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Sample records for accurate image segmentation

  1. Evolving generalized Voronoi diagrams for accurate cellular image segmentation.

    PubMed

    Yu, Weimiao; Lee, Hwee Kuan; Hariharan, Srivats; Bu, Wenyu; Ahmed, Sohail

    2010-04-01

    Analyzing cellular morphologies on a cell-by-cell basis is vital for drug discovery, cell biology, and many other biological studies. Interactions between cells in their culture environments cause cells to touch each other in acquired microscopy images. Because of this phenomenon, cell segmentation is a challenging task, especially when the cells are of similar brightness and of highly variable shapes. The concept of topological dependence and the maximum common boundary (MCB) algorithm are presented in our previous work (Yu et al., Cytometry Part A 2009;75A:289-297). However, the MCB algorithm suffers a few shortcomings, such as low computational efficiency and difficulties in generalizing to higher dimensions. To overcome these limitations, we present the evolving generalized Voronoi diagram (EGVD) algorithm. Utilizing image intensity and geometric information, EGVD preserves topological dependence easily in both 2D and 3D images, such that touching cells can be segmented satisfactorily. A systematic comparison with other methods demonstrates that EGVD is accurate and much more efficient. PMID:20169588

  2. [Research on maize multispectral image accurate segmentation and chlorophyll index estimation].

    PubMed

    Wu, Qian; Sun, Hong; Li, Min-zan; Song, Yuan-yuan; Zhang, Yan-e

    2015-01-01

    In order to rapidly acquire maize growing information in the field, a non-destructive method of maize chlorophyll content index measurement was conducted based on multi-spectral imaging technique and imaging processing technology. The experiment was conducted at Yangling in Shaanxi province of China and the crop was Zheng-dan 958 planted in about 1 000 m X 600 m experiment field. Firstly, a 2-CCD multi-spectral image monitoring system was available to acquire the canopy images. The system was based on a dichroic prism, allowing precise separation of the visible (Blue (B), Green (G), Red (R): 400-700 nm) and near-infrared (NIR, 760-1 000 nm) band. The multispectral images were output as RGB and NIR images via the system vertically fixed to the ground with vertical distance of 2 m and angular field of 50°. SPAD index of each sample was'measured synchronously to show the chlorophyll content index. Secondly, after the image smoothing using adaptive smooth filtering algorithm, the NIR maize image was selected to segment the maize leaves from background, because there was a big difference showed in gray histogram between plant and soil background. The NIR image segmentation algorithm was conducted following steps of preliminary and accuracy segmentation: (1) The results of OTSU image segmentation method and the variable threshold algorithm were discussed. It was revealed that the latter was better one in corn plant and weed segmentation. As a result, the variable threshold algorithm based on local statistics was selected for the preliminary image segmentation. The expansion and corrosion were used to optimize the segmented image. (2) The region labeling algorithm was used to segment corn plants from soil and weed background with an accuracy of 95. 59 %. And then, the multi-spectral image of maize canopy was accurately segmented in R, G and B band separately. Thirdly, the image parameters were abstracted based on the segmented visible and NIR images. The average gray

  3. Automatic lung segmentation in CT images with accurate handling of the hilar region.

    PubMed

    De Nunzio, Giorgio; Tommasi, Eleonora; Agrusti, Antonella; Cataldo, Rosella; De Mitri, Ivan; Favetta, Marco; Maglio, Silvio; Massafra, Andrea; Quarta, Maurizio; Torsello, Massimo; Zecca, Ilaria; Bellotti, Roberto; Tangaro, Sabina; Calvini, Piero; Camarlinghi, Niccolò; Falaschi, Fabio; Cerello, Piergiorgio; Oliva, Piernicola

    2011-02-01

    A fully automated and three-dimensional (3D) segmentation method for the identification of the pulmonary parenchyma in thorax X-ray computed tomography (CT) datasets is proposed. It is meant to be used as pre-processing step in the computer-assisted detection (CAD) system for malignant lung nodule detection that is being developed by the Medical Applications in a Grid Infrastructure Connection (MAGIC-5) Project. In this new approach the segmentation of the external airways (trachea and bronchi), is obtained by 3D region growing with wavefront simulation and suitable stop conditions, thus allowing an accurate handling of the hilar region, notoriously difficult to be segmented. Particular attention was also devoted to checking and solving the problem of the apparent 'fusion' between the lungs, caused by partial-volume effects, while 3D morphology operations ensure the accurate inclusion of all the nodules (internal, pleural, and vascular) in the segmented volume. The new algorithm was initially developed and tested on a dataset of 130 CT scans from the Italung-CT trial, and was then applied to the ANODE09-competition images (55 scans) and to the LIDC database (84 scans), giving very satisfactory results. In particular, the lung contour was adequately located in 96% of the CT scans, with incorrect segmentation of the external airways in the remaining cases. Segmentation metrics were calculated that quantitatively express the consistency between automatic and manual segmentations: the mean overlap degree of the segmentation masks is 0.96 ± 0.02, and the mean and the maximum distance between the mask borders (averaged on the whole dataset) are 0.74 ± 0.05 and 4.5 ± 1.5, respectively, which confirms that the automatic segmentations quite correctly reproduce the borders traced by the radiologist. Moreover, no tissue containing internal and pleural nodules was removed in the segmentation process, so that this method proved to be fit for the use in the

  4. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model

    SciTech Connect

    Gan, Yangzhou; Zhao, Qunfei; Xia, Zeyang E-mail: jing.xiong@siat.ac.cn; Hu, Ying; Xiong, Jing E-mail: jing.xiong@siat.ac.cn; Zhang, Jianwei

    2015-01-15

    Purpose: A three-dimensional (3D) model of the teeth provides important information for orthodontic diagnosis and treatment planning. Tooth segmentation is an essential step in generating the 3D digital model from computed tomography (CT) images. The aim of this study is to develop an accurate and efficient tooth segmentation method from CT images. Methods: The 3D dental CT volumetric images are segmented slice by slice in a two-dimensional (2D) transverse plane. The 2D segmentation is composed of a manual initialization step and an automatic slice by slice segmentation step. In the manual initialization step, the user manually picks a starting slice and selects a seed point for each tooth in this slice. In the automatic slice segmentation step, a developed hybrid level set model is applied to segment tooth contours from each slice. Tooth contour propagation strategy is employed to initialize the level set function automatically. Cone beam CT (CBCT) images of two subjects were used to tune the parameters. Images of 16 additional subjects were used to validate the performance of the method. Volume overlap metrics and surface distance metrics were adopted to assess the segmentation accuracy quantitatively. The volume overlap metrics were volume difference (VD, mm{sup 3}) and Dice similarity coefficient (DSC, %). The surface distance metrics were average symmetric surface distance (ASSD, mm), RMS (root mean square) symmetric surface distance (RMSSSD, mm), and maximum symmetric surface distance (MSSD, mm). Computation time was recorded to assess the efficiency. The performance of the proposed method has been compared with two state-of-the-art methods. Results: For the tested CBCT images, the VD, DSC, ASSD, RMSSSD, and MSSD for the incisor were 38.16 ± 12.94 mm{sup 3}, 88.82 ± 2.14%, 0.29 ± 0.03 mm, 0.32 ± 0.08 mm, and 1.25 ± 0.58 mm, respectively; the VD, DSC, ASSD, RMSSSD, and MSSD for the canine were 49.12 ± 9.33 mm{sup 3}, 91.57 ± 0.82%, 0.27 ± 0.02 mm, 0

  5. Accurate and reliable segmentation of the optic disc in digital fundus images

    PubMed Central

    Giachetti, Andrea; Ballerini, Lucia; Trucco, Emanuele

    2014-01-01

    Abstract. We describe a complete pipeline for the detection and accurate automatic segmentation of the optic disc in digital fundus images. This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks. Validation experiments, performed on a large dataset of images of healthy and pathological eyes, annotated by experts and partially graded with a quality label, demonstrate the good performances of the proposed approach. The method is able to detect the optic disc and trace its contours better than the other systems presented in the literature and tested on the same data. The average error in the obtained contour masks is reasonably close to the interoperator errors and suitable for practical applications. The optic disc segmentation pipeline is currently integrated in a complete software suite for the semiautomatic quantification of retinal vessel properties from fundus camera images (VAMPIRE). PMID:26158034

  6. Accurate and reliable segmentation of the optic disc in digital fundus images.

    PubMed

    Giachetti, Andrea; Ballerini, Lucia; Trucco, Emanuele

    2014-07-01

    We describe a complete pipeline for the detection and accurate automatic segmentation of the optic disc in digital fundus images. This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks. Validation experiments, performed on a large dataset of images of healthy and pathological eyes, annotated by experts and partially graded with a quality label, demonstrate the good performances of the proposed approach. The method is able to detect the optic disc and trace its contours better than the other systems presented in the literature and tested on the same data. The average error in the obtained contour masks is reasonably close to the interoperator errors and suitable for practical applications. The optic disc segmentation pipeline is currently integrated in a complete software suite for the semiautomatic quantification of retinal vessel properties from fundus camera images (VAMPIRE). PMID:26158034

  7. Fast and Accurate Semiautomatic Segmentation of Individual Teeth from Dental CT Images.

    PubMed

    Kang, Ho Chul; Choi, Chankyu; Shin, Juneseuk; Lee, Jeongjin; Shin, Yeong-Gil

    2015-01-01

    In this paper, we propose a fast and accurate semiautomatic method to effectively distinguish individual teeth from the sockets of teeth in dental CT images. Parameter values of thresholding and shapes of the teeth are propagated to the neighboring slice, based on the separated teeth from reference images. After the propagation of threshold values and shapes of the teeth, the histogram of the current slice was analyzed. The individual teeth are automatically separated and segmented by using seeded region growing. Then, the newly generated separation information is iteratively propagated to the neighboring slice. Our method was validated by ten sets of dental CT scans, and the results were compared with the manually segmented result and conventional methods. The average error of absolute value of volume measurement was 2.29 ± 0.56%, which was more accurate than conventional methods. Boosting up the speed with the multicore processors was shown to be 2.4 times faster than a single core processor. The proposed method identified the individual teeth accurately, demonstrating that it can give dentists substantial assistance during dental surgery. PMID:26413143

  8. Fast and Accurate Semiautomatic Segmentation of Individual Teeth from Dental CT Images

    PubMed Central

    Kang, Ho Chul; Choi, Chankyu; Shin, Juneseuk; Lee, Jeongjin; Shin, Yeong-Gil

    2015-01-01

    DIn this paper, we propose a fast and accurate semiautomatic method to effectively distinguish individual teeth from the sockets of teeth in dental CT images. Parameter values of thresholding and shapes of the teeth are propagated to the neighboring slice, based on the separated teeth from reference images. After the propagation of threshold values and shapes of the teeth, the histogram of the current slice was analyzed. The individual teeth are automatically separated and segmented by using seeded region growing. Then, the newly generated separation information is iteratively propagated to the neighboring slice. Our method was validated by ten sets of dental CT scans, and the results were compared with the manually segmented result and conventional methods. The average error of absolute value of volume measurement was 2.29 ± 0.56%, which was more accurate than conventional methods. Boosting up the speed with the multicore processors was shown to be 2.4 times faster than a single core processor. The proposed method identified the individual teeth accurately, demonstrating that it can give dentists substantial assistance during dental surgery. PMID:26413143

  9. An efficient method for accurate segmentation of LV in contrast-enhanced cardiac MR images

    NASA Astrophysics Data System (ADS)

    Suryanarayana K., Venkata; Mitra, Abhishek; Srikrishnan, V.; Jo, Hyun Hee; Bidesi, Anup

    2016-03-01

    Segmentation of left ventricle (LV) in contrast-enhanced cardiac MR images is a challenging task because of high variability in the image intensity. This is due to a) wash-in and wash-out of the contrast agent over time and b) poor contrast around the epicardium (outer wall) region. Current approaches for segmentation of the endocardium (inner wall) usually involve application of a threshold within the region of interest, followed by refinement techniques like active contours. A limitation of this method is under-segmentation of the inner wall because of gradual loss of contrast at the wall boundary. On the other hand, the challenge in outer wall segmentation is the lack of reliable boundaries because of poor contrast. There are four main contributions in this paper to address the aforementioned issues. First, a seed image is selected using variance based approach on 4D time-frame images over which initial endocardium and epicardium is segmented. Secondly, we propose a patch based feature which overcomes the problem of gradual contrast loss for LV endocardium segmentation. Third, we propose a novel Iterative-Edge-Refinement (IER) technique for epicardium segmentation. Fourth, we propose a greedy search algorithm for propagating the initial contour segmented on seed-image across other time frame images. We have experimented our technique on five contrast-enhanced cardiac MR Datasets (4D) having a total of 1097 images. The segmentation results for all 1097 images have been visually inspected by a clinical expert and have shown good accuracy.

  10. Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images

    PubMed Central

    Zhao, Minghua; Liu, Yonghong; Feng, Yaning; Zhang, Ming; He, Lifeng; Suzuki, Kenji

    2016-01-01

    Accurate lung segmentation is an essential step in developing a computer-aided lung disease diagnosis system. However, because of the high variability of computerized tomography (CT) images, it remains a difficult task to accurately segment lung tissue in CT slices using a simple strategy. Motived by the aforementioned, a novel CT lung segmentation method based on the integration of multiple strategies was proposed in this paper. Firstly, in order to avoid noise, the input CT slice was smoothed using the guided filter. Then, the smoothed slice was transformed into a binary image using an optimized threshold. Next, a region growing strategy was employed to extract thorax regions. Then, lung regions were segmented from the thorax regions using a seed-based random walk algorithm. The segmented lung contour was then smoothed and corrected with a curvature-based correction method on each axis slice. Finally, with the lung masks, the lung region was automatically segmented from a CT slice. The proposed method was validated on a CT database consisting of 23 scans, including a number of 883 2D slices (the number of slices per scan is 38 slices), by comparing it to the commonly used lung segmentation method. Experimental results show that the proposed method accurately segmented lung regions in CT slices.

  11. Possibilistic-clustering-based MR brain image segmentation with accurate initialization

    NASA Astrophysics Data System (ADS)

    Liao, Qingmin; Deng, Yingying; Dou, Weibei; Ruan, Su; Bloyet, Daniel

    2004-01-01

    Magnetic resonance image analysis by computer is useful to aid diagnosis of malady. We present in this paper a automatic segmentation method for principal brain tissues. It is based on the possibilistic clustering approach, which is an improved fuzzy c-means clustering method. In order to improve the efficiency of clustering process, the initial value problem is discussed and solved by combining with a histogram analysis method. Our method can automatically determine number of classes to cluster and the initial values for each class. It has been tested on a set of forty MR brain images with or without the presence of tumor. The experimental results showed that it is simple, rapid and robust to segment the principal brain tissues.

  12. Can masses of non-experts train highly accurate image classifiers? A crowdsourcing approach to instrument segmentation in laparoscopic images.

    PubMed

    Maier-Hein, Lena; Mersmann, Sven; Kondermann, Daniel; Bodenstedt, Sebastian; Sanchez, Alexandro; Stock, Christian; Kenngott, Hannes Gotz; Eisenmann, Mathias; Speidel, Stefanie

    2014-01-01

    Machine learning algorithms are gaining increasing interest in the context of computer-assisted interventions. One of the bottlenecks so far, however, has been the availability of training data, typically generated by medical experts with very limited resources. Crowdsourcing is a new trend that is based on outsourcing cognitive tasks to many anonymous untrained individuals from an online community. In this work, we investigate the potential of crowdsourcing for segmenting medical instruments in endoscopic image data. Our study suggests that (1) segmentations computed from annotations of multiple anonymous non-experts are comparable to those made by medical experts and (2) training data generated by the crowd is of the same quality as that annotated by medical experts. Given the speed of annotation, scalability and low costs, this implies that the scientific community might no longer need to rely on experts to generate reference or training data for certain applications. To trigger further research in endoscopic image processing, the data used in this study will be made publicly available. PMID:25485409

  13. Automated medical image segmentation techniques

    PubMed Central

    Sharma, Neeraj; Aggarwal, Lalit M.

    2010-01-01

    Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images. PMID:20177565

  14. Segmentation of stereo terrain images

    NASA Astrophysics Data System (ADS)

    George, Debra A.; Privitera, Claudio M.; Blackmon, Theodore T.; Zbinden, Eric; Stark, Lawrence W.

    2000-06-01

    We have studied four approaches to segmentation of images: three automatic ones using image processing algorithms and a fourth approach, human manual segmentation. We were motivated toward helping with an important NASA Mars rover mission task -- replacing laborious manual path planning with automatic navigation of the rover on the Mars terrain. The goal of the automatic segmentations was to identify an obstacle map on the Mars terrain to enable automatic path planning for the rover. The automatic segmentation was first explored with two different segmentation methods: one based on pixel luminance, and the other based on pixel altitude generated through stereo image processing. The third automatic segmentation was achieved by combining these two types of image segmentation. Human manual segmentation of Martian terrain images was used for evaluating the effectiveness of the combined automatic segmentation as well as for determining how different humans segment the same images. Comparisons between two different segmentations, manual or automatic, were measured using a similarity metric, SAB. Based on this metric, the combined automatic segmentation did fairly well in agreeing with the manual segmentation. This was a demonstration of a positive step towards automatically creating the accurate obstacle maps necessary for automatic path planning and rover navigation.

  15. A three-dimensional image processing program for accurate, rapid, and semi-automated segmentation of neuronal somata with dense neurite outgrowth

    PubMed Central

    Ross, James D.; Cullen, D. Kacy; Harris, James P.; LaPlaca, Michelle C.; DeWeerth, Stephen P.

    2015-01-01

    Three-dimensional (3-D) image analysis techniques provide a powerful means to rapidly and accurately assess complex morphological and functional interactions between neural cells. Current software-based identification methods of neural cells generally fall into two applications: (1) segmentation of cell nuclei in high-density constructs or (2) tracing of cell neurites in single cell investigations. We have developed novel methodologies to permit the systematic identification of populations of neuronal somata possessing rich morphological detail and dense neurite arborization throughout thick tissue or 3-D in vitro constructs. The image analysis incorporates several novel automated features for the discrimination of neurites and somata by initially classifying features in 2-D and merging these classifications into 3-D objects; the 3-D reconstructions automatically identify and adjust for over and under segmentation errors. Additionally, the platform provides for software-assisted error corrections to further minimize error. These features attain very accurate cell boundary identifications to handle a wide range of morphological complexities. We validated these tools using confocal z-stacks from thick 3-D neural constructs where neuronal somata had varying degrees of neurite arborization and complexity, achieving an accuracy of ≥95%. We demonstrated the robustness of these algorithms in a more complex arena through the automated segmentation of neural cells in ex vivo brain slices. These novel methods surpass previous techniques by improving the robustness and accuracy by: (1) the ability to process neurites and somata, (2) bidirectional segmentation correction, and (3) validation via software-assisted user input. This 3-D image analysis platform provides valuable tools for the unbiased analysis of neural tissue or tissue surrogates within a 3-D context, appropriate for the study of multi-dimensional cell-cell and cell-extracellular matrix interactions. PMID

  16. Fast and Accurate Semi-Automated Segmentation Method of Spinal Cord MR Images at 3T Applied to the Construction of a Cervical Spinal Cord Template

    PubMed Central

    El Mendili, Mohamed-Mounir; Trunet, Stéphanie; Pélégrini-Issac, Mélanie; Lehéricy, Stéphane; Pradat, Pierre-François; Benali, Habib

    2015-01-01

    Objective To design a fast and accurate semi-automated segmentation method for spinal cord 3T MR images and to construct a template of the cervical spinal cord. Materials and Methods A semi-automated double threshold-based method (DTbM) was proposed enabling both cross-sectional and volumetric measures from 3D T2-weighted turbo spin echo MR scans of the spinal cord at 3T. Eighty-two healthy subjects, 10 patients with amyotrophic lateral sclerosis, 10 with spinal muscular atrophy and 10 with spinal cord injuries were studied. DTbM was compared with active surface method (ASM), threshold-based method (TbM) and manual outlining (ground truth). Accuracy of segmentations was scored visually by a radiologist in cervical and thoracic cord regions. Accuracy was also quantified at the cervical and thoracic levels as well as at C2 vertebral level. To construct a cervical template from healthy subjects’ images (n=59), a standardization pipeline was designed leading to well-centered straight spinal cord images and accurate probability tissue map. Results Visual scoring showed better performance for DTbM than for ASM. Mean Dice similarity coefficient (DSC) was 95.71% for DTbM and 90.78% for ASM at the cervical level and 94.27% for DTbM and 89.93% for ASM at the thoracic level. Finally, at C2 vertebral level, mean DSC was 97.98% for DTbM compared with 98.02% for TbM and 96.76% for ASM. DTbM showed similar accuracy compared with TbM, but with the advantage of limited manual interaction. Conclusion A semi-automated segmentation method with limited manual intervention was introduced and validated on 3T images, enabling the construction of a cervical spinal cord template. PMID:25816143

  17. Color image segmentation

    NASA Astrophysics Data System (ADS)

    McCrae, Kimberley A.; Ruck, Dennis W.; Rogers, Steven K.; Oxley, Mark E.

    1994-03-01

    The most difficult stage of automated target recognition is segmentation. Current segmentation problems include faces and tactical targets; previous efforts to segment these objects have used intensity and motion cues. This paper develops a color preprocessing scheme to be used with the other segmentation techniques. A neural network is trained to identify the color of a desired object, eliminating all but that color from the scene. Gabor correlations and 2D wavelet transformations will be performed on stationary images; and 3D wavelet transforms on multispectral data will incorporate color and motion detection into the machine visual system. The paper will demonstrate that color and motion cues can enhance a computer segmentation system. Results from segmenting faces both from the AFIT data base and from video taped television are presented; results from tactical targets such as tanks and airplanes are also given. Color preprocessing is shown to greatly improve the segmentation in most cases.

  18. Image segmentation survey

    NASA Technical Reports Server (NTRS)

    Haralick, R. M.

    1982-01-01

    The methodologies and capabilities of image segmentation techniques are reviewed. Single linkage schemes, hybrid linkage schemes, centroid linkage schemes, histogram mode seeking, spatial clustering, and split and merge schemes are addressed.

  19. Segmentation of SAR images

    NASA Technical Reports Server (NTRS)

    Kwok, Ronald

    1989-01-01

    The statistical characteristics of image speckle are reviewed. Existing segmentation techniques that have been used for speckle filtering, edge detection, and texture extraction are sumamrized. The relative effectiveness of each technique is briefly discussed.

  20. Scorpion image segmentation system

    NASA Astrophysics Data System (ADS)

    Joseph, E.; Aibinu, A. M.; Sadiq, B. A.; Bello Salau, H.; Salami, M. J. E.

    2013-12-01

    Death as a result of scorpion sting has been a major public health problem in developing countries. Despite the high rate of death as a result of scorpion sting, little report exists in literature of intelligent device and system for automatic detection of scorpion. This paper proposed a digital image processing approach based on the floresencing characteristics of Scorpion under Ultra-violet (UV) light for automatic detection and identification of scorpion. The acquired UV-based images undergo pre-processing to equalize uneven illumination and colour space channel separation. The extracted channels are then segmented into two non-overlapping classes. It has been observed that simple thresholding of the green channel of the acquired RGB UV-based image is sufficient for segmenting Scorpion from other background components in the acquired image. Two approaches to image segmentation have also been proposed in this work, namely, the simple average segmentation technique and K-means image segmentation. The proposed algorithm has been tested on over 40 UV scorpion images obtained from different part of the world and results obtained show an average accuracy of 97.7% in correctly classifying the pixel into two non-overlapping clusters. The proposed 1system will eliminate the problem associated with some of the existing manual approaches presently in use for scorpion detection.

  1. Robust, accurate and fast automatic segmentation of the spinal cord.

    PubMed

    De Leener, Benjamin; Kadoury, Samuel; Cohen-Adad, Julien

    2014-09-01

    Spinal cord segmentation provides measures of atrophy and facilitates group analysis via inter-subject correspondence. Automatizing this procedure enables studies with large throughput and minimizes user bias. Although several automatic segmentation methods exist, they are often restricted in terms of image contrast and field-of-view. This paper presents a new automatic segmentation method (PropSeg) optimized for robustness, accuracy and speed. The algorithm is based on the propagation of a deformable model and is divided into three parts: firstly, an initialization step detects the spinal cord position and orientation using a circular Hough transform on multiple axial slices rostral and caudal to the starting plane and builds an initial elliptical tubular mesh. Secondly, a low-resolution deformable model is propagated along the spinal cord. To deal with highly variable contrast levels between the spinal cord and the cerebrospinal fluid, the deformation is coupled with a local contrast-to-noise adaptation at each iteration. Thirdly, a refinement process and a global deformation are applied on the propagated mesh to provide an accurate segmentation of the spinal cord. Validation was performed in 15 healthy subjects and two patients with spinal cord injury, using T1- and T2-weighted images of the entire spinal cord and on multiecho T2*-weighted images. Our method was compared against manual segmentation and against an active surface method. Results show high precision for all the MR sequences. Dice coefficients were 0.9 for the T1- and T2-weighted cohorts and 0.86 for the T2*-weighted images. The proposed method runs in less than 1min on a normal computer and can be used to quantify morphological features such as cross-sectional area along the whole spinal cord. PMID:24780696

  2. Hybrid image segmentation using watersheds

    NASA Astrophysics Data System (ADS)

    Haris, Kostas; Efstratiadis, Serafim N.; Maglaveras, Nicos; Pappas, Costas

    1996-02-01

    A hybrid image segmentation algorithm is proposed which combines edge- and region-based techniques through the morphological algorithm of watersheds. The algorithm consists of the following steps: (1) edge-preserving statistical noise reduction, (2) gradient approximation, (3) detection of watersheds on gradient magnitude image, and (4) hierarchical region merging (HRM) in order to get semantically meaningful segmentations. The HRM process uses the region adjacency graph (RAG) representation of the image regions. At each step, the most similar pair of regions is determined (minimum cost RAG edge), the regions are merged and the RAG is updated. Traditionally, the above is implemented by storing all the RAG edges in a priority queue (heap). We propose a significantly faster algorithm which maintains an additional graph, the most similar neighbor graph, through which the priority queue size and processing time are drastically reduced. The final segmentation is an image partition which, through the RAG, provides information that can be used by knowledge-based high level processes, i.e. recognition. In addition, this region based representation provides one-pixel wide, closed, and accurately localized contours/surfaces. Due to the small number of free parameters, the algorithm can be quite effectively used in interactive image processing. Experimental results obtained with 2D MR images are presented.

  3. Toward accurate and fast iris segmentation for iris biometrics.

    PubMed

    He, Zhaofeng; Tan, Tieniu; Sun, Zhenan; Qiu, Xianchao

    2009-09-01

    Iris segmentation is an essential module in iris recognition because it defines the effective image region used for subsequent processing such as feature extraction. Traditional iris segmentation methods often involve an exhaustive search of a large parameter space, which is time consuming and sensitive to noise. To address these problems, this paper presents a novel algorithm for accurate and fast iris segmentation. After efficient reflection removal, an Adaboost-cascade iris detector is first built to extract a rough position of the iris center. Edge points of iris boundaries are then detected, and an elastic model named pulling and pushing is established. Under this model, the center and radius of the circular iris boundaries are iteratively refined in a way driven by the restoring forces of Hooke's law. Furthermore, a smoothing spline-based edge fitting scheme is presented to deal with noncircular iris boundaries. After that, eyelids are localized via edge detection followed by curve fitting. The novelty here is the adoption of a rank filter for noise elimination and a histogram filter for tackling the shape irregularity of eyelids. Finally, eyelashes and shadows are detected via a learned prediction model. This model provides an adaptive threshold for eyelash and shadow detection by analyzing the intensity distributions of different iris regions. Experimental results on three challenging iris image databases demonstrate that the proposed algorithm outperforms state-of-the-art methods in both accuracy and speed. PMID:19574626

  4. Cooperative processes in image segmentation

    NASA Technical Reports Server (NTRS)

    Davis, L. S.

    1982-01-01

    Research into the role of cooperative, or relaxation, processes in image segmentation is surveyed. Cooperative processes can be employed at several levels of the segmentation process as a preprocessing enhancement step, during supervised or unsupervised pixel classification and, finally, for the interpretation of image segments based on segment properties and relations.

  5. Automatic setae segmentation from Chaetoceros microscopic images.

    PubMed

    Zheng, Haiyong; Zhao, Hongmiao; Sun, Xue; Gao, Huihui; Ji, Guangrong

    2014-09-01

    A novel image processing model Grayscale Surface Direction Angle Model (GSDAM) is presented and the algorithm based on GSDAM is developed to segment setae from Chaetoceros microscopic images. The proposed model combines the setae characteristics of the microscopic images with the spatial analysis of image grayscale surface to detect and segment the direction thin and long setae from the low contrast background as well as noise which may make the commonly used segmentation methods invalid. The experimental results show that our algorithm based on GSDAM outperforms the boundary-based and region-based segmentation methods Canny edge detector, iterative threshold selection, Otsu's thresholding, minimum error thresholding, K-means clustering, and marker-controlled watershed on the setae segmentation more accurately and completely. PMID:24913015

  6. Note-accurate audio segmentation based on MPEG-7

    NASA Astrophysics Data System (ADS)

    Wellhausen, Jens

    2003-12-01

    Segmenting audio data into the smallest musical components is the basis for many further meta data extraction algorithms. For example, an automatic music transcription system needs to know where the exact boundaries of each tone are. In this paper a note accurate audio segmentation algorithm based on MPEG-7 low level descriptors is introduced. For a reliable detection of different notes, both features in the time and the frequency domain are used. Because of this, polyphonic instrument mixes and even melodies characterized by human voices can be examined with this alogrithm. For testing and verification of the note accurate segmentation, a simple music transcription system was implemented. The dominant frequency within each segment is used to build a MIDI file representing the processed audio data.

  7. Review methods for image segmentation from computed tomography images

    SciTech Connect

    Mamat, Nurwahidah; Rahman, Wan Eny Zarina Wan Abdul; Soh, Shaharuddin Cik; Mahmud, Rozi

    2014-12-04

    Image segmentation is a challenging process in order to get the accuracy of segmentation, automation and robustness especially in medical images. There exist many segmentation methods that can be implemented to medical images but not all methods are suitable. For the medical purposes, the aims of image segmentation are to study the anatomical structure, identify the region of interest, measure tissue volume to measure growth of tumor and help in treatment planning prior to radiation therapy. In this paper, we present a review method for segmentation purposes using Computed Tomography (CT) images. CT images has their own characteristics that affect the ability to visualize anatomic structures and pathologic features such as blurring of the image and visual noise. The details about the methods, the goodness and the problem incurred in the methods will be defined and explained. It is necessary to know the suitable segmentation method in order to get accurate segmentation. This paper can be a guide to researcher to choose the suitable segmentation method especially in segmenting the images from CT scan.

  8. Image segmentation using random features

    NASA Astrophysics Data System (ADS)

    Bull, Geoff; Gao, Junbin; Antolovich, Michael

    2014-01-01

    This paper presents a novel algorithm for selecting random features via compressed sensing to improve the performance of Normalized Cuts in image segmentation. Normalized Cuts is a clustering algorithm that has been widely applied to segmenting images, using features such as brightness, intervening contours and Gabor filter responses. Some drawbacks of Normalized Cuts are that computation times and memory usage can be excessive, and the obtained segmentations are often poor. This paper addresses the need to improve the processing time of Normalized Cuts while improving the segmentations. A significant proportion of the time in calculating Normalized Cuts is spent computing an affinity matrix. A new algorithm has been developed that selects random features using compressed sensing techniques to reduce the computation needed for the affinity matrix. The new algorithm, when compared to the standard implementation of Normalized Cuts for segmenting images from the BSDS500, produces better segmentations in significantly less time.

  9. Simplified labeling process for medical image segmentation.

    PubMed

    Gao, Mingchen; Huang, Junzhou; Huang, Xiaolei; Zhang, Shaoting; Metaxas, Dimitris N

    2012-01-01

    Image segmentation plays a crucial role in many medical imaging applications by automatically locating the regions of interest. Typically supervised learning based segmentation methods require a large set of accurately labeled training data. However, thel labeling process is tedious, time consuming and sometimes not necessary. We propose a robust logistic regression algorithm to handle label outliers such that doctors do not need to waste time on precisely labeling images for training set. To validate its effectiveness and efficiency, we conduct carefully designed experiments on cervigram image segmentation while there exist label outliers. Experimental results show that the proposed robust logistic regression algorithms achieve superior performance compared to previous methods, which validates the benefits of the proposed algorithms. PMID:23286072

  10. Bayesian segmentation of hyperspectral images

    NASA Astrophysics Data System (ADS)

    Mohammadpour, Adel; Féron, Olivier; Mohammad-Djafari, Ali

    2004-11-01

    In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.

  11. Neurosphere segmentation in brightfield images

    NASA Astrophysics Data System (ADS)

    Cheng, Jierong; Xiong, Wei; Chia, Shue Ching; Lim, Joo Hwee; Sankaran, Shvetha; Ahmed, Sohail

    2014-03-01

    The challenge of segmenting neurospheres (NSPs) from brightfield images includes uneven background illumination (vignetting), low contrast and shadow-casting appearance near the well wall. We propose a pipeline for neurosphere segmentation in brightfield images, focusing on shadow-casting removal. Firstly, we remove vignetting by creating a synthetic blank field image from a set of brightfield images of the whole well. Then, radial line integration is proposed to remove the shadow-casting and therefore facilitate automatic segmentation. Furthermore, a weighted bi-directional decay function is introduced to prevent undesired gradient effect of line integration on NSPs without shadow-casting. Afterward, multiscale Laplacian of Gaussian (LoG) and localized region-based level set are used to detect the NSP boundaries. Experimental results show that our proposed radial line integration method (RLI) achieves higher detection accuracy over existing methods in terms of precision, recall and F-score with less computational time.

  12. A Review on Segmentation of Positron Emission Tomography Images

    PubMed Central

    Foster, Brent; Bagci, Ulas; Mansoor, Awais; Xu, Ziyue; Mollura, Daniel J.

    2014-01-01

    Positron Emission Tomography (PET), a non-invasive functional imaging method at the molecular level, images the distribution of biologically targeted radiotracers with high sensitivity. PET imaging provides detailed quantitative information about many diseases and is often used to evaluate inflammation, infection, and cancer by detecting emitted photons from a radiotracer localized to abnormal cells. In order to differentiate abnormal tissue from surrounding areas in PET images, image segmentation methods play a vital role; therefore, accurate image segmentation is often necessary for proper disease detection, diagnosis, treatment planning, and follow-ups. In this review paper, we present state-of-the-art PET image segmentation methods, as well as the recent advances in image segmentation techniques. In order to make this manuscript self-contained, we also briefly explain the fundamentals of PET imaging, the challenges of diagnostic PET image analysis, and the effects of these challenges on the segmentation results. PMID:24845019

  13. Contrast Driven Elastica for Image Segmentation.

    PubMed

    El-Zehiry, Noha Youssry; Grady, Leo

    2016-06-01

    Minimization of boundary curvature is a classic regularization technique for image segmentation in the presence of noisy image data. Techniques for minimizing curvature have historically been derived from gradient descent methods which could be trapped by a local minimum and, therefore, required a good initialization. Recently, combinatorial optimization techniques have overcome this barrier by providing solutions that can achieve a global optimum. However, curvature regularization methods can fail when the true object has high curvature. In these circumstances, existing methods depend on a data term to overcome the high curvature of the object. Unfortunately, the data term may be ambiguous in some images, which causes these methods also to fail. To overcome these problems, we propose a contrast driven elastica model (including curvature), which can accommodate high curvature objects and an ambiguous data model. We demonstrate that we can accurately segment extremely challenging synthetic and real images with ambiguous data discrimination, poor boundary contrast, and sharp corners. We provide a quantitative evaluation of our segmentation approach when applied to a standard image segmentation data set. PMID:27019488

  14. Image Information Mining Utilizing Hierarchical Segmentation

    NASA Technical Reports Server (NTRS)

    Tilton, James C.; Marchisio, Giovanni; Koperski, Krzysztof; Datcu, Mihai

    2002-01-01

    The Hierarchical Segmentation (HSEG) algorithm is an approach for producing high quality, hierarchically related image segmentations. The VisiMine image information mining system utilizes clustering and segmentation algorithms for reducing visual information in multispectral images to a manageable size. The project discussed herein seeks to enhance the VisiMine system through incorporating hierarchical segmentations from HSEG into the VisiMine system.

  15. Distribution Metrics and Image Segmentation

    PubMed Central

    Georgiou, Tryphon; Michailovich, Oleg; Rathi, Yogesh; Malcolm, James; Tannenbaum, Allen

    2007-01-01

    The purpose of this paper is to describe certain alternative metrics for quantifying distances between distributions, and to explain their use and relevance in visual tracking. Besides the theoretical interest, such metrics may be used to design filters for image segmentation, that is for solving the key visual task of separating an object from the background in an image. The segmenting curve is represented as the zero level set of a signed distance function. Most existing methods in the geometric active contour framework perform segmentation by maximizing the separation of intensity moments between the interior and the exterior of an evolving contour. Here one can use the given distributional metric to determine a flow which minimizes changes in the distribution inside and outside the curve. PMID:18769529

  16. Adaptive image segmentation by quantization

    NASA Astrophysics Data System (ADS)

    Liu, Hui; Yun, David Y.

