junctions of the curve skeleton, or based on user input. We use these points t

  2. Superresolution improves MRI cortical segmentation with FACE

    DEFF Research Database (Denmark)

    Eskildsen, Simon Fristed; Manjón, José V.; Coupé, Pierrick;

    Brain cortical surface extraction from MRI has applications for measurement of gray matter (GM) atrophy, functional mapping, source localization and preoperative neurosurgical planning. Accurate cortex segmentation requires high resolution morphological images and several methods for extracting...... the cerebral cortex have been developed during the last decade (Dale 1999, Kim 2005, Eskildsen 2006). In many studies, the resolution of the morphological image acquisition sequence is chosen to be relatively low (~1mm3) due to time and equipment constraints. To improve segmentation accuracy, such low...... the ability to effectively increase the image resolution while preserving sharp features of the underlying anatomy. In this study, we investigated the effect of applying superresolution as proposed in (Manjon 2010a) to the accuracy of cerebral cortex segmentation....

  3. Image Series Segmentation and Improved MC Algorithm

    Institute of Scientific and Technical Information of China (English)

    WAN Wei-bing; SHI Peng-fei

    2008-01-01

    A semiautomatic segmentation method based on active contour is proposed for computed tomog-raphy (CT) image series. First, to get initial contour, one image slice was segmented exactly by C-V method based on Mumford-Shah model. Next, the computer will segment the nearby slice automatically using the snake model one by one. During segmenting of image slices, former slice boundary, as next slice initial con-tour, may cross over next slice real boundary and never return to right position. To avoid contour skipping over, the distance variance between two slices is evaluated by an threshold, which decides whether to initiate again. Moreover, a new improved marching cubes (MC) algorithm based on 2D images series segmentation boundary is given for 3D image reconstruction. Compared with the standard method, the proposed algorithm reduces detecting time and needs less storing memory. The effectiveness and capabilities of the algorithm were illustrated by experimental results.

  4. 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.

  5. 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...

  6. 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.

  7. 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.

  8. 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…

  9. Robust Shape Regression for Supervised Vessel Segmentation and its application to Coronary Segmentation in CTA

    DEFF Research Database (Denmark)

    Schaap, M.; van Walsum, T.; Neefjes, L.;

    2011-01-01

    This paper presents a vessel segmentation method which learns the geometry and appearance of vessels in medical images from annotated data and uses this knowledge to segment vessels in unseen images. Vessels are segmented in a coarse-to-fine fashion. First, the vessel boundaries are estimated...... with multivariate linear regression using image intensities sampled in a region of interest around an initialization curve. Subsequently, the position of the vessel boundary is refined with a robust nonlinear regression technique using intensity profiles sampled across the boundary of the rough segmentation...... and using information about plausible cross-sectional vessel shapes. The method was evaluated by quantitatively comparing segmentation results to manual annotations of 229 coronary arteries. On average the difference between the automatically obtained segmentations and manual contours was smaller than...

  10. Toward automatic regional analysis of pulmonary function using inspiration and expiration thoracic CT

    DEFF Research Database (Denmark)

    Murphy, Keelin; Pluim, Josien P. W.; Rikxoort, Eva M. van;

    2012-01-01

    Purpose: To analyze pulmonary function using a fully automatic technique which processes pairs of thoracic CT scans acquired at breath-hold inspiration and expiration, respectively. The following research objectives are identified to: (a) describe and systematically analyze the processing pipeline...... disorder). Lungs, fissures, airways, lobes, and vessels are automatically segmented in both scans and the expiration scan is registered with the inspiration scan using a fully automatic nonrigid registration algorithm. Segmentations and registrations are examined and scored by expert observers to analyze...

  11. 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.

  12. 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)

  13. The future of gross anatomy teaching.

    Science.gov (United States)

    Malamed, S; Seiden, D

    1995-01-01

    A survey of U.S. departments of anatomy, physiology, and biochemistry shows that 39% of the respondent anatomy departments reported declines in the numbers of graduate students taking the human gross anatomy course. Similarly, 42% of the departments reported decreases in the numbers of graduate students teaching human gross anatomy. These decreases were greater in anatomy than in physiology and in biochemistry. The percentages of departments reporting increases in students taking or teaching their courses was 6% for human gross anatomy and 0% to 19% for physiology and biochemistry courses. To reverse this trend the establishment of specific programs for the training of gross anatomy teachers is advocated. These new teachers will be available as the need for them is increasingly recognized in the future.

  14. 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.

  15. 基于改进分水岭算法和凹点搜索的乳腺癌粘连细胞分割%Automatic Segmentation of Clustered Breast Cancer Cells Based on Modified Watershed Algorithm and Concavity Points Searching

    Institute of Scientific and Technical Information of China (English)

    童振; 蒲立新; 董方杰

    2013-01-01

    As a common malignant tumor,breast cancer has seriously affected women's physical and psychological health even threatened their lives.Breast cancer has even begun to show a gradual trend of high incidence in some places in the world.As a kind of common pathological assist diagnosis technique,immunohistochemical technique plays an important role in the diagnosis of breast cancer.Usually,Pathologists isolate positive cells from the stained specimen which were processed by immunohistochemical technique and calculate the ratio of positive cells which is a core indicator of breast cancer in diagnosis.In this paper,we present a new algorithm which was based on modified watershed algorithm and concavity points searching to identify the positive cells and segment the clustered cells automatically,and then realize automatic counting.By comparison of the results of our experiments with those of other methods,our method can exactly segment the clustered cells without losing any geometrical cell features and give the exact number of separating cells.%乳腺癌作为一种常见的恶性肿瘤,已经严重影响妇女身心健康甚至危及生命,在某些地区还呈现高发趋势.免疫组织化学技术作为一种常用的辅助病理诊断技术,在乳腺癌的诊断上发挥了重要作用,从免疫组化技术处理的标本中,识别阳性细胞,并统计阳性细胞百分率这一个重要的诊断指标.本文提出一种基于改进分水岭算法和凹点搜索的方法,识别阳性细胞并自动分割粘连细胞,最后实现自动计数,试验结果对比显示,本方法能够在不损失细胞几何特性的基础上准确地分离粘连细胞,实现自动计数并辅助完成统计.

  16. Anatomy of Teaching Anatomy: Do Prosected Cross Sections Improve Students Understanding of Spatial and Radiological Anatomy?

    Science.gov (United States)

    Vithoosan, S.; Kokulan, S.; Dissanayake, M. M.; Dissanayake, Vajira; Jayasekara, Rohan

    2016-01-01

    Introduction. Cadaveric dissections and prosections have traditionally been part of undergraduate medical teaching. Materials and Methods. Hundred and fifty-nine first-year students in the Faculty of Medicine, University of Colombo, were invited to participate in the above study. Students were randomly allocated to two age and gender matched groups. Both groups were exposed to identical series of lectures regarding anatomy of the abdomen and conventional cadaveric prosections of the abdomen. The test group (n = 77, 48.4%) was also exposed to cadaveric cross-sectional slices of the abdomen to which the control group (n = 82, 51.6%) was blinded. At the end of the teaching session both groups were assessed by using their performance in a timed multiple choice question paper as well as ability to identify structures in abdominal CT films. Results. Scores for spatial and radiological anatomy were significantly higher among the test group when compared with the control group (P < 0.05, CI 95%). Majority of the students in both control and test groups agreed that cadaveric cross section may be useful for them to understand spatial and radiological anatomy. Conclusion. Introduction of cadaveric cross-sectional prosections may help students to understand spatial and radiological anatomy better. PMID:27579181

  17. Anatomy of Teaching Anatomy: Do Prosected Cross Sections Improve Students Understanding of Spatial and Radiological Anatomy?

    Directory of Open Access Journals (Sweden)

    L. B. Samarakoon

    2016-01-01

    Full Text Available Introduction. Cadaveric dissections and prosections have traditionally been part of undergraduate medical teaching. Materials and Methods. Hundred and fifty-nine first-year students in the Faculty of Medicine, University of Colombo, were invited to participate in the above study. Students were randomly allocated to two age and gender matched groups. Both groups were exposed to identical series of lectures regarding anatomy of the abdomen and conventional cadaveric prosections of the abdomen. The test group (n=77, 48.4% was also exposed to cadaveric cross-sectional slices of the abdomen to which the control group (n=82, 51.6% was blinded. At the end of the teaching session both groups were assessed by using their performance in a timed multiple choice question paper as well as ability to identify structures in abdominal CT films. Results. Scores for spatial and radiological anatomy were significantly higher among the test group when compared with the control group (P<0.05, CI 95%. Majority of the students in both control and test groups agreed that cadaveric cross section may be useful for them to understand spatial and radiological anatomy. Conclusion. Introduction of cadaveric cross-sectional prosections may help students to understand spatial and radiological anatomy better.