    1992-12-01

    Segmentation of images into textural homogeneous regions is a fundamental problem in an image understanding system. Most region-oriented segmentation approaches suffer from the problem of different thresholds selecting for different images. In this paper an adaptive image segmentation based on vector quantization is presented. It automatically segments images without preset thresholds. The approach contains a feature extraction module and a two-layer hierarchical clustering module, a vector quantizer (VQ) implemented by a competitive learning neural network in the first layer. A near-optimal competitive learning algorithm (NOLA) is employed to train the vector quantizer. NOLA combines the advantages of both Kohonen self- organizing feature map (KSFM) and K-means clustering algorithm. After the VQ is trained, the weights of the network and the number of input vectors clustered by each neuron form a 3- D topological feature map with separable hills aggregated by similar vectors. This overcomes the inability to visualize the geometric properties of data in a high-dimensional space for most other clustering algorithms. The second clustering algorithm operates in the feature map instead of the input set itself. Since the number of units in the feature map is much less than the number of feature vectors in the feature set, it is easy to check all peaks and find the `correct' number of clusters, also a key problem in current clustering techniques. In the experiments, we compare our algorithm with K-means clustering method on a variety of images. The results show that our algorithm achieves better performance.

  17. Colony image acquisition and segmentation

    NASA Astrophysics Data System (ADS)

    Wang, W. X.

    2007-12-01

    For counting of both colonies and plaques, there is a large number of applications including food, dairy, beverages, hygiene, environmental monitoring, water, toxicology, sterility testing, AMES testing, pharmaceuticals, paints, sterile fluids and fungal contamination. Recently, many researchers and developers have made efforts for this kind of systems. By investigation, some existing systems have some problems. The main problems are image acquisition and image segmentation. In order to acquire colony images with good quality, an illumination box was constructed as: the box includes front lightning and back lightning, which can be selected by users based on properties of colony dishes. With the illumination box, lightning can be uniform; colony dish can be put in the same place every time, which make image processing easy. The developed colony image segmentation algorithm consists of the sub-algorithms: (1) image classification; (2) image processing; and (3) colony delineation. The colony delineation algorithm main contain: the procedures based on grey level similarity, on boundary tracing, on shape information and colony excluding. In addition, a number of algorithms are developed for colony analysis. The system has been tested and satisfactory.

  18. Accurate and robust line segment extraction using minimum entropy with Hough transform.

    PubMed

    Xu, Zezhong; Shin, Bok-Suk; Klette, Reinhard

    2015-03-01

    The Hough transform is a popular technique used in the field of image processing and computer vision. With a Hough transform technique, not only the normal angle and distance of a line but also the line-segment's length and midpoint (centroid) can be extracted by analysing the voting distribution around a peak in the Hough space. In this paper, a method based on minimum-entropy analysis is proposed to extract the set of parameters of a line segment. In each column around a peak in Hough space, the voting values specify probabilistic distributions. The corresponding entropies and statistical means are computed. The line-segment's normal angle and length are simultaneously computed by fitting a quadratic polynomial curve to the voting entropies. The line-segment's midpoint and normal distance are computed by fitting and interpolating a linear curve to the voting means. The proposed method is tested on simulated images for detection accuracy by providing comparative results. Experimental results on real-world images verify the method as well. The proposed method for line-segment detection is both accurate and robust in the presence of quantization error, background noise, or pixel disturbances. PMID:25561596

  19. Metric Learning to Enhance Hyperspectral Image Segmentation

    NASA Technical Reports Server (NTRS)

    Thompson, David R.; Castano, Rebecca; Bue, Brian; Gilmore, Martha S.

    2013-01-01

    Unsupervised hyperspectral image segmentation can reveal spatial trends that show the physical structure of the scene to an analyst. They highlight borders and reveal areas of homogeneity and change. Segmentations are independently helpful for object recognition, and assist with automated production of symbolic maps. Additionally, a good segmentation can dramatically reduce the number of effective spectra in an image, enabling analyses that would otherwise be computationally prohibitive. Specifically, using an over-segmentation of the image instead of individual pixels can reduce noise and potentially improve the results of statistical post-analysis. In this innovation, a metric learning approach is presented to improve the performance of unsupervised hyperspectral image segmentation. The prototype demonstrations attempt a superpixel segmentation in which the image is conservatively over-segmented; that is, the single surface features may be split into multiple segments, but each individual segment, or superpixel, is ensured to have homogenous mineralogy.

  20. Image segmentation via piecewise constant regression

    NASA Astrophysics Data System (ADS)

    Acton, Scott T.; Bovik, Alan C.

    1994-09-01

    We introduce a novel unsupervised image segmentation technique that is based on piecewise constant (PICO) regression. Given an input image, a PICO output image for a specified feature size (scale) is computed via nonlinear regression. The regression effectively provides the constant region segmentation of the input image that has a minimum deviation from the input image. PICO regression-based segmentation avoids the problems of region merging, poor localization, region boundary ambiguity, and region fragmentation. Additionally, our segmentation method is particularly well-suited for corrupted (noisy) input data. An application to segmentation and classification of remotely sensed imagery is provided.

  1. Fast planar segmentation of depth images

    NASA Astrophysics Data System (ADS)

    Javan Hemmat, Hani; Pourtaherian, Arash; Bondarev, Egor; de With, Peter H. N.

    2015-03-01

    One of the major challenges for applications dealing with the 3D concept is the real-time execution of the algorithms. Besides this, for the indoor environments, perceiving the geometry of surrounding structures plays a prominent role in terms of application performance. Since indoor structures mainly consist of planar surfaces, fast and accurate detection of such features has a crucial impact on quality and functionality of the 3D applications, e.g. decreasing model size (decimation), enhancing localization, mapping, and semantic reconstruction. The available planar-segmentation algorithms are mostly developed using surface normals and/or curvatures. Therefore, they are computationally expensive and challenging for real-time performance. In this paper, we introduce a fast planar-segmentation method for depth images avoiding surface normal calculations. Firstly, the proposed method searches for 3D edges in a depth image and finds the lines between identified edges. Secondly, it merges all the points on each pair of intersecting lines into a plane. Finally, various enhancements (e.g. filtering) are applied to improve the segmentation quality. The proposed algorithm is capable of handling VGA-resolution depth images at a 6 FPS frame-rate with a single-thread implementation. Furthermore, due to the multi-threaded design of the algorithm, we achieve a factor of 10 speedup by deploying a GPU implementation.

  2. Cerebral microbleed segmentation from susceptibility weighted images

    NASA Astrophysics Data System (ADS)

    Roy, Snehashis; Jog, Amod; Magrath, Elizabeth; Butman, John A.; Pham, Dzung L.

    2015-03-01

    Cerebral microbleeds (CMB) are a common marker of traumatic brain injury. Accurate detection and quantification of the CMBs are important for better understanding the progression and prognosis of the injury. Previous microbleed detection methods have suffered from a high rate of false positives, which is time consuming to manually correct. In this paper, we propose a fully automatic, example-based method to segment CMBs from susceptibility-weighted (SWI) scans, where examples from an already segmented template SWI image are used to detect CMBs in a new image. First, multiple radial symmetry transforms (RST) are performed on the template SWI to detect small ellipsoidal structures, which serve as potential microbleed candidates. Then 3D patches from the SWI and its RSTs are combined to form a feature vector at each voxel of the image. A random forest regression is trained using the feature vectors, where the dependent variable is the binary segmentation voxel of the template. Once the regression is learnt, it is applied to a new SWI scan, whose feature vectors contain patches from SWI and its RSTs. Experiments on 26 subjects with mild to severe brain injury show a CMB detection sensitivity of 85:7%, specificity 99:5%, and a false positive to true positive ratio of 1:73, which is competitive with published methods while providing a significant reduction in computation time.

  3. An accurate registration technique for distorted images

    NASA Technical Reports Server (NTRS)

    Delapena, Michele; Shaw, Richard A.; Linde, Peter; Dravins, Dainis

    1990-01-01

    Accurate registration of International Ultraviolet Explorer (IUE) images is crucial because the variability of the geometrical distortions that are introduced by the SEC-Vidicon cameras ensures that raw science images are never perfectly aligned with the Intensity Transfer Functions (ITFs) (i.e., graded floodlamp exposures that are used to linearize and normalize the camera response). A technique for precisely registering IUE images which uses a cross correlation of the fixed pattern that exists in all raw IUE images is described.

  4. Image segmentation using fuzzy LVQ clustering networks

    NASA Technical Reports Server (NTRS)

    Tsao, Eric Chen-Kuo; Bezdek, James C.; Pal, Nikhil R.

    1992-01-01

    In this note we formulate image segmentation as a clustering problem. Feature vectors extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of a Kohonen learning vector quantization (LVQ) which integrates the Fuzzy c-Means (FCM) model with the learning rate and updating strategies of the LVQ is used for this task. This network, which segments images in an unsupervised manner, is thus related to the FCM optimization problem. Numerical examples on photographic and magnetic resonance images are given to illustrate this approach to image segmentation.

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

    NASA Astrophysics Data System (ADS)

    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.

  6. Image Segmentation, Registration, Compression, and Matching

    NASA Technical Reports Server (NTRS)

    Yadegar, Jacob; Wei, Hai; Yadegar, Joseph; Ray, Nilanjan; Zabuawala, Sakina

    2011-01-01

    A novel computational framework was developed of a 2D affine invariant matching exploiting a parameter space. Named as affine invariant parameter space (AIPS), the technique can be applied to many image-processing and computer-vision problems, including image registration, template matching, and object tracking from image sequence. The AIPS is formed by the parameters in an affine combination of a set of feature points in the image plane. In cases where the entire image can be assumed to have undergone a single affine transformation, the new AIPS match metric and matching framework becomes very effective (compared with the state-of-the-art methods at the time of this reporting). No knowledge about scaling or any other transformation parameters need to be known a priori to apply the AIPS framework. An automated suite of software tools has been created to provide accurate image segmentation (for data cleaning) and high-quality 2D image and 3D surface registration (for fusing multi-resolution terrain, image, and map data). These tools are capable of supporting existing GIS toolkits already in the marketplace, and will also be usable in a stand-alone fashion. The toolkit applies novel algorithmic approaches for image segmentation, feature extraction, and registration of 2D imagery and 3D surface data, which supports first-pass, batched, fully automatic feature extraction (for segmentation), and registration. A hierarchical and adaptive approach is taken for achieving automatic feature extraction, segmentation, and registration. Surface registration is the process of aligning two (or more) data sets to a common coordinate system, during which the transformation between their different coordinate systems is determined. Also developed here are a novel, volumetric surface modeling and compression technique that provide both quality-guaranteed mesh surface approximations and compaction of the model sizes by efficiently coding the geometry and connectivity

  7. Optimal retinal cyst segmentation from OCT images

    NASA Astrophysics Data System (ADS)

    Oguz, Ipek; Zhang, Li; Abramoff, Michael D.; Sonka, Milan

    2016-03-01

    Accurate and reproducible segmentation of cysts and fluid-filled regions from retinal OCT images is an important step allowing quantification of the disease status, longitudinal disease progression, and response to therapy in wet-pathology retinal diseases. However, segmentation of fluid-filled regions from OCT images is a challenging task due to their inhomogeneous appearance, the unpredictability of their number, size and location, as well as the intensity profile similarity between such regions and certain healthy tissue types. While machine learning techniques can be beneficial for this task, they require large training datasets and are often over-fitted to the appearance models of specific scanner vendors. We propose a knowledge-based approach that leverages a carefully designed cost function and graph-based segmentation techniques to provide a vendor-independent solution to this problem. We illustrate the results of this approach on two publicly available datasets with a variety of scanner vendors and retinal disease status. Compared to a previous machine-learning based approach, the volume similarity error was dramatically reduced from 81:3+/-56:4% to 22:2+/-21:3% (paired t-test, p << 0:001).

  8. Hierarchical image segmentation for learning object priors

    SciTech Connect

    Prasad, Lakshman; Yang, Xingwei; Latecki, Longin J; Li, Nan

    2010-11-10

    The proposed segmentation approach naturally combines experience based and image based information. The experience based information is obtained by training a classifier for each object class. For a given test image, the result of each classifier is represented as a probability map. The final segmentation is obtained with a hierarchial image segmentation algorithm that considers both the probability maps and the image features such as color and edge strength. We also utilize image region hierarchy to obtain not only local but also semi-global features as input to the classifiers. Moreover, to get robust probability maps, we take into account the region context information by averaging the probability maps over different levels of the hierarchical segmentation algorithm. The obtained segmentation results are superior to the state-of-the-art supervised image segmentation algorithms.

  9. Unsupervised Performance Evaluation of Image Segmentation

    NASA Astrophysics Data System (ADS)

    Chabrier, Sebastien; Emile, Bruno; Rosenberger, Christophe; Laurent, Helene

    2006-12-01

    We present in this paper a study of unsupervised evaluation criteria that enable the quantification of the quality of an image segmentation result. These evaluation criteria compute some statistics for each region or class in a segmentation result. Such an evaluation criterion can be useful for different applications: the comparison of segmentation results, the automatic choice of the best fitted parameters of a segmentation method for a given image, or the definition of new segmentation methods by optimization. We first present the state of art of unsupervised evaluation, and then, we compare six unsupervised evaluation criteria. For this comparative study, we use a database composed of 8400 synthetic gray-level images segmented in four different ways. Vinet's measure (correct classification rate) is used as an objective criterion to compare the behavior of the different criteria. Finally, we present the experimental results on the segmentation evaluation of a few gray-level natural images.

  10. Core Recursive Hierarchical Image Segmentation

    NASA Technical Reports Server (NTRS)

    Tilton, James

    2011-01-01

    The Recursive Hierarchical Image Segmentation (RHSEG) software has been repackaged to provide a version of the RHSEG software that is not subject to patent restrictions and that can be released to the general public through NASA GSFC's Open Source release process. Like the Core HSEG Software Package, this Core RHSEG Software Package also includes a visualization program called HSEGViewer along with a utility program HSEGReader. It also includes an additional utility program called HSEGExtract. The unique feature of the Core RHSEG package is that it is a repackaging of the RHSEG technology designed to specifically avoid the inclusion of the certain software technology. Unlike the Core HSEG package, it includes the recursive portions of the technology, but does not include processing window artifact elimination technology.

  11. Hyperspectral image segmentation using active contours

    NASA Astrophysics Data System (ADS)

    Lee, Cheolha P.; Snyder, Wesley E.

    2004-08-01

    Multispectral or hyperspectral image processing has been studied as a possible approach to automatic target recognition (ATR). Hundreds of spectral bands may provide high data redundancy, compensating the low contrast in medium wavelength infrared (MWIR) and long wavelength infrared (LWIR) images. Thus, the combination of spectral (image intensity) and spatial (geometric feature) information analysis could produce a substantial improvement. Active contours provide segments with continuous boundaries, while edge detectors based on local filtering often provide discontinuous boundaries. The segmentation by active contours depends on geometric feature of the object as well as image intensity. However, the application of active contours to multispectral images has been limited to the cases of simply textured images with low number of frames. This paper presents a supervised active contour model, which is applicable to vector-valued images with non-homogeneous regions and high number of frames. In the training stage, histogram models of target classes are estimated from sample vector-pixels. In the test stage, contours are evolved based on two different metrics: the histogram models of the corresponding segments and the histogram models estimated from sample target vector-pixels. The proposed segmentation method integrates segmentation and model-based pattern matching using supervised segmentation and multi-phase active contour model, while traditional methods apply pattern matching only after the segmentation. The proposed algorithm is implemented with both synthetic and real multispectral images, and shows desirable segmentation and classification results even in images with non-homogeneous regions.

  12. Neural cell image segmentation method based on support vector machine

    NASA Astrophysics Data System (ADS)

    Niu, Shiwei; Ren, Kan

    2015-10-01

    In the analysis of neural cell images gained by optical microscope, accurate and rapid segmentation is the foundation of nerve cell detection system. In this paper, a modified image segmentation method based on Support Vector Machine (SVM) is proposed to reduce the adverse impact caused by low contrast ratio between objects and background, adherent and clustered cells' interference etc. Firstly, Morphological Filtering and OTSU Method are applied to preprocess images for extracting the neural cells roughly. Secondly, the Stellate Vector, Circularity and Histogram of Oriented Gradient (HOG) features are computed to train SVM model. Finally, the incremental learning SVM classifier is used to classify the preprocessed images, and the initial recognition areas identified by the SVM classifier are added to the library as the positive samples for training SVM model. Experiment results show that the proposed algorithm can achieve much better segmented results than the classic segmentation algorithms.

  13. Skin lesion image segmentation using Delaunay Triangulation for melanoma detection.

    PubMed

    Pennisi, Andrea; Bloisi, Domenico D; Nardi, Daniele; Giampetruzzi, Anna Rita; Mondino, Chiara; Facchiano, Antonio

    2016-09-01

    Developing automatic diagnostic tools for the early detection of skin cancer lesions in dermoscopic images can help to reduce melanoma-induced mortality. Image segmentation is a key step in the automated skin lesion diagnosis pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion segmentation in dermoscopic images is presented. Delaunay Triangulation is used to extract a binary mask of the lesion region, without the need of any training stage. A quantitative experimental evaluation has been conducted on a publicly available database, by taking into account six well-known state-of-the-art segmentation methods for comparison. The results of the experimental analysis demonstrate that the proposed approach is highly accurate when dealing with benign lesions, while the segmentation accuracy significantly decreases when melanoma images are processed. This behavior led us to consider geometrical and color features extracted from the binary masks generated by our algorithm for classification, achieving promising results for melanoma detection. PMID:27215953

  14. Semiautomatic segmentation of liver metastases on volumetric CT images

    SciTech Connect

    Yan, Jiayong; Schwartz, Lawrence H.; Zhao, Binsheng

    2015-11-15

    Purpose: Accurate segmentation and quantification of liver metastases on CT images are critical to surgery/radiation treatment planning and therapy response assessment. To date, there are no reliable methods to perform such segmentation automatically. In this work, the authors present a method for semiautomatic delineation of liver metastases on contrast-enhanced volumetric CT images. Methods: The first step is to manually place a seed region-of-interest (ROI) in the lesion on an image. This ROI will (1) serve as an internal marker and (2) assist in automatically identifying an external marker. With these two markers, lesion contour on the image can be accurately delineated using traditional watershed transformation. Density information will then be extracted from the segmented 2D lesion and help determine the 3D connected object that is a candidate of the lesion volume. The authors have developed a robust strategy to automatically determine internal and external markers for marker-controlled watershed segmentation. By manually placing a seed region-of-interest in the lesion to be delineated on a reference image, the method can automatically determine dual threshold values to approximately separate the lesion from its surrounding structures and refine the thresholds from the segmented lesion for the accurate segmentation of the lesion volume. This method was applied to 69 liver metastases (1.1–10.3 cm in diameter) from a total of 15 patients. An independent radiologist manually delineated all lesions and the resultant lesion volumes served as the “gold standard” for validation of the method’s accuracy. Results: The algorithm received a median overlap, overestimation ratio, and underestimation ratio of 82.3%, 6.0%, and 11.5%, respectively, and a median average boundary distance of 1.2 mm. Conclusions: Preliminary results have shown that volumes of liver metastases on contrast-enhanced CT images can be accurately estimated by a semiautomatic segmentation

  15. Metric Learning for Hyperspectral Image Segmentation

    NASA Technical Reports Server (NTRS)

    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.

  16. Iterative Vessel Segmentation of Fundus Images.

    PubMed

    Roychowdhury, Sohini; Koozekanani, Dara D; Parhi, Keshab K

    2015-07-01

    This paper presents a novel unsupervised iterative blood vessel segmentation algorithm using fundus images. First, a vessel enhanced image is generated by tophat reconstruction of the negative green plane image. An initial estimate of the segmented vasculature is extracted by global thresholding the vessel enhanced image. Next, new vessel pixels are identified iteratively by adaptive thresholding of the residual image generated by masking out the existing segmented vessel estimate from the vessel enhanced image. The new vessel pixels are, then, region grown into the existing vessel, thereby resulting in an iterative enhancement of the segmented vessel structure. As the iterations progress, the number of false edge pixels identified as new vessel pixels increases compared to the number of actual vessel pixels. A key contribution of this paper is a novel stopping criterion that terminates the iterative process leading to higher vessel segmentation accuracy. This iterative algorithm is robust to the rate of new vessel pixel addition since it achieves 93.2-95.35% vessel segmentation accuracy with 0.9577-0.9638 area under ROC curve (AUC) on abnormal retinal images from the STARE dataset. The proposed algorithm is computationally efficient and consistent in vessel segmentation performance for retinal images with variations due to pathology, uneven illumination, pigmentation, and fields of view since it achieves a vessel segmentation accuracy of about 95% in an average time of 2.45, 3.95, and 8 s on images from three public datasets DRIVE, STARE, and CHASE_DB1, respectively. Additionally, the proposed algorithm has more than 90% segmentation accuracy for segmenting peripapillary blood vessels in the images from the DRIVE and CHASE_DB1 datasets. PMID:25700436

  17. Relaxed image foresting transforms for interactive volume image segmentation

    NASA Astrophysics Data System (ADS)

    Malmberg, Filip; Nyström, Ingela; Mehnert, Andrew; Engstrom, Craig; Bengtsson, Ewert

    2010-03-01

    The Image Foresting Transform (IFT) is a framework for image partitioning, commonly used for interactive segmentation. Given an image where a subset of the image elements (seed-points) have been assigned correct segmentation labels, the IFT completes the labeling by computing minimal cost paths from all image elements to the seed-points. Each image element is then given the same label as the closest seed-point. Here, we propose the relaxed IFT (RIFT). This modified version of the IFT features an additional parameter to control the smoothness of the segmentation boundary. The RIFT yields more intuitive segmentation results in the presence of noise and weak edges, while maintaining a low computational complexity. We show an application of the method to the refinement of manual segmentations of a thoracolumbar muscle in magnetic resonance images. The performed study shows that the refined segmentations are qualitatively similar to the manual segmentations, while intra-user variations are reduced by more than 50%.

  18. Improving image segmentation by learning region affinities

    SciTech Connect

    Prasad, Lakshman; Yang, Xingwei; Latecki, Longin J

    2010-11-03

    We utilize the context information of other regions in hierarchical image segmentation to learn new regions affinities. It is well known that a single choice of quantization of an image space is highly unlikely to be a common optimal quantization level for all categories. Each level of quantization has its own benefits. Therefore, we utilize the hierarchical information among different quantizations as well as spatial proximity of their regions. The proposed affinity learning takes into account higher order relations among image regions, both local and long range relations, making it robust to instabilities and errors of the original, pairwise region affinities. Once the learnt affinities are obtained, we use a standard image segmentation algorithm to get the final segmentation. Moreover, the learnt affinities can be naturally unutilized in interactive segmentation. Experimental results on Berkeley Segmentation Dataset and MSRC Object Recognition Dataset are comparable and in some aspects better than the state-of-art methods.

  19. Regression Segmentation for M³ Spinal Images.

    PubMed

    Wang, Zhijie; Zhen, Xiantong; Tay, KengYeow; Osman, Said; Romano, Walter; Li, Shuo

    2015-08-01

    Clinical routine often requires to analyze spinal images of multiple anatomic structures in multiple anatomic planes from multiple imaging modalities (M(3)). Unfortunately, existing methods for segmenting spinal images are still limited to one specific structure, in one specific plane or from one specific modality (S(3)). In this paper, we propose a novel approach, Regression Segmentation, that is for the first time able to segment M(3) spinal images in one single unified framework. This approach formulates the segmentation task innovatively as a boundary regression problem: modeling a highly nonlinear mapping function from substantially diverse M(3) images directly to desired object boundaries. Leveraging the advancement of sparse kernel machines, regression segmentation is fulfilled by a multi-dimensional support vector regressor (MSVR) which operates in an implicit, high dimensional feature space where M(3) diversity and specificity can be systematically categorized, extracted, and handled. The proposed regression segmentation approach was thoroughly tested on images from 113 clinical subjects including both disc and vertebral structures, in both sagittal and axial planes, and from both MRI and CT modalities. The overall result reaches a high dice similarity index (DSI) 0.912 and a low boundary distance (BD) 0.928 mm. With our unified and expendable framework, an efficient clinical tool for M(3) spinal image segmentation can be easily achieved, and will substantially benefit the diagnosis and treatment of spinal diseases. PMID:25361503

  20. Renal compartment segmentation in DCE-MRI images.

    PubMed

    Yang, Xin; Le Minh, Hung; Tim Cheng, Kwang-Ting; Sung, Kyung Hyun; Liu, Wenyu

    2016-08-01

    Renal compartment segmentation from Dynamic Contrast-Enhanced MRI (DCE-MRI) images is an important task for functional kidney evaluation. Despite advancement in segmentation methods, most of them focus on segmenting an entire kidney on CT images, there still lacks effective and automatic solutions for accurate segmentation of internal renal structures (i.e. cortex, medulla and renal pelvis) from DCE-MRI images. In this paper, we introduce a method for renal compartment segmentation which can robustly achieve high segmentation accuracy for a wide range of DCE-MRI data, and meanwhile requires little manual operations and parameter settings. The proposed method consists of five main steps. First, we pre-process the image time series to reduce the motion artifacts caused by the movement of the patients during the scans and enhance the kidney regions. Second, the kidney is segmented as a whole based on the concept of Maximally Stable Temporal Volume (MSTV). The proposed MSTV detects anatomical structures that are homogeneous in the spatial domain and stable in terms of temporal dynamics. MSTV-based kidney segmentation is robust to noises and does not require a training phase. It can well adapt to kidney shape variations caused by renal dysfunction. Third, voxels in the segmented kidney are described by principal components (PCs) to remove temporal redundancy and noises. And then k-means clustering of PCs is applied to separate voxels into multiple clusters. Fourth, the clusters are automatically labeled as cortex, medulla and pelvis based on voxels' geometric locations and intensity distribution. Finally, an iterative refinement method is introduced to further remove noises in each segmented compartment. Experiments on 14 real clinical kidney datasets and 12 synthetic dataset demonstrate that results produced by our method match very well with those segmented manually and the performance of our method is superior to the other five existing methods. PMID:27236222

  1. Low level image segmentation: an expert system.

    PubMed

    Nazif, A M; Levine, M D

    1984-05-01

    A major problem in robotic vision is the segmentation of images of natural scenes in order to understand their content. This paper presents a new solution to the image segmentation problem that is based on the design of a rule-based expert system. General knowledge about low level properties of processes employ the rules to segment the image into uniform regions and connected lines. In addition to the knowledge rules, a set of control rules are also employed. These include metarules that embody inferences about the order in which the knowledge rules are matched. They also incorporate focus of attention rules that determine the path of processing within the image. Furthermore, an additional set of higher level rules dynamically alters the processing strategy. This paper discusses the structure and content of the knowledge and control rules for image segmentation. PMID:21869225

  2. Image segmentation based on competitive learning

    NASA Astrophysics Data System (ADS)

    Zhang, Jing; Liu, Qun; Baikunth, Nath

    2004-06-01

    Image segment is a primary step in image analysis of unexploded ordnance (UXO) detection by ground penetrating 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.

  3. Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests.

    PubMed

    Gao, Yaozong; Shao, Yeqin; Lian, Jun; Wang, Andrew Z; Chen, Ronald C; Shen, Dinggang

    2016-06-01

    Segmenting male pelvic organs from CT images is a prerequisite for prostate cancer radiotherapy. The efficacy of radiation treatment highly depends on segmentation accuracy. However, accurate segmentation of male pelvic organs is challenging due to low tissue contrast of CT images, as well as large variations of shape and appearance of the pelvic organs. Among existing segmentation methods, deformable models are the most popular, as shape prior can be easily incorporated to regularize the segmentation. Nonetheless, the sensitivity to initialization often limits their performance, especially for segmenting organs with large shape variations. In this paper, we propose a novel approach to guide deformable models, thus making them robust against arbitrary initializations. Specifically, we learn a displacement regressor, which predicts 3D displacement from any image voxel to the target organ boundary based on the local patch appearance. This regressor provides a non-local external force for each vertex of deformable model, thus overcoming the initialization problem suffered by the traditional deformable models. To learn a reliable displacement regressor, two strategies are particularly proposed. 1) A multi-task random forest is proposed to learn the displacement regressor jointly with the organ classifier; 2) an auto-context model is used to iteratively enforce structural information during voxel-wise prediction. Extensive experiments on 313 planning CT scans of 313 patients show that our method achieves better results than alternative classification or regression based methods, and also several other existing methods in CT pelvic organ segmentation. PMID:26800531

  4. A Unified Framework for Brain Segmentation in MR Images

    PubMed Central

    Yazdani, S.; Yusof, R.; Karimian, A.; Riazi, A. H.; Bennamoun, M.

    2015-01-01

    Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets. PMID:26089978

  5. A Unified Framework for Brain Segmentation in MR Images.

    PubMed

    Yazdani, S; Yusof, R; Karimian, A; Riazi, A H; Bennamoun, M

    2015-01-01

    Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets. PMID:26089978

  6. Hierarchical Image Segmentation Using Correlation Clustering.

    PubMed

    Alush, Amir; Goldberger, Jacob

    2016-06-01

    In this paper, we apply efficient implementations of integer linear programming to the problem of image segmentation. The image is first grouped into superpixels and then local information is extracted for each pair of spatially adjacent superpixels. Given local scores on a map of several hundred superpixels, we use correlation clustering to find the global segmentation that is most consistent with the local evidence. We show that, although correlation clustering is known to be NP-hard, finding the exact global solution is still feasible by breaking the segmentation problem down into subproblems. Each such sub-problem can be viewed as an automatically detected image part. We can further accelerate the process by using the cutting-plane method, which provides a hierarchical structure of the segmentations. The efficiency and improved performance of the proposed method is compared to several state-of-the-art methods and demonstrated on several standard segmentation data sets. PMID:26701901

  7. Segmentation-based CT image compression

    NASA Astrophysics Data System (ADS)

    Thammineni, Arunoday; Mukhopadhyay, Sudipta; Kamath, Vidya

    2004-04-01

    The existing image compression standards like JPEG and JPEG 2000, compress the whole image as a single frame. This makes the system simple but inefficient. The problem is acute for applications where lossless compression is mandatory viz. medical image compression. If the spatial characteristics of the image are considered, it can give rise to a more efficient coding scheme. For example, CT reconstructed images have uniform background outside the field of view (FOV). Even the portion within the FOV can be divided as anatomically relevant and irrelevant parts. They have distinctly different statistics. Hence coding them separately will result in more efficient compression. Segmentation is done based on thresholding and shape information is stored using 8-connected differential chain code. Simple 1-D DPCM is used as the prediction scheme. The experiments show that the 1st order entropies of images fall by more than 11% when each segment is coded separately. For simplicity and speed of decoding Huffman code is chosen for entropy coding. Segment based coding will have an overhead of one table per segment but the overhead is minimal. Lossless compression of image based on segmentation resulted in reduction of bit rate by 7%-9% compared to lossless compression of whole image as a single frame by the same prediction coder. Segmentation based scheme also has the advantage of natural ROI based progressive decoding. If it is allowed to delete the diagnostically irrelevant portions, the bit budget can go down as much as 40%. This concept can be extended to other modalities.

  8. SAR Image Segmentation Using Morphological Attribute Profiles

    NASA Astrophysics Data System (ADS)

    Boldt, M.; Thiele, A.; Schulz, K.; Hinz, S.

    2014-08-01

    In the last years, the spatial resolution of remote sensing sensors and imagery has continuously improved. Focusing on spaceborne Synthetic Aperture Radar (SAR) sensors, the satellites of the current generation (TerraSAR-X, COSMO-SykMed) are able to acquire images with sub-meter resolution. Indeed, high resolution imagery is visually much better interpretable, but most of the established pixel-based analysis methods have become more or less impracticable since, in high resolution images, self-sufficient objects (vehicle, building) are represented by a large number of pixels. Methods dealing with Object-Based Image Analysis (OBIA) provide help. Objects (segments) are groupings of pixels resulting from image segmentation algorithms based on homogeneity criteria. The image set is represented by image segments, which allows the development of rule-based analysis schemes. For example, segments can be described or categorized by their local neighborhood in a context-based manner. In this paper, a novel method for the segmentation of high resolution SAR images is presented. It is based on the calculation of morphological differential attribute profiles (DAP) which are analyzed pixel-wise in a region growing procedure. The method distinguishes between heterogeneous and homogeneous image content and delivers a precise segmentation result.

  9. Segmentation of white rat sperm image

    NASA Astrophysics Data System (ADS)

    Bai, Weiguo; Liu, Jianguo; Chen, Guoyuan

    2011-11-01

    The segmentation of sperm image exerts a profound influence in the analysis of sperm morphology, which plays a significant role in the research of animals' infertility and reproduction. To overcome the microscope image's properties of low contrast and highly polluted noise, and to get better segmentation results of sperm image, this paper presents a multi-scale gradient operator combined with a multi-structuring element for the micro-spermatozoa image of white rat, as the multi-scale gradient operator can smooth the noise of an image, while the multi-structuring element can retain more shape details of the sperms. Then, we use the Otsu method to segment the modified gradient image whose gray scale processed is strong in sperms and weak in the background, converting it into a binary sperm image. As the obtained binary image owns impurities that are not similar with sperms in the shape, we choose a form factor to filter those objects whose form factor value is larger than the select critical value, and retain those objects whose not. And then, we can get the final binary image of the segmented sperms. The experiment shows this method's great advantage in the segmentation of the micro-spermatozoa image.

  10. FISICO: Fast Image SegmentatIon COrrection

    PubMed Central

    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

  11. Gel image segmentation based on discontinuity and region information

    NASA Astrophysics Data System (ADS)

    Wang, Weixing

    2005-10-01

    2-D electrophoresis gel images can be used for identifying and characterizing many forms of a particular protein encoded by a single gene. Conventional approaches to gel analysis require the three steps: (1) Spot detection on each gel; (2) Spot matching between gels; and (3) Spot quantification and comparison. Many researchers and developers attempt to automate all steps as much as possible, but errors in the detection and matching stages are common. In order to carry out gel image analysis, one first needs to accurately detect and measure the protein spots in a gel image. As other image analysis or computer vision areas, image segmentation is still a hard problem. This paper presents algorithms for automatically delineating gel spots. Two types of segmentation algorithms were implemented, the one is edge (discontinuity) based type, and the other is region based type. For the different classes of gel images, the two types of algorithms were tested; the advantages and disadvantages were discussed. Based on the testing and analysis results, authors suggested using a fusion of edge information and region information for gel image segmentation is a good complementary. The primary integration of the two types of image segmentation algorithms have been tested too, the result clearly show that the integrated algorithm can automatically delineate gel not only on a simple image and also on a complex image, and it is much better than that either only edge based algorithm or only region based algorithm.

  12. Automatic labeling and segmentation of vertebrae in CT images

    NASA Astrophysics Data System (ADS)

    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.

  13. Active contour based segmentation of resected livers in CT images

    NASA Astrophysics Data System (ADS)

    Oelmann, Simon; Oyarzun Laura, Cristina; Drechsler, Klaus; Wesarg, Stefan

    2015-03-01

    The majority of state of the art segmentation algorithms are able to give proper results in healthy organs but not in pathological ones. However, many clinical applications require an accurate segmentation of pathological organs. The determination of the target boundaries for radiotherapy or liver volumetry calculations are examples of this. Volumetry measurements are of special interest after tumor resection for follow up of liver regrow. The segmentation of resected livers presents additional challenges that were not addressed by state of the art algorithms. This paper presents a snakes based algorithm specially developed for the segmentation of resected livers. The algorithm is enhanced with a novel dynamic smoothing technique that allows the active contour to propagate with different speeds depending on the intensities visible in its neighborhood. The algorithm is evaluated in 6 clinical CT images as well as 18 artificial datasets generated from additional clinical CT images.

  14. Convergent Coarseness Regulation for Segmented Images

    SciTech Connect

    Paglieroni, D W

    2004-05-27

    In segmentation of remotely sensed images, the number of pixel classes and their spectral representations are often unknown a priori. Even with prior knowledge, pixels with spectral components from multiple classes lead to classification errors and undesired small region artifacts. Coarseness regulation for segmented images is proposed as an efficient novel technique for handling these problems. Beginning with an over-segmented image, perceptually similar connected regions are iteratively merged using a method reminiscent of region growing, except the primitives are regions, not pixels. Interactive coarseness regulation is achieved by specifying the area {alpha} of the largest region eligible for merging. A region with area less than {alpha} is merged with the most spectrally similar connected region, unless the regions are perceived as spectrally dissimilar. In convergent coarseness regulation, which requires no user interaction, {alpha} is specified as the total number of pixels in the image, and the coarseness regulation output converges to a steady-state segmentation that remains unchanged as {alpha} is further increased. By applying convergent coarseness regulation to AVIRIS, IKONOS and DigitalGlobe images, and quantitatively comparing computer-generated segmentations to segmentations generated manually by a human analyst, it was found that the quality of the input segmentations was consistently and dramatically improved.

  15. Medical image segmentation using genetic algorithms.

    PubMed

    Maulik, Ujjwal

    2009-03-01

    Genetic algorithms (GAs) have been found to be effective in the domain of medical image segmentation, since the problem can often be mapped to one of search in a complex and multimodal landscape. The challenges in medical image segmentation arise due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. The resulting search space is therefore often noisy with a multitude of local optima. Not only does the genetic algorithmic framework prove to be effective in coming out of local optima, it also brings considerable flexibility into the segmentation procedure. In this paper, an attempt has been made to review the major applications of GAs to the domain of medical image segmentation. PMID:19272859

  16. A summary of image segmentation techniques

    NASA Technical Reports Server (NTRS)

    Spirkovska, Lilly

    1993-01-01

    Machine vision systems are often considered to be composed of two subsystems: low-level vision and high-level vision. Low level vision consists primarily of image processing operations performed on the input image to produce another image with more favorable characteristics. These operations may yield images with reduced noise or cause certain features of the image to be emphasized (such as edges). High-level vision includes object recognition and, at the highest level, scene interpretation. The bridge between these two subsystems is the segmentation system. Through segmentation, the enhanced input image is mapped into a description involving regions with common features which can be used by the higher level vision tasks. There is no theory on image segmentation. Instead, image segmentation techniques are basically ad hoc and differ mostly in the way they emphasize one or more of the desired properties of an ideal segmenter and in the way they balance and compromise one desired property against another. These techniques can be categorized in a number of different groups including local vs. global, parallel vs. sequential, contextual vs. noncontextual, interactive vs. automatic. In this paper, we categorize the schemes into three main groups: pixel-based, edge-based, and region-based. Pixel-based segmentation schemes classify pixels based solely on their gray levels. Edge-based schemes first detect local discontinuities (edges) and then use that information to separate the image into regions. Finally, region-based schemes start with a seed pixel (or group of pixels) and then grow or split the seed until the original image is composed of only homogeneous regions. Because there are a number of survey papers available, we will not discuss all segmentation schemes. Rather than a survey, we take the approach of a detailed overview. We focus only on the more common approaches in order to give the reader a flavor for the variety of techniques available yet present enough

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-01-15

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

  19. Segmentation of confocal microscopic image of insect brain

    NASA Astrophysics Data System (ADS)

    Wu, Ming-Jin; Lin, Chih-Yang; Ching, Yu-Tai

    2002-05-01

    Accurate analysis of insect brain structures in digital confocal microscopic images is valuable and important to biology research needs. The first step is to segment meaningful structures from images. Active contour model, known as snakes, is widely used for segmentation of medical images. A new class of active contour model called gradient vector flow snake has been introduced in 1998 to overcome some critical problems encountered in the traditional snake. In this paper, we use gradient vector flow snake to segment the mushroom body and the central body from the confocal microscopic insect brain images. First, an edge map is created from images by some edge filters. Second, a gradient vector flow field is calculated from the edge map using a computational diffusion process. Finally, a traditional snake deformation process starts until it reaches a stable configuration. User interface is also provided here, allowing users to edit the snake during deformation process, if desired. Using the gradient vector flow snake as the main segmentation method and assist with user interface, we can properly segment the confocal microscopic insect brain image for most of the cases. The identified mushroom and central body can then be used as the preliminary results toward a 3-D reconstruction process for further biology researches.

  20. Automatic image segmentation by dynamic region merging.

    PubMed

    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

  1. Combining CRF and multi-hypothesis detection for accurate lesion segmentation in breast sonograms.

    PubMed

    Hao, Zhihui; Wang, Qiang; Seong, Yeong Kyeong; Lee, Jong-Ha; Ren, Haibing; Kim, Ji-yeun

    2012-01-01

    The implementation of lesion segmentation for breast ultrasound image relies on several diagnostic rules on intensity, texture, etc. In this paper, we propose a novel algorithm to achieve a comprehensive decision upon these rules by incorporating image over-segmentation and lesion detection in a pairwise CRF model, rather than a term-by-term translation. Multiple detection hypotheses are used to propagate object-level cues to segments and a unified classifier is trained based on the concatenated features. The experimental results show that our algorithm can avoid the drawbacks of separate detection or bottom-up segmentation, and can deal with very complicated cases. PMID:23285589

  2. Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Images

    PubMed Central

    Lee, Kyungmoo; Buitendijk, Gabriëlle H.S.; Bogunovic, Hrvoje; Springelkamp, Henriët; Hofman, Albert; Wahle, Andreas; Sonka, Milan; Vingerling, Johannes R.; Klaver, Caroline C.W.; Abràmoff, Michael D.

    2016-01-01

    Purpose To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies. Methods Six hundred ninety macular SD-OCT image volumes (6.0 × 6.0 × 2.3 mm3) were obtained from one eyes of 690 subjects (74.6 ± 9.7 [mean ± SD] years, 37.8% of males) randomly selected from the population-based Rotterdam Study. The dataset consisted of 420 OCT volumes with successful automated retinal nerve fiber layer (RNFL) segmentations obtained from our previously reported graph-based segmentation method and 270 volumes with failed segmentations. To evaluate the reliability of the layer segmentations, we have developed a new metric, segmentability index SI, which is obtained from a random forest regressor based on 12 features using OCT voxel intensities, edge-based costs, and on-surface costs. The SI was compared with well-known quality indices, quality index (QI), and maximum tissue contrast index (mTCI), using receiver operating characteristic (ROC) analysis. Results The 95% confidence interval (CI) and the area under the curve (AUC) for the QI are 0.621 to 0.805 with AUC 0.713, for the mTCI 0.673 to 0.838 with AUC 0.756, and for the SI 0.784 to 0.920 with AUC 0.852. The SI AUC is significantly larger than either the QI or mTCI AUC (P < 0.01). Conclusions The segmentability index SI is well suited to identify SD-OCT scans for which successful automated intraretinal layer segmentations can be expected. Translational Relevance Interpreting the quantification of SD-OCT images requires the underlying segmentation to be reliable, but standard SD-OCT quality metrics do not predict which segmentations are reliable and which are not. The segmentability index SI presented in this study does allow reliable segmentations to be identified, which is important for more accurate layer thickness analyses in research and population studies. PMID:27066311

  3. Active appearance model and deep learning for more accurate prostate segmentation on MRI

    NASA Astrophysics Data System (ADS)

    Cheng, Ruida; Roth, Holger R.; Lu, Le; Wang, Shijun; Turkbey, Baris; Gandler, William; McCreedy, Evan S.; Agarwal, Harsh K.; Choyke, Peter; Summers, Ronald M.; McAuliffe, Matthew J.

    2016-03-01

    Prostate segmentation on 3D MR images is a challenging task due to image artifacts, large inter-patient prostate shape and texture variability, and lack of a clear prostate boundary specifically at apex and base levels. We propose a supervised machine learning model that combines atlas based Active Appearance Model (AAM) with a Deep Learning model to segment the prostate on MR images. The performance of the segmentation method is evaluated on 20 unseen MR image datasets. The proposed method combining AAM and Deep Learning achieves a mean Dice Similarity Coefficient (DSC) of 0.925 for whole 3D MR images of the prostate using axial cross-sections. The proposed model utilizes the adaptive atlas-based AAM model and Deep Learning to achieve significant segmentation accuracy.

  4. Multimodality medical image fusion: probabilistic quantification, segmentation, and registration

    NASA Astrophysics Data System (ADS)

    Wang, Yue J.; Freedman, Matthew T.; Xuan, Jian Hua; Zheng, Qinfen; Mun, Seong K.

    1998-06-01

    Multimodality medical image fusion is becoming increasingly important in clinical applications, which involves information processing, registration and visualization of interventional and/or diagnostic images obtained from different modalities. This work is to develop a multimodality medical image fusion technique through probabilistic quantification, segmentation, and registration, based on statistical data mapping, multiple feature correlation, and probabilistic mean ergodic theorems. The goal of image fusion is to geometrically align two or more image areas/volumes so that pixels/voxels representing the same underlying anatomical structure can be superimposed meaningfully. Three steps are involved. To accurately extract the regions of interest, we developed the model supported Bayesian relaxation labeling, and edge detection and region growing integrated algorithms to segment the images into objects. After identifying the shift-invariant features (i.e., edge and region information), we provided an accurate and robust registration technique which is based on matching multiple binary feature images through a site model based image re-projection. The image was initially segmented into specified number of regions. A rough contour can be obtained by delineating and merging some of the segmented regions. We applied region growing and morphological filtering to extract the contour and get rid of some disconnected residual pixels after segmentation. The matching algorithm is implemented as follows: (1) the centroids of PET/CT and MR images are computed and then translated to the center of both images. (2) preliminary registration is performed first to determine an initial range of scaling factors and rotations, and the MR image is then resampled according to the specified parameters. (3) the total binary difference of the corresponding binary maps in both images is calculated for the selected registration parameters, and the final registration is achieved when the

  5. 3-D segmentation of human sternum in lung MDCT images.

    PubMed

    Pazokifard, Banafsheh; Sowmya, Arcot

    2013-01-01

    A fully automatic novel algorithm is presented for accurate 3-D segmentation of the human sternum in lung multi detector computed tomography (MDCT) images. The segmentation result is refined by employing active contours to remove calcified costal cartilage that is attached to the sternum. For each dataset, costal notches (sternocostal joints) are localized in 3-D by using a sternum mask and positions of the costal notches on it as reference. The proposed algorithm for sternum segmentation was tested on 16 complete lung MDCT datasets and comparison of the segmentation results to the reference delineation provided by a radiologist, shows high sensitivity (92.49%) and specificity (99.51%) and small mean distance (dmean=1.07 mm). Total average of the Euclidean distance error for costal notches positioning in 3-D is 4.2 mm. PMID:24110446

  6. Light Field Imaging Based Accurate Image Specular Highlight Removal

    PubMed Central

    Wang, Haoqian; Xu, Chenxue; Wang, Xingzheng; Zhang, Yongbing; Peng, Bo

    2016-01-01

    Specular reflection removal is indispensable to many computer vision tasks. However, most existing methods fail or degrade in complex real scenarios for their individual drawbacks. Benefiting from the light field imaging technology, this paper proposes a novel and accurate approach to remove specularity and improve image quality. We first capture images with specularity by the light field camera (Lytro ILLUM). After accurately estimating the image depth, a simple and concise threshold strategy is adopted to cluster the specular pixels into “unsaturated” and “saturated” category. Finally, a color variance analysis of multiple views and a local color refinement are individually conducted on the two categories to recover diffuse color information. Experimental evaluation by comparison with existed methods based on our light field dataset together with Stanford light field archive verifies the effectiveness of our proposed algorithm. PMID:27253083

  7. Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation.

    PubMed

    Liu, Guoying; Zhang, Yun; Wang, Aimin

    2015-11-01

    Fuzzy c-means (FCM) clustering with spatial constraints has attracted great attention in the field of image segmentation. However, most of the popular techniques fail to resolve misclassification problems due to the inaccuracy of their spatial models. This paper presents a new unsupervised FCM-based image segmentation method by paying closer attention to the selection of local information. In this method, region-level local information is incorporated into the fuzzy clustering procedure to adaptively control the range and strength of interactive pixels. First, a novel dissimilarity function is established by combining region-based and pixel-based distance functions together, in order to enhance the relationship between pixels which have similar local characteristics. Second, a novel prior probability function is developed by integrating the differences between neighboring regions into the mean template of the fuzzy membership function, which adaptively selects local spatial constraints by a tradeoff weight depending upon whether a pixel belongs to a homogeneous region or not. Through incorporating region-based information into the spatial constraints, the proposed method strengthens the interactions between pixels within the same region and prevents over smoothing across region boundaries. Experimental results over synthetic noise images, natural color images, and synthetic aperture radar images show that the proposed method achieves more accurate segmentation results, compared with five state-of-the-art image segmentation methods. PMID:26186787

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

    SciTech Connect

    Alirezaie, J.; Jernigan, M.E.; Nahmias, C.

    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.

  9. OCT image segmentation of the prostate nerves

    NASA Astrophysics Data System (ADS)

    Chitchian, Shahab; Weldon, Thomas P.; Fried, Nathaniel M.

    2009-08-01

    The cavernous nerves course along the surface of the prostate and are responsible for erectile function. Improvements in identification, imaging, and visualization of the cavernous nerves during prostate cancer surgery may improve nerve preservation and postoperative sexual potency. In this study, 2-D OCT images of the rat prostate were segmented to differentiate the cavernous nerves from the prostate gland. Three image features were employed: Gabor filter, Daubechies wavelet, and Laws filter. The features were segmented using a nearestneighbor classifier. N-ary morphological post-processing was used to remove small voids. The cavernous nerves were differentiated from the prostate gland with a segmentation error rate of only 0.058 +/- 0.019.

  10. Hierarchical Segmentation Enhances Diagnostic Imaging

    NASA Technical Reports Server (NTRS)

    2007-01-01

    Bartron Medical Imaging LLC (BMI), of New Haven, Connecticut, gained a nonexclusive license from Goddard Space Flight Center to use the RHSEG software in medical imaging. To manage image data, BMI then licensed two pattern-matching software programs from NASA's Jet Propulsion Laboratory that were used in image analysis and three data-mining and edge-detection programs from Kennedy Space Center. More recently, BMI made NASA history by being the first company to partner with the Space Agency through a Cooperative Research and Development Agreement to develop a 3-D version of RHSEG. With U.S. Food and Drug Administration clearance, BMI will sell its Med-Seg imaging system with the 2-D version of the RHSEG software to analyze medical imagery from CAT and PET scans, MRI, ultrasound, digitized X-rays, digitized mammographies, dental X-rays, soft tissue analyses, moving object analyses, and soft-tissue slides such as Pap smears for the diagnoses and management of diseases. Extending the software's capabilities to three dimensions will eventually enable production of pixel-level views of a tumor or lesion, early identification of plaque build-up in arteries, and identification of density levels of microcalcification in mammographies.

  11. Fully automated liver segmentation from SPIR image series.

    PubMed

    Göçeri, Evgin; Gürcan, Metin N; Dicle, Oğuz

    2014-10-01

    Accurate liver segmentation is an important component of surgery planning for liver transplantation, which enables patients with liver disease a chance to survive. Spectral pre-saturation inversion recovery (SPIR) image sequences are useful for liver vessel segmentation because vascular structures in the liver are clearly visible in these sequences. Although level-set based segmentation techniques are frequently used in liver segmentation due to their flexibility to adapt to different problems by incorporating prior knowledge, the need to initialize the contours on each slice is a common drawback of such techniques. In this paper, we present a fully automated variational level set approach for liver segmentation from SPIR image sequences. Our approach is designed to be efficient while achieving high accuracy. The efficiency is achieved by (1) automatically defining an initial contour for each slice, and (2) automatically computing weight values of each term in the applied energy functional at each iteration during evolution. Automated detection and exclusion of spurious structures (e.g. cysts and other bright white regions on the skin) in the pre-processing stage increases the accuracy and robustness. We also present a novel approach to reduce computational cost by employing binary regularization of level set function. A signed pressure force function controls the evolution of the active contour. The method was applied to ten data sets. In each image, the performance of the algorithm was measured using the receiver operating characteristics method in terms of accuracy, sensitivity and specificity. The accuracy of the proposed method was 96%. Quantitative analyses of results indicate that the proposed method can accurately, efficiently and consistently segment liver images. PMID:25192606

  12. Segmenting Images for a Better Diagnosis

    NASA Technical Reports Server (NTRS)

    2004-01-01

    NASA's Hierarchical Segmentation (HSEG) software has been adapted by Bartron Medical Imaging, LLC, for use in segmentation feature extraction, pattern recognition, and classification of medical images. Bartron acquired licenses from NASA Goddard Space Flight Center for application of the HSEG concept to medical imaging, from the California Institute of Technology/Jet Propulsion Laboratory to incorporate pattern-matching software, and from Kennedy Space Center for data-mining and edge-detection programs. The Med-Seg[TM] united developed by Bartron provides improved diagnoses for a wide range of medical images, including computed tomography scans, positron emission tomography scans, magnetic resonance imaging, ultrasound, digitized Z-ray, digitized mammography, dental X-ray, soft tissue analysis, and moving object analysis. It also can be used in analysis of soft-tissue slides. Bartron's future plans include the application of HSEG technology to drug development. NASA is advancing it's HSEG software to learn more about the Earth's magnetosphere.

  13. Retinal layer segmentation of macular OCT images using boundary classification

    PubMed Central

    Lang, Andrew; Carass, Aaron; Hauser, Matthew; Sotirchos, Elias S.; Calabresi, Peter A.; Ying, Howard S.; Prince, Jerry L.

    2013-01-01

    Optical coherence tomography (OCT) has proven to be an essential imaging modality for ophthalmology and is proving to be very important in neurology. OCT enables high resolution imaging of the retina, both at the optic nerve head and the macula. Macular retinal layer thicknesses provide useful diagnostic information and have been shown to correlate well with measures of disease severity in several diseases. Since manual segmentation of these layers is time consuming and prone to bias, automatic segmentation methods are critical for full utilization of this technology. In this work, we build a random forest classifier to segment eight retinal layers in macular cube images acquired by OCT. The random forest classifier learns the boundary pixels between layers, producing an accurate probability map for each boundary, which is then processed to finalize the boundaries. Using this algorithm, we can accurately segment the entire retina contained in the macular cube to an accuracy of at least 4.3 microns for any of the nine boundaries. Experiments were carried out on both healthy and multiple sclerosis subjects, with no difference in the accuracy of our algorithm found between the groups. PMID:23847738

  14. Adaptive geodesic transform for segmentation of vertebrae on CT images

    NASA Astrophysics Data System (ADS)

    Gaonkar, Bilwaj; Shu, Liao; Hermosillo, Gerardo; Zhan, Yiqiang

    2014-03-01

    Vertebral segmentation is a critical first step in any quantitative evaluation of vertebral pathology using CT images. This is especially challenging because bone marrow tissue has the same intensity profile as the muscle surrounding the bone. Thus simple methods such as thresholding or adaptive k-means fail to accurately segment vertebrae. While several other algorithms such as level sets may be used for segmentation any algorithm that is clinically deployable has to work in under a few seconds. To address these dual challenges we present here, a new algorithm based on the geodesic distance transform that is capable of segmenting the spinal vertebrae in under one second. To achieve this we extend the theory of the geodesic distance transforms proposed in1 to incorporate high level anatomical knowledge through adaptive weighting of image gradients. Such knowledge may be provided by the user directly or may be automatically generated by another algorithm. We incorporate information 'learnt' using a previously published machine learning algorithm2 to segment the L1 to L5 vertebrae. While we present a particular application here, the adaptive geodesic transform is a generic concept which can be applied to segmentation of other organs as well.

  15. Performance evaluation of image segmentation algorithms on microscopic image data.

    PubMed

    Beneš, Miroslav; Zitová, Barbara

    2015-01-01

    In our paper, we present a performance evaluation of image segmentation algorithms on microscopic image data. In spite of the existence of many algorithms for image data partitioning, there is no universal and 'the best' method yet. Moreover, images of microscopic samples can be of various character and quality which can negatively influence the performance of image segmentation algorithms. Thus, the issue of selecting suitable method for a given set of image data is of big interest. We carried out a large number of experiments with a variety of segmentation methods to evaluate the behaviour of individual approaches on the testing set of microscopic images (cross-section images taken in three different modalities from the field of art restoration). The segmentation results were assessed by several indices used for measuring the output quality of image segmentation algorithms. In the end, the benefit of segmentation combination approach is studied and applicability of achieved results on another representatives of microscopic data category - biological samples - is shown. PMID:25233873

  16. Segmentation of dermoscopy images using wavelet networks.

    PubMed

    Sadri, Amir Reza; Zekri, Maryam; Sadri, Saeed; Gheissari, Niloofar; Mokhtari, Mojgan; Kolahdouzan, Farzaneh

    2013-04-01

    This paper introduces a new approach for the segmentation of skin lesions in dermoscopic images based on wavelet network (WN). The WN presented here is a member of fixed-grid WNs that is formed with no need of training. In this WN, after formation of wavelet lattice, determining shift and scale parameters of wavelets with two screening stage and selecting effective wavelets, orthogonal least squares algorithm is used to calculate the network weights and to optimize the network structure. The existence of two stages of screening increases globality of the wavelet lattice and provides a better estimation of the function especially for larger scales. R, G, and B values of a dermoscopy image are considered as the network inputs and the network structure formation. Then, the image is segmented and the skin lesions exact boundary is determined accordingly. The segmentation algorithm were applied to 30 dermoscopic images and evaluated with 11 different metrics, using the segmentation result obtained by a skilled pathologist as the ground truth. Experimental results show that our method acts more effectively in comparison with some modern techniques that have been successfully used in many medical imaging problems. PMID:23193305

  17. Image texture segmentation using a neural network

    NASA Astrophysics Data System (ADS)

    Sayeh, Mohammed R.; Athinarayanan, Ragu; Dhali, Pushpuak

    1992-09-01

    In this paper we use a neural network called the Lyapunov associative memory (LYAM) system to segment image texture into different categories or clusters. The LYAM system is constructed by a set of ordinary differential equations which are simulated on a digital computer. The clustering can be achieved by using a single tuning parameter in the simplest model. Pattern classes are represented by the stable equilibrium states of the system. Design of the system is based on synthesizing two local energy functions, namely, the learning and recall energy functions. Before the implementation of the segmentation process, a Gauss-Markov random field (GMRF) model is applied to the raw image. This application suitably reduces the image data and prepares the texture information for the neural network process. We give a simple image example illustrating the capability of the technique. The GMRF-generated features are also used for a clustering, based on the Euclidean distance.

  18. Multiresolution segmentation technique for spine MRI images

    NASA Astrophysics Data System (ADS)

    Li, Haiyun; Yan, Chye H.; Ong, Sim Heng; Chui, Cheekong K.; Teoh, Swee H.

    2002-05-01

    In this paper, we describe a hybrid method for segmentation of spinal magnetic resonance imaging that has been developed based on the natural phenomenon of stones appearing as water recedes. The candidate segmentation region corresponds to the stones with characteristics similar to that of intensity extrema, edges, intensity ridge and grey-level blobs. The segmentation method is implemented based on a combination of wavelet multiresolution decomposition and fuzzy clustering. First thresholding is performed dynamically according to local characteristic to detect possible target areas, We then use fuzzy c-means clustering in concert with wavelet multiscale edge detection to identify the maximum likelihood anatomical and functional target areas. Fuzzy C-Means uses iterative optimization of an objective function based on a weighted similarity measure between the pixels in the image and each of c cluster centers. Local extrema of this objective function are indicative of an optimal clustering of the input data. The multiscale edges can be detected and characterized from local maxima of the modulus of the wavelet transform while the noise can be reduced to some extent by enacting thresholds. The method provides an efficient and robust algorithm for spinal image segmentation. Examples are presented to demonstrate the efficiency of the technique on some spinal MRI images.

  19. Automatic segmentation of MR brain images in multiple sclerosis patients

    NASA Astrophysics Data System (ADS)

    Avula, Ramesh T. V.; Erickson, Bradley J.

    1996-04-01

    A totally automatic scheme for segmenting brain from extracranial tissues and to classify all intracranial voxels as CSF, gray matter (GM), white matter (WM), or abnormality such as multiple sclerosis (MS) lesions is presented in this paper. It is observed that in MR head images, if a tissue's intensity values are normalized, its relationship to the other tissues is essentially constant for a given type of image. Based on this approach, the subcutaneous fat surrounding the head is normalized to classify other tissues. Spatially registered 3 mm MR head image slices of T1 weighted, fast spin echo [dual echo T2 weighted and proton density (PD) weighted images] and fast fluid attenuated inversion recovery (FLAIR) sequences are used for segmentation. Subcutaneous fat surrounding the skull was identified based on intensity thresholding from T1 weighted images. A multiparametric space map was developed for CSF, GM and WM by normalizing each tissue with respect to the mean value of corresponding subcutaneous fat on each pulse sequence. To reduce the low frequency noise without blurring the fine morphological high frequency details an anisotropic diffusion filter was applied to all images before segmentation. An initial slice by slice classification was followed by morphological operations to delete any brides connecting extracranial segments. Finally 3-dimensional region growing of the segmented brain extracts GM, WM and pathology. The algorithm was tested on sequential scans of 10 patients with MS lesions. For well registered sequences, tissues and pathology have been accurately classified. This procedure does not require user input or image training data sets, and shows promise for automatic classification of brain and pathology.

  20. Liver segmentation for CT images using GVF snake

    SciTech Connect

    Liu Fan; Zhao Binsheng; Kijewski, Peter K.; Wang Liang; Schwartz, Lawrence H.

    2005-12-15

    Accurate liver segmentation on computed tomography (CT) images is a challenging task especially at sites where surrounding tissues (e.g., stomach, kidney) have densities similar to that of the liver and lesions reside at the liver edges. We have developed a method for semiautomatic delineation of the liver contours on contrast-enhanced CT images. The method utilizes a snake algorithm with a gradient vector flow (GVF) field as its external force. To improve the performance of the GVF snake in the segmentation of the liver contour, an edge map was obtained with a Canny edge detector, followed by modifications using a liver template and a concavity removal algorithm. With the modified edge map, for which unwanted edges inside the liver were eliminated, the GVF field was computed and an initial liver contour was formed. The snake algorithm was then applied to obtain the actual liver contour. This algorithm was extended to segment the liver volume in a slice-by-slice fashion, where the result of the preceding slice constrained the segmentation of the adjacent slice. 551 two-dimensional liver images from 20 volumetric images with colorectal metastases spreading throughout the livers were delineated using this method, and also manually by a radiologist for evaluation. The difference ratio, which is defined as the percentage ratio of mismatching volume between the computer and the radiologist's results, ranged from 2.9% to 7.6% with a median value of 5.3%.

  1. Image feature based GPS trace filtering for road network generation and road segmentation

    SciTech Connect

    Yuan, Jiangye; Cheriyadat, Anil M.

    2015-10-19

    We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segment road regions. Lastly, our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.

  2. Image Segmentation With Eigenfunctions of an Anisotropic Diffusion Operator.

    PubMed

    Wang, Jingyue; Huang, Weizhang

    2016-05-01

    We propose the eigenvalue problem of an anisotropic diffusion operator for image segmentation. The diffusion matrix is defined based on the input image. The eigenfunctions and the projection of the input image in some eigenspace capture key features of the input image. An important property of the model is that for many input images, the first few eigenfunctions are close to being piecewise constant, which makes them useful as the basis for a variety of applications, such as image segmentation and edge detection. The eigenvalue problem is shown to be related to the algebraic eigenvalue problems resulting from several commonly used discrete spectral clustering models. The relation provides a better understanding and helps developing more efficient numerical implementation and rigorous numerical analysis for discrete spectral segmentation methods. The new continuous model is also different from energy-minimization methods such as active contour models in that no initial guess is required for in the current model. A numerical implementation based on a finite-element method with an anisotropic mesh adaptation strategy is presented. It is shown that the numerical scheme gives much more accurate results on eigenfunctions than uniform meshes. Several interesting features of the model are examined in numerical examples, and possible applications are discussed. PMID:26992021

  3. Side scan sonar image segmentation based on neutrosophic set and quantum-behaved particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Zhao, Jianhu; Wang, Xiao; Zhang, Hongmei; Hu, Jun; Jian, Xiaomin

    2016-06-01

    To fulfill side scan sonar (SSS) image segmentation accurately and efficiently, a novel segmentation algorithm based on neutrosophic set (NS) and quantum-behaved particle swarm optimization (QPSO) is proposed in this paper. Firstly, the neutrosophic subset images are obtained by transforming the input image into the NS domain. Then, a co-occurrence matrix is accurately constructed based on these subset images, and the entropy of the gray level image is described to serve as the fitness function of the QPSO algorithm. Moreover, the optimal two-dimensional segmentation threshold vector is quickly obtained by QPSO. Finally, the contours of the interested target are segmented with the threshold vector and extracted by the mathematic morphology operation. To further improve the segmentation efficiency, the single threshold segmentation, an alternative algorithm, is recommended for the shadow segmentation by considering the gray level characteristics of the shadow. The accuracy and efficiency of the proposed algorithm are assessed with experiments of SSS image segmentation.

  4. Segmentation of prostate cancer tissue microarray images

    NASA Astrophysics Data System (ADS)

    Cline, Harvey E.; Can, Ali; Padfield, Dirk

    2006-02-01

    Prostate cancer is diagnosed by histopathology interpretation of hematoxylin and eosin (H and E)-stained tissue sections. Gland and nuclei distributions vary with the disease grade. The morphological features vary with the advance of cancer where the epithelial regions grow into the stroma. An efficient pathology slide image analysis method involved using a tissue microarray with known disease stages. Digital 24-bit RGB images were acquired for each tissue element on the slide with both 10X and 40X objectives. Initial segmentation at low magnification was accomplished using prior spectral characteristics from a training tissue set composed of four tissue clusters; namely, glands, epithelia, stroma and nuclei. The segmentation method was automated by using the training RGB values as an initial guess and iterating the averaging process 10 times to find the four cluster centers. Labels were assigned to the nearest cluster center in red-blue spectral feature space. An automatic threshold algorithm separated the glands from the tissue. A visual pseudo color representation of 60 segmented tissue microarray image was generated where white, pink, red, blue colors represent glands, epithelia, stroma and nuclei, respectively. The higher magnification images provided refined nuclei morphology. The nuclei were detected with a RGB color space principle component analysis that resulted in a grey scale image. The shape metrics such as compactness, elongation, minimum and maximum diameters were calculated based on the eigenvalues of the best-fitting ellipses to the nuclei.

  5. Hepatic lesions segmentation in ultrasound nonlinear imaging

    NASA Astrophysics Data System (ADS)

    Kissi, Adelaide A.; Cormier, Stephane; Pourcelot, Leandre; Tranquart, Francois

    2005-04-01

    Doppler has been used for many years for cardiovascular exploration in order to visualize the vessels walls and anatomical or functional diseases. The use of ultrasound contrast agents makes it possible to improve ultrasonic information. Nonlinear ultrasound imaging highlights the detection of these agents within an organ and hence is a powerful technique to image perfusion of an organ in real-time. The visualization of flow and perfusion provides important information for the diagnosis of various diseases as well as for the detection of tumors. However, the images are buried in noise, the speckle, inherent in the image formation. Furthermore at portal phase, there is often an absence of clear contrast between lesions and surrounding tissues because the organ is filled with agents. In this context, we propose a new method of automatic liver lesions segmentation in nonlinear imaging sequences for the quantification of perfusion. Our method of segmentation is divided into two stages. Initially, we developed an anisotropic diffusion step which raised the structural characteristics to eliminate the speckle. Then, a fuzzy competitive clustering process allowed us to delineate liver lesions. This method has been used to detect focal hepatic lesions (metastasis, nodular hyperplasia, adenoma). Compared to medical expert"s report obtained on 15 varied lesions, the automatic segmentation allows us to identify and delineate focal liver lesions during the portal phase which high accuracy. Our results show that this method improves markedly the recognition of focal hepatic lesions and opens the way for future precise quantification of contrast enhancement.

  6. Image Segmentation With Cage Active Contours.

    PubMed

    Garrido, Lluís; Guerrieri, Marité; Igual, Laura

    2015-12-01

    In this paper, we present a framework for image segmentation based on parametrized active contours. The evolving contour is parametrized according to a reduced set of control points that form a closed polygon and have a clear visual interpretation. The parametrization, called mean value coordinates, stems from the techniques used in computer graphics to animate virtual models. Our framework allows to easily formulate region-based energies to segment an image. In particular, we present three different local region-based energy terms: 1) the mean model; 2) the Gaussian model; 3) and the histogram model. We show the behavior of our method on synthetic and real images and compare the performance with state-of-the-art level set methods. PMID:26316128

  7. An approach to multi-temporal MODIS image analysis using image classification and segmentation

    NASA Astrophysics Data System (ADS)

    Senthilnath, J.; Bajpai, Shivesh; Omkar, S. N.; Diwakar, P. G.; Mani, V.

    2012-11-01

    This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation for extracting water-covered regions. Analysis of MODIS satellite images is applied in three stages: before flood, during flood and after flood. Water regions are extracted from the MODIS images using image classification (based on spectral information) and image segmentation (based on spatial information). Multi-temporal MODIS images from "normal" (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification (SVM and ANN) and region-based image segmentation is an accurate and reliable approach for the extraction of water-covered regions.

  8. Three-dimensional segmentation of luminal and adventitial borders in serial intravascular ultrasound images

    NASA Technical Reports Server (NTRS)

    Shekhar, R.; Cothren, R. M.; Vince, D. G.; Chandra, S.; Thomas, J. D.; Cornhill, J. F.

    1999-01-01

    Intravascular ultrasound (IVUS) provides exact anatomy of arteries, allowing accurate quantitative analysis. Automated segmentation of IVUS images is a prerequisite for routine quantitative analyses. We present a new three-dimensional (3D) segmentation technique, called active surface segmentation, which detects luminal and adventitial borders in IVUS pullback examinations of coronary arteries. The technique was validated against expert tracings by computing correlation coefficients (range 0.83-0.97) and William's index values (range 0.37-0.66). The technique was statistically accurate, robust to image artifacts, and capable of segmenting a large number of images rapidly. Active surface segmentation enabled geometrically accurate 3D reconstruction and visualization of coronary arteries and volumetric measurements.

  9. A caGRID-ENABLED, LEARNING BASED IMAGE SEGMENTATION METHOD FOR HISTOPATHOLOGY SPECIMENS

    PubMed Central

    Foran, David J.; Yang, Lin; Tuzel, Oncel; Chen, Wenjin; Hu, Jun; Kurc, Tahsin M.; Ferreira, Renato; Saltz, Joel H.

    2009-01-01

    Accurate segmentation of tissue microarrays is a challenging topic because of some of the similarities exhibited by normal tissue and tumor regions. Processing speed is another consideration when dealing with imaged tissue microarrays as each microscopic slide may contain hundreds of digitized tissue discs. In this paper, a fast and accurate image segmentation algorithm is presented. Both a whole disc delineation algorithm and a learning based tumor region segmentation approach which utilizes multiple scale texton histograms are introduced. The algorithm is completely automatic and computationally efficient. The mean pixel-wise segmentation accuracy is about 90%. It requires about 1 second for whole disc (1024×1024 pixels) segmentation and less than 5 seconds for segmenting tumor regions. In order to enable remote access to the algorithm and collaborative studies, an analytical service is implemented using the caGrid infrastructure. This service wraps the algorithm and provides interfaces for remote clients to submit images for analysis and retrieve analysis results. PMID:19936299

  10. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

    SciTech Connect

    Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang; Chen, Ken Chung; Shen, Steve G. F.; Yan, Jin; Lee, Philip K. M.; Chow, Ben; Liu, Nancy X.; Xia, James J.; Shen, Dinggang

    2014-04-15

    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT

  11. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

    PubMed Central

    Wang, Li; Chen, Ken Chung; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang; Shen, Steve G. F.; Yan, Jin; Lee, Philip K. M.; Chow, Ben; Liu, Nancy X.; Xia, James J.; Shen, Dinggang

    2014-01-01

    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into a maximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT

  12. Automatic segmentation of medical images using image registration: diagnostic and simulation applications.

    PubMed

    Barber, D C; Hose, D R

    2005-01-01

    Automatic identification of the boundaries of significant structure (segmentation) within a medical image is an are of ongoing research. Various approaches have been proposed but only two methods have achieved widespread use: manual delineation of boundaries and segmentation using intensity values. In this paper we describe an approach based on image registration. A reference image is prepared and segmented, by hand or otherwise. A patient image is registered to the reference image and the mapping then applied to ther reference segmentation to map it back to the patient image. In general a high-resolution nonlinear mapping is required to achieve accurate segmentation. This paper describes an algorithm that can efficiently generate such mappings, and outlines the uses of this tool in two relevant applications. An important feature of the approach described in this paper is that the algorithm is independent of the segmentation problem being addresses. All knowledge about the problem at hand is contained in files of reference data. A secondary benefit is that the continuous three-dimensional mapping generated is well suited to the generation of patient-specific numerical models (e.g. finite element meshes) from the library models. Smoothness constraints in the morphing algorithm tend to maintain the geometric quality of the reference mesh. PMID:15804853

  13. Volume rendering of segmented image objects.

    PubMed

    Bullitt, Elizabeth; Aylward, Stephen R

    2002-08-01

    This paper describes a new method of combining ray-casting with segmentation. Volume rendering is performed at interactive rates on personal computers, and visualizations include both "superficial" ray-casting through a shell at each object's surface and "deep" ray-casting through the confines of each object. A feature of the approach is the option to smoothly and interactively dilate segmentation boundaries along all axes. This ability, when combined with selective "turning off" of extraneous image objects, can help clinicians detect and evaluate segmentation errors that may affect surgical planning. We describe both a method optimized for displaying tubular objects and a more general method applicable to objects of arbitrary geometry. In both cases, select three-dimensional points are projected onto a modified z buffer that records additional information about the projected objects. A subsequent step selectively volume renders only through the object volumes indicated by the z buffer. We describe how our approach differs from other reported methods for combining segmentation with ray-casting, and illustrate how our method can be useful in helping to detect segmentation errors. PMID:12472272

  14. Segmentation of nasopharyngeal carcinoma (NPC) lesions in MR images

    SciTech Connect

    Lee, Francis K.H. . E-mail: fkhlee@cuhk.edu.hk; Yeung, David K.W.; King, Ann D.; Leung, S.F.; Ahuja, Anil

    2005-02-01

    Purpose: An accurate and reproducible method to delineate tumor margins from uninvolved tissues is of vital importance in guiding radiation therapy (RT). In nasopharyngeal carcinoma (NPC), tumor margin may be difficult to identify in magnetic resonance (MR) images, making the task of optimizing RT treatment more difficult. Our aim in this study is to develop a semiautomatic image segmentation method for NPC that requires minimal human intervention and is capable of delineating tumor margins with good accuracy and reproducibility. Methods and materials: The segmentation algorithm includes 5 stages: masking, Bayesian probability calculation, smoothing, thresholding and seed growing, and finally dilation and overlaying of results with different thresholds. The algorithm is based on information obtained from the contrast enhancement ratio of T1-weighted images and signal intensity of T2-weighted images. The algorithm is initiated by the selection of a valid anatomical seed point within the tumor by the user. The algorithm was evaluated on MR images from 7 NPC patients and was compared against the radiologist's reference outline. Results: The algorithm was successfully implemented on all 7 subjects. With a threshold of 1, the average percent match is 78.5 {+-} 3.86 (standard deviation) %, and the correspondence ratio is 66.5 {+-} 7%. Discussion: The segmentation algorithm presented here may be useful for diagnosing NPC and may guide RT treatment planning. Further improvement will be desirable to improve the accuracy and versatility of the method.