  18. A Generative Model for Image Segmentation Based on Label Fusion

    OpenAIRE

    Sabuncu, Mert R.; Thomas Yeo, B.T.; Van Leemput, Koen; Fischl, Bruce; Golland, Polina

    2010-01-01

    We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of mu...

  19. 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 ...

  20. 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

  1. 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

  2. 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.

  3. [Functional dental anatomy and amalgam].

    Science.gov (United States)

    Tavernier, B; Colon, P

    1989-01-01

    Very often, the functional dental anatomy are reflected during the rehabilitation of posterior quadrants. However, the placement, the shaping in correct relation of the different dental components are indispensable conditions to respect, in order to achieve an adequate integration of the restoration within the neuro-muscular system. A clinical protocol is proposed in order to reconcile the anatomical and biological prerequisite and the setting time of modern alloys.

  4. 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.

  5. 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

  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. 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.

  8. Model-based segmentation of short-axis MR cardiac images

    NARCIS (Netherlands)

    Spreeuwers, L.J.; Breeuwer, M.

    2005-01-01

    Reliable automatic segmentation of MR cardiac images is still an important problem in medical image processing. Although image data quality has improved considerably during the last years, this segmentation is still considered a difficult problem. Manual segmentation is hardly an option as this is e

  9. An image segmentation method for apple sorting and grading using support vector machine and Otsu's method

    Science.gov (United States)

    Segmentation is the first step in image analysis to subdivide an image into meaningful regions. The segmentation result directly affects the subsequent image analysis. The objective of the research was to develop an automatic adjustable algorithm for segmentation of color images, using linear suppor...

  10. Interactive lung segmentation in abnormal human and animal chest CT scans

    NARCIS (Netherlands)

    Kockelkorn, T.T.J.P.; Schaefer-Prokop, C.M.; Bozovic, G.; Muñoz-Barrutia, A.; Rikxoort, E.M. van; Brown, M.S.; Jong, P.A. de; Viergever, M.A.; Ginneken, B. van

    2014-01-01

    Many medical image analysis systems require segmentation of the structures of interest as a first step. For scans with gross pathology, automatic segmentation methods may fail. The authors' aim is to develop a versatile, fast, and reliable interactive system to segment anatomical structures. In this

  11. Evaluation for Uncertain Image Classification and Segmentation

    CERN Document Server

    Martin, Arnaud; Arnold-Bos, Andreas

    2008-01-01

    Each year, numerous segmentation and classification algorithms are invented or reused to solve problems where machine vision is needed. Generally, the efficiency of these algorithms is compared against the results given by one or many human experts. However, in many situations, the location of the real boundaries of the objects as well as their classes are not known with certainty by the human experts. Furthermore, only one aspect of the segmentation and classification problem is generally evaluated. In this paper we present a new evaluation method for classification and segmentation of image, where we take into account both the classification and segmentation results as well as the level of certainty given by the experts. As a concrete example of our method, we evaluate an automatic seabed characterization algorithm based on sonar images.

  12. Visual Knowledge Representation and Intelligent Image Segmentation

    Institute of Scientific and Technical Information of China (English)

    1992-01-01

    Automatic medical image analysis shows that image segmentation is a crucial task for any practical AI system in this field.On the basis of evaluation of the existing segmentation methods,a new image segmentation method is presented.To seek the perfct solution to knowledge representation in low level machine vision,a new knowledge representation approach--“Notebbok”approach is proposed and the processing of visual knowledge is discussed at all levels.To integrate the computer vision theory with Gestalt psychology and knowledge engineering,a new integrated method for intelligent image segmentation of sonargraphs- “Generalized-pattern guided segmentation”is proposed.With the methods and techniques mentioned above,the medical diagnosis expert system for sonargraphs can be built The work on the preliminary experiments is also introduced.

  13. MEDICAL IMAGE SEGMENTATION FOR ANATOMICAL KNOWLEDGE EXTRACTION

    Directory of Open Access Journals (Sweden)

    Ms Maya Eapen

    2014-01-01

    Full Text Available Computed Tomography-Angiography (CTA images of the abdomen, followed by precise segmentation and subsequent computation of shape based features of liver play an important role in hepatic surgery, patient/donor diagnosis during liver transplantation and at various treatment stages. Nevertheless, the issues like intensity similarity and Partial Volume Effect (PVE between the neighboring organs; left the task of liver segmentation critical. The accurate segmentation of liver helps the surgeons to perfectly classify the patients based on their liver anatomy which in turn helps them in the treatment decision phase. In this study, we propose an effective Advanced Region Growing (ARG algorithm for segmentation of liver from CTA images. The performance of the proposed technique was tested with several CTA images acquired across a wide range of patients. The proposed ARG algorithm identifies the liver regions on the images based on the statistical features (intensity distribution and orientation value. The proposed technique addressed the aforementioned issues and been evaluated both quantitatively and qualitatively. For quantitative analysis proposed method was compared with manual segmentation (gold standard. The method was also compared with standard region growing.

  14. Brachial Plexus Anatomy: Normal and Variant

    Directory of Open Access Journals (Sweden)

    Steven L. Orebaugh

    2009-01-01

    Full Text Available 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: "brachial plexus", "median nerve", "ulnar nerve", "radial nerve", "axillary nerve", and "musculocutanous nerve". Each of these was then paired with the MESH terms "anatomy", "nerve block", "anomaly", "variation", and "ultrasound". Resulting articles were hand searched for additional relevant literature. A total of 68 searches were conducted, with a total of 377 possible articles for inclusion. Of these, 57 were found to provide substantive information for this review. The normal anatomy of the brachial plexus is briefly reviewed, with an emphasis on those features revealed by use of imaging technologies. Anomalies of the anatomy that might affect the conduct of the various brachial plexus blocks are noted. Brachial plexus blockade has been effectively utilized as a component of anesthesia for upper extremity surgery for a century. Over that period, our understanding of anatomy and its variations has improved significantly. The ability to explore anatomy at the bedside, with real-time ultrasonography, has improved our appreciation of brachial plexus anatomy as well.

  15. [New findings of clinical anatomy in pelvis].

    Science.gov (United States)

    Muraoka, Kuniyasu; Takenaka, Atsushi

    2016-01-01

    Surgical anatomy involves clarifying the mutual relationships of each structure in the operative field. Knowledge of new surgical anatomy has arisen via new methods or approaches. Associated with the development and spread of laparoscopic surgery in recent years, adaptation to changes in surgical techniques using knowledge of classical pelvic anatomy has been difficult. Better knowledge of the delicate structures surrounding the prostate is essential in order to provide both cancer control and functional preservation with regard to radical prostatectomy. In this report, we review the progress in knowledge of pelvic anatomy, particularly regarding the endopelvic fascia, prostatic fascia and Denonvilliers' fascia.

  16. Partial differential equations-based segmentation for radiotherapy treatment planning.

    Science.gov (United States)

    Gibou, Frederic; Levy, Doron; Cardenas, Carlos; Liu, Pingyu; Boyer, Arthur

    2005-04-01

    The purpose of this study is to develop automatic algorithms for the segmentation phase of radiotherapy treatment planning. We develop new image processing techniques that are based on solving a partial diferential equation for the evolution of the curve that identifies the segmented organ. The velocity function is based on the piecewise Mumford-Shah functional. Our method incorporates information about the target organ into classical segmentation algorithms. This information, which is given in terms of a three- dimensional wireframe representation of the organ, serves as an initial guess for the segmentation algorithm. We check the performance of the new algorithm on eight data sets of three diferent organs: rectum, bladder, and kidney. The results of the automatic segmentation were compared with a manual seg- mentation of each data set by radiation oncology faculty and residents. The quality of the automatic segmentation was measured with the k-statistics", and with a count of over- and undersegmented frames, and was shown in most cases to be very close to the manual segmentation of the same data. A typical segmentation of an organ with sixty slices takes less than ten seconds on a Pentium IV laptop.