  15. Deep convolutional networks for pancreas segmentation in CT imaging

    NASA Astrophysics Data System (ADS)

    Roth, Holger R.; Farag, Amal; Lu, Le; Turkbey, Evrim B.; Summers, Ronald M.

    2015-03-01

    Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve maximum Dice scores of an average 68% +/- 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.

  16. Automatic segmentation of mammogram and tomosynthesis images

    NASA Astrophysics Data System (ADS)

    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

  17. Automatic scale selection for medical image segmentation

    NASA Astrophysics Data System (ADS)

    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.

  18. Image segmentation by background extraction refinements

    NASA Technical Reports Server (NTRS)

    Rodriguez, Arturo A.; Mitchell, O. Robert

    1990-01-01

    An image segmentation method refining background extraction in two phases is presented. In the first phase, the method detects homogeneous-background blocks and estimates the local background to be extracted throughout the image. A block is classified homogeneous if its left and right standard deviations are small. The second phase of the method refines background extraction in nonhomogeneous blocks by recomputing the shoulder thresholds. Rules that predict the final background extraction are derived by observing the behavior of successive background statistical measurements in the regions under the presence of dark and/or bright object pixels. Good results are shown for a number of outdoor scenes.

  19. An improved vein image segmentation algorithm based on SLIC and Niblack threshold method

    NASA Astrophysics Data System (ADS)

    Zhou, Muqing; Wu, Zhaoguo; Chen, Difan; Zhou, Ya

    2013-12-01

    Subcutaneous vein images are often obtained by using the absorbency difference of near-infrared (NIR) light between vein and its surrounding tissue under NIR light illumination. Vein images with high quality are critical to biometric identification, which requires segmenting the vein skeleton from the original images accurately. To address this issue, we proposed a vein image segmentation method which based on simple linear iterative clustering (SLIC) method and Niblack threshold method. The SLIC method was used to pre-segment the original images into superpixels and all the information in superpixels were transferred into a matrix (Block Matrix). Subsequently, Niblack thresholding method is adopted to binarize Block Matrix. Finally, we obtained segmented vein images from binarized Block Matrix. According to several experiments, most part of vein skeleton is revealed compared to traditional Niblack segmentation algorithm.

  20. Generalized expectation-maximization segmentation of brain MR images

    NASA Astrophysics Data System (ADS)

    Devalkeneer, Arnaud A.; Robe, Pierre A.; Verly, Jacques G.; Phillips, Christophe L. M.

    2006-03-01

    Manual segmentation of medical images is unpractical because it is time consuming, not reproducible, and prone to human error. It is also very difficult to take into account the 3D nature of the images. Thus, semi- or fully-automatic methods are of great interest. Current segmentation algorithms based on an Expectation- Maximization (EM) procedure present some limitations. The algorithm by Ashburner et al., 2005, does not allow multichannel inputs, e.g. two MR images of different contrast, and does not use spatial constraints between adjacent voxels, e.g. Markov random field (MRF) constraints. The solution of Van Leemput et al., 1999, employs a simplified model (mixture coefficients are not estimated and only one Gaussian is used by tissue class, with three for the image background). We have thus implemented an algorithm that combines the features of these two approaches: multichannel inputs, intensity bias correction, multi-Gaussian histogram model, and Markov random field (MRF) constraints. Our proposed method classifies tissues in three iterative main stages by way of a Generalized-EM (GEM) algorithm: (1) estimation of the Gaussian parameters modeling the histogram of the images, (2) correction of image intensity non-uniformity, and (3) modification of prior classification knowledge by MRF techniques. The goal of the GEM algorithm is to maximize the log-likelihood across the classes and voxels. Our segmentation algorithm was validated on synthetic data (with the Dice metric criterion) and real data (by a neurosurgeon) and compared to the original algorithms by Ashburner et al. and Van Leemput et al. Our combined approach leads to more robust and accurate segmentation.

  1. Accurate Non-parametric Estimation of Recent Effective Population Size from Segments of Identity by Descent

    PubMed Central

    Browning, Sharon R.; Browning, Brian L.

    2015-01-01

    Existing methods for estimating historical effective population size from genetic data have been unable to accurately estimate effective population size during the most recent past. We present a non-parametric method for accurately estimating recent effective population size by using inferred long segments of identity by descent (IBD). We found that inferred segments of IBD contain information about effective population size from around 4 generations to around 50 generations ago for SNP array data and to over 200 generations ago for sequence data. In human populations that we examined, the estimates of effective size were approximately one-third of the census size. We estimate the effective population size of European-ancestry individuals in the UK four generations ago to be eight million and the effective population size of Finland four generations ago to be 0.7 million. Our method is implemented in the open-source IBDNe software package. PMID:26299365

  2. Perceived visual speed constrained by image segmentation

    NASA Technical Reports Server (NTRS)

    Verghese, P.; Stone, L. S.

    1996-01-01

    Little is known about how or where the visual system parses the visual scene into objects or surfaces. However, it is generally assumed that the segmentation and grouping of pieces of the image into discrete entities is due to 'later' processing stages, after the 'early' processing of the visual image by local mechanisms selective for attributes such as colour, orientation, depth, and motion. Speed perception is also thought to be mediated by early mechanisms tuned for speed. Here we show that manipulating the way in which an image is parsed changes the way in which local speed information is processed. Manipulations that cause multiple stimuli to appear as parts of a single patch degrade speed discrimination, whereas manipulations that perceptually divide a single large stimulus into parts improve discrimination. These results indicate that processes as early as speed perception may be constrained by the parsing of the visual image into discrete entities.

  3. Automated segmentation of breast in 3-D MR images using a robust atlas.

    PubMed

    Khalvati, Farzad; Gallego-Ortiz, Cristina; Balasingham, Sharmila; Martel, Anne L

    2015-01-01

    This paper presents a robust atlas-based segmentation (ABS) algorithm for segmentation of the breast boundary in 3-D MR images. The proposed algorithm combines the well-known methodologies of ABS namely probabilistic atlas and atlas selection approaches into a single framework where two configurations are realized. The algorithm uses phase congruency maps to create an atlas which is robust to intensity variations. This allows an atlas derived from images acquired with one MR imaging sequence to be used to segment images acquired with a different MR imaging sequence and eliminates the need for intensity-based registration. Images acquired using a Dixon sequence were used to create an atlas which was used to segment both Dixon images (intra-sequence) and T1-weighted images (inter-sequence). In both cases, highly accurate results were achieved with the median Dice similarity coefficient values of 94% ±4% and 87 ±6.5%, respectively. PMID:25137725

  4. Image feature based GPS trace filtering for road network generation and road segmentation

    DOE PAGESBeta

    Yuan, Jiangye; Cheriyadat, Anil M.

    2015-10-19

    We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segmentmore » road regions. Lastly, our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.« less

  5. Model-based segmentation of the middle phalanx in digital radiographic images of the hand.

    PubMed

    Dendere, Ronald; Kabelitz, Gordian; Douglas, Tania S

    2013-01-01

    We present techniques for segmenting the middle phalanx of the middle finger in digital radiographic images using deformable models and active shape models (ASMs). The result of segmentation may be used in the estimation of bone mineral density which in turn may be used in the diagnosis of osteoporosis. A technique for minimizing user dependence is described. The segmentation accuracy of the two methods is assessed by comparing contours produced by the algorithms to those produced by manual segmentation, using the Hausdorff distance measure. The ASM technique produces more accurate segmentation. PMID:24110534

  6. Theoretical analysis of multispectral image segmentation criteria.

    PubMed

    Kerfoot, I B; Bresler, Y

    1999-01-01

    Markov random field (MRF) image segmentation algorithms have been extensively studied, and have gained wide acceptance. However, almost all of the work on them has been experimental. This provides a good understanding of the performance of existing algorithms, but not a unified explanation of the significance of each component. To address this issue, we present a theoretical analysis of several MRF image segmentation criteria. Standard methods of signal detection and estimation are used in the theoretical analysis, which quantitatively predicts the performance at realistic noise levels. The analysis is decoupled into the problems of false alarm rate, parameter selection (Neyman-Pearson and receiver operating characteristics), detection threshold, expected a priori boundary roughness, and supervision. Only the performance inherent to a criterion, with perfect global optimization, is considered. The analysis indicates that boundary and region penalties are very useful, while distinct-mean penalties are of questionable merit. Region penalties are far more important for multispectral segmentation than for greyscale. This observation also holds for Gauss-Markov random fields, and for many separable within-class PDFs. To validate the analysis, we present optimization algorithms for several criteria. Theoretical and experimental results agree fairly well. PMID:18267494

  7. WERITAS: weighted ensemble of regional image textures for ASM segmentation

    NASA Astrophysics Data System (ADS)

    Toth, Robert; Doyle, Scott; Rosen, Mark; Kalyanpur, Arjun; Pungavkar, Sona; Bloch, B. Nicolas; Genega, Elizabeth; Rofsky, Neil; Lenkinski, Robert; Madabhushi, Anant

    2009-02-01

    In this paper we present WERITAS, which is based in part on the traditional Active Shape Model (ASM) segmentation system. WERITAS generates multiple statistical texture features, and finds the optimal weighted average of those texture features by maximizing the correlation between the Euclidean distance to the ground truth and the Mahalanobis distance to the training data. The weighted average is used a multi-resolution segmentation system to more accurately detect the object border. A rigorous evaluation was performed on over 200 clinical images comprising of prostate images and breast images from 1.5 Tesla and 3 Tesla MRI machines via 6 distinct metrics. WERITAS was tested against a traditional multi-resolution ASM in addition to an ASM system which uses a plethora of random features to determine if the selection of features is improving the results rather than simply the use of multiple features. The results indicate that WERITAS outperforms all other methods to a high degree of statistical significance. For 1.5T prostate MRI images, the overlap from WERITAS is 83%, the overlap from the random features is 81%, and the overlap from the traditional ASM is only 66%. In addition, using 3T prostate MRI images, the overlap from WERITAS is 77%, the overlap from the random features is 54%, and the overlap from the traditional ASM is 59%, suggesting the usefulness of WERITAS. The only metrics in which WERITAS was outperformed did not hold any degree of statistical significance. WERITAS is a robust, efficient, and accurate segmentation system with a wide range of applications.

  8. Embedded Implementation of VHR Satellite Image Segmentation.

    PubMed

    Li, Chao; Balla-Arabé, Souleymane; Ginhac, Dominique; Yang, Fan

    2016-01-01

    Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage. PMID:27240370

  9. Embedded Implementation of VHR Satellite Image Segmentation

    PubMed Central

    Li, Chao; Balla-Arabé, Souleymane; Ginhac, Dominique; Yang, Fan

    2016-01-01

    Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage. PMID:27240370

  10. An Interactive Method Based on the Live Wire for Segmentation of the Breast in Mammography Images

    PubMed Central

    Zewei, Zhang; Tianyue, Wang; Li, Guo; Tingting, Wang; Lu, Xu

    2014-01-01

    In order to improve accuracy of computer-aided diagnosis of breast lumps, the authors introduce an improved interactive segmentation method based on Live Wire. This paper presents the Gabor filters and FCM clustering algorithm is introduced to the Live Wire cost function definition. According to the image FCM analysis for image edge enhancement, we eliminate the interference of weak edge and access external features clear segmentation results of breast lumps through improving Live Wire on two cases of breast segmentation data. Compared with the traditional method of image segmentation, experimental results show that the method achieves more accurate segmentation of breast lumps and provides more accurate objective basis on quantitative and qualitative analysis of breast lumps. PMID:25024740

  11. Initialisation of 3D level set for hippocampus segmentation from volumetric brain MR images

    NASA Astrophysics Data System (ADS)

    Hajiesmaeili, Maryam; Dehmeshki, Jamshid; Bagheri Nakhjavanlo, Bashir; Ellis, Tim

    2014-04-01

    Shrinkage of the hippocampus is a primary biomarker for Alzheimer's disease and can be measured through accurate segmentation of brain MR images. The paper will describe the problem of initialisation of a 3D level set algorithm for hippocampus segmentation that must cope with the some challenging characteristics, such as small size, wide range of intensities, narrow width, and shape variation. In addition, MR images require bias correction, to account for additional inhomogeneity associated with the scanner technology. Due to these inhomogeneities, using a single initialisation seed region inside the hippocampus is prone to failure. Alternative initialisation strategies are explored, such as using multiple initialisations in different sections (such as the head, body and tail) of the hippocampus. The Dice metric is used to validate our segmentation results with respect to ground truth for a dataset of 25 MR images. Experimental results indicate significant improvement in segmentation performance using the multiple initialisations techniques, yielding more accurate segmentation results for the hippocampus.

  12. Imaging segmentation along the Cascadia subduction zone

    NASA Astrophysics Data System (ADS)

    Allen, R. M.; Hawley, W. B.; Martin-Short, R.

    2015-12-01

    As we learn more about the Cascadia subduction zone, there is clear evidence for segmentation in the character of the many physical processes along its 1000 km length. There is segmentation in the arc magmas, in the seismicity, episodic tremor and slip, crustal structure and mantle structure all the way down to ~400 km depth. What is striking is the fact that the segment boundaries for these processes at depths of a few kilometers to hundreds of kilometers align. We must determine if this is coincidence, or if not, what the causative process is. The seismic deployments of the Cascadia Initiative onshore and offshore allow us to image the structure of the subduction zone, including the incoming Juan de Fuca plate, with unprecedented resolution. We use data from three one-year deployments of 70 ocean bottom seismometers across the Juan de Fuca plate, along with hundreds of onshore stations from the Pacific Northwest Seismic Network, the Berkeley Digital Seismic Network, the Earthscope Transportable Array, and smaller temporary seismic deployments. Our 3D tomographic models show significant variation in the structure of the subducting slab along its length. It extends deepest in the south (the Gorda section) where the plate is youngest, and shallows to the north across southern Oregon. There is a gap in the slab beneath northern Oregon, which appears to correlate with the geochemistry of the arc magmas. The slab is then visible again beneath Washington. We also constrain mantle flow paths using shear-wave splitting measurements at the offshore and onshore seismic stations. Beneath the Juan de Fuca plate the flow is sub-parallel to the motion of the plate. However, beneath the Gorda section of the Juan de Fuca place the flow is sub-parallel to the motion of the Pacific plate, not the Juan de Fuca plate. We are thus beginning to image a complex mantle flow pattern that may also play a role in the observed segmentation.

  13. Segmentation of polycystic kidneys from MR images

    NASA Astrophysics Data System (ADS)

    Racimora, Dimitri; Vivier, Pierre-Hugues; Chandarana, Hersh; Rusinek, Henry

    2010-03-01

    Polycystic kidney disease (PKD) is a disorder characterized by the growth of numerous fluid filled cysts in the kidneys. Measuring cystic kidney volume is thus crucial to monitoring the evolution of the disease. While T2-weighted MRI delineates the organ, automatic segmentation is very difficult due to highly variable shape and image contrast. The interactive stereology methods used currently involve a compromise between segmentation accuracy and time. We have investigated semi-automated methods: active contours and a sub-voxel morphology based algorithm. Coronal T2- weighted images of 17 patients were acquired in four breath-holds using the HASTE sequence on a 1.5 Tesla MRI unit. The segmentation results were compared to ground truth kidney masks obtained as a consensus of experts. Automatic active contour algorithm yielded an average 22% +/- 8.6% volume error. A recently developed method (Bridge Burner) based on thresholding and constrained morphology failed to separate PKD from the spleen, yielding 37.4% +/- 8.7% volume error. Manual post-editing reduced the volume error to 3.2% +/- 0.8% for active contours and 3.2% +/- 0.6% for Bridge Burner. The total time (automated algorithm plus editing) was 15 min +/- 5 min for active contours and 19 min +/- 11 min for Bridge Burner. The average volume errors for stereology method were 5.9%, 6.2%, 5.4% for mesh size 6.6, 11, 16.5 mm. The average processing times were 17, 7, 4 min. These results show that nearly two-fold improvement in PKD segmentation accuracy over stereology technique can be achieved with a combination of active contours and postediting.

  14. Unsupervised segmentation of ultrasound images by fusion of spatio-frequential textural features

    NASA Astrophysics Data System (ADS)

    Benameur, S.; Mignotte, M.; Lavoie, F.

    2011-03-01

    Image segmentation plays an important role in both qualitative and quantitative analysis of medical ultrasound images. However, due to their poor resolution and strong speckle noise, segmenting objects from this imaging modality remains a challenging task and may not be satisfactory with traditional image segmentation methods. To this end, this paper presents a simple, reliable, and conceptually different segmentation technique to locate and extract bone contours from ultrasound images. Instead of considering a new elaborate (texture) segmentation model specifically adapted for the ultrasound images, our technique proposes to fuse (i.e. efficiently combine) several segmentation maps associated with simpler segmentation models in order to get a final reliable and accurate segmentation result. More precisely, our segmentation model aims at fusing several K-means clustering results, each one exploiting, as simple cues, a set of complementary textural features, either spatial or frequential. Eligible models include the gray-level co-occurrence matrix, the re-quantized histogram, the Gabor filter bank, and local DCT coefficients. The experiments reported in this paper demonstrate the efficiency and illustrate all the potential of this segmentation approach.

  15. Iterative fuzzy segmentation for an accurate delimitation of the breast region.

    PubMed

    Touil, Asma; Kalti, Karim

    2016-08-01

    In mammographic images, extracting different anatomical structures and tissues types is a critical requirement for the breast cancer diagnosis. For instance, separating breast and background regions increases the accuracy and efficiency of mammographic processing algorithms. In this paper, we propose a new region-based method to properly segment breast and background regions in mammographic images. These regions are estimated by an Iterative Fuzzy Breast Segmentation method (IFBS). Based on the Fuzzy C-Means (FCM) algorithm, IFBS method iteratively increases the precision of an initially extracted breast region. This proposal is evaluated using the MIAS database. Experimental results show high accuracy and reliability in breast extraction when compared with Ground-Truth (GT) images. PMID:27282234

  16. Robust vessel segmentation in fundus images.

    PubMed

    Budai, A; Bock, R; Maier, A; Hornegger, J; Michelson, G

    2013-01-01

    One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods. PMID:24416040

  17. Nonlinear image labeling for multivalued segmentation.

    PubMed

    Dellepiane, S G; Fontana, F; Vernazza, G L

    1996-01-01

    We describe a framework for multivalued segmentation and demonstrate that some of the problems affecting common region-based algorithms can be overcome by integrating statistical and topological methods in a nonlinear fashion. We address the sensitivity to parameter setting, the difficulty with handling global contextual information, and the dependence of results on analysis order and on initial conditions. We develop our method within a theoretical framework and resort to the definition of image segmentation as an estimation problem. We show that, thanks to an adaptive image scanning mechanism, there is no need of iterations to propagate a global context efficiently. The keyword multivalued refers to a result property, which spans over a set of solutions. The advantage is twofold: first, there is no necessity for setting a priori input thresholds; secondly, we are able to cope successfully with the problem of uncertainties in the signal model. To this end, we adopt a modified version of fuzzy connectedness, which proves particularly useful to account for densitometric and topological information simultaneously. The algorithm was tested on several synthetic and real images. The peculiarities of the method are assessed both qualitatively and quantitatively. PMID:18285129

  18. A combinatorial Bayesian and Dirichlet model for prostate MR image segmentation using probabilistic image features

    NASA Astrophysics Data System (ADS)

    Li, Ang; Li, Changyang; Wang, Xiuying; Eberl, Stefan; Feng, Dagan; Fulham, Michael

    2016-08-01

    Blurred boundaries and heterogeneous intensities make accurate prostate MR image segmentation problematic. To improve prostate MR image segmentation we suggest an approach that includes: (a) an image patch division method to partition the prostate into homogeneous segments for feature extraction; (b) an image feature formulation and classification method, using the relevance vector machine, to provide probabilistic prior knowledge for graph energy construction; (c) a graph energy formulation scheme with Bayesian priors and Dirichlet graph energy and (d) a non-iterative graph energy minimization scheme, based on matrix differentiation, to perform the probabilistic pixel membership optimization. The segmentation output was obtained by assigning pixels with foreground and background labels based on derived membership probabilities. We evaluated our approach on the PROMISE-12 dataset with 50 prostate MR image volumes. Our approach achieved a mean dice similarity coefficient (DSC) of 0.90  ±  0.02, which surpassed the five best prior-based methods in the PROMISE-12 segmentation challenge.

  19. A combinatorial Bayesian and Dirichlet model for prostate MR image segmentation using probabilistic image features.

    PubMed

    Li, Ang; Li, Changyang; Wang, Xiuying; Eberl, Stefan; Feng, Dagan; Fulham, Michael

    2016-08-21

    Blurred boundaries and heterogeneous intensities make accurate prostate MR image segmentation problematic. To improve prostate MR image segmentation we suggest an approach that includes: (a) an image patch division method to partition the prostate into homogeneous segments for feature extraction; (b) an image feature formulation and classification method, using the relevance vector machine, to provide probabilistic prior knowledge for graph energy construction; (c) a graph energy formulation scheme with Bayesian priors and Dirichlet graph energy and (d) a non-iterative graph energy minimization scheme, based on matrix differentiation, to perform the probabilistic pixel membership optimization. The segmentation output was obtained by assigning pixels with foreground and background labels based on derived membership probabilities. We evaluated our approach on the PROMISE-12 dataset with 50 prostate MR image volumes. Our approach achieved a mean dice similarity coefficient (DSC) of 0.90  ±  0.02, which surpassed the five best prior-based methods in the PROMISE-12 segmentation challenge. PMID:27461085

  20. Segmenting time-lapse phase contrast images of adjacent NIH 3T3 cells.

    PubMed

    Chalfoun, J; Kociolek, M; Dima, A; Halter, M; Cardone, A; Peskin, A; Bajcsy, P; Brady, M

    2013-01-01

    We present a new method for segmenting phase contrast images of NIH 3T3 fibroblast cells that is accurate even when cells are physically in contact with each other. The problem of segmentation, when cells are in contact, poses a challenge to the accurate automation of cell counting, tracking and lineage modelling in cell biology. The segmentation method presented in this paper consists of (1) background reconstruction to obtain noise-free foreground pixels and (2) incorporation of biological insight about dividing and nondividing cells into the segmentation process to achieve reliable separation of foreground pixels defined as pixels associated with individual cells. The segmentation results for a time-lapse image stack were compared against 238 manually segmented images (8219 cells) provided by experts, which we consider as reference data. We chose two metrics to measure the accuracy of segmentation: the 'Adjusted Rand Index' which compares similarities at a pixel level between masks resulting from manual and automated segmentation, and the 'Number of Cells per Field' (NCF) which compares the number of cells identified in the field by manual versus automated analysis. Our results show that the automated segmentation compared to manual segmentation has an average adjusted rand index of 0.96 (1 being a perfect match), with a standard deviation of 0.03, and an average difference of the two numbers of cells per field equal to 5.39% with a standard deviation of 4.6%. PMID:23126432

  1. Accurate segmentation framework for the left ventricle wall from cardiac cine MRI

    NASA Astrophysics Data System (ADS)

    Sliman, H.; Khalifa, F.; Elnakib, A.; Soliman, A.; Beache, G. M.; Gimel'farb, G.; Emam, A.; Elmaghraby, A.; El-Baz, A.

    2013-10-01

    We propose a novel, fast, robust, bi-directional coupled parametric deformable model to segment the left ventricle (LV) wall borders using first- and second-order visual appearance features. These features are embedded in a new stochastic external force that preserves the topology of LV wall to track the evolution of the parametric deformable models control points. To accurately estimate the marginal density of each deformable model control point, the empirical marginal grey level distributions (first-order appearance) inside and outside the boundary of the deformable model are modeled with adaptive linear combinations of discrete Gaussians (LCDG). The second order visual appearance of the LV wall is accurately modeled with a new rotationally invariant second-order Markov-Gibbs random field (MGRF). We tested the proposed segmentation approach on 15 data sets in 6 infarction patients using the Dice similarity coefficient (DSC) and the average distance (AD) between the ground truth and automated segmentation contours. Our approach achieves a mean DSC value of 0.926±0.022 and AD value of 2.16±0.60 compared to two other level set methods that achieve 0.904±0.033 and 0.885±0.02 for DSC; and 2.86±1.35 and 5.72±4.70 for AD, respectively.

  2. Segmentation of uterine fibroid ultrasound images using a dynamic statistical shape model in HIFU therapy.

    PubMed

    Ni, Bo; He, Fazhi; Yuan, ZhiYong

    2015-12-01

    Segmenting the lesion areas from ultrasound (US) images is an important step in the intra-operative planning of high-intensity focused ultrasound (HIFU). However, accurate segmentation remains a challenge due to intensity inhomogeneity, blurry boundaries in HIFU US images and the deformation of uterine fibroids caused by patient's breathing or external force. This paper presents a novel dynamic statistical shape model (SSM)-based segmentation method to accurately and efficiently segment the target region in HIFU US images of uterine fibroids. For accurately learning the prior shape information of lesion boundary fluctuations in the training set, the dynamic properties of stochastic differential equation and Fokker-Planck equation are incorporated into SSM (referred to as SF-SSM). Then, a new observation model of lesion areas (named to RPFM) in HIFU US images is developed to describe the features of the lesion areas and provide a likelihood probability to the prior shape given by SF-SSM. SF-SSM and RPFM are integrated into active contour model to improve the accuracy and robustness of segmentation in HIFU US images. We compare the proposed method with four well-known US segmentation methods to demonstrate its superiority. The experimental results in clinical HIFU US images validate the high accuracy and robustness of our approach, even when the quality of the images is unsatisfactory, indicating its potential for practical application in HIFU therapy. PMID:26459767

  3. a Minimum Spanning Tree Based Method for Uav Image Segmentation

    NASA Astrophysics Data System (ADS)

    Wang, Ping; Wei, Zheng; Cui, Weihong; Lin, Zhiyong

    2016-06-01

    This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary of objects.

  4. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data

    NASA Technical Reports Server (NTRS)

    Tilton, James C.; Lawrence, William T.

    2005-01-01

    NASA's Goddard Space Flight Center has developed a fast and effective method for generating image segmentation hierarchies. These segmentation hierarchies organize image data in a manner that makes their information content more accessible for analysis. Image segmentation enables analysis through the examination of image regions rather than individual image pixels. In addition, the segmentation hierarchy provides additional analysis clues through the tracing of the behavior of image region characteristics at several levels of segmentation detail. The potential for extracting the information content from imagery data based on segmentation hierarchies has not been fully explored for the benefit of the Earth and space science communities. This paper explores the potential of exploiting these segmentation hierarchies for the analysis of multi-date data sets, and for the particular application of change monitoring.

  5. Towards Automatic Image Segmentation Using Optimised Region Growing Technique

    NASA Astrophysics Data System (ADS)

    Alazab, Mamoun; Islam, Mofakharul; Venkatraman, Sitalakshmi

    Image analysis is being adopted extensively in many applications such as digital forensics, medical treatment, industrial inspection, etc. primarily for diagnostic purposes. Hence, there is a growing interest among researches in developing new segmentation techniques to aid the diagnosis process. Manual segmentation of images is labour intensive, extremely time consuming and prone to human errors and hence an automated real-time technique is warranted in such applications. There is no universally applicable automated segmentation technique that will work for all images as the image segmentation is quite complex and unique depending upon the domain application. Hence, to fill the gap, this paper presents an efficient segmentation algorithm that can segment a digital image of interest into a more meaningful arrangement of regions and objects. Our algorithm combines region growing approach with optimised elimination of false boundaries to arrive at more meaningful segments automatically. We demonstrate this using X-ray teeth images that were taken for real-life dental diagnosis.

  6. Semisupervised synthetic aperture radar image segmentation with multilayer superpixels

    NASA Astrophysics Data System (ADS)

    Wang, Can; Su, Weimin; Gu, Hong; Gong, Dachen

    2015-01-01

    Image segmentation plays a significant role in synthetic aperture radar (SAR) image processing. However, SAR image segmentation is challenging due to speckle. We propose a semisupervised bipartite graph method for segmentation of an SAR image. First, the multilayer over-segmentation of the SAR image, referred to as superpixels, is computed using existing segmentation algorithms. Second, an unbalanced bipartite graph is constructed in which the correlation between pixels is replaced by the texture similarity between superpixels, to reduce the dimension of the edge matrix. To also improve efficiency, we define a new method, called the combination of the Manhattan distance and symmetric Kullback-Leibler divergence, to measure texture similarity. Third, by the Moore-Penrose inverse matrix and semisupervised learning, we construct an across-affinity matrix. A quantitative evaluation using SAR images shows that the new algorithm produces significantly high-quality segmentations as compared with state-of-the-art segmentation algorithms.

  7. Segmentation Fusion Techniques with Application to Plenoptic Images: A Survey.

    NASA Astrophysics Data System (ADS)

    Evin, D.; Hadad, A.; Solano, A.; Drozdowicz, B.

    2016-04-01

    The segmentation of anatomical and pathological structures plays a key role in the characterization of clinically relevant evidence from digital images. Recently, plenoptic imaging has emerged as a new promise to enrich the diagnostic potential of conventional photography. Since the plenoptic images comprises a set of slightly different versions of the target scene, we propose to make use of those images to improve the segmentation quality in relation to the scenario of a single image segmentation. The problem of finding a segmentation solution from multiple images of a single scene, is called segmentation fusion. This paper reviews the issue of segmentation fusion in order to find solutions that can be applied to plenoptic images, particularly images from the ophthalmological domain.

  8. Interactive segmentation of abdominal aortic aneurysms in CTA images.

    PubMed

    de Bruijne, Marleen; van Ginneken, Bram; Viergever, Max A; Niessen, Wiro J

    2004-06-01

    A model-based approach to interactive segmentation of abdominal aortic aneurysms from CTA data is presented. After manual delineation of the aneurysm sac in the first slice, the method automatically detects the contour in subsequent slices, using the result from the previous slice as a reference. If an obtained contour is not sufficiently accurate, the user can intervene and provide an additional manual reference contour. The method is inspired by the active shape model (ASM) segmentation scheme (), in which a statistical shape model, derived from corresponding landmark points in manually labeled training images, is fitted to the image in an iterative manner. In our method, a shape model of the contours in two adjacent image slices is progressively fitted to the entire volume. The contour obtained in one slice thus constrains the possible shapes in the next slice. The optimal fit is determined on the basis of multi-resolution gray level models constructed from gray value patches sampled around each landmark. We propose to use the similarity of adjacent image slices for this gray level model, and compare these to single-slice features that are more generally used with ASM. The performance of various image features is evaluated in leave-one-out experiments on 23 data sets. Features that use the similarity of adjacent image slices outperform measures based on single-slice features in all cases. The average number of slices in our datasets is 51, while on average eight manual initializations are required, which decreases operator segmentation time by a factor of 6. PMID:15063862

  9. Fuzzy watershed segmentation algorithm: an enhanced algorithm for 2D gel electrophoresis image segmentation.

    PubMed

    Rashwan, Shaheera; Sarhan, Amany; Faheem, Muhamed Talaat; Youssef, Bayumy A

    2015-01-01

    Detection and quantification of protein spots is an important issue in the analysis of two-dimensional electrophoresis images. However, there is a main challenge in the segmentation of 2DGE images which is to separate overlapping protein spots correctly and to find the weak protein spots. In this paper, we describe a new robust technique to segment and model the different spots present in the gels. The watershed segmentation algorithm is modified to handle the problem of over-segmentation by initially partitioning the image to mosaic regions using the composition of fuzzy relations. The experimental results showed the effectiveness of the proposed algorithm to overcome the over segmentation problem associated with the available algorithm. We also use a wavelet denoising function to enhance the quality of the segmented image. The results of using a denoising function before the proposed fuzzy watershed segmentation algorithm is promising as they are better than those without denoising. PMID:26510287

  10. Efficient segmentation of skin epidermis in whole slide histopathological images.

    PubMed

    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

  11. Segmentation of deformable organs from medical images using particle swarm optimization and nonlinear shape priors

    NASA Astrophysics Data System (ADS)

    Afifi, Ahmed; Nakaguchi, Toshiya; Tsumura, Norimichi

    2010-03-01

    In many medical applications, the automatic segmentation of deformable organs from medical images is indispensable and its accuracy is of a special interest. However, the automatic segmentation of these organs is a challenging task according to its complex shape. Moreover, the medical images usually have noise, clutter, or occlusion and considering the image information only often leads to meager image segmentation. In this paper, we propose a fully automated technique for the segmentation of deformable organs from medical images. In this technique, the segmentation is performed by fitting a nonlinear shape model with pre-segmented images. The kernel principle component analysis (KPCA) is utilized to capture the complex organs deformation and to construct the nonlinear shape model. The presegmentation is carried out by labeling each pixel according to its high level texture features extracted using the overcomplete wavelet packet decomposition. Furthermore, to guarantee an accurate fitting between the nonlinear model and the pre-segmented images, the particle swarm optimization (PSO) algorithm is employed to adapt the model parameters for the novel images. In this paper, we demonstrate the competence of proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans of different patients.

  12. Segmentation of the liver from abdominal MR images: a level-set approach

    NASA Astrophysics Data System (ADS)

    Abdalbari, Anwar; Huang, Xishi; Ren, Jing

    2015-03-01

    The usage of prior knowledge in segmentation of abdominal MR images enables more accurate and comprehensive interpretation about the organ to segment. Prior knowledge about abdominal organ like liver vessels can be employed to get an accurate segmentation of the liver that leads to accurate diagnosis or treatment plan. In this paper, a new method for segmenting the liver from abdominal MR images using liver vessels as prior knowledge is proposed. This paper employs the technique of level set method to segment the liver from MR abdominal images. The speed image used in the level set method is responsible for propagating and stopping region growing at boundaries. As a result of the poor contrast of the MR images between the liver and the surrounding organs i.e. stomach, kidneys, and heart causes leak of the segmented liver to those organs that lead to inaccurate or incorrect segmentation. For that reason, a second speed image is developed, as an extra term to the level set, to control the front propagation at weak edges with the help of the original speed image. The basic idea of the proposed approach is to use the second speed image as a boundary surface which is approximately orthogonal to the area of the leak. The aim of the new speed image is to slow down the level set propagation and prevent the leak in the regions close to liver boundary. The new speed image is a surface created by filling holes to reconstruct the liver surface. These holes are formed as a result of the exit and the entry of the liver vessels, and are considered the main cause of the segmentation leak. The result of the proposed method shows superior outcome than other methods in the literature.

  13. Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images.

    PubMed

    Boix, Macarena; Cantó, Begoña

    2013-04-01

    Accurate image segmentation is used in medical diagnosis since this technique is a noninvasive pre-processing step for biomedical treatment. In this work we present an efficient segmentation method for medical image analysis. In particular, with this method blood cells can be segmented. For that, we combine the wavelet transform with morphological operations. Moreover, the wavelet thresholding technique is used to eliminate the noise and prepare the image for suitable segmentation. In wavelet denoising we determine the best wavelet that shows a segmentation with the largest area in the cell. We study different wavelet families and we conclude that the wavelet db1 is the best and it can serve for posterior works on blood pathologies. The proposed method generates goods results when it is applied on several images. Finally, the proposed algorithm made in MatLab environment is verified for a selected blood cells. PMID:23458301

  14. Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours

    PubMed Central

    Molnar, Csaba; Jermyn, Ian H.; Kato, Zoltan; Rahkama, Vesa; Östling, Päivi; Mikkonen, Piia; Pietiäinen, Vilja; Horvath, Peter

    2016-01-01

    The identification of fluorescently stained cell nuclei is the basis of cell detection, segmentation, and feature extraction in high content microscopy experiments. The nuclear morphology of single cells is also one of the essential indicators of phenotypic variation. However, the cells used in experiments can lose their contact inhibition, and can therefore pile up on top of each other, making the detection of single cells extremely challenging using current segmentation methods. The model we present here can detect cell nuclei and their morphology even in high-confluency cell cultures with many overlapping cell nuclei. We combine the “gas of near circles” active contour model, which favors circular shapes but allows slight variations around them, with a new data model. This captures a common property of many microscopic imaging techniques: the intensities from superposed nuclei are additive, so that two overlapping nuclei, for example, have a total intensity that is approximately double the intensity of a single nucleus. We demonstrate the power of our method on microscopic images of cells, comparing the results with those obtained from a widely used approach, and with manual image segmentations by experts. PMID:27561654

  15. Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours.

    PubMed

    Molnar, Csaba; Jermyn, Ian H; Kato, Zoltan; Rahkama, Vesa; Östling, Päivi; Mikkonen, Piia; Pietiäinen, Vilja; Horvath, Peter

    2016-01-01

    The identification of fluorescently stained cell nuclei is the basis of cell detection, segmentation, and feature extraction in high content microscopy experiments. The nuclear morphology of single cells is also one of the essential indicators of phenotypic variation. However, the cells used in experiments can lose their contact inhibition, and can therefore pile up on top of each other, making the detection of single cells extremely challenging using current segmentation methods. The model we present here can detect cell nuclei and their morphology even in high-confluency cell cultures with many overlapping cell nuclei. We combine the "gas of near circles" active contour model, which favors circular shapes but allows slight variations around them, with a new data model. This captures a common property of many microscopic imaging techniques: the intensities from superposed nuclei are additive, so that two overlapping nuclei, for example, have a total intensity that is approximately double the intensity of a single nucleus. We demonstrate the power of our method on microscopic images of cells, comparing the results with those obtained from a widely used approach, and with manual image segmentations by experts. PMID:27561654

  16. Replica inference approach to unsupervised multiscale image segmentation

    NASA Astrophysics Data System (ADS)

    Hu, Dandan; Ronhovde, Peter; Nussinov, Zohar

    2012-01-01

    We apply a replica-inference-based Potts model method to unsupervised image segmentation on multiple scales. This approach was inspired by the statistical mechanics problem of “community detection” and its phase diagram. Specifically, the problem is cast as identifying tightly bound clusters (“communities” or “solutes”) against a background or “solvent.” Within our multiresolution approach, we compute information-theory-based correlations among multiple solutions (“replicas”) of the same graph over a range of resolutions. Significant multiresolution structures are identified by replica correlations manifest by information theory overlaps. We further employ such information theory measures (such as normalized mutual information and variation of information), thermodynamic quantities such as the system entropy and energy, and dynamic measures monitoring the convergence time to viable solutions as metrics for transitions between various solvable and unsolvable phases. Within the solvable phase, transitions between contending solutions (such as those corresponding to segmentations on different scales) may also appear. With the aid of these correlations as well as thermodynamic measures, the phase diagram of the corresponding Potts model is analyzed at both zero and finite temperatures. Optimal parameters corresponding to a sensible unsupervised segmentations appear within the “easy phase” of the Potts model. Our algorithm is fast and shown to be at least as accurate as the best algorithms to date and to be especially suited to the detection of camouflaged images.

  17. Segmentation of interstitial lung disease patterns in HRCT images

    NASA Astrophysics Data System (ADS)

    Dash, Jatindra K.; Madhavi, Vaddepalli; Mukhopadhyay, Sudipta; Khandelwal, Niranjan; Kumar, Prafulla

    2015-03-01

    Automated segmentation of pathological bearing region is the first step towards the development of lung CAD. Most of the work reported in the literature related to automated analysis of lung tissue aims towards classification of fixed sized block into one of the classes. This block level classification of lung tissues in the image never results in accurate or smooth boundaries between different regions. In this work, effort is taken to investigate the performance of three automated image segmentation algorithms those results in smooth boundaries among lung tissue patterns commonly encountered in HRCT images of the thorax. A public database that consists of HRCT images taken from patients affected with Interstitial Lung Diseases (ILDs) is used for the evaluation. The algorithms considered are Markov Random Field (MRF), Gaussian Mixture Model (GMM) and Mean Shift (MS). 2-fold cross validation approach is followed for the selection of the best parameter value for individual algorithm as well as to evaluate the performance of all the algorithms. Mean shift algorithm is observed as the best performer in terms of Jaccard Index, Modified Hausdorff Distance, accuracy, Dice Similarity Coefficient and execution speed.

  18. Microscopy image segmentation tool: Robust image data analysis

    SciTech Connect

    Valmianski, Ilya Monton, Carlos; Schuller, Ivan K.

    2014-03-15

    We present a software package called Microscopy Image Segmentation Tool (MIST). MIST is designed for analysis of microscopy images which contain large collections of small regions of interest (ROIs). Originally developed for analysis of porous anodic alumina scanning electron images, MIST capabilities have been expanded to allow use in a large variety of problems including analysis of biological tissue, inorganic and organic film grain structure, as well as nano- and meso-scopic structures. MIST provides a robust segmentation algorithm for the ROIs, includes many useful analysis capabilities, and is highly flexible allowing incorporation of specialized user developed analysis. We describe the unique advantages MIST has over existing analysis software. In addition, we present a number of diverse applications to scanning electron microscopy, atomic force microscopy, magnetic force microscopy, scanning tunneling microscopy, and fluorescent confocal laser scanning microscopy.

  19. Quantitative Analysis of Mouse Retinal Layers Using Automated Segmentation of Spectral Domain Optical Coherence Tomography Images

    PubMed Central

    Dysli, Chantal; Enzmann, Volker; Sznitman, Raphael; Zinkernagel, Martin S.

    2015-01-01

    Purpose Quantification of retinal layers using automated segmentation of optical coherence tomography (OCT) images allows for longitudinal studies of retinal and neurological disorders in mice. The purpose of this study was to compare the performance of automated retinal layer segmentation algorithms with data from manual segmentation in mice using the Spectralis OCT. Methods Spectral domain OCT images from 55 mice from three different mouse strains were analyzed in total. The OCT scans from 22 C57Bl/6, 22 BALBc, and 11 C3A.Cg-Pde6b+Prph2Rd2/J mice were automatically segmented using three commercially available automated retinal segmentation algorithms and compared to manual segmentation. Results Fully automated segmentation performed well in mice and showed coefficients of variation (CV) of below 5% for the total retinal volume. However, all three automated segmentation algorithms yielded much thicker total retinal thickness values compared to manual segmentation data (P < 0.0001) due to segmentation errors in the basement membrane. Conclusions Whereas the automated retinal segmentation algorithms performed well for the inner layers, the retinal pigmentation epithelium (RPE) was delineated within the sclera, leading to consistently thicker measurements of the photoreceptor layer and the total retina. Translational Relevance The introduction of spectral domain OCT allows for accurate imaging of the mouse retina. Exact quantification of retinal layer thicknesses in mice is important to study layers of interest under various pathological conditions. PMID:26336634

  20. Image segmentation using trainable fuzzy set classifiers

    NASA Astrophysics Data System (ADS)

    Schalkoff, Robert J.; Carver, Albrecht E.; Gurbuz, Sabri

    1999-07-01

    A general image analysis and segmentation method using fuzzy set classification and learning is described. The method uses a learned fuzzy representation of pixel region characteristics, based upon the conjunction and disjunction of extracted and derived fuzzy color and texture features. Both positive and negative exemplars of some visually apparent characteristic which forms the basis of the inspection, input by a human operator, are used together with a clustering algorithm to construct positive similarity membership functions and negative similarity membership functions. Using these composite fuzzified images, P and N, are produced using fuzzy union. Classification is accomplished via image defuzzification, whereby linguistic meaning is assigned to each pixel in the fuzzy set using a fuzzy inference operation. The technique permits: (1) strict color and texture discrimination, (2) machine learning of color and texture characteristics of regions, (3) and judicious labeling of each pixel based upon leaned fuzzy representation and fuzzy classification. This approach appears ideal for applications involving visual inspection and allows the development of image-based inspection systems which may be trained and used by relatively unskilled workers. We show three different examples involving the visual inspection of mixed waste drums, lumber and woven fabric.

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

    PubMed Central

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

    2014-01-01

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

  2. Fast and accurate circle detection using gradient-direction-based segmentation.

    PubMed

    Wu, Jianping; Chen, Ke; Gao, Xiaohui

    2013-06-01

    We present what is to our knowledge the first-ever fitting-based circle detection algorithm, namely, the fast and accurate circle (FACILE) detection algorithm, based on gradient-direction-based edge clustering and direct least square fitting. Edges are segmented into sections based on gradient directions, and each section is validated separately; valid arcs are then fitted and further merged to extract more accurate circle information. We implemented the algorithm with the C++ language and compared it with four other algorithms. Testing on simulated data showed FACILE was far superior to the randomized Hough transform, standard Hough transform, and fast circle detection using gradient pair vectors with regard to processing speed and detection reliability. Testing on publicly available standard datasets showed FACILE outperformed robust and precise circular detection, a state-of-art arc detection method, by 35% with regard to recognition rate and is also a significant improvement over the latter in processing speed. PMID:24323106

  3. Improved automatic detection and segmentation of cell nuclei in histopathology images.

    PubMed

    Al-Kofahi, Yousef; Lassoued, Wiem; Lee, William; Roysam, Badrinath

    2010-04-01

    Automatic segmentation of cell nuclei is an essential step in image cytometry and histometry. Despite substantial progress, there is a need to improve accuracy, speed, level of automation, and adaptability to new applications. This paper presents a robust and accurate novel method for segmenting cell nuclei using a combination of ideas. The image foreground is extracted automatically using a graph-cuts-based binarization. Next, nuclear seed points are detected by a novel method combining multiscale Laplacian-of-Gaussian filtering constrained by distance-map-based adaptive scale selection. These points are used to perform an initial segmentation that is refined using a second graph-cuts-based algorithm incorporating the method of alpha expansions and graph coloring to reduce computational complexity. Nuclear segmentation results were manually validated over 25 representative images (15 in vitro images and 10 in vivo images, containing more than 7400 nuclei) drawn from diverse cancer histopathology studies, and four types of segmentation errors were investigated. The overall accuracy of the proposed segmentation algorithm exceeded 86%. The accuracy was found to exceed 94% when only over- and undersegmentation errors were considered. The confounding image characteristics that led to most detection/segmentation errors were high cell density, high degree of clustering, poor image contrast and noisy background, damaged/irregular nuclei, and poor edge information. We present an efficient semiautomated approach to editing automated segmentation results that requires two mouse clicks per operation. PMID:19884070

  4. An image fusion method based region segmentation and complex wavelets

    NASA Astrophysics Data System (ADS)

    Zhang, Junju; Yuan, Yihui; Chang, Benkang; Han, Yiyong; Liu, Lei; Qiu, Yafeng

    2009-07-01

    A fusion algorithm for infrared and visible light images based on region segmentation and the dual-tree complex wavelet transform. Before image segmentation, morphological top-hat filtering is firstly performed on the IR image and visual images respectively and the details of the luminous area are eliminated. Morphological bottom-hat filtering is then performed on the two kinds of images respectively and the details of the dark area are eliminated. Make the top-hat filtered image subtract the bottom-hat filtered image and obtain the enhanced images. Then the threshold method is used to segment the enhanced images. After image segmentation, the DTCWT coefficients from different regions are merged separately. Finally the fused image is obtained by performing inverse DTCWT. The evaluation results show the validity of the presented algorithm.

  5. Building roof segmentation from aerial images using a lineand region-based watershed segmentation technique.

    PubMed

    El Merabet, Youssef; Meurie, Cyril; Ruichek, Yassine; Sbihi, Abderrahmane; Touahni, Raja

    2015-01-01

    In this paper, we present a novel strategy for roof segmentation from aerial images (orthophotoplans) based on the cooperation of edge- and region-based segmentation methods. The proposed strategy is composed of three major steps. The first one, called the pre-processing step, consists of simplifying the acquired image with an appropriate couple of invariant and gradient, optimized for the application, in order to limit illumination changes (shadows, brightness, etc.) affecting the images. The second step is composed of two main parallel treatments: on the one hand, the simplified image is segmented by watershed regions. Even if the first segmentation of this step provides good results in general, the image is often over-segmented. To alleviate this problem, an efficient region merging strategy adapted to the orthophotoplan particularities, with a 2D modeling of roof ridges technique, is applied. On the other hand, the simplified image is segmented by watershed lines. The third step consists of integrating both watershed segmentation strategies into a single cooperative segmentation scheme in order to achieve satisfactory segmentation results. Tests have been performed on orthophotoplans containing 100 roofs with varying complexity, and the results are evaluated with the VINETcriterion using ground-truth image segmentation. A comparison with five popular segmentation techniques of the literature demonstrates the effectiveness and the reliability of the proposed approach. Indeed, we obtain a good segmentation rate of 96% with the proposed method compared to 87.5% with statistical region merging (SRM), 84% with mean shift, 82% with color structure code (CSC), 80% with efficient graph-based segmentation algorithm (EGBIS) and 71% with JSEG. PMID:25648706

  6. Image segmentation using an improved differential algorithm

    NASA Astrophysics Data System (ADS)

    Gao, Hao; Shi, Yujiao; Wu, Dongmei

    2014-10-01

    Among all the existing segmentation techniques, the thresholding technique is one of the most popular due to its simplicity, robustness, and accuracy (e.g. the maximum entropy method, Otsu's method, and K-means clustering). However, the computation time of these algorithms grows exponentially with the number of thresholds due to their exhaustive searching strategy. As a population-based optimization algorithm, differential algorithm (DE) uses a population of potential solutions and decision-making processes. It has shown considerable success in solving complex optimization problems within a reasonable time limit. Thus, applying this method into segmentation algorithm should be a good choice during to its fast computational ability. In this paper, we first propose a new differential algorithm with a balance strategy, which seeks a balance between the exploration of new regions and the exploitation of the already sampled regions. Then, we apply the new DE into the traditional Otsu's method to shorten the computation time. Experimental results of the new algorithm on a variety of images show that, compared with the EA-based thresholding methods, the proposed DE algorithm gets more effective and efficient results. It also shortens the computation time of the traditional Otsu method.

  7. Placental fetal stem segmentation in a sequence of histology images

    NASA Astrophysics Data System (ADS)

    Athavale, Prashant; Vese, Luminita A.

    2012-02-01

    Recent research in perinatal pathology argues that analyzing properties of the placenta may reveal important information on how certain diseases progress. One important property is the structure of the placental fetal stems. Analysis of the fetal stems in a placenta could be useful in the study and diagnosis of some diseases like autism. To study the fetal stem structure effectively, we need to automatically and accurately track fetal stems through a sequence of digitized hematoxylin and eosin (H&E) stained histology slides. There are many problems in successfully achieving this goal. A few of the problems are: large size of images, misalignment of the consecutive H&E slides, unpredictable inaccuracies of manual tracing, very complicated texture patterns of various tissue types without clear characteristics, just to name a few. In this paper we propose a novel algorithm to achieve automatic tracing of the fetal stem in a sequence of H&E images, based on an inaccurate manual segmentation of a fetal stem in one of the images. This algorithm combines global affine registration, local non-affine registration and a novel 'dynamic' version of the active contours model without edges. We first use global affine image registration of all the images based on displacement, scaling and rotation. This gives us approximate location of the corresponding fetal stem in the image that needs to be traced. We then use the affine registration algorithm "locally" near this location. At this point, we use a fast non-affine registration based on L2-similarity measure and diffusion regularization to get a better location of the fetal stem. Finally, we have to take into account inaccuracies in the initial tracing. This is achieved through a novel dynamic version of the active contours model without edges where the coefficients of the fitting terms are computed iteratively to ensure that we obtain a unique stem in the segmentation. The segmentation thus obtained can then be used as an

  8. Segmentation of neuroanatomy in magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Simmons, Andrew; Arridge, Simon R.; Barker, G. J.; Tofts, Paul S.

    1992-06-01

    Segmentation in neurological magnetic resonance imaging (MRI) is necessary for feature extraction, volume measurement and for the three-dimensional display of neuroanatomy. Automated and semi-automated methods offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. We have used dual echo multi-slice spin-echo data sets which take advantage of the intrinsically multispectral nature of MRI. As a pre-processing step, a rf non-uniformity correction is applied and if the data is noisy the images are smoothed using a non-isotropic blurring method. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing has been used to significantly simplify edge images and thus allow simple postprocessing to pick out the brain contour in each slice of the data set. Edge- focusing is a technique which locates significant edges using a high degree of smoothing at a coarse level and tracks these edges to a fine level where the edges can be determined with high positional accuracy. Both 2-D and 3-D edge-detection methods have been compared. Once isolated, the brain is further processed to identify CSF, and, depending upon the MR pulse sequence used, the brain itself may be sub-divided into gray matter and white matter using semi-automatic contrast enhancement and clustering methods.

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

    NASA Astrophysics Data System (ADS)

    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.

  10. Cerebella segmentation on MR images of pediatric patients with medulloblastoma

    NASA Astrophysics Data System (ADS)

    Shan, Zu Y.; Ji, Qing; Glass, John; Gajjar, Amar; Reddick, Wilburn E.

    2005-04-01

    In this study, an automated method has been developed to identify the cerebellum from T1-weighted MR brain images of patients with medulloblastoma. A new objective function that is similar to Gibbs free energy in classic physics was defined; and the brain structure delineation was viewed as a process of minimizing Gibbs free energy. We used a rigid-body registration and an active contour (snake) method to minimize the Gibbs free energy in this study. The method was applied to 20 patient data sets to generate cerebellum images and volumetric results. The generated cerebellum images were compared with two manually drawn results. Strong correlations were found between the automatically and manually generated volumetric results, the correlation coefficients with each of manual results were 0.971 and 0.974, respectively. The average Jaccard similarities with each of two manual results were 0.89 and 0.88, respectively. The average Kappa indexes with each of two manual results were 0.94 and 0.93, respectively. These results showed this method was both robust and accurate for cerebellum segmentation. The method may be applied to various research and clinical investigation in which cerebellum segmentation and quantitative MR measurement of cerebellum are needed.

  11. Applications of magnetic resonance image segmentation in neurology

    NASA Astrophysics Data System (ADS)

    Heinonen, Tomi; Lahtinen, Antti J.; Dastidar, Prasun; Ryymin, Pertti; Laarne, Paeivi; Malmivuo, Jaakko; Laasonen, Erkki; Frey, Harry; Eskola, Hannu

    1999-05-01

    After the introduction of digital imagin devices in medicine computerized tissue recognition and classification have become important in research and clinical applications. Segmented data can be applied among numerous research fields including volumetric analysis of particular tissues and structures, construction of anatomical modes, 3D visualization, and multimodal visualization, hence making segmentation essential in modern image analysis. In this research project several PC based software were developed in order to segment medical images, to visualize raw and segmented images in 3D, and to produce EEG brain maps in which MR images and EEG signals were integrated. The software package was tested and validated in numerous clinical research projects in hospital environment.

  12. Detection of bone disease by hybrid SST-watershed x-ray image segmentation

    NASA Astrophysics Data System (ADS)

    Sanei, Saeid; Azron, Mohammad; Heng, Ong Sim

    2001-07-01

    Detection of diagnostic features from X-ray images is favorable due to the low cost of these images. Accurate detection of the bone metastasis region greatly assists physicians to monitor the treatment and to remove the cancerous tissue by surgery. A hybrid SST-watershed algorithm, here, efficiently detects the boundary of the diseased regions. Shortest Spanning Tree (SST), based on graph theory, is one of the most powerful tools in grey level image segmentation. The method converts the images into arbitrary-shape closed segments of distinct grey levels. To do that, the image is initially mapped to a tree. Then using RSST algorithm the image is segmented to a certain number of arbitrary-shaped regions. However, in fine segmentation, over-segmentation causes loss of objects of interest. In coarse segmentation, on the other hand, SST-based method suffers from merging the regions belonged to different objects. By applying watershed algorithm, the large segments are divided into the smaller regions based on the number of catchment's basins for each segment. The process exploits bi-level watershed concept to separate each multi-lobe region into a number of areas each corresponding to an object (in our case a cancerous region of the bone,) disregarding their homogeneity in grey level.

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

    PubMed

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

    2016-05-01

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

  14. Perceptual segmentation: combining image segmentation with object tagging.

    PubMed

    Bergman, Ruth; Nachlieli, Hila

    2011-06-01

    Human observers understand the content of an image intuitively. Based upon image content, they perform many image-related tasks, such as creating slide shows and photo albums, and organizing their image archives. For example, to select photos for an album, people assess image quality based upon the main objects in the image. They modify colors in an image based upon the color of important objects, such as sky, grass or skin. Serious photographers might modify each object separately. Photo applications, in contrast, use low-level descriptors to guide similar tasks. Typical descriptors, such as color histograms, noise level, JPEG artifacts and overall sharpness, can guide an imaging application and safeguard against blunders. However, there is a gap between the outcome of such operations and the same task performed by a person. We believe that the gap can be bridged by automatically understanding the content of the image. This paper presents algorithms for automatic tagging of perceptual objects in images, including sky, skin, and foliage, which constitutes an important step toward this goal. PMID:21592914

  15. A method for scale parameter selection and segments refinement for multi-resolution image segmentation

    NASA Astrophysics Data System (ADS)

    Li, Hui; Tang, Yunwei; Liu, Qingjie; Ding, Haifeng; Chen, Yu; Jing, Linhai

    2014-11-01

    Image segmentation is the basis of object-based information extraction from remote sensing imagery. Image segmentation based on multiple features, multi-scale, and spatial context is one current research focus. The scale parameters selected in the segmentation severely impact on the average size of segments obtained by multi-scale segmentation method, such as the Fractal Network Evolution Approach (FNEA) employed in the eCognition software. It is important for the FNEA method to select an appropriate scale parameter that causes no neither over- nor undersegmentation. A method for scale parameter selection and segments refinement is proposed in this paper by modifying a method proposed by Johnson. In a test on two images, the segmentation maps obtained using the proposed method contain less under-segmentation and over-segmentation than that generated by the Johnson's method. It was demonstrated that the proposed method is effective in scale parameter selection and segment refinement for multi-scale segmentation algorithms, such as the FNEA method.

  16. Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching

    PubMed Central

    Chen, Wenan; Smith, Rebecca; Ji, Soo-Yeon; Ward, Kevin R; Najarian, Kayvan

    2009-01-01

    Background Accurate analysis of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI). Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and identify the ventricular systems. The segmentation of ventricles provides quantitative measures on the changes of ventricles in the brain that form vital diagnosis information. Methods First all CT slices are aligned by detecting the ideal midlines in all images. The initial estimation of the ideal midline of the brain is found based on skull symmetry and then the initial estimate is further refined using detected anatomical features. Then a two-step method is used for ventricle segmentation. First a low-level segmentation on each pixel is applied on the CT images. For this step, both Iterated Conditional Mode (ICM) and Maximum A Posteriori Spatial Probability (MASP) are evaluated and compared. The second step applies template matching algorithm to identify objects in the initial low-level segmentation as ventricles. Experiments for ventricle segmentation are conducted using a relatively large CT dataset containing mild and severe TBI cases. Results Experiments show that the acceptable rate of the ideal midline detection is over 95%. Two measurements are defined to evaluate ventricle recognition results. The first measure is a sensitivity-like measure and the second is a false positive-like measure. For the first measurement, the rate is 100% indicating that all ventricles are identified in all slices. The false positives-like measurement is 8.59%. We also point out the similarities and differences between ICM and MASP algorithms through both mathematically relationships and segmentation results on CT images. Conclusion The experiments show the reliability of the proposed algorithms. The novelty of the proposed

  17. Automatic bone segmentation in knee MR images using a coarse-to-fine strategy

    NASA Astrophysics Data System (ADS)

    Park, Sang Hyun; Lee, Soochahn; Yun, Il Dong; Lee, Sang Uk

    2012-02-01

    Segmentation of bone and cartilage from a three dimensional knee magnetic resonance (MR) image is a crucial element in monitoring and understanding of development and progress of osteoarthritis. Until now, various segmentation methods have been proposed to separate the bone from other tissues, but it still remains challenging problem due to different modality of MR images, low contrast between bone and tissues, and shape irregularity. In this paper, we present a new fully-automatic segmentation method of bone compartments using relevant bone atlases from a training set. To find the relevant bone atlases and obtain the segmentation, a coarse-to-fine strategy is proposed. In the coarse step, the best atlas among the training set and an initial segmentation are simultaneously detected using branch and bound tree search. Since the best atlas in the coarse step is not accurately aligned, all atlases from the training set are aligned to the initial segmentation, and the best aligned atlas is selected in the middle step. Finally, in the fine step, segmentation is conducted as adaptively integrating shape of the best aligned atlas and appearance prior based on characteristics of local regions. For experiment, femur and tibia bones of forty test MR images are segmented by the proposed method using sixty training MR images. Experimental results show that a performance of the segmentation and the registration becomes better as going near the fine step, and the proposed method obtain the comparable performance with the state-of-the-art methods.

  18. Hybrid segmentation framework for 3D medical image analysis

    NASA Astrophysics Data System (ADS)

    Chen, Ting; Metaxas, Dimitri N.

    2003-05-01

    Medical image segmentation is the process that defines the region of interest in the image volume. Classical segmentation methods such as region-based methods and boundary-based methods cannot make full use of the information provided by the image. In this paper we proposed a general hybrid framework for 3D medical image segmentation purposes. In our approach we combine the Gibbs Prior model, and the deformable model. First, Gibbs Prior models are applied onto each slice in a 3D medical image volume and the segmentation results are combined to a 3D binary masks of the object. Then we create a deformable mesh based on this 3D binary mask. The deformable model will be lead to the edge features in the volume with the help of image derived external forces. The deformable model segmentation result can be used to update the parameters for Gibbs Prior models. These methods will then work recursively to reach a global segmentation solution. The hybrid segmentation framework has been applied to images with the objective of lung, heart, colon, jaw, tumor, and brain. The experimental data includes MRI (T1, T2, PD), CT, X-ray, Ultra-Sound images. High quality results are achieved with relatively efficient time cost. We also did validation work using expert manual segmentation as the ground truth. The result shows that the hybrid segmentation may have further clinical use.

  19. Segmentation of solid nodules in ultrasonographic breast image based on wavelet transform.

    PubMed

    Park, Sangyun; Kong, Hyoun-Joong; Moon, Woo Kyoung; Kim, Hee Chan

    2007-01-01

    An accurate segmentation of solid nodules in ultrasonographic (US) breast image is presented. 1-level 2-dimensional Discrete Wavelet Transform (DWT) is used to create features reflecting the texture information of the original image. Using these features, the texture classification is achieved. Finally, solid nodule region is segmented from the classified texture region. Proper threshold for texture classification is automatically decided. Empirically acquired information about the relationship between the texture characteristic of the original image and the optimal threshold is examined and used. Presented algorithm is applied to 284 malignant solid nodules and 300 benign solid nodules and the resulting images are presented. PMID:18003294

  20. Myocardial Infarct Segmentation from Magnetic Resonance Images for Personalized Modeling of Cardiac Electrophysiology

    PubMed Central

    Ukwatta, Eranga; Arevalo, Hermenegild; Li, Kristina; Yuan, Jing; Qiu, Wu; Malamas, Peter; Wu, Katherine C.

    2016-01-01

    Accurate representation of myocardial infarct geometry is crucial to patient-specific computational modeling of the heart in ischemic cardiomyopathy. We have developed a methodology for segmentation of left ventricular (LV) infarct from clinically acquired, two-dimensional (2D), late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) images, for personalized modeling of ventricular electrophysiology. The infarct segmentation was expressed as a continuous min-cut optimization problem, which was solved using its dual formulation, the continuous max-flow (CMF). The optimization objective comprised of a smoothness term, and a data term that quantified the similarity between image intensity histograms of segmented regions and those of a set of training images. A manual segmentation of the LV myocardium was used to initialize and constrain the developed method. The three-dimensional geometry of infarct was reconstructed from its segmentation using an implicit, shape-based interpolation method. The proposed methodology was extensively evaluated using metrics based on geometry, and outcomes of individualized electrophysiological simulations of cardiac dys(function). Several existing LV infarct segmentation approaches were implemented, and compared with the proposed method. Our results demonstrated that the CMF method was more accurate than the existing approaches in reproducing expert manual LV infarct segmentations, and in electrophysiological simulations. The infarct segmentation method we have developed and comprehensively evaluated in this study constitutes an important step in advancing clinical applications of personalized simulations of cardiac electrophysiology. PMID:26731693

  1. Myocardial Infarct Segmentation From Magnetic Resonance Images for Personalized Modeling of Cardiac Electrophysiology.

    PubMed

    Ukwatta, Eranga; Arevalo, Hermenegild; Li, Kristina; Yuan, Jing; Qiu, Wu; Malamas, Peter; Wu, Katherine C; Trayanova, Natalia A; Vadakkumpadan, Fijoy

    2016-06-01

    Accurate representation of myocardial infarct geometry is crucial to patient-specific computational modeling of the heart in ischemic cardiomyopathy. We have developed a methodology for segmentation of left ventricular (LV) infarct from clinically acquired, two-dimensional (2D), late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) images, for personalized modeling of ventricular electrophysiology. The infarct segmentation was expressed as a continuous min-cut optimization problem, which was solved using its dual formulation, the continuous max-flow (CMF). The optimization objective comprised of a smoothness term, and a data term that quantified the similarity between image intensity histograms of segmented regions and those of a set of training images. A manual segmentation of the LV myocardium was used to initialize and constrain the developed method. The three-dimensional geometry of infarct was reconstructed from its segmentation using an implicit, shape-based interpolation method. The proposed methodology was extensively evaluated using metrics based on geometry, and outcomes of individualized electrophysiological simulations of cardiac dys(function). Several existing LV infarct segmentation approaches were implemented, and compared with the proposed method. Our results demonstrated that the CMF method was more accurate than the existing approaches in reproducing expert manual LV infarct segmentations, and in electrophysiological simulations. The infarct segmentation method we have developed and comprehensively evaluated in this study constitutes an important step in advancing clinical applications of personalized simulations of cardiac electrophysiology. PMID:26731693

  2. A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

    NASA Technical Reports Server (NTRS)

    Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.

    2012-01-01

    The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.

  3. Boundary-constrained multi-scale segmentation method for remote sensing images

    NASA Astrophysics Data System (ADS)

    Zhang, Xueliang; Xiao, Pengfeng; Song, Xiaoqun; She, Jiangfeng

    2013-04-01

    Image segmentation is the key step of Object-Based Image Analysis (OBIA) in remote sensing. This paper proposes a Boundary-Constrained Multi-Scale Segmentation (BCMS) method. Firstly, adjacent pixels are aggregated to generate initial segmentation according to the local best region growing strategy. Then, the Region Adjacency Graph (RAG) is built based on initial segmentation. Finally, the local mutual best region merging strategy is applied on RAG to produce multi-scale segmentation results. During the region merging process, a Step-Wise Scale Parameter (SWSP) strategy is proposed to produce boundary-constrained multi-scale segmentation results. Moreover, in order to improve the accuracy of object boundaries, the property of edge strength is introduced as a merging criterion. A set of high spatial resolution remote sensing images is used in the experiment, e.g., QuickBird, WorldView, and aerial image, to evaluate the effectiveness of the proposed method. The segmentation results of BCMS are compared with those of the commercial image analysis software eCognition. The experiment shows that BCMS can produce nested multi-scale segmentations with accurate and smooth boundaries, which proves the robustness of the proposed method.

  4. Multimodal Correlative Preclinical Whole Body Imaging and Segmentation.

    PubMed

    Akselrod-Ballin, Ayelet; Dafni, Hagit; Addadi, Yoseph; Biton, Inbal; Avni, Reut; Brenner, Yafit; Neeman, Michal

    2016-01-01

    Segmentation of anatomical structures and particularly abdominal organs is a fundamental problem for quantitative image analysis in preclinical research. This paper presents a novel approach for whole body segmentation of small animals in a multimodal setting of MR, CT and optical imaging. The algorithm integrates multiple imaging sequences into a machine learning framework, which generates supervoxels by an efficient hierarchical agglomerative strategy and utilizes multiple SVM-kNN classifiers each constrained by a heatmap prior region to compose the segmentation. We demonstrate results showing segmentation of mice images into several structures including the heart, lungs, liver, kidneys, stomach, vena cava, bladder, tumor, and skeleton structures. Experimental validation on a large set of mice and organs, indicated that our system outperforms alternative state of the art approaches. The system proposed can be generalized to various tissues and imaging modalities to produce automatic atlas-free segmentation, thereby enabling a wide range of applications in preclinical studies of small animal imaging. PMID:27325178

  5. Multimodal Correlative Preclinical Whole Body Imaging and Segmentation

    PubMed Central

    Akselrod-Ballin, Ayelet; Dafni, Hagit; Addadi, Yoseph; Biton, Inbal; Avni, Reut; Brenner, Yafit; Neeman, Michal

    2016-01-01

    Segmentation of anatomical structures and particularly abdominal organs is a fundamental problem for quantitative image analysis in preclinical research. This paper presents a novel approach for whole body segmentation of small animals in a multimodal setting of MR, CT and optical imaging. The algorithm integrates multiple imaging sequences into a machine learning framework, which generates supervoxels by an efficient hierarchical agglomerative strategy and utilizes multiple SVM-kNN classifiers each constrained by a heatmap prior region to compose the segmentation. We demonstrate results showing segmentation of mice images into several structures including the heart, lungs, liver, kidneys, stomach, vena cava, bladder, tumor, and skeleton structures. Experimental validation on a large set of mice and organs, indicated that our system outperforms alternative state of the art approaches. The system proposed can be generalized to various tissues and imaging modalities to produce automatic atlas-free segmentation, thereby enabling a wide range of applications in preclinical studies of small animal imaging. PMID:27325178

  6. A statistical multi-vertebrae shape+pose model for segmentation of CT images

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

    Segmentation of the spinal column from CT images is a pre-processing step for a range of image guided interventions. Current techniques focus on identification and separate segmentation of each vertebra. Recently, statistical multi-object shape models have been introduced to extract common statistical characteristics between several anatomies. These models are also used for segmentation purposes and are shown to be robust, accurate, and computationally tractable. In this paper, we reconstruct a statistical multi-vertebrae shape+pose model and propose a novel technique to register such a model to CT images. We validate our technique in terms of accuracy of the multi-vertebrae segmentation of CT images acquired from 16 subjects. The mean distance error achieved for all vertebrae is 1.17 mm with standard deviation of 0.38 mm.

  7. Segmentation and learning in the quantitative analysis of microscopy images

    NASA Astrophysics Data System (ADS)

    Ruggiero, Christy; Ross, Amy; Porter, Reid

    2015-02-01

    In material science and bio-medical domains the quantity and quality of microscopy images is rapidly increasing and there is a great need to automatically detect, delineate and quantify particles, grains, cells, neurons and other functional "objects" within these images. These are challenging problems for image processing because of the variability in object appearance that inevitably arises in real world image acquisition and analysis. One of the most promising (and practical) ways to address these challenges is interactive image segmentation. These algorithms are designed to incorporate input from a human operator to tailor the segmentation method to the image at hand. Interactive image segmentation is now a key tool in a wide range of applications in microscopy and elsewhere. Historically, interactive image segmentation algorithms have tailored segmentation on an image-by-image basis, and information derived from operator input is not transferred between images. But recently there has been increasing interest to use machine learning in segmentation to provide interactive tools that accumulate and learn from the operator input over longer periods of time. These new learning algorithms reduce the need for operator input over time, and can potentially provide a more dynamic balance between customization and automation for different applications. This paper reviews the state of the art in this area, provides a unified view of these algorithms, and compares the segmentation performance of various design choices.

  8. Semantic image retrieval through human subject segmentation and characterization

    NASA Astrophysics Data System (ADS)

    Li, Yanbing; Tao, Bo; Kei, Shun; Wolf, Wayne H.

    1997-01-01

    Video databases can be searched for visual content by searching over automatically extracted key frames rather than the complete video sequence. Many video materials used in the humanities and social sciences contain a preponderance of shots of people. In this paper, we describe our work in semantic image retrieval of person-rich scenes (key frames) for video databases and libraries. We use an approach called retrieval through segmentation. A key-frame image is first segmented into human subjects and background. We developed a specialized segmentation technique that utilizes both human flesh-tone detection and contour analysis. Experimental results show that this technique can effectively segment images in a low time complexity. Once the image has been segmented, we can then extract features or pose queries about both the people and the background. We propose a retrieval framework that is based on the segmentation results and the extracted features of people and background.

  9. Unsupervised learning of categorical segments in image collections.

    PubMed

    Andreetto, Marco; Zelnik-Manor, Lihi; Perona, Pietro

    2012-09-01

    Which one comes first: segmentation or recognition? We propose a unified framework for carrying out the two simultaneously and without supervision. The framework combines a flexible probabilistic model, for representing the shape and appearance of each segment, with the popular “bag of visual words” model for recognition. If applied to a collection of images, our framework can simultaneously discover the segments of each image and the correspondence between such segments, without supervision. Such recurring segments may be thought of as the “parts” of corresponding objects that appear multiple times in the image collection. Thus, the model may be used for learning new categories, detecting/classifying objects, and segmenting images, without using expensive human annotation. PMID:22201050

  10. Segmentation of knee injury swelling on infrared images

    NASA Astrophysics Data System (ADS)

    Puentes, John; Langet, Hélène; Herry, Christophe; Frize, Monique

    2011-03-01

    Interpretation of medical infrared images is complex due to thermal noise, absence of texture, and small temperature differences in pathological zones. Acute inflammatory response is a characteristic symptom of some knee injuries like anterior cruciate ligament sprains, muscle or tendons strains, and meniscus tear. Whereas artificial coloring of the original grey level images may allow to visually assess the extent inflammation in the area, their automated segmentation remains a challenging problem. This paper presents a hybrid segmentation algorithm to evaluate the extent of inflammation after knee injury, in terms of temperature variations and surface shape. It is based on the intersection of rapid color segmentation and homogeneous region segmentation, to which a Laplacian of a Gaussian filter is applied. While rapid color segmentation enables to properly detect the observed core of swollen area, homogeneous region segmentation identifies possible inflammation zones, combining homogeneous grey level and hue area segmentation. The hybrid segmentation algorithm compares the potential inflammation regions partially detected by each method to identify overlapping areas. Noise filtering and edge segmentation are then applied to common zones in order to segment the swelling surfaces of the injury. Experimental results on images of a patient with anterior cruciate ligament sprain show the improved performance of the hybrid algorithm with respect to its separated components. The main contribution of this work is a meaningful automatic segmentation of abnormal skin temperature variations on infrared thermography images of knee injury swelling.