  17. Microsurgical anatomy of the anterior cerebral artery in Indian cadavers

    Directory of Open Access Journals (Sweden)

    Shweta Kedia

    2013-01-01

    Full Text Available Background: The microanatomy features of cerebral arteries may be variable and may be different in different ethnic groups. Aim: To study the anterior cerebral artery (ACA anatomy in North-West Indian cadavers. Materials and Methods: Microanatomy features of the ACA were studied in 15 formalin fixed human cadaveric brains under microscope. The outer diameter, length, and number of perforating branches with respective anomalies were measured for each of the following vessels: ACA (proximal A1 segment to distal A2 segment, anterior communicating artery (ACoA, Recurrent artery of Heubner (RAH, and callosomarginal artery and photographed for documentation. Results: The mean length and external diameter of right and left A1 segment was 12.09 mm and 12.0 mm and 2.32 mm and 2.36 mm respectively. Narrowing, duplication, and median ACA were seen in 6.6%, 3.3% and 6.6% of the vessels respectively. Complex ACoA type was seen in 40% cadavers. RAH originated at an average point of 0.2 mm distal to ACoA, but in one cadaver it arose 5 mm proximal to ACoA. Double RAH was found in 26.6%. The course of RAH in relation to A1 was superiorly in 60%, in anteriorly 30% and posteriorly in 10% of cadavers. The orbitofrontal artery (OFA and frontopolar artery (FPA arose from A2 in 83.3% to 40% respectively. The mean distance of OFA and FPA from ACoA was 4.17 mm and 8.5 mm respectively. After giving rise to central, callosal and cortical branches, pericallosal artery terminated near the splenium of the corpus callosum or on the precuneus as the inferomedial parietal artery. Conclusion: Knowledge of the microvascular anatomy is indispensable and it is mandatory to be aware of the possible variations in the anomalies to minimize morbidity.

  18. Effective Analysis of Chinese Word-Segmentation Accuracy

    Institute of Scientific and Technical Information of China (English)

    MA Weiyin

    2007-01-01

    Automatic word-segmentation is widely used in the ambiguity cancellation when processing large-scale real text,but during the process of unknown word detection in Chinese word segmentation,many detected word candidates are invalid.These false unknown word candidates deteriorate the overall segmentation accuracy,as it will affect the segmentation accuracy of known words.In this paper,we propose several methods for reducing the difficulties and improving the accuracy of the word-segmentation of written Chinese,such as full segmentation of a sentence,processing the duplicative word,idioms and statistical identification for unknown words.A simulation shows the feasibility of our proposed methods in improving the accuracy of word-segmentation of Chinese.

  19. Auto-segmentation of normal and target structures in head and neck CT images: A feature-driven model-based approach

    International Nuclear Information System (INIS)

    Purpose: Intensity modulated radiation therapy (IMRT) allows greater control over dose distribution, which leads to a decrease in radiation related toxicity. IMRT, however, requires precise and accurate delineation of the organs at risk and target volumes. Manual delineation is tedious and suffers from both interobserver and intraobserver variability. State of the art auto-segmentation methods are either atlas-based, model-based or hybrid however, robust fully automated segmentation is often difficult due to the insufficient discriminative information provided by standard medical imaging modalities for certain tissue types. In this paper, the authors present a fully automated hybrid approach which combines deformable registration with the model-based approach to accurately segment normal and target tissues from head and neck CT images. Methods: The segmentation process starts by using an average atlas to reliably identify salient landmarks in the patient image. The relationship between these landmarks and the reference dataset serves to guide a deformable registration algorithm, which allows for a close initialization of a set of organ-specific deformable models in the patient image, ensuring their robust adaptation to the boundaries of the structures. Finally, the models are automatically fine adjusted by our boundary refinement approach which attempts to model the uncertainty in model adaptation using a probabilistic mask. This uncertainty is subsequently resolved by voxel classification based on local low-level organ-specific features. Results: To quantitatively evaluate the method, they auto-segment several organs at risk and target tissues from 10 head and neck CT images. They compare the segmentations to the manual delineations outlined by the expert. The evaluation is carried out by estimating two common quantitative measures on 10 datasets: volume overlap fraction or the Dice similarity coefficient (DSC), and a geometrical metric, the median symmetric