  11. Automated image segmentation using support vector machines

    NASA Astrophysics Data System (ADS)

    Powell, Stephanie; Magnotta, Vincent A.; Andreasen, Nancy C.

    2007-03-01

    Neurodegenerative and neurodevelopmental diseases demonstrate problems associated with brain maturation and aging. Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like that being collected in several on going multi-center studies. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures including the thalamus (0.88), caudate (0.85) and the putamen (0.81). In this work, apriori probability information was generated using Thirion's demons registration algorithm. The input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. We have applied the support vector machine (SVM) machine learning algorithm to automatically segment subcortical and cerebellar regions using the same input vector information. SVM architecture was derived from the ANN framework. Training was completed using a radial-basis function kernel with gamma equal to 5.5. Training was performed using 15,000 vectors collected from 15 training images in approximately 10 minutes. The resulting support vectors were applied to delineate 10 images not part of the training set. Relative overlap calculated for the subcortical structures was 0.87 for the thalamus, 0.84 for the caudate, 0.84 for the putamen, and 0.72 for the hippocampus. Relative overlap for the cerebellar lobes ranged from 0.76 to 0.86. The reliability of the SVM based algorithm was similar to the inter-rater reliability between manual raters and can be achieved without rater intervention.

  12. Automatic registration and segmentation algorithm for multiple electrophoresis images

    NASA Astrophysics Data System (ADS)

    Baker, Matthew S.; Busse, Harald; Vogt, Martin

    2000-06-01

    We present an algorithm for registering, segmenting and quantifying multiple scanned electrophoresis images. (2D gel) Electrophoresis is a technique for separating proteins or other macromolecules in organic material according to net charge and molecular mass and results in scanned grayscale images with dark spots against a light background marking the presence of such macromolecules. The algorithm begins by registering each of the images using a non-rigid registration algorithm. The registered images are then jointly segmented using a Markov random field approach to obtain a single segmentation. By using multiple images, the effect of noise is greatly reduced. We demonstrate the algorithm on several sets of real data.

  13. Atherosclerotic carotid lumen segmentation in combined B-mode and contrast enhanced ultrasound images

    NASA Astrophysics Data System (ADS)

    Akkus, Zeynettin; Carvalho, Diego D. B.; Klein, Stefan; van den Oord, Stijn C. H.; Schinkel, Arend F. L.; de Jong, Nico; van der Steen, Antonius F. W.; Bosch, Johan G.

    2014-03-01

    Patients with carotid atherosclerotic plaques carry an increased risk of cardiovascular events such as stroke. Ultrasound has been employed as a standard for diagnosis of carotid atherosclerosis. To assess atherosclerosis, the intima contour of the carotid artery lumen should be accurately outlined. For this purpose, we use simultaneously acquired side-by-side longitudinal contrast enhanced ultrasound (CEUS) and B-mode ultrasound (BMUS) images and exploit the information in the two imaging modalities for accurate lumen segmentation. First, nonrigid motion compensation is performed on both BMUS and CEUS image sequences, followed by averaging over the 150 time frames to produce an image with improved signal-to-noise ratio (SNR). After that, we segment the lumen from these images using a novel method based on dynamic programming which uses the joint histogram of the CEUS and BMUS pair of images to distinguish between background, lumen, tissue and artifacts. Finally, the obtained lumen contour in the improved-SNR mean image is transformed back to each time frame of the original image sequence. Validation was done by comparing manual lumen segmentations of two independent observers with automated lumen segmentations in the improved-SNR images of 9 carotid arteries from 7 patients. The root mean square error between the two observers was 0.17+/-0.10mm and between automated and average of manual segmentation of two observers was 0.19+/-0.06mm. In conclusion, we present a robust and accurate carotid lumen segmentation method which overcomes the complexity of anatomical structures, noise in the lumen, artifacts and echolucent plaques by exploiting the information in this combined imaging modality.

  14. Accurate airway segmentation based on intensity structure analysis and graph-cut

    NASA Astrophysics Data System (ADS)

    Meng, Qier; Kitsaka, Takayuki; Nimura, Yukitaka; Oda, Masahiro; Mori, Kensaku

    2016-03-01

    This paper presents a novel airway segmentation method based on intensity structure analysis and graph-cut. Airway segmentation is an important step in analyzing chest CT volumes for computerized lung cancer detection, emphysema diagnosis, asthma diagnosis, and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3-D airway tree structure from a CT volume is quite challenging. Several researchers have proposed automated algorithms basically based on region growing and machine learning techniques. However these methods failed to detect the peripheral bronchi branches. They caused a large amount of leakage. This paper presents a novel approach that permits more accurate extraction of complex bronchial airway region. Our method are composed of three steps. First, the Hessian analysis is utilized for enhancing the line-like structure in CT volumes, then a multiscale cavity-enhancement filter is employed to detect the cavity-like structure from the previous enhanced result. In the second step, we utilize the support vector machine (SVM) to construct a classifier for removing the FP regions generated. Finally, the graph-cut algorithm is utilized to connect all of the candidate voxels to form an integrated airway tree. We applied this method to sixteen cases of 3D chest CT volumes. The results showed that the branch detection rate of this method can reach about 77.7% without leaking into the lung parenchyma areas.

  15. Markerless motion capture of multiple characters using multiview image segmentation.

    PubMed

    Liu, Yebin; Gall, Juergen; Stoll, Carsten; Dai, Qionghai; Seidel, Hans-Peter; Theobalt, Christian

    2013-11-01

    Capturing the skeleton motion and detailed time-varying surface geometry of multiple, closely interacting peoples is a very challenging task, even in a multicamera setup, due to frequent occlusions and ambiguities in feature-to-person assignments. To address this task, we propose a framework that exploits multiview image segmentation. To this end, a probabilistic shape and appearance model is employed to segment the input images and to assign each pixel uniquely to one person. Given the articulated template models of each person and the labeled pixels, a combined optimization scheme, which splits the skeleton pose optimization problem into a local one and a lower dimensional global one, is applied one by one to each individual, followed with surface estimation to capture detailed nonrigid deformations. We show on various sequences that our approach can capture the 3D motion of humans accurately even if they move rapidly, if they wear wide apparel, and if they are engaged in challenging multiperson motions, including dancing, wrestling, and hugging. PMID:24051731

  16. Bright-field cell image segmentation by principal component pursuit with an Ncut penalization

    NASA Astrophysics Data System (ADS)

    Chen, Yuehuan; Wan, Justin W. L.

    2015-03-01

    Segmentation of cells in time-lapse bright-field microscopic images is crucial in understanding cell behaviours for oncological research. However, the complex nature of the cells makes it difficult to segment cells accurately. Furthermore, poor contrast, broken cell boundaries and the halo artifact pose additional challenges to this problem. Standard segmentation techniques such as edged-based methods, watershed, or active contours result in poor segmentation. Other existing methods for bright-field images cannot provide good results without localized segmentation steps. In this paper, we present two robust mathematical models to segment bright-field cells automatically for the entire image. These models treat cell image segmentation as a background subtraction problem, which can be formulated as a Principal Component Pursuit (PCP) problem. Our first segmentation model is formulated as a PCP with nonnegative constraints. We exploit the sparse component of the PCP solution for identifying the cell pixels. However, there is no control on the quality of the sparse component and the nonzero entries can scatter all over the image, resulting in a noisy segmentation. The second model is an improvement of the first model by combining PCP with spectral clustering. Seemingly unrelated approaches, we combine the two techniques by incorporating normalized-cut in the PCP as a measure for the quality of the segmentation. These two models have been applied to a set of C2C12 cells obtained from bright-field microscopy. Experimental results demonstrate that the proposed models are effective in segmenting cells from bright-field images.

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

    PubMed

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

    2008-01-01

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

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

    NASA Astrophysics Data System (ADS)

    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.

  19. Segmentation of medical images using explicit anatomical knowledge

    NASA Astrophysics Data System (ADS)

    Wilson, Laurie S.; Brown, Stephen; Brown, Matthew S.; Young, Jeanne; Li, Rongxin; Luo, Suhuai; Brandt, Lee

    1999-07-01

    Knowledge-based image segmentation is defined in terms of the separation of image analysis procedures and representation of knowledge. Such architecture is particularly suitable for medical image segmentation, because of the large amount of structured domain knowledge. A general methodology for the application of knowledge-based methods to medical image segmentation is described. This includes frames for knowledge representation, fuzzy logic for anatomical variations, and a strategy for determining the order of segmentation from the modal specification. This method has been applied to three separate problems, 3D thoracic CT, chest X-rays and CT angiography. The application of the same methodology to such a range of applications suggests a major role in medical imaging for segmentation methods incorporating representation of anatomical knowledge.

  20. Analysis of image thresholding segmentation algorithms based on swarm intelligence

    NASA Astrophysics Data System (ADS)

    Zhang, Yi; Lu, Kai; Gao, Yinghui; Yang, Bo

    2013-03-01

    Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt & Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.

  1. Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

    NASA Astrophysics Data System (ADS)

    Dewalle-Vignion, Anne-Sophie; Betrouni, Nacim; Baillet, Clio; Vermandel, Maximilien

    2015-12-01

    Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians’ manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging. Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used. Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results. The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging.

  2. Cellular image segmentation using n-agent cooperative game theory

    NASA Astrophysics Data System (ADS)

    Dimock, Ian B.; Wan, Justin W. L.

    2016-03-01

    Image segmentation is an important problem in computer vision and has significant applications in the segmentation of cellular images. Many different imaging techniques exist and produce a variety of image properties which pose difficulties to image segmentation routines. Bright-field images are particularly challenging because of the non-uniform shape of the cells, the low contrast between cells and background, and imaging artifacts such as halos and broken edges. Classical segmentation techniques often produce poor results on these challenging images. Previous attempts at bright-field imaging are often limited in scope to the images that they segment. In this paper, we introduce a new algorithm for automatically segmenting cellular images. The algorithm incorporates two game theoretic models which allow each pixel to act as an independent agent with the goal of selecting their best labelling strategy. In the non-cooperative model, the pixels choose strategies greedily based only on local information. In the cooperative model, the pixels can form coalitions, which select labelling strategies that benefit the entire group. Combining these two models produces a method which allows the pixels to balance both local and global information when selecting their label. With the addition of k-means and active contour techniques for initialization and post-processing purposes, we achieve a robust segmentation routine. The algorithm is applied to several cell image datasets including bright-field images, fluorescent images and simulated images. Experiments show that the algorithm produces good segmentation results across the variety of datasets which differ in cell density, cell shape, contrast, and noise levels.

  3. Dental x-ray image segmentation

    NASA Astrophysics Data System (ADS)

    Said, Eyad; Fahmy, Gamal F.; Nassar, Diaa; Ammar, Hany

    2004-08-01

    Law enforcement agencies have been exploiting biometric identifiers for decades as key tools in forensic identification. With the evolution in information technology and the huge volume of cases that need to be investigated by forensic specialists, it has become important to automate forensic identification systems. While, ante mortem (AM) identification, that is identification prior to death, is usually possible through comparison of many biometric identifiers, postmortem (PM) identification, that is identification after death, is impossible using behavioral biometrics (e.g. speech, gait). Moreover, under severe circumstances, such as those encountered in mass disasters (e.g. airplane crashers) or if identification is being attempted more than a couple of weeks postmortem, under such circumstances, most physiological biometrics may not be employed for identification, because of the decay of soft tissues of the body to unidentifiable states. Therefore, a postmortem biometric identifier has to resist the early decay that affects body tissues. Because of their survivability and diversity, the best candidates for postmortem biometric identification are the dental features. In this paper we present an over view about an automated dental identification system for Missing and Unidentified Persons. This dental identification system can be used by both law enforcement and security agencies in both forensic and biometric identification. We will also present techniques for dental segmentation of X-ray images. These techniques address the problem of identifying each individual tooth and how the contours of each tooth are extracted.

  4. Medical Image Watermarking Technique for Accurate Tamper Detection in ROI and Exact Recovery of ROI

    PubMed Central

    Eswaraiah, R.; Sreenivasa Reddy, E.

    2014-01-01

    In telemedicine while transferring medical images tampers may be introduced. Before making any diagnostic decisions, the integrity of region of interest (ROI) of the received medical image must be verified to avoid misdiagnosis. In this paper, we propose a novel fragile block based medical image watermarking technique to avoid embedding distortion inside ROI, verify integrity of ROI, detect accurately the tampered blocks inside ROI, and recover the original ROI with zero loss. In this proposed method, the medical image is segmented into three sets of pixels: ROI pixels, region of noninterest (RONI) pixels, and border pixels. Then, authentication data and information of ROI are embedded in border pixels. Recovery data of ROI is embedded into RONI. Results of experiments conducted on a number of medical images reveal that the proposed method produces high quality watermarked medical images, identifies the presence of tampers inside ROI with 100% accuracy, and recovers the original ROI without any loss. PMID:25328515

  5. Anterior segment imaging in glaucoma: An updated review

    PubMed Central

    Maslin, Jessica S; Barkana, Yaniv; Dorairaj, Syril K

    2015-01-01

    Anterior segment imaging allows for an objective method of visualizing the anterior segment angle. Two of the most commonly used devices for anterior segment imaging include the anterior segment optical coherence tomography (AS-OCT) and the ultrasound biomicroscopy (UBM). AS-OCT technology has several types, including time-domain, swept-source, and spectral-domain-based configurations. We performed a literature search on PubMed for articles containing the text “anterior segment OCT,” “ultrasound biomicroscopy,” and “anterior segment imaging” since 2004, with some pertinent references before 2004 included for completeness. This review compares the advantages and disadvantages of AS-OCT and UBM, and summarizes the most recent literature regarding the importance of these devices in glaucoma diagnosis and management. These devices not only aid in visualization of the angle, but also have important postsurgical applications in bleb and tube imaging. PMID:26576519

  6. The comparison index: A tool for assessing the accuracy of image segmentation

    NASA Astrophysics Data System (ADS)

    Möller, M.; Lymburner, L.; Volk, M.

    2007-08-01

    Segmentation algorithms applied to remote sensing data provide valuable information about the size, distribution and context of landscape objects at a range of scales. However, there is a need for well-defined and robust validation tools to assessing the reliability of segmentation results. Such tools are required to assess whether image segments are based on 'real' objects, such as field boundaries, or on artefacts of the image segmentation algorithm. These tools can be used to improve the reliability of any land-use/land-cover classifications or landscape analyses that is based on the image segments. The validation algorithm developed in this paper aims to: (a) localize and quantify segmentation inaccuracies; and (b) allow the assessment of segmentation results on the whole. The first aim is achieved using object metrics that enable the quantification of topological and geometric object differences. The second aim is achieved by combining these object metrics into a 'Comparison Index', which allows a relative comparison of different segmentation results. The approach demonstrates how the Comparison Index CI can be used to guide trial-and-error techniques, enabling the identification of a segmentation scale H that is close to optimal. Once this scale has been identified a more detailed examination of the CI- H- diagrams can be used to identify precisely what H value and associated parameter settings will yield the most accurate image segmentation results. The procedure is applied to segmented Landsat scenes in an agricultural area in Saxony-Anhalt, Germany. The segmentations were generated using the 'Fractal Net Evolution Approach', which is implemented in the eCognition software.

  7. Outstanding-objects-oriented color image segmentation using fuzzy logic

    NASA Astrophysics Data System (ADS)

    Hayasaka, Rina; Zhao, Jiying; Matsushita, Yutaka

    1997-10-01

    This paper presents a novel fuzzy-logic-based color image segmentation scheme focusing on outstanding objects to human eyes. The scheme first segments the image into rough fuzzy regions, chooses visually significant regions, and conducts fine segmentation on the chosen regions. It can not only reduce the computational load, but also make contour detection easy because the brief object externals has been previously determined. The scheme reflects human sense, and it can be sued efficiently in automatic extraction of image retrieval key, robot vision and region-adaptive image compression.

  8. Improved document image segmentation algorithm using multiresolution morphology

    NASA Astrophysics Data System (ADS)

    Bukhari, Syed Saqib; Shafait, Faisal; Breuel, Thomas M.

    2011-01-01

    Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper describes modifications to the text/non-text segmentation algorithm presented by Bloomberg,1 which is also available in his open-source Leptonica library.2The modifications result in significant improvements and achieved better segmentation accuracy than the original algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram datasets.

  9. Single 3D cell segmentation from optical CT microscope images

    NASA Astrophysics Data System (ADS)

    Xie, Yiting; Reeves, Anthony P.

    2014-03-01

    The automated segmentation of the nucleus and cytoplasm regions in 3D optical CT microscope images has been achieved with two methods, a global threshold gradient based approach and a graph-cut approach. For the first method, the first two peaks of a gradient figure of merit curve are selected as the thresholds for cytoplasm and nucleus segmentation. The second method applies a graph-cut segmentation twice: the first identifies the nucleus region and the second identifies the cytoplasm region. Image segmentation of single cells is important for automated disease diagnostic systems. The segmentation methods were evaluated with 200 3D images consisting of 40 samples of 5 different cell types. The cell types consisted of columnar, macrophage, metaplastic and squamous human cells and cultured A549 cancer cells. The segmented cells were compared with both 2D and 3D reference images and the quality of segmentation was determined by the Dice Similarity Coefficient (DSC). In general, the graph-cut method had a superior performance to the gradient-based method. The graph-cut method achieved an average DSC of 86% and 72% for nucleus and cytoplasm segmentations respectively for the 2D reference images and 83% and 75% for the 3D reference images. The gradient method achieved an average DSC of 72% and 51% for nucleus and cytoplasm segmentation for the 2D reference images and 71% and 51% for the 3D reference images. The DSC of cytoplasm segmentation was significantly lower than for the nucleus since the cytoplasm was not differentiated as well by image intensity from the background.

  10. Accurate estimation of motion blur parameters in noisy remote sensing image

    NASA Astrophysics Data System (ADS)

    Shi, Xueyan; Wang, Lin; Shao, Xiaopeng; Wang, Huilin; Tao, Zhong

    2015-05-01

    The relative motion between remote sensing satellite sensor and objects is one of the most common reasons for remote sensing image degradation. It seriously weakens image data interpretation and information extraction. In practice, point spread function (PSF) should be estimated firstly for image restoration. Identifying motion blur direction and length accurately is very crucial for PSF and restoring image with precision. In general, the regular light-and-dark stripes in the spectrum can be employed to obtain the parameters by using Radon transform. However, serious noise existing in actual remote sensing images often causes the stripes unobvious. The parameters would be difficult to calculate and the error of the result relatively big. In this paper, an improved motion blur parameter identification method to noisy remote sensing image is proposed to solve this problem. The spectrum characteristic of noisy remote sensing image is analyzed firstly. An interactive image segmentation method based on graph theory called GrabCut is adopted to effectively extract the edge of the light center in the spectrum. Motion blur direction is estimated by applying Radon transform on the segmentation result. In order to reduce random error, a method based on whole column statistics is used during calculating blur length. Finally, Lucy-Richardson algorithm is applied to restore the remote sensing images of the moon after estimating blur parameters. The experimental results verify the effectiveness and robustness of our algorithm.

  11. Robust image modeling technique with a bioluminescence image segmentation application

    NASA Astrophysics Data System (ADS)

    Zhong, Jianghong; Wang, Ruiping; Tian, Jie

    2009-02-01

    A robust pattern classifier algorithm for the variable symmetric plane model, where the driving noise is a mixture of a Gaussian and an outlier process, is developed. The veracity and high-speed performance of the pattern recognition algorithm is proved. Bioluminescence tomography (BLT) has recently gained wide acceptance in the field of in vivo small animal molecular imaging. So that it is very important for BLT to how to acquire the highprecision region of interest in a bioluminescence image (BLI) in order to decrease loss of the customers because of inaccuracy in quantitative analysis. An algorithm in the mode is developed to improve operation speed, which estimates parameters and original image intensity simultaneously from the noise corrupted image derived from the BLT optical hardware system. The focus pixel value is obtained from the symmetric plane according to a more realistic assumption for the noise sequence in the restored image. The size of neighborhood is adaptive and small. What's more, the classifier function is base on the statistic features. If the qualifications for the classifier are satisfied, the focus pixel intensity is setup as the largest value in the neighborhood.Otherwise, it will be zeros.Finally,pseudo-color is added up to the result of the bioluminescence segmented image. The whole process has been implemented in our 2D BLT optical system platform and the model is proved.

  12. An improved variational level set method for MR image segmentation and bias field correction.

    PubMed

    Zhan, Tianming; Zhang, Jun; Xiao, Liang; Chen, Yunjie; Wei, Zhihui

    2013-04-01

    In this paper, we propose an improved variational level set approach to correct the bias and to segment the magnetic resonance (MR) images with inhomogeneous intensity. First, we use a Gaussian distribution with bias field as a local region descriptor in two-phase level set formulation for segmentation and bias field correction of the images with inhomogeneous intensities. By using the information of the local variance in this descriptor, our method is able to obtain accurate segmentation results. Furthermore, we extend this method to three-phase level set formulation for brain MR image segmentation and bias field correction. By using this three-phase level set function to replace the four-phase level set function, we can reduce the number of convolution operations in each iteration and improve the efficiency. Compared with other approaches, this algorithm demonstrates a superior performance. PMID:23219273

  13. Computed Tomographic Image Analysis Based on FEM Performance Comparison of Segmentation on Knee Joint Reconstruction

    PubMed Central

    Jang, Seong-Wook; Seo, Young-Jin; Yoo, Yon-Sik

    2014-01-01

    The demand for an accurate and accessible image segmentation to generate 3D models from CT scan data has been increasing as such models are required in many areas of orthopedics. In this paper, to find the optimal image segmentation to create a 3D model of the knee CT data, we compared and validated segmentation algorithms based on both objective comparisons and finite element (FE) analysis. For comparison purposes, we used 1 model reconstructed in accordance with the instructions of a clinical professional and 3 models reconstructed using image processing algorithms (Sobel operator, Laplacian of Gaussian operator, and Canny edge detection). Comparison was performed by inspecting intermodel morphological deviations with the iterative closest point (ICP) algorithm, and FE analysis was performed to examine the effects of the segmentation algorithm on the results of the knee joint movement analysis. PMID:25538950

  14. Computed tomographic image analysis based on FEM performance comparison of segmentation on knee joint reconstruction.

    PubMed

    Jang, Seong-Wook; Seo, Young-Jin; Yoo, Yon-Sik; Kim, Yoon Sang

    2014-01-01

    The demand for an accurate and accessible image segmentation to generate 3D models from CT scan data has been increasing as such models are required in many areas of orthopedics. In this paper, to find the optimal image segmentation to create a 3D model of the knee CT data, we compared and validated segmentation algorithms based on both objective comparisons and finite element (FE) analysis. For comparison purposes, we used 1 model reconstructed in accordance with the instructions of a clinical professional and 3 models reconstructed using image processing algorithms (Sobel operator, Laplacian of Gaussian operator, and Canny edge detection). Comparison was performed by inspecting intermodel morphological deviations with the iterative closest point (ICP) algorithm, and FE analysis was performed to examine the effects of the segmentation algorithm on the results of the knee joint movement analysis. PMID:25538950

  15. On the Performance of Stochastic Model-Based Image Segmentation

    NASA Astrophysics Data System (ADS)

    Lei, Tianhu; Sewchand, Wilfred

    1989-11-01

    A new stochastic model-based image segmentation technique for X-ray CT image has been developed and has been extended to the more general nondiffraction CT images which include MRI, SPELT, and certain type of ultrasound images [1,2]. The nondiffraction CT image is modeled by a Finite Normal Mixture. The technique utilizes the information theoretic criterion to detect the number of the region images, uses the Expectation-Maximization algorithm to estimate the parameters of the image, and uses the Bayesian classifier to segment the observed image. How does this technique over/under-estimate the number of the region images? What is the probability of errors in the segmentation of this technique? This paper addresses these two problems and is a continuation of [1,2].

  16. Multiscale Segmentation of Polarimetric SAR Image Based on Srm Superpixels

    NASA Astrophysics Data System (ADS)

    Lang, F.; Yang, J.; Wu, L.; Li, D.

    2016-06-01

    Multi-scale segmentation of remote sensing image is more systematic and more convenient for the object-oriented image analysis compared to single-scale segmentation. However, the existing pixel-based polarimetric SAR (PolSAR) image multi-scale segmentation algorithms are usually inefficient and impractical. In this paper, we proposed a superpixel-based binary partition tree (BPT) segmentation algorithm by combining the generalized statistical region merging (GSRM) algorithm and the BPT algorithm. First, superpixels are obtained by setting a maximum region number threshold to GSRM. Then, the region merging process of the BPT algorithm is implemented based on superpixels but not pixels. The proposed algorithm inherits the advantages of both GSRM and BPT. The operation efficiency is obviously improved compared to the pixel-based BPT segmentation. Experiments using the Lband ESAR image over the Oberpfaffenhofen test site proved the effectiveness of the proposed method.

  17. Progress in the robust automated segmentation of real cell images

    NASA Astrophysics Data System (ADS)

    Bamford, P.; Jackway, P.; Lovell, Brian

    1999-07-01

    We propose a collection of robust algorithms for the segmentation of cell images from Papanicolaou stained cervical smears (`Pap' smears). This problem is deceptively difficult and often results on laboratory datasets do not carry over to real world data. Our approach is in 3 parts. First, we segment the cytoplasm from the background using a novel method based on the Wilson and Spann multi-resolution framework. Second, we segment the nucleus from the cytoplasm using an active contour method, where the best contour is found by a global minimization method. Third, we implement a method to determine a confidence measure for the segmentation of each object. This uses a stability criterion over the regularization parameter (lambda) in the active contour. We present the results of thorough testing of the algorithms on large numbers of cell images. A database of 20,120 images is used for the segmentation tests and 18,718 images for the robustness tests.

  18. Segmenting images analytically in shape space

    NASA Astrophysics Data System (ADS)

    Rathi, Yogesh; Dambreville, Samuel; Niethammer, Marc; Malcolm, James; Levitt, James; Shenton, Martha E.; Tannenbaum, Allen

    2008-03-01

    This paper presents a novel analytic technique to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert a given test volume into a binary map representation, and a novel energy functional is proposed whose minimum can be analytically computed to obtain the desired segmentation in the shape space. We compare the proposed method with the log-likelihood based energy to elucidate some key differences. Our algorithm is applied to the segmentation of brain caudate nucleus and hippocampus from MRI data, which is of interest in the study of schizophrenia and Alzheimer's disease. Our validation (we compute the Hausdorff distance and the DICE coefficient between the automatic segmentation and ground-truth) shows that the proposed algorithm is very fast, requires no initialization and outperforms the log-likelihood based energy.

  19. Live minimal path for interactive segmentation of medical images

    NASA Astrophysics Data System (ADS)

    Chartrand, Gabriel; Tang, An; Chav, Ramnada; Cresson, Thierry; Chantrel, Steeve; De Guise, Jacques A.

    2015-03-01

    Medical image segmentation is nowadays required for medical device development and in a growing number of clinical and research applications. Since dedicated automatic segmentation methods are not always available, generic and efficient interactive tools can alleviate the burden of manual segmentation. In this paper we propose an interactive segmentation tool based on image warping and minimal path segmentation that is efficient for a wide variety of segmentation tasks. While the user roughly delineates the desired organs boundary, a narrow band along the cursors path is straightened, providing an ideal subspace for feature aligned filtering and minimal path algorithm. Once the segmentation is performed on the narrow band, the path is warped back onto the original image, precisely delineating the desired structure. This tool was found to have a highly intuitive dynamic behavior. It is especially efficient against misleading edges and required only coarse interaction from the user to achieve good precision. The proposed segmentation method was tested for 10 difficult liver segmentations on CT and MRI images, and the resulting 2D overlap Dice coefficient was 99% on average..

  20. A web-based procedure for liver segmentation in CT images

    NASA Astrophysics Data System (ADS)

    Yuan, Rong; Luo, Ming; Wang, Luyao; Xie, Qingguo

    2015-03-01

    Liver segmentation in CT images has been acknowledged as a basic and indispensable part in systems of computer aided liver surgery for operation design and risk evaluation. In this paper, we will introduce and implement a web-based procedure for liver segmentation to help radiologists and surgeons get an accurate result efficiently and expediently. Several clinical datasets are used to evaluate the accessibility and the accuracy. This procedure seems a promising approach for extraction of liver volumetry of various shapes. Moreover, it is possible for user to access the segmentation wherever the Internet is available without any specific machine.

  1. Volume quantization of the mouse cerebellum by semiautomatic 3D segmentation of magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Sijbers, Jan; Van der Linden, Anne-Marie; Scheunders, Paul; Van Audekerke, Johan; Van Dyck, Dirk; Raman, Erik R.

    1996-04-01

    The aim of this work is the development of a non-invasive technique for efficient and accurate volume quantization of the cerebellum of mice. This enables an in-vivo study on the development of the cerebellum in order to define possible alterations in cerebellum volume of transgenic mice. We concentrate on a semi-automatic segmentation procedure to extract the cerebellum from 3D magnetic resonance data. The proposed technique uses a 3D variant of Vincent and Soille's immersion based watershed algorithm which is applied to the gradient magnitude of the MR data. The algorithm results in a partitioning of the data in volume primitives. The known drawback of the watershed algorithm, over-segmentation, is strongly reduced by a priori application of an adaptive anisotropic diffusion filter on the gradient magnitude data. In addition, over-segmentation is a posteriori contingently reduced by properly merging volume primitives, based on the minimum description length principle. The outcome of the preceding image processing step is presented to the user for manual segmentation. The first slice which contains the object of interest is quickly segmented by the user through selection of basic image regions. In the sequel, the subsequent slices are automatically segmented. The segmentation results are contingently manually corrected. The technique is tested on phantom objects, where segmentation errors less than 2% were observed. Three-dimensional reconstructions of the segmented data are shown for the mouse cerebellum and the mouse brains in toto.

  2. From image segmentation to anti-textons.

    PubMed

    van Tonder, G J; Ejima, Y

    2000-01-01

    We apply the 'patchwork engine' (PE; van Tonder and Ejima, 2000 Neural Networks forthcoming) to encode spaces between textons in an attempt to find a suitable feature representation of anti-textons [Williams and Julesz, 1991, in Neural Networks for Perception volume 1: Human and Machine Perception Ed. H Wechsler (San Diego, CA: Academic Press); 1992, Proceedings of the National Academy of Sciences of the USA 89 6531-6534]. With computed anti-textons it is possible to show that tessellation and distribution of anti-textons can differ from that of textons depending on the ratio of texton size to anti-texton size. From this we hypothesise that variability of anti-textons can enhance texture segregation, and test our hypothesis in two psychophysical experiments. Texture segregation asymmetry is the topic of the first test. We found that targets on backgrounds with regular anti-textons segregate more strongly than on backgrounds with highly variable anti-textons. This neatly complements other explanations for texture segregation asymmetry (e.g. Rubenstein and Sagi, 1990 Journal of the Optical Society of America A 7 1632-1643). Second the relative significance of textons and anti-textons in human texture segregation is investigated for a limited set of texture patterns. Subjects consistently judged a combination of texton and anti-texton gradients as more conspicuous than texton-only gradients, and judged texton-only gradients as being more conspicuous than anti-texton-only gradients. In the absence of strong texton gradients the regularity versus irregularity of anti-textons agrees with perceived texture segregation. Using PE outputs as anti-texton features thus enabled the conception of various useful tests on texture segregation. The PE is originally intended as a general image segmentation method based on symmetry axes. With this paper we therefore hope to relate anti-textons with visual processing in a wider sense. PMID:11220214

  3. Pixel classification based color image segmentation using quaternion exponent moments.

    PubMed

    Wang, Xiang-Yang; Wu, Zhi-Fang; Chen, Liang; Zheng, Hong-Liang; Yang, Hong-Ying

    2016-02-01

    Image segmentation remains an important, but hard-to-solve, problem since it appears to be application dependent with usually no a priori information available regarding the image structure. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. In this paper, we propose a pixel classification based color image segmentation using quaternion exponent moments. Firstly, the pixel-level image feature is extracted based on quaternion exponent moments (QEMs), which can capture effectively the image pixel content by considering the correlation between different color channels. Then, the pixel-level image feature is used as input of twin support vector machines (TSVM) classifier, and the TSVM model is trained by selecting the training samples with Arimoto entropy thresholding. Finally, the color image is segmented with the trained TSVM model. The proposed scheme has the following advantages: (1) the effective QEMs is introduced to describe color image pixel content, which considers the correlation between different color channels, (2) the excellent TSVM classifier is utilized, which has lower computation time and higher classification accuracy. Experimental results show that our proposed method has very promising segmentation performance compared with the state-of-the-art segmentation approaches recently proposed in the literature. PMID:26618250

  4. Locally-constrained boundary regression for segmentation of prostate and rectum in the planning CT images.

    PubMed

    Shao, Yeqin; Gao, Yaozong; Wang, Qian; Yang, Xin; Shen, Dinggang

    2015-12-01

    Automatic and accurate segmentation of the prostate and rectum in planning CT images is a challenging task due to low image contrast, unpredictable organ (relative) position, and uncertain existence of bowel gas across different patients. Recently, regression forest was adopted for organ deformable segmentation on 2D medical images by training one landmark detector for each point on the shape model. However, it seems impractical for regression forest to guide 3D deformable segmentation as a landmark detector, due to large number of vertices in the 3D shape model as well as the difficulty in building accurate 3D vertex correspondence for each landmark detector. In this paper, we propose a novel boundary detection method by exploiting the power of regression forest for prostate and rectum segmentation. The contributions of this paper are as follows: (1) we introduce regression forest as a local boundary regressor to vote the entire boundary of a target organ, which avoids training a large number of landmark detectors and building an accurate 3D vertex correspondence for each landmark detector; (2) an auto-context model is integrated with regression forest to improve the accuracy of the boundary regression; (3) we further combine a deformable segmentation method with the proposed local boundary regressor for the final organ segmentation by integrating organ shape priors. Our method is evaluated on a planning CT image dataset with 70 images from 70 different patients. The experimental results show that our proposed boundary regression method outperforms the conventional boundary classification method in guiding the deformable model for prostate and rectum segmentations. Compared with other state-of-the-art methods, our method also shows a competitive performance. PMID:26439938

  5. Image segmentation in wavelet transform space implemented on DSP

    NASA Astrophysics Data System (ADS)

    Ponomaryov, Volodymyr I.; Castillejos, Heydy; Peralta-Fabi, Ricardo

    2012-06-01

    A novel approach in the segmentation for the images of different nature employing the feature extraction in WT space before the segmentation process is presented. The designed frameworks (W-FCM, W-CPSFCM and WK-Means) according to AUC analysis have demonstrated better performance novel frameworks against other algorithms existing in literature during numerous simulation experiments with synthetic and dermoscopic images. The novel W-CPSFCM algorithm estimates a number of clusters in automatic mode without the intervention of a specialist. The implementation of the proposed segmentation algorithms on the Texas Instruments DSP TMS320DM642 demonstrates possible real time processing mode for images of different nature.

  6. Segmentation of Whole Cells and Cell Nuclei From 3-D Optical Microscope Images Using Dynamic Programming

    PubMed Central

    McCullough, Dean P.; Gudla, Prabhakar R.; Harris, Bradley S.; Collins, Jason A.; Meaburn, Karen J.; Nakaya, Masa-Aki; Yamaguchi, Terry P.; Misteli, Tom; Lockett, Stephen J.

    2009-01-01

    Communications between cells in large part drive tissue development and function, as well as disease-related processes such as tumorigenesis. Understanding the mechanistic bases of these processes necessitates quantifying specific molecules in adjacent cells or cell nuclei of intact tissue. However, a major restriction on such analyses is the lack of an efficient method that correctly segments each object (cell or nucleus) from 3-D images of an intact tissue specimen. We report a highly reliable and accurate semi-automatic algorithmic method for segmenting fluorescence-labeled cells or nuclei from 3-D tissue images. Segmentation begins with semi-automatic, 2-D object delineation in a user-selected plane, using dynamic programming (DP) to locate the border with an accumulated intensity per unit length greater that any other possible border around the same object. Then the two surfaces of the object in planes above and below the selected plane are found using an algorithm that combines DP and combinatorial searching. Following segmentation, any perceived errors can be interactively corrected. Segmentation accuracy is not significantly affected by intermittent labeling of object surfaces, diffuse surfaces, or spurious signals away from surfaces. The unique strength of the segmentation method was demonstrated on a variety of biological tissue samples where all cells, including irregularly shaped cells, were accurately segmented based on visual inspection. PMID:18450544

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

    PubMed

    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

  8. A Bayesian Approach for Image Segmentation with Shape Priors

    SciTech Connect

    Chang, Hang; Yang, Qing; Parvin, Bahram

    2008-06-20

    Color and texture have been widely used in image segmentation; however, their performance is often hindered by scene ambiguities, overlapping objects, or missingparts. In this paper, we propose an interactive image segmentation approach with shape prior models within a Bayesian framework. Interactive features, through mouse strokes, reduce ambiguities, and the incorporation of shape priors enhances quality of the segmentation where color and/or texture are not solely adequate. The novelties of our approach are in (i) formulating the segmentation problem in a well-de?ned Bayesian framework with multiple shape priors, (ii) ef?ciently estimating parameters of the Bayesian model, and (iii) multi-object segmentation through user-speci?ed priors. We demonstrate the effectiveness of our method on a set of natural and synthetic images.

  9. Optoelectronic complex inner product for evaluating quality of image segmentation

    NASA Astrophysics Data System (ADS)

    Power, Gregory J.; Awwal, Abdul Ahad S.

    2000-11-01

    In automatic target recognition and machine vision applications, segmentation of the images is a key step. Poor segmentation reduces the recognition performance. For some imaging systems such as MRI and Synthetic Aperture Radar (SAR) it is difficult even for humans to agree on the location of the edge which allows for segmentation. A real- time dynamic approach to determine the quality of segmentation can enable vision systems to refocus of apply appropriate algorithms to ensure high quality segmentation for recognition. A recent approach to evaluate the quality of image segmentation uses percent-pixels-different (PPD). For some cases, PPD provides a reasonable quality evaluation, but it has a weakness in providing a measure for how well the shape of the segmentation matches the true shape. This paper introduces the complex inner product approach for providing a goodness measure for evaluating the segmentation quality based on shape. The complex inner product approach is demonstrated on SAR target chips obtained from the Moving and Stationary Target Acquisition and Recognition (MSTAR) program sponsored by the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL). The results are compared to the PPD approach. A design for an optoelectronic implementation of the complex inner product for dynamic segmentation evaluation is introduced.

  10. Template-driven segmentation of confocal microscopy images.

    PubMed

    Chen, Ying-Cheng; Chen, Yung-Chang; Chiang, Ann-Shyn

    2008-03-01

    High quality 3D visualization of anatomic structures is necessary for many applications. The anatomic structures first need to be segmented. A variety of segmentation algorithms have been developed for this purpose. For confocal microscopy images, the noise introduced during the specimen preparation process, such as the procedure of penetration or staining, may cause images to be of low contrast in some regions. This property will make segmentation difficult. Also, the segmented structures may have rugged surfaces in 3D visualization. In this paper, we present a hybrid method that is suitable for segmentation of confocal microscopy images. A rough segmentation result is obtained from the atlas-based segmentation via affine registration. The boundaries of the segmentation result are close to the object boundaries, and are regarded as the initial contours of the active contour models. After convergence of the snake algorithm, the resulting contours in regions of low contrast are locally refined by parametric bicubic surfaces to alleviate the problem of incorrect convergence. The proposed method increases the accuracy of the snake algorithm because of better initial contours. Besides, it can provide smoother segmented results in 3D visualization. PMID:18178286

  11. Optimized mean shift algorithm for color segmentation in image sequences

    NASA Astrophysics Data System (ADS)

    Bailer, Werner; Schallauer, Peter; Haraldsson, Harald B.; Rehatschek, Herwig

    2005-03-01

    The application of the mean shift algorithm to color image segmentation has been proposed in 1997 by Comaniciu and Meer. We apply the mean shift color segmentation to image sequences, as the first step of a moving object segmentation algorithm. Previous work has shown that it is well suited for this task, because it provides better temporal stability of the segmentation result than other approaches. The drawback is higher computational cost. For speed up of processing on image sequences we exploit the fact that subsequent frames are similar and use the cluster centers of previous frames as initial estimates, which also enhances spatial segmentation continuity. In contrast to other implementations we use the originally proposed CIE LUV color space to ensure high quality segmentation results. We show that moderate quantization of the input data before conversion to CIE LUV has little influence on the segmentation quality but results in significant speed up. We also propose changes in the post-processing step to increase the temporal stability of border pixels. We perform objective evaluation of the segmentation results to compare the original algorithm with our modified version. We show that our optimized algorithm reduces processing time and increases the temporal stability of the segmentation.

  12. 3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning

    NASA Astrophysics Data System (ADS)

    Yang, Xiaofeng; Fei, Baowei

    2012-02-01

    We developed a three-dimensional (3D) segmentation method for transrectal ultrasound (TRUS) images, which is based on longitudinal image registration and machine learning. Using longitudinal images of each individual patient, we register previously acquired images to the new images of the same subject. Three orthogonal Gabor filter banks were used to extract texture features from each registered image. Patient-specific Gabor features from the registered images are used to train kernel support vector machines (KSVMs) and then to segment the newly acquired prostate image. The segmentation method was tested in TRUS data from five patients. The average surface distance between our and manual segmentation is 1.18 +/- 0.31 mm, indicating that our automatic segmentation method based on longitudinal image registration is feasible for segmenting the prostate in TRUS images.

  13. iCut: an Integrative Cut Algorithm Enables Accurate Segmentation of Touching Cells

    PubMed Central

    He, Yong; Gong, Hui; Xiong, Benyi; Xu, Xiaofeng; Li, Anan; Jiang, Tao; Sun, Qingtao; Wang, Simin; Luo, Qingming; Chen, Shangbin

    2015-01-01

    Individual cells play essential roles in the biological processes of the brain. The number of neurons changes during both normal development and disease progression. High-resolution imaging has made it possible to directly count cells. However, the automatic and precise segmentation of touching cells continues to be a major challenge for massive and highly complex datasets. Thus, an integrative cut (iCut) algorithm, which combines information regarding spatial location and intervening and concave contours with the established normalized cut, has been developed. iCut involves two key steps: (1) a weighting matrix is first constructed with the abovementioned information regarding the touching cells and (2) a normalized cut algorithm that uses the weighting matrix is implemented to separate the touching cells into isolated cells. This novel algorithm was evaluated using two types of data: the open SIMCEP benchmark dataset and our micro-optical imaging dataset from a Nissl-stained mouse brain. It has achieved a promising recall/precision of 91.2 ± 2.1%/94.1 ± 1.8% and 86.8 ± 4.1%/87.5 ± 5.7%, respectively, for the two datasets. As quantified using the harmonic mean of recall and precision, the accuracy of iCut is higher than that of some state-of-the-art algorithms. The better performance of this fully automated algorithm can benefit studies of brain cytoarchitecture. PMID:26168908

  14. Image segmentation by iterative parallel region growing with application to data compression and image analysis

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    1988-01-01

    Image segmentation can be a key step in data compression and image analysis. However, the segmentation results produced by most previous approaches to region growing are suspect because they depend on the order in which portions of the image are processed. An iterative parallel segmentation algorithm avoids this problem by performing globally best merges first. Such a segmentation approach, and two implementations of the approach on NASA's Massively Parallel Processor (MPP) are described. Application of the segmentation approach to data compression and image analysis is then described, and results of such application are given for a LANDSAT Thematic Mapper image.

  15. Segmentation of 4D cardiac images: investigation on statistical shape models.

    PubMed

    Renno, Markus S; Shang, Yan; Sweeney, James; Dossel, Olaf

    2006-01-01

    The purpose of this research was two-fold: (1) to investigate the properties of statistical shape models constructed from manually segmented cardiac ventricular chambers to confirm the validity of an automatic 4-dimensional (4D) segmentation model that uses gradient vector flow (GVF) images of the original data and (2) to develop software to further automate the steps necessary in active shape model (ASM) training. These goals were achieved by first constructing ASMs from manually segmented ventricular models by allowing the user to cite entire datasets for processing using a GVF-based landmarking procedure and principal component analysis (PCA) to construct the statistical shape model. The statistical shape model of one dataset was used to regulate the segmentation of another dataset according to its GVF, and these results were then analyzed and found to accurately represent the original cardiac data when compared to the manual segmentation results as the golden standard. PMID:17947007

  16. A translational registration system for LANDSAT image segments

    NASA Technical Reports Server (NTRS)

    Parada, N. D. J. (Principal Investigator); Erthal, G. J.; Velasco, F. R. D.; Mascarenhas, N. D. D.

    1983-01-01

    The use of satellite images obtained from various dates is essential for crop forecast systems. In order to make possible a multitemporal analysis, it is necessary that images belonging to each acquisition have pixel-wise correspondence. A system developed to obtain, register and record image segments from LANDSAT images in computer compatible tapes is described. The translational registration of the segments is performed by correlating image edges in different acquisitions. The system was constructed for the Burroughs B6800 computer in ALGOL language.

  17. Comparison of automated and manual segmentation of hippocampus MR images

    NASA Astrophysics Data System (ADS)

    Haller, John W.; Christensen, Gary E.; Miller, Michael I.; Joshi, Sarang C.; Gado, Mokhtar; Csernansky, John G.; Vannier, Michael W.

    1995-05-01

    The precision and accuracy of area estimates from magnetic resonance (MR) brain images and using manual and automated segmentation methods are determined. Areas of the human hippocampus were measured to compare a new automatic method of segmentation with regions of interest drawn by an expert. MR images of nine normal subjects and nine schizophrenic patients were acquired with a 1.5-T unit (Siemens Medical Systems, Inc., Iselin, New Jersey). From each individual MPRAGE 3D volume image a single comparable 2-D slice (matrix equals 256 X 256) was chosen which corresponds to the same coronal slice of the hippocampus. The hippocampus was first manually segmented, then segmented using high dimensional transformations of a digital brain atlas to individual brain MR images. The repeatability of a trained rater was assessed by comparing two measurements from each individual subject. Variability was also compared within and between subject groups of schizophrenics and normal subjects. Finally, the precision and accuracy of automated segmentation of hippocampal areas were determined by comparing automated measurements to manual segmentation measurements made by the trained rater on MR and brain slice images. The results demonstrate the high repeatability of area measurement from MR images of the human hippocampus. Automated segmentation using high dimensional transformations from a digital brain atlas provides repeatability superior to that of manual segmentation. Furthermore, the validity of automated measurements was demonstrated by a high correlation with manual segmentation measurements made by a trained rater. Quantitative morphometry of brain substructures (e.g. hippocampus) is feasible by use of a high dimensional transformation of a digital brain atlas to an individual MR image. This method automates the search for neuromorphological correlates of schizophrenia by a new mathematically robust method with unprecedented sensitivity to small local and regional differences.

  18. Nucleus and cytoplasm segmentation in microscopic images using K-means clustering and region growing

    PubMed Central

    Sarrafzadeh, Omid; Dehnavi, Alireza Mehri

    2015-01-01

    Background: Segmentation of leukocytes acts as the foundation for all automated image-based hematological disease recognition systems. Most of the time, hematologists are interested in evaluation of white blood cells only. Digital image processing techniques can help them in their analysis and diagnosis. Materials and Methods: The main objective of this paper is to detect leukocytes from a blood smear microscopic image and segment them into their two dominant elements, nucleus and cytoplasm. The segmentation is conducted using two stages of applying K-means clustering. First, the nuclei are segmented using K-means clustering. Then, a proposed method based on region growing is applied to separate the connected nuclei. Next, the nuclei are subtracted from the original image. Finally, the cytoplasm is segmented using the second stage of K-means clustering. Results: The results indicate that the proposed method is able to extract the nucleus and cytoplasm regions accurately and works well even though there is no significant contrast between the components in the image. Conclusions: In this paper, a method based on K-means clustering and region growing is proposed in order to detect leukocytes from a blood smear microscopic image and segment its components, the nucleus and the cytoplasm. As region growing step of the algorithm relies on the information of edges, it will not able to separate the connected nuclei more accurately in poor edges and it requires at least a weak edge to exist between the nuclei. The nucleus and cytoplasm segments of a leukocyte can be used for feature extraction and classification which leads to automated leukemia detection. PMID:26605213

  19. Bayesian Analysis and Segmentation of Multichannel Image Sequences

    NASA Astrophysics Data System (ADS)

    Chang, Michael Ming Hsin

    This thesis is concerned with the segmentation and analysis of multichannel image sequence data. In particular, we use maximum a posteriori probability (MAP) criterion and Gibbs random fields (GRF) to formulate the problems. We start by reviewing the significance of MAP estimation with GRF priors and study the feasibility of various optimization methods for implementing the MAP estimator. We proceed to investigate three areas where image data and parameter estimates are present in multichannels, multiframes, and interrelated in complicated manners. These areas of study include color image segmentation, multislice MR image segmentation, and optical flow estimation and segmentation in multiframe temporal sequences. Besides developing novel algorithms in each of these areas, we demonstrate how to exploit the potential of MAP estimation and GRFs, and we propose practical and efficient implementations. Illustrative examples and relevant experimental results are included.

  20. Spectral segmentation of polygonized images with normalized cuts

    SciTech Connect

    Matsekh, Anna; Skurikhin, Alexei; Rosten, Edward

    2009-01-01

    We analyze numerical behavior of the eigenvectors corresponding to the lowest eigenvalues of the generalized graph Laplacians arising in the Normalized Cuts formulations of the image segmentation problem on coarse polygonal grids.

  1. Automatic segmentation of kidneys from non-contrast CT images using efficient belief propagation

    NASA Astrophysics Data System (ADS)

    Liu, Jianfei; Linguraru, Marius George; Wang, Shijun; Summers, Ronald M.

    2013-03-01

    CT colonography (CTC) can increase the chance of detecting high-risk lesions not only within the colon but anywhere in the abdomen with a low cost. Extracolonic findings such as calculi and masses are frequently found in the kidneys on CTC. Accurate kidney segmentation is an important step to detect extracolonic findings in the kidneys. However, noncontrast CTC images make the task of kidney segmentation substantially challenging because the intensity values of kidney parenchyma are similar to those of adjacent structures. In this paper, we present a fully automatic kidney segmentation algorithm to support extracolonic diagnosis from CTC data. It is built upon three major contributions: 1) localize kidney search regions by exploiting the segmented liver and spleen as well as body symmetry; 2) construct a probabilistic shape prior handling the issue of kidney touching other organs; 3) employ efficient belief propagation on the shape prior to extract the kidneys. We evaluated the accuracy of our algorithm on five non-contrast CTC datasets with manual kidney segmentation as the ground-truth. The Dice volume overlaps were 88%/89%, the root-mean-squared errors were 3.4 mm/2.8 mm, and the average surface distances were 2.1 mm/1.9 mm for the left/right kidney respectively. We also validated the robustness on 27 additional CTC cases, and 23 datasets were successfully segmented. In four problematic cases, the segmentation of the left kidney failed due to problems with the spleen segmentation. The results demonstrated that the proposed algorithm could automatically and accurately segment kidneys from CTC images, given the prior correct segmentation of the liver and spleen.

  2. 3CCD image segmentation and edge detection based on MATLAB

    NASA Astrophysics Data System (ADS)

    He, Yong; Pan, Jiazhi; Zhang, Yun

    2006-09-01

    This research aimed to identify weeds from crops in early stage in the field operation by using image-processing technology. As 3CCD images offer greater binary value difference between weed and crop section than ordinary digital images taken by common cameras. It has 3 channels (green, red, ifred) which takes a snap-photo of the same area, and the three images can be composed into one image, which facilitates the segmentation of different areas. By the application of image-processing toolkit on MATLAB, the different areas in the image can be segmented clearly. As edge detection technique is the first and very important step in image processing, The different result of different processing method was compared. Especially, by using the wavelet packet transform toolkit on MATLAB, An image was preprocessed and then the edge was extracted, and getting more clearly cut image of edge. The segmentation methods include operations as erosion, dilation and other algorithms to preprocess the images. It is of great importance to segment different areas in digital images in field real time, so as to be applied in precision farming, to saving energy and herbicide and many other materials. At present time Large scale software as MATLAB on PC was used, but the computation can be reduced and integrated into a small embed system, which means that the application of this technique in agricultural engineering is feasible and of great economical value.

  3. Fat segmentation on chest CT images via fuzzy models

    NASA Astrophysics Data System (ADS)

    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.

  4. Object segmentation based on guided layering from video image

    NASA Astrophysics Data System (ADS)

    Lin, Guangfeng; Zhu, Hong; Fan, Caixia; Zhang, Erhu

    2011-09-01

    When the object is similar to the background, it is difficult to segment the completed human body object from video images. To solve the problem, this paper proposes an object segmentation algorithm based on guided layering from video images. This algorithm adopts the structure of advance by degrees, including three parts altogether. Each part constructs the different energy function in terms of the spatiotemporal information to maximize the posterior probability of segmentation label. In part one, the energy functions are established, respectively, with the frame difference information in the first layer and second layer. By optimization, the initial segmentation is solved in the first layer, and then the amended segmentation is obtained in the second layer. In part two, the energy function is built in the interframe with the shape feature as the prior guiding to eliminate the interframe difference of the segmentation result. In art three, the segmentation results in the previous two parts are fused to suppress or inhibit the over-repairing segmentation and the object shape variations in the adjacent two-frame. The results from the compared experiment indicate that this algorithm can obtain the completed human body object in the case of the video image with similarity between object and background.

  5. Gaussian Mixtures on Tensor Fields for Segmentation: Applications to Medical Imaging

    PubMed Central

    de Luis-García, Rodrigo; Westin, Carl-Fredrik; Alberola-López, Carlos

    2012-01-01

    In this paper, we introduce a new approach for tensor field segmentation based on the definition of mixtures of Gaussians on tensors as a statistical model. Working over the well-known Geodesic Active Regions segmentation framework, this scheme presents several interesting advantages. First, it yields a more flexible model than the use of a single Gaussian distribution, which enables the method to better adapt to the complexity of the data. Second, it can work directly on tensor-valued images or, through a parallel scheme that processes independently the intensity and the local structure tensor, on scalar textured images. Two different applications have been considered to show the suitability of the proposed method for medical imaging segmentation. First, we address DT-MRI segmentation on a dataset of 32 volumes, showing a successful segmentation of the corpus callosum and favourable comparisons with related approaches in the literature. Second, the segmentation of bones from hand radiographs is studied, and a complete automatic-semiautomatic approach has been developed that makes use of anatomical prior knowledge to produce accurate segmentation results. PMID:20932717

  6. 3D segmentation of the true and false lumens on CT aortic dissection images

    NASA Astrophysics Data System (ADS)

    Fetnaci, Nawel; Łubniewski, Paweł; Miguel, Bruno; Lohou, Christophe

    2013-03-01

    Our works are related to aortic dissections which are a medical emergency and can quickly lead to death. In this paper, we want to retrieve in CT images the false and the true lumens which are aortic dissection features. Our aim is to provide a 3D view of the lumens that we can difficultly obtain either by volume rendering or by another visualization tool which only directly gives the outer contour of the aorta; or by other segmentation methods because they mainly directly segment either only the outer contour of the aorta or other connected arteries and organs both. In our work, we need to segment the two lumens separately; this segmentation will allow us to: distinguish them automatically, facilitate the landing of the aortic prosthesis, propose a virtual 3d navigation and do quantitative analysis. We chose to segment these data by using a deformable model based on the fast marching method. In the classical fast marching approach, a speed function is used to control the front propagation of a deforming curve. The speed function is only based on the image gradient. In our CT images, due to the low resolution, with the fast marching the front propagates from a lumen to the other; therefore, the gradient data is insufficient to have accurate segmentation results. In the paper, we have adapted the fast marching method more particularly by modifying the speed function and we succeed in segmenting the two lumens separately.

  7. Watershed Merge Tree Classification for Electron Microscopy Image Segmentation

    SciTech Connect

    Liu, TIng; Jurrus, Elizabeth R.; Seyedhosseini, Mojtaba; Ellisman, Mark; Tasdizen, Tolga

    2012-11-11

    Automated segmentation of electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that utilizes a hierarchical structure and boundary classification for 2D neuron segmentation. With a membrane detection probability map, a watershed merge tree is built for the representation of hierarchical region merging from the watershed algorithm. A boundary classifier is learned with non-local image features to predict each potential merge in the tree, upon which merge decisions are made with consistency constraints in the sense of optimization to acquire the final segmentation. Independent of classifiers and decision strategies, our approach proposes a general framework for efficient hierarchical segmentation with statistical learning. We demonstrate that our method leads to a substantial improvement in segmentation accuracy.

  8. An enhanced fast scanning algorithm for image segmentation

    NASA Astrophysics Data System (ADS)

    Ismael, Ahmed Naser; Yusof, Yuhanis binti

    2015-12-01

    Segmentation is an essential and important process that separates an image into regions that have similar characteristics or features. This will transform the image for a better image analysis and evaluation. An important benefit of segmentation is the identification of region of interest in a particular image. Various algorithms have been proposed for image segmentation and this includes the Fast Scanning algorithm which has been employed on food, sport and medical images. It scans all pixels in the image and cluster each pixel according to the upper and left neighbor pixels. The clustering process in Fast Scanning algorithm is performed by merging pixels with similar neighbor based on an identified threshold. Such an approach will lead to a weak reliability and shape matching of the produced segments. This paper proposes an adaptive threshold function to be used in the clustering process of the Fast Scanning algorithm. This function used the gray'value in the image's pixels and variance Also, the level of the image that is more the threshold are converted into intensity values between 0 and 1, and other values are converted into intensity values zero. The proposed enhanced Fast Scanning algorithm is realized on images of the public and private transportation in Iraq. Evaluation is later made by comparing the produced images of proposed algorithm and the standard Fast Scanning algorithm. The results showed that proposed algorithm is faster in terms the time from standard fast scanning.

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

    PubMed

    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 radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (< 1 ms) with a satisfying accuracy (Dice = 0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of

  10. Fuzzy fusion of results of medical image segmentation

    NASA Astrophysics Data System (ADS)

    Guliato, Denise; Rangayyan, Rangaraj M.; Carnielli, Walter A.; Zuffo, Joao A.; Desautels, J. E. Leo

    1999-05-01

    We propose an abstract concept of data fusion based on finite automata and fuzzy sets to integrate and evaluate different sources of information, in particular results of multiple image segmentation procedures. We give an example of how the method may be applied to the problem of mammographic image segmentation to combine results of region growing and closed- contour detection techniques. We further propose a measure of fuzziness to assess the agreement between a segmented region and a reference contour. Results of application to breast tumor detection in mammograms indicate that the fusion results agree with reference contours provided by a radiologist to a higher extent than the results of the individual methods.

  11. Refining image segmentation by integration of edge and region data

    NASA Technical Reports Server (NTRS)

    Le Moigne, Jacqueline; Tilton, James C.

    1992-01-01

    An iterative parallel region growing (IPRG) algorithm previously developed by Tilton (1989) produces hierarchical segmentations of images from finer to coarser resolution. An ideal segmentation does not always correspond to one single iteration but to several different ones, each one producing the 'best' result for a separate part of the image. With the goal of finding this ideal segmentation, the results of the IPRG algorithm are refined by utilizing some additional information, such as edge features, and by interpreting the tree of hierarchical regions.

  12. A segmentation algorithm of intracranial hemorrhage CT image

    NASA Astrophysics Data System (ADS)

    Wang, Haibo; Chen, Zhiguo; Wang, Jianzhi

    2011-10-01

    To develop a computer aided detection (CAD) system that improves diagnostic accuracy of intracranial hemorrhage on cerebral CT. A method for CT image segmentation of brain is proposed, with which, several regions that are suspicious of hemorrhage can be segmented rapidly and effectively. Extracting intracranial area algorithm is introduced firstly to extract intracranial area. Secondly, FCM is employed twice, we named it with TFCM. FCM is first employed to identify areas of intracranial hemorrhage. Finally, FCM is employed to segment the lesions. Experimental results on real medical images demonstrate the efficiency and effectiveness.

  13. Semi-automatic segmentation of major aorto-pulmonary collateral arteries (MAPCAs) for image guided procedures

    NASA Astrophysics Data System (ADS)

    Rivest-Hénault, David; Duong, Luc; Lapierre, Chantale; Desch"nes, Sylvain; Cheriet, Mohamed

    2010-02-01

    Manual segmentation of pre-operative volumetric dataset is generally time consuming and results are subject to large inter-user variabilities. Level-set methods have been proposed to improve segmentation consistency by finding interactively the segmentation boundaries with respect to some priors. However, in thin and elongated structures, such as major aorto-pulmonary collateral arteries (MAPCAs), edge-based level set methods might be subject to flooding whereas region-based level set methods may not be selective enough. The main contribution of this work is to propose a novel expert-guided technique for the segmentation of the aorta and of the attached MAPCAs that is resilient to flooding while keeping the localization properties of an edge-based level set method. In practice, a two stages approach is used. First, the aorta is delineated by using manually inserted seed points at key locations and an automatic segmentation algorithm. The latter includes an intensity likelihood term that prevents leakage of the contour in regions of weak image gradients. Second, the origins of the MAPCAs are identified by using another set of seed points, then the MAPCAs' segmentation boundaries are evolved while being constrained by the aorta segmentation. This prevents the aorta to interfere with the segmentation of the MAPCAs. Our preliminary results are promising and constitute an indication that an accurate segmentation of the aorta and MAPCAs can be obtained with reasonable amount of effort.

  14. Accurate statistical associating fluid theory for chain molecules formed from Mie segments.

    PubMed

    Lafitte, Thomas; Apostolakou, Anastasia; Avendaño, Carlos; Galindo, Amparo; Adjiman, Claire S; Müller, Erich A; Jackson, George

    2013-10-21

    A highly accurate equation of state (EOS) for chain molecules formed from spherical segments interacting through Mie potentials (i.e., a generalized Lennard-Jones form with variable repulsive and attractive exponents) is presented. The quality of the theoretical description of the vapour-liquid equilibria (coexistence densities and vapour pressures) and the second-derivative thermophysical properties (heat capacities, isobaric thermal expansivities, and speed of sound) are critically assessed by comparison with molecular simulation and with experimental data of representative real substances. Our new EOS represents a notable improvement with respect to previous versions of the statistical associating fluid theory for variable range interactions (SAFT-VR) of the generic Mie form. The approach makes rigorous use of the Barker and Henderson high-temperature perturbation expansion up to third order in the free energy of the monomer Mie system. The radial distribution function of the reference monomer fluid, which is a prerequisite for the representation of the properties of the fluid of Mie chains within a Wertheim first-order thermodynamic perturbation theory (TPT1), is calculated from a second-order expansion. The resulting SAFT-VR Mie EOS can now be applied to molecular fluids characterized by a broad range of interactions spanning from soft to very repulsive and short-ranged Mie potentials. A good representation of the corresponding molecular-simulation data is achieved for model monomer and chain fluids. When applied to the particular case of the ubiquitous Lennard-Jones potential, our rigorous description of the thermodynamic properties is of equivalent quality to that obtained with the empirical EOSs for LJ monomer (EOS of Johnson et al.) and LJ chain (soft-SAFT) fluids. A key feature of our reformulated SAFT-VR approach is the greatly enhanced accuracy in the near-critical region for chain molecules. This attribute, combined with the accurate modeling of second

  15. Study on the application of MRF and the D-S theory to image segmentation of the human brain and quantitative analysis of the brain tissue

    NASA Astrophysics Data System (ADS)

    Guan, Yihong; Luo, Yatao; Yang, Tao; Qiu, Lei; Li, Junchang

    2012-01-01

    The features of the spatial information of Markov random field image was used in image segmentation. It can effectively remove the noise, and get a more accurate segmentation results. Based on the fuzziness and clustering of pixel grayscale information, we find clustering center of the medical image different organizations and background through Fuzzy cmeans clustering method. Then we find each threshold point of multi-threshold segmentation through two dimensional histogram method, and segment it. The features of fusing multivariate information based on the Dempster-Shafer evidence theory, getting image fusion and segmentation. This paper will adopt the above three theories to propose a new human brain image segmentation method. Experimental result shows that the segmentation result is more in line with human vision, and is of vital significance to accurate analysis and application of tissues.

  16. Region-Based Convolutional Networks for Accurate Object Detection and Segmentation.

    PubMed

    Girshick, Ross; Donahue, Jeff; Darrell, Trevor; Malik, Jitendra

    2016-01-01

    Object detection performance, as measured on the canonical PASCAL VOC Challenge datasets, plateaued in the final years of the competition. The best-performing methods were complex ensemble systems that typically combined multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 50 percent relative to the previous best result on VOC 2012-achieving a mAP of 62.4 percent. Our approach combines two ideas: (1) one can apply high-capacity convolutional networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data are scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, boosts performance significantly. Since we combine region proposals with CNNs, we call the resulting model an R-CNN or Region-based Convolutional Network. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn. PMID:26656583

  17. Image segmentation using random-walks on the histogram

    NASA Astrophysics Data System (ADS)

    Morin, Jean-Philippe; Desrosiers, Christian; Duong, Luc

    2012-02-01

    This document presents a novel method for the problem of image segmentation, based on random-walks. This method shares similarities with the Mean-shift algorithm, as it finds the modes of the intensity histogram of images. However, unlike Mean-shift, our proposed method is stochastic and also provides class membership probabilities. Also, unlike other random-walk based methods, our approach does not require any form of user interaction, and can scale to very large images. To illustrate the usefulness, efficiency and scalability of our method, we test it on the task of segmenting anatomical structures present in cardiac CT and brain MRI images.

  18. Segmentation and image navigation in digitized spine x rays

    NASA Astrophysics Data System (ADS)

    Long, L. Rodney; Thoma, George R.

    2000-06-01

    The National Library of Medicine has archived a collection of 17,000 digitized x-rays of the cervical and lumbar spines. Extensive health information has been collected on the subjects of these x-rays, but no information has been derived from the image contents themselves. We are researching algorithms to segment anatomy in these images and to derive from the segmented data measurements useful for indexing this image set for characteristics important to researchers in rheumatology, bone morphometry, and related areas. Active Shape Modeling is currently being investigated for use in location and boundary definition for the vertebrae in these images.

  19. A skull segmentation method for brain MR images based on multiscale bilateral filtering scheme

    NASA Astrophysics Data System (ADS)

    Yang, Xiaofeng; Fei, Baowei

    2010-03-01

    We present a novel automatic segmentation method for the skull on brain MR images for attenuation correction in combined PET/MRI applications. Our method transforms T1-weighted MR images to the Radon domain and then detects the feature of the skull. In the Radon domain we use a bilateral filter to construct a multiscale images series. For the repeated convolution we increase the spatial smoothing at each scale and make the cumulative width of the spatial and range Gaussian doubled at each scale. Two filters with different kernels along the vertical direction are applied along the scales from the coarse to fine levels. The results from a coarse scale give a mask for the next fine scale and supervise the segmentation in the next fine scale. The method is robust for noise MR images because of its multiscale bilateral filtering scheme. After combining the two filtered sinogram, the reciprocal binary sinogram of the skull is obtained for the reconstruction of the skull image. We use the filtered back projection method to reconstruct the segmented skull image. We define six metrics to evaluate our segmentation method. The method has been tested with brain phantom data, simulated brain data, and real MRI data. Evaluation results showed that our method is robust and accurate, which is useful for skull segmentation and subsequently for attenuation correction in combined PET/MRI applications.

  20. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI.

    PubMed

    Li, Chunming; Huang, Rui; Ding, Zhaohua; Gatenby, J Chris; Metaxas, Dimitris N; Gore, John C

    2011-07-01

    Intensity inhomogeneity often occurs in real-world images, which presents a considerable challenge in image segmentation. The most widely used image segmentation algorithms are region-based and typically rely on the homogeneity of the image intensities in the regions of interest, which often fail to provide accurate segmentation results due to the intensity inhomogeneity. This paper proposes a novel region-based method for image segmentation, which is able to deal with intensity inhomogeneities in the segmentation. First, based on the model of images with intensity inhomogeneities, we derive a local intensity clustering property of the image intensities, and define a local clustering criterion function for the image intensities in a neighborhood of each point. This local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. In a level set formulation, this criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, by minimizing this energy, our method is able to simultaneously segment the image and estimate the bias field, and the estimated bias field can be used for intensity inhomogeneity correction (or bias correction). Our method has been validated on synthetic images and real images of various modalities, with desirable performance in the presence of intensity inhomogeneities. Experiments show that our method is more robust to initialization, faster and more accurate than the well-known piecewise smooth model. As an application, our method has been used for segmentation and bias correction of magnetic resonance (MR) images with promising results. PMID:21518662

  1. Coadding Techniques for Image-based Wavefront Sensing for Segmented-mirror Telescopes

    NASA Technical Reports Server (NTRS)

    Smith, Scott; Aronstein, David; Dean, Bruce; Acton, Scott

    2007-01-01

    Image-based wavefront sensing algorithms are being used to characterize optical performance for a variety of current and planned astronomical telescopes. Phase retrieval recovers the optical wavefront that correlates to a series of diversity-defocused point-spread functions (PSFs), where multiple frames can be acquired at each defocus setting. Multiple frames of data can be coadded in different ways; two extremes are in "image-plane space," to average the frames for each defocused PSF and use phase retrieval once on the averaged images, or in "pupil-plane space," to use phase retrieval on every set of PSFs individually and average the resulting wavefronts. The choice of coadd methodology is particularly noteworthy for segmented-mirror telescopes that are subject to noise that causes uncorrelated motions between groups of segments. Using data collected on and simulations of the James Webb Space Telescope Testbed Telescope (TBT) commissioned at Ball Aerospace, we show how different sources of noise (uncorrelated segment jitter, turbulence, and common-mode noise) and different parts of the optical wavefront, segment and global aberrations, contribute to choosing the coadd method. Of particular interest, segment piston is more accurately recovered in "image-plane space" coadding, while segment tip/tilt is recovered in "pupil-plane space" coadding.

  2. Colour Image Segmentation Using Homogeneity Method and Data Fusion Techniques

    NASA Astrophysics Data System (ADS)

    Ben Chaabane, Salim; Sayadi, Mounir; Fnaiech, Farhat; Brassart, Eric

    2009-12-01

    A novel method of colour image segmentation based on fuzzy homogeneity and data fusion techniques is presented. The general idea of mass function estimation in the Dempster-Shafer evidence theory of the histogram is extended to the homogeneity domain. The fuzzy homogeneity vector is used to determine the fuzzy region in each primitive colour, whereas, the evidence theory is employed to merge different data sources in order to increase the quality of the information and to obtain an optimal segmented image. Segmentation results from the proposed method are validated and the classification accuracy for the test data available is evaluated, and then a comparative study versus existing techniques is presented. The experimental results demonstrate the superiority of introducing the fuzzy homogeneity method in evidence theory for image segmentation.

  3. Accelerated Gaussian mixture model and its application on image segmentation

    NASA Astrophysics Data System (ADS)

    Zhao, Jianhui; Zhang, Yuanyuan; Ding, Yihua; Long, Chengjiang; Yuan, Zhiyong; Zhang, Dengyi

    2013-03-01

    Gaussian mixture model (GMM) has been widely used for image segmentation in recent years due to its superior adaptability and simplicity of implementation. However, traditional GMM has the disadvantage of high computational complexity. In this paper an accelerated GMM is designed, for which the following approaches are adopted: establish the lookup table for Gaussian probability matrix to avoid the repetitive probability calculations on all pixels, employ the blocking detection method on each block of pixels to further decrease the complexity, change the structure of lookup table from 3D to 1D with more simple data type to reduce the space requirement. The accelerated GMM is applied on image segmentation with the help of OTSU method to decide the threshold value automatically. Our algorithm has been tested through image segmenting of flames and faces from a set of real pictures, and the experimental results prove its efficiency in segmentation precision and computational cost.

  4. Leaf image segmentation method based on multifractal detrended fluctuation analysis

    NASA Astrophysics Data System (ADS)

    Wang, Fang; Li, Jin-Wei; Shi, Wen; Liao, Gui-Ping

    2013-12-01

    To identify singular regions of crop leaf affected by diseases, based on multifractal detrended fluctuation analysis (MF-DFA), an image segmentation method is proposed. In the proposed method, first, we defend a new texture descriptor: local generalized Hurst exponent, recorded as LHq based on MF-DFA. And then, box-counting dimension f(LHq) is calculated for sub-images constituted by the LHq of some pixels, which come from a specific region. Consequently, series of f(LHq) of the different regions can be obtained. Finally, the singular regions are segmented according to the corresponding f(LHq). Six kinds of corn diseases leaf's images are tested in our experiments. Both the proposed method and other two segmentation methods—multifractal spectrum based and fuzzy C-means clustering have been compared in the experiments. The comparison results demonstrate that the proposed method can recognize the lesion regions more effectively and provide more robust segmentations.

  5. Segmentation method for in vivo meibomian gland OCT image

    NASA Astrophysics Data System (ADS)

    Shin, Jun Geun; Lee, Byeong Ha; Eom, Tae Joong

    2014-02-01

    We demonstrate segmentation of human MGs based on several image processing technic. 3D volumetric data of upper eyelid was acquired from real-time FD-OCT, and its acini area of MGs was segmented. Three dimensional volume informations of meibomian glands should be helpful to diagnose meibomian gland related disease. In order to reveal boundary between tarsal plate and acini, each B-scan images were obtained before averaged three times. Imaging area was 10x10mm and 700x1000x500 voxels. The acquisition time was 60ms for B-scan and 30sec for C-scan. The 3D data was flattened to remove curvature and axial vibration, and resized to reduce computational costs, and filtered to minimize speckles, and segmented. Marker based watershed transform was employed to segment each acini area of meibomian gland.