  20. GrabCut-Based Human Segmentation in Video Sequences

    Directory of Open Access Journals (Sweden)

    Sergio Escalera

    2012-11-01

    Full Text Available In this paper, we present a fully-automatic Spatio-Temporal GrabCut human segmentation methodology that combines tracking and segmentation. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model. Spatial information is included by Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, full face and pose recovery is obtained by combining human segmentation with Active Appearance Models and Conditional Random Fields. Results over public datasets and in a new Human Limb dataset show a robust segmentation and recovery of both face and pose using the presented methodology.

  1. Unsupervised Multiresolution Image Segmentation Integrating Color and Texture

    Institute of Scientific and Technical Information of China (English)

    XINGQiang; YUANBaozong; TANGXiaofang

    2004-01-01

    Unsupervised segmentation of images is highly useful in various applications including contentbased image retrieval. A novel multiresolution image segmentation algorithm, designed to separate a focused object of interest from background automatically, is described in this paper. According to the principle of human vision system, our algorithm first searches the salient block representing object in global image domain. Then all image blocks are clustered using the feature of color moments and texture in salient block. At last the algorithm classifies the image blocks belonging to object class in high resolution. Experiment shows that our algorithm achieves better segmentation results at higher speed compared with the traditional image segmentation approach using global optimization.

  2. 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.

  3. Amodal Instance Segmentation

    OpenAIRE

    Li, Ke; Malik, Jitendra

    2016-01-01

    We consider the problem of amodal instance segmentation, the objective of which is to predict the region encompassing both visible and occluded parts of each object. Thus far, the lack of publicly available amodal segmentation annotations has stymied the development of amodal segmentation methods. In this paper, we sidestep this issue by relying solely on standard modal instance segmentation annotations to train our model. The result is a new method for amodal instance segmentation, which rep...

  4. Anatomy Education Faces Challenges in Pakistan

    Science.gov (United States)

    Memon, Ismail K.

    2009-01-01

    Anatomy education in Pakistan is facing many of the same challenges as in other parts of the world. Roughly, a decade ago, all medical and dental colleges in Pakistan emphasized anatomy as a core basic discipline within a traditional medical science curriculum. Now institutions are adopting problem based learning (PBL) teaching philosophies, and…

  5. Anatomy of a Cancer Treatment Scam

    Medline Plus

    Full Text Available ... All Events Weekly Calendar Weekly Calendar Archive Speeches Audio/Video Featured Videos FTC Events For Consumers For ... in Adjudicative Proceedings You are here News & Events » Audio/Video » Anatomy of a Cancer Treatment Scam Anatomy ...

  6. DESIGNING A CONTEMPORARY ANATOMY MUSEUM: ANATOMISTS’ PERSPECTIVE

    Directory of Open Access Journals (Sweden)

    Venkatesh. G. Kamath

    2015-06-01

    Full Text Available Background: A research study was conducted in sixteen anatomy museums across India. Aim: The aim of the study is to have an integrated approach while designing a museum. Objective: The objective is to stress on the need to have a holistic approach while designing a museum so that that the museum is well planned and organised and has a huge sectional diversity that spans all aspects related to anatomy. Materials and Methods: All the museums were studied using a planned proforma that emphasised on special features of the museum with special emphasis on sectional variety. Observations: The various techniques of specimen preparation, preservation, mounting and display were observed and photographed. The sectional variety was noted. Moreover the various methods of maintaining specimen related information in pictorial and computerised catalogues was observed. Results and Conclusion: A design of a contemporary anatomy museum can now be conceived that incorporates all aspects of anatomy from history, evolution, embryology, cross-sectional anatomy, comparative anatomy, teratology, genetics and clinical anatomy to sections with modern techniques like plastination. Such a museum will certainly have a more holistic approach to anatomy and will be more educative and scientific.

  7. Design Projects in Human Anatomy & Physiology

    Science.gov (United States)

    Polizzotto, Kristin; Ortiz, Mary T.

    2008-01-01

    Very often, some type of writing assignment is required in college entry-level Human Anatomy and Physiology courses. This assignment can be anything from an essay to a research paper on the literature, focusing on a faculty-approved topic of interest to the student. As educators who teach Human Anatomy and Physiology at an urban community college,…

  8. Shark Attack! Sinking Your Teeth into Anatomy.

    Science.gov (United States)

    House, Herbert

    2002-01-01

    Presents a real life shark attack story and studies arm reattachment surgery to teach human anatomy. Discusses how knowledge of anatomy can be put to use in the real world and how the arm functions. Includes teaching notes and suggestions for classroom management. (YDS)

  9. 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? ... June 24, 2016 The first-person story of a retired educator’s Medicare scam experience – and the FTC ...

  10. Pulmonary Lobe Segmentation in CT Examinations Using Implicit Surface Fitting

    OpenAIRE

    Pu, Jiantao; Zheng, Bin; Leader, Joseph Ken; Fuhrman, Carl; Knollmann, Friedrich; Klym, Amy; Gur, David

    2009-01-01

    Lobe identification in computed tomography (CT) examinations is often an important consideration during the diagnostic process as well as during treatment planning because of their relative independence of each other in terms of anatomy and function. In this study, we present a new automated scheme for segmenting lung lobes depicted on three-dimensional CT examinations. The unique characteristic of this scheme is the representation of fissures in the form of implicit functions using Radial Ba...

  11. William, a voxel model of child anatomy from tomographic images for Monte Carlo dosimetry calculations

    International Nuclear Information System (INIS)

    Full text: Medical imaging provides two-dimensional pictures of the human internal anatomy from which may be constructed a three-dimensional model of organs and tissues suitable for calculation of dose from radiation. Diagnostic CT provides the greatest exposure to radiation per examination and the frequency of CT examination is high. Esti mates of dose from diagnostic radiography are still determined from data derived from geometric models (rather than anatomical models), models scaled from adult bodies (rather than bodies of children) and CT scanner hardware that is no longer used. The aim of anatomical modelling is to produce a mathematical representation of internal anatomy that has organs of realistic size, shape and positioning. The organs and tissues are represented by a great many cuboidal volumes (voxels). The conversion of medical images to voxels is called segmentation and on completion every pixel in an image is assigned to a tissue or organ. Segmentation is time consuming. An image processing pack age is used to identify organ boundaries in each image. Thirty to forty tomographic voxel models of anatomy have been reported in the literature. Each model is of an individual, or a composite from several individuals. Images of children are particularly scarce. So there remains a need for more paediatric anatomical models. I am working on segmenting ''William'' who is 368 PET-CT images from head to toe of a seven year old boy. William will be used for Monte Carlo dose calculations of dose from CT examination using a simulated modern CT scanner.

  12. The Large-Scale Sugarcane Stripper with Automatic Feeding

    OpenAIRE

    Jiaxiang Lin; Wenjie Yan; Jiaping Lin

    2012-01-01

    This study mainly introduce the large-scale sugarcane stripper with automatic feeding, which including the automatic feeding module, cleaning leaves module, collecting module and control module. The machine is an important part of the segmental type sugarcane harvester, using to solve the highest labor intensity problem of cleaning leaves. Collecting the hilly areas sugarcane and cleaning their leaves, can greatly improve the labor productivity and changing the current mode of sugarcane harvest.

  13. Automatic quantitative analysis of morphology of apoptotic HL-60 cells

    OpenAIRE

    Liu, Yahui; Lin, Wang; Yang, Xu; Liang, Weizi; Zhang, Jun; Meng, Maobin; Rice, John R.; Sa, Yu; Feng, Yuanming

    2014-01-01

    Morphological identification is a widespread procedure to assess the presence of apoptosis by visual inspection of the morphological characteristics or the fluorescence images. The procedure is lengthy and results are observer dependent. A quantitative automatic analysis is objective and would greatly help the routine work. We developed an image processing and segmentation method which combined the Otsu thresholding and morphological operators for apoptosis study. An automatic determina...

  14. Automated segmentation of pigmented skin lesions in multispectral imaging

    International Nuclear Information System (INIS)

    The aim of this study was to develop an algorithm for the automatic segmentation of multispectral images of pigmented skin lesions. The study involved 1700 patients with 1856 cutaneous pigmented lesions, which were analysed in vivo by a novel spectrophotometric system, before excision. The system is able to acquire a set of 15 different multispectral images at equally spaced wavelengths between 483 and 951 nm. An original segmentation algorithm was developed and applied to the whole set of lesions and was able to automatically contour them all. The obtained lesion boundaries were shown to two expert clinicians, who, independently, rejected 54 of them. The 97.1% contour accuracy indicates that the developed algorithm could be a helpful and effective instrument for the automatic segmentation of skin pigmented lesions. (note)

  15. FULLY AUTOMATIC FRAMEWORK FOR SEGMENTATION OF BRAIN MRI IMAGE

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Automaticbraintissuesegmentationfrommag neticresonanceimages(MRI)isofgreatimportance forresearchandclinicalstudyofmuchneurological pathology.Duringthepastdecade,theMRIhashad agreatimpactonthediagnosticimagingofmosthu manorgansystem.ThesegmentationofbrainMRI imagesplaysanimportantroleinthevolumerecon structionforavarietyofmedicalimageanalysis, computer aideddiagnosis,three dimensionalrecon structionandvisualizationapplications.Theaccu rateSegmentationofMRimagesintodifferenttis sueclasses,especiallygray...

  16. A Review on Automatic Mammographic Density and Parenchymal Segmentation

    OpenAIRE

    Wenda He; Arne Juette; Denton, Erika R. E.; Arnau Oliver; Robert Martí; Reyer Zwiggelaar

    2015-01-01

    Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk fa...

  17. 3D segmentation of great vessels using active contours and morphological image processing techniques

    OpenAIRE

    Montejo Garcia, Cristina

    2010-01-01

    The scope of this project is to present a semi-automated vessels segmentation algorithm, to describe its usage and results. This will be achieved combining several segmentation algorithms to get proper vessel segmentation and visualization. Consequently, automatic segmentation can significantly reduce the scan-to-diagnosis time, thus helping the clinicians to reach the fundamental goal of efficient patient management. In order to complete our project, we can identify different phases...

  18. Improving cerebellar segmentation with statistical fusion

    Science.gov (United States)

    Plassard, Andrew J.; Yang, Zhen; Prince, Jerry L.; Claassen, Daniel O.; Landman, Bennett A.

    2016-03-01

    The cerebellum is a somatotopically organized central component of the central nervous system well known to be involved with motor coordination and increasingly recognized roles in cognition and planning. Recent work in multiatlas labeling has created methods that offer the potential for fully automated 3-D parcellation of the cerebellar lobules and vermis (which are organizationally equivalent to cortical gray matter areas). This work explores the trade offs of using different statistical fusion techniques and post hoc optimizations in two datasets with distinct imaging protocols. We offer a novel fusion technique by extending the ideas of the Selective and Iterative Method for Performance Level Estimation (SIMPLE) to a patch-based performance model. We demonstrate the effectiveness of our algorithm, Non- Local SIMPLE, for segmentation of a mixed population of healthy subjects and patients with severe cerebellar anatomy. Under the first imaging protocol, we show that Non-Local SIMPLE outperforms previous gold-standard segmentation techniques. In the second imaging protocol, we show that Non-Local SIMPLE outperforms previous gold standard techniques but is outperformed by a non-locally weighted vote with the deeper population of atlases available. This work advances the state of the art in open source cerebellar segmentation algorithms and offers the opportunity for routinely including cerebellar segmentation in magnetic resonance imaging studies that acquire whole brain T1-weighted volumes with approximately 1 mm isotropic resolution.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2003-11-01

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

  20. Automatic Fiscal Stabilizers

    Directory of Open Access Journals (Sweden)

    Narcis Eduard Mitu

    2013-11-01

    Full Text Available Policies or institutions (built into an economic system that automatically tend to dampen economic cycle fluctuations in income, employment, etc., without direct government intervention. For example, in boom times, progressive income tax automatically reduces money supply as incomes and spendings rise. Similarly, in recessionary times, payment of unemployment benefits injects more money in the system and stimulates demand. Also called automatic stabilizers or built-in stabilizers.