  6. Automatic image segmentation by dynamic region growth and multiresolution merging.

    PubMed

    Ugarriza, Luis Garcia; Saber, Eli; Vantaram, Sreenath Rao; Amuso, Vincent; Shaw, Mark; Bhaskar, Ranjit

    2009-10-01

    Image segmentation is a fundamental task in many computer vision applications. In this paper, we propose a new unsupervised color image segmentation algorithm, which exploits the information obtained from detecting edges in color images in the CIE L *a *b * color space. To this effect, by using a color gradient detection technique, pixels without edges are clustered and labeled individually to identify some initial portion of the input image content. Elements that contain higher gradient densities are included by the dynamic generation of clusters as the algorithm progresses. Texture modeling is performed by color quantization and local entropy computation of the quantized image. The obtained texture and color information along with a region growth map consisting of all fully grown regions are used to perform a unique multiresolution merging procedure to blend regions with similar characteristics. Experimental results obtained in comparison to published segmentation techniques demonstrate the performance advantages of the proposed method. PMID:19535323

  7. Image mosaic method based on SIFT features of line segment.

    PubMed

    Zhu, Jun; Ren, Mingwu

    2014-01-01

    This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform) feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling. PMID:24511326

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

    NASA Astrophysics Data System (ADS)

    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.

  9. Automatic segmentation of MR images using self-organizing feature mapping and neural networks

    NASA Astrophysics Data System (ADS)

    Alirezaie, Javad; Jernigan, M. Ed; Nahmias, Claude

    1997-04-01

    In this paper we present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Our scheme utilizes the self-organizing feature map (SOFM) artificial neural network (ANN) for feature mapping and generates a set of codebook vectors for each tissue class. Features are selected from three image spectra: T1, T2 and proton density (PD) weighted images. An algorithm has been developed for isolating the cerebrum from the head scan prior to the segmentation. To classify the map, we extend the network by adding an associative layer. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. Any unclassified tissues were remained as unknown tissue class.

  10. Performance benchmarking of liver CT image segmentation and volume estimation

    NASA Astrophysics Data System (ADS)

    Xiong, Wei; Zhou, Jiayin; Tian, Qi; Liu, Jimmy J.; Qi, Yingyi; Leow, Wee Kheng; Han, Thazin; Wang, Shih-chang

    2008-03-01

    In recent years more and more computer aided diagnosis (CAD) systems are being used routinely in hospitals. Image-based knowledge discovery plays important roles in many CAD applications, which have great potential to be integrated into the next-generation picture archiving and communication systems (PACS). Robust medical image segmentation tools are essentials for such discovery in many CAD applications. In this paper we present a platform with necessary tools for performance benchmarking for algorithms of liver segmentation and volume estimation used for liver transplantation planning. It includes an abdominal computer tomography (CT) image database (DB), annotation tools, a ground truth DB, and performance measure protocols. The proposed architecture is generic and can be used for other organs and imaging modalities. In the current study, approximately 70 sets of abdominal CT images with normal livers have been collected and a user-friendly annotation tool is developed to generate ground truth data for a variety of organs, including 2D contours of liver, two kidneys, spleen, aorta and spinal canal. Abdominal organ segmentation algorithms using 2D atlases and 3D probabilistic atlases can be evaluated on the platform. Preliminary benchmark results from the liver segmentation algorithms which make use of statistical knowledge extracted from the abdominal CT image DB are also reported. We target to increase the CT scans to about 300 sets in the near future and plan to make the DBs built available to medical imaging research community for performance benchmarking of liver segmentation algorithms.

  11. Vectorized image segmentation via trixel agglomeration

    DOEpatents

    Prasad, Lakshman; Skourikhine, Alexei N.

    2006-10-24

    A computer implemented method transforms an image comprised of pixels into a vectorized image specified by a plurality of polygons that can be subsequently used to aid in image processing and understanding. The pixelated image is processed to extract edge pixels that separate different colors and a constrained Delaunay triangulation of the edge pixels forms a plurality of triangles having edges that cover the pixelated image. A color for each one of the plurality of triangles is determined from the color pixels within each triangle. A filter is formed with a set of grouping rules related to features of the pixelated image and applied to the plurality of triangle edges to merge adjacent triangles consistent with the filter into polygons having a plurality of vertices. The pixelated image may be then reformed into an array of the polygons, that can be represented collectively and efficiently by standard vector image.

  12. Image segmentation for automated dental identification

    NASA Astrophysics Data System (ADS)

    Haj Said, Eyad; Nassar, Diaa Eldin M.; Ammar, Hany H.

    2006-02-01

    Dental features are one of few biometric identifiers that qualify for postmortem identification; therefore, creation of an Automated Dental Identification System (ADIS) with goals and objectives similar to the Automated Fingerprint Identification System (AFIS) has received increased attention. As a part of ADIS, teeth segmentation from dental radiographs films is an essential step in the identification process. In this paper, we introduce a fully automated approach for teeth segmentation with goal to extract at least one tooth from the dental radiograph film. We evaluate our approach based on theoretical and empirical basis, and we compare its performance with the performance of other approaches introduced in the literature. The results show that our approach exhibits the lowest failure rate and the highest optimality among all full automated approaches introduced in the literature.

  13. Generalized method for partial volume estimation and tissue segmentation in cerebral magnetic resonance images

    PubMed Central

    Khademi, April; Venetsanopoulos, Anastasios; Moody, Alan R.

    2014-01-01

    Abstract. An artifact found in magnetic resonance images (MRI) called partial volume averaging (PVA) has received much attention since accurate segmentation of cerebral anatomy and pathology is impeded by this artifact. Traditional neurological segmentation techniques rely on Gaussian mixture models to handle noise and PVA, or high-dimensional feature sets that exploit redundancy in multispectral datasets. Unfortunately, model-based techniques may not be optimal for images with non-Gaussian noise distributions and/or pathology, and multispectral techniques model probabilities instead of the partial volume (PV) fraction. For robust segmentation, a PV fraction estimation approach is developed for cerebral MRI that does not depend on predetermined intensity distribution models or multispectral scans. Instead, the PV fraction is estimated directly from each image using an adaptively defined global edge map constructed by exploiting a relationship between edge content and PVA. The final PVA map is used to segment anatomy and pathology with subvoxel accuracy. Validation on simulated and real, pathology-free T1 MRI (Gaussian noise), as well as pathological fluid attenuation inversion recovery MRI (non-Gaussian noise), demonstrate that the PV fraction is accurately estimated and the resultant segmentation is robust. Comparison to model-based methods further highlight the benefits of the current approach. PMID:26158022

  14. Colony image acquisition and genetic segmentation algorithm and colony analyses

    NASA Astrophysics Data System (ADS)

    Wang, W. X.

    2012-01-01

    Colony anaysis is used in a large number of engineerings such as food, dairy, beverages, hygiene, environmental monitoring, water, toxicology, sterility testing. In order to reduce laboring and increase analysis acuracy, many researchers and developers have made efforts for image analysis systems. The main problems in the systems are image acquisition, image segmentation and image analysis. In this paper, to acquire colony images with good quality, an illumination box was constructed. In the box, the distances between lights and dishe, camra lens and lights, and camera lens and dishe are adjusted optimally. In image segmentation, It is based on a genetic approach that allow one to consider the segmentation problem as a global optimization,. After image pre-processing and image segmentation, the colony analyses are perfomed. The colony image analysis consists of (1) basic colony parameter measurements; (2) colony size analysis; (3) colony shape analysis; and (4) colony surface measurements. All the above visual colony parameters can be selected and combined together, used to make a new engineeing parameters. The colony analysis can be applied into different applications.

  15. 3D ultrasound image segmentation using wavelet support vector machines

    PubMed Central

    Akbari, Hamed; Fei, Baowei

    2012-01-01

    Purpose: Transrectal ultrasound (TRUS) imaging is clinically used in prostate biopsy and therapy. Segmentation of the prostate on TRUS images has many applications. In this study, a three-dimensional (3D) segmentation method for TRUS images of the prostate is presented for 3D ultrasound-guided biopsy. Methods: This segmentation method utilizes a statistical shape, texture information, and intensity profiles. A set of wavelet support vector machines (W-SVMs) is applied to the images at various subregions of the prostate. The W-SVMs are trained to adaptively capture the features of the ultrasound images in order to differentiate the prostate and nonprostate tissue. This method consists of a set of wavelet transforms for extraction of prostate texture features and a kernel-based support vector machine to classify the textures. The voxels around the surface of the prostate are labeled in sagittal, coronal, and transverse planes. The weight functions are defined for each labeled voxel on each plane and on the model at each region. In the 3D segmentation procedure, the intensity profiles around the boundary between the tentatively labeled prostate and nonprostate tissue are compared to the prostate model. Consequently, the surfaces are modified based on the model intensity profiles. The segmented prostate is updated and compared to the shape model. These two steps are repeated until they converge. Manual segmentation of the prostate serves as the gold standard and a variety of methods are used to evaluate the performance of the segmentation method. Results: The results from 40 TRUS image volumes of 20 patients show that the Dice overlap ratio is 90.3% ± 2.3% and that the sensitivity is 87.7% ± 4.9%. Conclusions: The proposed method provides a useful tool in our 3D ultrasound image-guided prostate biopsy and can also be applied to other applications in the prostate. PMID:22755682

  16. Segmentation of neonatal brain MR images using patch-driven level sets.

    PubMed

    Wang, Li; Shi, Feng; Li, Gang; Gao, Yaozong; Lin, Weili; Gilmore, John H; Shen, Dinggang

    2014-01-01

    The segmentation of neonatal brain MR image into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), is challenging due to the low spatial resolution, severe partial volume effect, high image noise, and dynamic myelination and maturation processes. Atlas-based methods have been widely used for guiding neonatal brain segmentation. Existing brain atlases were generally constructed by equally averaging all the aligned template images from a population. However, such population-based atlases might not be representative of a testing subject in the regions with high inter-subject variability and thus often lead to a low capability in guiding segmentation in those regions. Recently, patch-based sparse representation techniques have been proposed to effectively select the most relevant elements from a large group of candidates, which can be used to generate a subject-specific representation with rich local anatomical details for guiding the segmentation. Accordingly, in this paper, we propose a novel patch-driven level set method for the segmentation of neonatal brain MR images by taking advantage of sparse representation techniques. Specifically, we first build a subject-specific atlas from a library of aligned, manually segmented images by using sparse representation in a patch-based fashion. Then, the spatial consistency in the probability maps from the subject-specific atlas is further enforced by considering the similarities of a patch with its neighboring patches. Finally, the probability maps are integrated into a coupled level set framework for more accurate segmentation. The proposed method has been extensively evaluated on 20 training subjects using leave-one-out cross validation, and also on 132 additional testing subjects. Our method achieved a high accuracy of 0.919±0.008 for white matter and 0.901±0.005 for gray matter, respectively, measured by Dice ratio for the overlap between the automated and manual segmentations in the cortical region

  17. In Situ Casting and Imaging of the Rat Airway Tree for Accurate 3D Reconstruction

    SciTech Connect

    Jacob, Rick E.; Colby, Sean M.; Kabilan, Senthil; Einstein, Daniel R.; Carson, James P.

    2013-08-01

    The use of anatomically accurate, animal-specific airway geometries is important for understanding and modeling the physiology of the respiratory system. One approach for acquiring detailed airway architecture is to create a bronchial cast of the conducting airways. However, typical casting procedures either do not faithfully preserve the in vivo branching angles, or produce rigid casts that when removed for imaging are fragile and thus easily damaged. We address these problems by creating an in situ bronchial cast of the conducting airways in rats that can be subsequently imaged in situ using 3D micro-CT imaging. We also demonstrate that deformations in airway branch angles resulting from the casting procedure are small, and that these angle deformations can be reversed through an interactive adjustment of the segmented cast geometry. Animal work was approved by the Institutional Animal Care and Use Committee of Pacific Northwest National Laboratory.

  18. Computer-aided kidney segmentation on abdominal CT images.

    PubMed

    Lin, Daw-Tung; Lei, Chung-Chih; Hung, Siu-Wan

    2006-01-01

    In this paper, an effective model-based approach for computer-aided kidney segmentation of abdominal CT images with anatomic structure consideration is presented. This automatic segmentation system is expected to assist physicians in both clinical diagnosis and educational training. The proposed method is a coarse to fine segmentation approach divided into two stages. First, the candidate kidney region is extracted according to the statistical geometric location of kidney within the abdomen. This approach is applicable to images of different sizes by using the relative distance of the kidney region to the spine. The second stage identifies the kidney by a series of image processing operations. The main elements of the proposed system are: 1) the location of the spine is used as the landmark for coordinate references; 2) elliptic candidate kidney region extraction with progressive positioning on the consecutive CT images; 3) novel directional model for a more reliable kidney region seed point identification; and 4) adaptive region growing controlled by the properties of image homogeneity. In addition, in order to provide different views for the physicians, we have implemented a visualization tool that will automatically show the renal contour through the method of second-order neighborhood edge detection. We considered segmentation of kidney regions from CT scans that contain pathologies in clinical practice. The results of a series of tests on 358 images from 30 patients indicate an average correlation coefficient of up to 88% between automatic and manual segmentation. PMID:16445250

  19. Image segmentation by hierarchial agglomeration of polygons using ecological statistics

    DOEpatents

    Prasad, Lakshman; Swaminarayan, Sriram

    2013-04-23

    A method for rapid hierarchical image segmentation based on perceptually driven contour completion and scene statistics is disclosed. The method begins with an initial fine-scale segmentation of an image, such as obtained by perceptual completion of partial contours into polygonal regions using region-contour correspondences established by Delaunay triangulation of edge pixels as implemented in VISTA. The resulting polygons are analyzed with respect to their size and color/intensity distributions and the structural properties of their boundaries. Statistical estimates of granularity of size, similarity of color, texture, and saliency of intervening boundaries are computed and formulated into logical (Boolean) predicates. The combined satisfiability of these Boolean predicates by a pair of adjacent polygons at a given segmentation level qualifies them for merging into a larger polygon representing a coarser, larger-scale feature of the pixel image and collectively obtains the next level of polygonal segments in a hierarchy of fine-to-coarse segmentations. The iterative application of this process precipitates textured regions as polygons with highly convolved boundaries and helps distinguish them from objects which typically have more regular boundaries. The method yields a multiscale decomposition of an image into constituent features that enjoy a hierarchical relationship with features at finer and coarser scales. This provides a traversable graph structure from which feature content and context in terms of other features can be derived, aiding in automated image understanding tasks. The method disclosed is highly efficient and can be used to decompose and analyze large images.

  20. Digital rock physics benchmarks—Part I: Imaging and segmentation

    NASA Astrophysics Data System (ADS)

    Andrä, Heiko; Combaret, Nicolas; Dvorkin, Jack; Glatt, Erik; Han, Junehee; Kabel, Matthias; Keehm, Youngseuk; Krzikalla, Fabian; Lee, Minhui; Madonna, Claudio; Marsh, Mike; Mukerji, Tapan; Saenger, Erik H.; Sain, Ratnanabha; Saxena, Nishank; Ricker, Sarah; Wiegmann, Andreas; Zhan, Xin

    2013-01-01

    The key paradigm of digital rock physics (DRP) "image and compute" implies imaging and digitizing the pore space and mineral matrix of natural rock and then numerically simulating various physical processes in this digital object to obtain such macroscopic rock properties as permeability, electrical conductivity, and elastic moduli. The steps of this process include image acquisition, image processing (noise reduction, smoothing, and segmentation); setting up the numerical experiment (object size and resolution as well as the boundary conditions); and numerically solving the field equations. Finally, we need to interpret the solution thus obtained in terms of the desired macroscopic properties. For each of these DRP steps, there is more than one method and implementation. Our goal is to explore and record the variability of the computed effective properties as a function of using different tools and workflows. Such benchmarking is the topic of the two present companion papers. Here, in the first part, we introduce four 3D microstructures, a segmented Fontainebleau sandstone sample (porosity 0.147), a gray-scale Berea sample; a gray-scale Grosmont carbonate sample; and a numerically constructed pack of solid spheres (porosity 0.343). Segmentation of the gray-scale images by three independent teams reveals the uncertainty of this process: the segmented porosity range is between 0.184 and 0.209 for Berea and between 0.195 and 0.271 for the carbonate. The implications of the uncertainty associated with image segmentation are explored in a second paper.

  1. Tissue Probability Map Constrained 4-D Clustering Algorithm for Increased Accuracy and Robustness in Serial MR Brain Image Segmentation

    PubMed Central

    Xue, Zhong; Shen, Dinggang; Li, Hai; Wong, Stephen

    2010-01-01

    The traditional fuzzy clustering algorithm and its extensions have been successfully applied in medical image segmentation. However, because of the variability of tissues and anatomical structures, the clustering results might be biased by the tissue population and intensity differences. For example, clustering-based algorithms tend to over-segment white matter tissues of MR brain images. To solve this problem, we introduce a tissue probability map constrained clustering algorithm and apply it to serial MR brain image segmentation, i.e., a series of 3-D MR brain images of the same subject at different time points. Using the new serial image segmentation algorithm in the framework of the CLASSIC framework, which iteratively segments the images and estimates the longitudinal deformations, we improved both accuracy and robustness for serial image computing, and at the mean time produced longitudinally consistent segmentation and stable measures. In the algorithm, the tissue probability maps consist of both the population-based and subject-specific segmentation priors. Experimental study using both simulated longitudinal MR brain data and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data confirmed that using both priors more accurate and robust segmentation results can be obtained. The proposed algorithm can be applied in longitudinal follow up studies of MR brain imaging with subtle morphological changes for neurological disorders. PMID:26566399

  2. Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study.

    PubMed

    Polan, Daniel F; Brady, Samuel L; Kaufman, Robert A

    2016-09-01

    patient image sections, were analyzed. The automated algorithm produced segmentation of seven material classes with a median DSC of 0.86  ±  0.03 for pediatric patient protocols, and 0.85  ±  0.04 for adult patient protocols. Additionally, 100 randomly selected patient examinations were segmented and analyzed, and a mean sensitivity of 0.91 (range: 0.82-0.98), specificity of 0.89 (range: 0.70-0.98), and accuracy of 0.90 (range: 0.76-0.98) were demonstrated. In this study, we demonstrate that this fully automated segmentation tool was able to produce fast and accurate segmentation of the neck and trunk of the body over a wide range of patient habitus and scan parameters. PMID:27530679

  3. Segmentation of vertebral bodies in CT and MR images based on 3D deterministic models

    NASA Astrophysics Data System (ADS)

    Štern, Darko; Vrtovec, Tomaž; Pernuš, Franjo; Likar, Boštjan

    2011-03-01

    The evaluation of vertebral deformations is of great importance in clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is oriented towards the computed tomography (CT) and magnetic resonance (MR) imaging techniques, as they can provide a detailed 3D representation of vertebrae, the established methods for the evaluation of vertebral deformations still provide only a two-dimensional (2D) geometrical description. Segmentation of vertebrae in 3D may therefore not only improve their visualization, but also provide reliable and accurate 3D measurements of vertebral deformations. In this paper we propose a method for 3D segmentation of individual vertebral bodies that can be performed in CT and MR images. Initialized with a single point inside the vertebral body, the segmentation is performed by optimizing the parameters of a 3D deterministic model of the vertebral body to achieve the best match of the model to the vertebral body in the image. The performance of the proposed method was evaluated on five CT (40 vertebrae) and five T2-weighted MR (40 vertebrae) spine images, among them five are normal and five are pathological. The results show that the proposed method can be used for 3D segmentation of vertebral bodies in CT and MR images and that the proposed model can describe a variety of vertebral body shapes. The method may be therefore used for initializing whole vertebra segmentation or reliably describing vertebral body deformations.

  4. Segmentation of pomegranate MR images using spatial fuzzy c-means (SFCM) algorithm

    NASA Astrophysics Data System (ADS)

    Moradi, Ghobad; Shamsi, Mousa; Sedaaghi, M. H.; Alsharif, M. R.

    2011-10-01

    Segmentation is one of the fundamental issues of image processing and machine vision. It plays a prominent role in a variety of image processing applications. In this paper, one of the most important applications of image processing in MRI segmentation of pomegranate is explored. Pomegranate is a fruit with pharmacological properties such as being anti-viral and anti-cancer. Having a high quality product in hand would be critical factor in its marketing. The internal quality of the product is comprehensively important in the sorting process. The determination of qualitative features cannot be manually made. Therefore, the segmentation of the internal structures of the fruit needs to be performed as accurately as possible in presence of noise. Fuzzy c-means (FCM) algorithm is noise-sensitive and pixels with noise are classified inversely. As a solution, in this paper, the spatial FCM algorithm in pomegranate MR images' segmentation is proposed. The algorithm is performed with setting the spatial neighborhood information in FCM and modification of fuzzy membership function for each class. The segmentation algorithm results on the original and the corrupted Pomegranate MR images by Gaussian, Salt Pepper and Speckle noises show that the SFCM algorithm operates much more significantly than FCM algorithm. Also, after diverse steps of qualitative and quantitative analysis, we have concluded that the SFCM algorithm with 5×5 window size is better than the other windows.

  5. PombeX: robust cell segmentation for fission yeast transillumination images.

    PubMed

    Peng, Jyh-Ying; Chen, Yen-Jen; Green, Marc D; Sabatinos, Sarah A; Forsburg, Susan L; Hsu, Chun-Nan

    2013-01-01

    Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo. Performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of a large amount of images, the first step of which requires robust segmentation of the cell. We developed a segmentation system, PombeX, that can segment cells from transmitted illumination images with focus gradient and varying contrast. Corrections for focus gradient are applied to the image to aid in accurate detection of cell membrane and cytoplasm pixels, which is used to generate initial contours for cells. Gradient vector flow snake evolution is used to obtain the final cell contours. Finally, a machine learning-based validation of cell contours removes most incorrect or spurious contours. Quantitative evaluations show overall good segmentation performance on a large set of images, regardless of differences in image quality, lighting condition, focus condition and phenotypic profile. Comparisons with recent related methods for yeast cells show that PombeX outperforms current methods, both in terms of segmentation accuracy and computational speed. PMID:24353754

  6. Supervised segmentation methods for the hippocampus in MR images

    NASA Astrophysics Data System (ADS)

    van Stralen, Marijn; Geerlings, Mirjam I.; Vincken, Koen L.; Pluim, Josien P. W.

    2011-03-01

    This study compares three different types of fully automated supervised methods for segmentation of the hippocampus in MR images. Many of such methods, trained using example data, have been presented for various medical imaging applications, but comparison of the methods is obscured because of optimization for, and evaluation on, different data. We compare three methods based on different methodological bases: atlas-based segmentation (ABS), active appearance model segmentation (AAM) and k-nearest neighbor voxel classification (KNN). All three methods are trained on 100 T1-weighted images with manual segmentations of the right hippocampus, and applied to 103 different images from the same study. Straightforward implementation of each of the three methods resulted in competitive segmentations, both mutually, as compared with methods currently reported in literature. AAM and KNN are favorable in terms of computational costs, requiring only a fraction of the time needed for ABS. The high accuracy and low computational cost make KNN the most favorable method based on this study. AAM achieves similar results as ABS in significantly less computation time. Further improvements might be achieved by fusion of the presented techniques, either methodologically or by direct fusion of the segmentation results.

  7. New AIRS: The medical imaging software for segmentation and registration of elastic organs in SPECT/CT

    NASA Astrophysics Data System (ADS)

    Widita, R.; Kurniadi, R.; Darma, Y.; Perkasa, Y. S.; Trianti, N.

    2012-06-01

    We have been successfully improved our software, Automated Image Registration and Segmentation (AIRS), to fuse the CT and SPECT images of elastic organs. Segmentation and registration of elastic organs presents many challenges. Many artifacts can arise in SPECT/CT scans. Also, different organs and tissues have very similar gray levels, which consign thresholding to limited utility. We have been developed a new software to solve different registration and segmentation problems that arises in tomographic data sets. It will be demonstrated that the information obtained by SPECT/CT is more accurate in evaluating patients/objects than that obtained from either SPECT or CT alone. We used multi-modality registration which is amenable for images produced by different modalities and having unclear boundaries between tissues. The segmentation components used in this software is region growing algorithms which have proven to be an effective approach for image segmentation. Our method is designed to perform with clinically acceptable speed, using accelerated techniques (multiresolution).

  8. Leukocyte segmentation and classification in blood-smear images.

    PubMed

    Ramoser, Herbert; Laurain, Vincent; Bischof, Horst; Ecker, Rupert

    2005-01-01

    The detection and classification of leukocytes in blood smear images is a routine task in medical diagnosis. In this paper we present a fully automated approach to leukocyte segmentation that is robust with respect to cell appearance and image quality. A set of features is used to describe cytoplasm and nucleus properties. Pairwise SVM classification is used to discriminate between different cell types. Evaluation on a set of 1166 images (13 classes) resulted in 95% correct segmentations and 75% to 99% correct classification (with reject option). PMID:17280945

  9. Color image segmentation considering human sensitivity for color pattern variations

    NASA Astrophysics Data System (ADS)

    Yoon, Kuk-Jin; Kweon, In-So

    2001-10-01

    Color image segmentation plays an important role in the computer vision and image processing area. In this paper, we propose a novel color image segmentation algorithm in consideration of human visual sensitivity for color pattern variations by generalizing K-means clustering. Human visual system has different color perception sensitivity according to the spatial color pattern variation. To reflect this effect, we define the CCM (Color Complexity Measure) by calculating the absolute deviation with Gaussian weighting within the local mask and assign weight value to each color vector using the CCM values.

  10. Fuzzy object models for newborn brain MR image segmentation

    NASA Astrophysics Data System (ADS)

    Kobashi, Syoji; Udupa, Jayaram K.

    2013-03-01

    Newborn brain MR image segmentation is a challenging problem because of variety of size, shape and MR signal although it is the fundamental study for quantitative radiology in brain MR images. Because of the large difference between the adult brain and the newborn brain, it is difficult to directly apply the conventional methods for the newborn brain. Inspired by the original fuzzy object model introduced by Udupa et al. at SPIE Medical Imaging 2011, called fuzzy shape object model (FSOM) here, this paper introduces fuzzy intensity object model (FIOM), and proposes a new image segmentation method which combines the FSOM and FIOM into fuzzy connected (FC) image segmentation. The fuzzy object models are built from training datasets in which the cerebral parenchyma is delineated by experts. After registering FSOM with the evaluating image, the proposed method roughly recognizes the cerebral parenchyma region based on a prior knowledge of location, shape, and the MR signal given by the registered FSOM and FIOM. Then, FC image segmentation delineates the cerebral parenchyma using the fuzzy object models. The proposed method has been evaluated using 9 newborn brain MR images using the leave-one-out strategy. The revised age was between -1 and 2 months. Quantitative evaluation using false positive volume fraction (FPVF) and false negative volume fraction (FNVF) has been conducted. Using the evaluation data, a FPVF of 0.75% and FNVF of 3.75% were achieved. More data collection and testing are underway.

  11. The use of the Kalman filter in the automated segmentation of EIT lung images.

    PubMed

    Zifan, A; Liatsis, P; Chapman, B E

    2013-06-01

    In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging. PMID:23719169

  12. A deformable model-based minimal path segmentation method for kidney MR images

    NASA Astrophysics Data System (ADS)

    Li, Ke; Fei, Baowei

    2008-03-01

    We developed a new minimal path segmentation method for mouse kidney MR images. We used dynamic programming and a minimal path segmentation approach to detect the optimal path within a weighted graph between two end points. The energy function combines distance and gradient information to guide the marching curve and thus to evaluate the best path and to span a broken edge. An algorithm was developed to automatically place initial end points. Dynamic programming was used to automatically optimize and update end points during the searching procedure. Principle component analysis (PCA) was used to generate a deformable model, which serves as the prior knowledge for the selection of initial end points and for the evaluation of the best path. The method has been tested for kidney MR images acquired from 44 mice. To quantitatively assess the automatic segmentation method, we compared the results with manual segmentation. The mean and standard deviation of the overlap ratios are 95.19%+/-0.03%. The distance error between the automatic and manual segmentation is 0.82+/-0.41 pixel. The automatic minimal path segmentation method is fast, accurate, and robust and it can be applied not only for kidney images but also for other organs.

  13. Segmenting CT prostate images using population and patient-specific statistics for radiotherapy

    SciTech Connect

    Feng, Qianjin; Foskey, Mark; Chen Wufan; Shen Dinggang

    2010-08-15

    Purpose: In the segmentation of sequential treatment-time CT prostate images acquired in image-guided radiotherapy, accurately capturing the intrapatient variation of the patient under therapy is more important than capturing interpatient variation. However, using the traditional deformable-model-based segmentation methods, it is difficult to capture intrapatient variation when the number of samples from the same patient is limited. This article presents a new deformable model, designed specifically for segmenting sequential CT images of the prostate, which leverages both population and patient-specific statistics to accurately capture the intrapatient variation of the patient under therapy. Methods: The novelty of the proposed method is twofold: First, a weighted combination of gradient and probability distribution function (PDF) features is used to build the appearance model to guide model deformation. The strengths of each feature type are emphasized by dynamically adjusting the weight between the profile-based gradient features and the local-region-based PDF features during the optimization process. An additional novel aspect of the gradient-based features is that, to alleviate the effect of feature inconsistency in the regions of gas and bone adjacent to the prostate, the optimal profile length at each landmark is calculated by statistically investigating the intensity profile in the training set. The resulting gradient-PDF combined feature produces more accurate and robust segmentations than general gradient features. Second, an online learning mechanism is used to build shape and appearance statistics for accurately capturing intrapatient variation. Results: The performance of the proposed method was evaluated on 306 images of the 24 patients. Compared to traditional gradient features, the proposed gradient-PDF combination features brought 5.2% increment in the success ratio of segmentation (from 94.1% to 99.3%). To evaluate the effectiveness of online

  14. Segmentation and quantification of adipose tissue by magnetic resonance imaging.

    PubMed

    Hu, Houchun Harry; Chen, Jun; Shen, Wei

    2016-04-01

    In this brief review, introductory concepts in animal and human adipose tissue segmentation using proton magnetic resonance imaging (MRI) and computed tomography are summarized in the context of obesity research. Adipose tissue segmentation and quantification using spin relaxation-based (e.g., T1-weighted, T2-weighted), relaxometry-based (e.g., T1-, T2-, T2*-mapping), chemical-shift selective, and chemical-shift encoded water-fat MRI pulse sequences are briefly discussed. The continuing interest to classify subcutaneous and visceral adipose tissue depots into smaller sub-depot compartments is mentioned. The use of a single slice, a stack of slices across a limited anatomical region, or a whole body protocol is considered. Common image post-processing steps and emerging atlas-based automated segmentation techniques are noted. Finally, the article identifies some directions of future research, including a discussion on the growing topic of brown adipose tissue and related segmentation considerations. PMID:26336839

  15. Supervised Evaluation of Image Segmentation and Object Proposal Techniques.

    PubMed

    Pont-Tuset, Jordi; Marques, Ferran

    2016-07-01

    This paper tackles the supervised evaluation of image segmentation and object proposal algorithms. It surveys, structures, and deduplicates the measures used to compare both segmentation results and object proposals with a ground truth database; and proposes a new measure: the precision-recall for objects and parts. To compare the quality of these measures, eight state-of-the-art object proposal techniques are analyzed and two quantitative meta-measures involving nine state of the art segmentation methods are presented. The meta-measures consist in assuming some plausible hypotheses about the results and assessing how well each measure reflects these hypotheses. As a conclusion of the performed experiments, this paper proposes the tandem of precision-recall curves for boundaries and for objects-and-parts as the tool of choice for the supervised evaluation of image segmentation. We make the datasets and code of all the measures publicly available. PMID:26415155

  16. Image segmentation using joint spatial-intensity-shape features: application to CT lung nodule segmentation

    NASA Astrophysics Data System (ADS)

    Ye, Xujiong; Siddique, Musib; Douiri, Abdel; Beddoe, Gareth; Slabaugh, Greg

    2009-02-01

    Automatic segmentation of medical images is a challenging problem due to the complexity and variability of human anatomy, poor contrast of the object being segmented, and noise resulting from the image acquisition process. This paper presents a novel feature-guided method for the segmentation of 3D medical lesions. The proposed algorithm combines 1) a volumetric shape feature (shape index) based on high-order partial derivatives; 2) mean shift clustering in a joint spatial-intensity-shape (JSIS) feature space; and 3) a modified expectation-maximization (MEM) algorithm on the mean shift mode map to merge the neighboring regions (modes). In such a scenario, the volumetric shape feature is integrated into the process of the segmentation algorithm. The joint spatial-intensity-shape features provide rich information for the segmentation of the anatomic structures or lesions (tumors). The proposed method has been evaluated on a clinical dataset of thoracic CT scans that contains 68 nodules. A volume overlap ratio between each segmented nodule and the ground truth annotation is calculated. Using the proposed method, the mean overlap ratio over all the nodules is 0.80. On visual inspection and using a quantitative evaluation, the experimental results demonstrate the potential of the proposed method. It can properly segment a variety of nodules including juxta-vascular and juxta-pleural nodules, which are challenging for conventional methods due to the high similarity of intensities between the nodules and their adjacent tissues. This approach could also be applied to lesion segmentation in other anatomies, such as polyps in the colon.

  17. A supervoxel-based segmentation method for prostate MR images

    NASA Astrophysics Data System (ADS)

    Tian, Zhiqiang; Liu, LiZhi; Fei, Baowei

    2015-03-01

    Accurate segmentation of the prostate has many applications in prostate cancer diagnosis and therapy. In this paper, we propose a "Supervoxel" based method for prostate segmentation. The prostate segmentation problem is considered as assigning a label to each supervoxel. An energy function with data and smoothness terms is used to model the labeling process. The data term estimates the likelihood of a supervoxel belongs to the prostate according to a shape feature. The geometric relationship between two neighboring supervoxels is used to construct a smoothness term. A threedimensional (3D) graph cut method is used to minimize the energy function in order to segment the prostate. A 3D level set is then used to get a smooth surface based on the output of the graph cut. The performance of the proposed segmentation algorithm was evaluated with respect to the manual segmentation ground truth. The experimental results on 12 prostate volumes showed that the proposed algorithm yields a mean Dice similarity coefficient of 86.9%+/-3.2%. The segmentation method can be used not only for the prostate but also for other organs.

  18. Segmentation Based Fuzzy Classification of High Resolution Images

    NASA Astrophysics Data System (ADS)

    Rao, Mukund; Rao, Suryaprakash; Masser, Ian; Kasturirangan, K.

    Information extraction from satellite images is the process of delineation of entities in the image which pertain to some feature on the earth and to which on associating an attribute, a classification of the image is obtained. Classification is a common technique to extract information from remote sensing data and, by and large, the common classification techniques mainly exploit the spectral characteristics of remote sensing images and attempt to detect patterns in spectral information to classify images. These are based on a per-pixel analysis of the spectral information, "clustering" or "grouping" of pixels is done to generate meaningful thematic information. Most of the classification techniques apply statistical pattern recognition of image spectral vectors to "label" each pixel with appropriate class information from a set of training information. On the other hand, Segmentation is not new, but it is yet seldom used in image processing of remotely sensed data. Although there has been a lot of development in segmentation of grey tone images in this field and other fields, like robotic vision, there has been little progress in segmentation of colour or multi-band imagery. Especially within the last two years many new segmentation algorithms as well as applications were developed, but not all of them lead to qualitatively convincing results while being robust and operational. One reason is that the segmentation of an image into a given number of regions is a problem with a huge number of possible solutions. Newer algorithms based on fractal approach could eventually revolutionize image processing of remotely sensed data. The paper looks at applying spatial concepts to image processing, paving the way to algorithmically formulate some more advanced aspects of cognition and inference. In GIS-based spatial analysis, vector-based tools already have been able to support advanced tasks generating new knowledge. By identifying objects (as segmentation results) from

  19. Segment fusion of ToF-SIMS images.

    PubMed

    Milillo, Tammy M; Miller, Mary E; Fischione, Remo; Montes, Angelina; Gardella, Joseph A

    2016-06-01

    The imaging capabilities of time-of-flight secondary ion mass spectrometry (ToF-SIMS) have not been used to their full potential in the analysis of polymer and biological samples. Imaging has been limited by the size of the dataset and the chemical complexity of the sample being imaged. Pixel and segment based image fusion algorithms commonly used in remote sensing, ecology, geography, and geology provide a way to improve spatial resolution and classification of biological images. In this study, a sample of Arabidopsis thaliana was treated with silver nanoparticles and imaged with ToF-SIMS. These images provide insight into the uptake mechanism for the silver nanoparticles into the plant tissue, giving new understanding to the mechanism of uptake of heavy metals in the environment. The Munechika algorithm was programmed in-house and applied to achieve pixel based fusion, which improved the spatial resolution of the image obtained. Multispectral and quadtree segment or region based fusion algorithms were performed using ecognition software, a commercially available remote sensing software suite, and used to classify the images. The Munechika fusion improved the spatial resolution for the images containing silver nanoparticles, while the segment fusion allowed classification and fusion based on the tissue types in the sample, suggesting potential pathways for the uptake of the silver nanoparticles. PMID:26746167

  20. Automatic needle segmentation in 3D ultrasound images using 3D improved Hough transform

    NASA Astrophysics Data System (ADS)

    Zhou, Hua; Qiu, Wu; Ding, Mingyue; Zhang, Songgen

    2008-03-01

    3D ultrasound (US) is a new technology that can be used for a variety of diagnostic applications, such as obstetrical, vascular, and urological imaging, and has been explored greatly potential in the applications of image-guided surgery and therapy. Uterine adenoma and uterine bleeding are the two most prevalent diseases in Chinese woman, and a minimally invasive ablation system using a needle-like RF button electrode is widely used to destroy tumor cells or stop bleeding. To avoid accidents or death of the patient by inaccurate localizations of the electrode and the tumor position during treatment, 3D US guidance system was developed. In this paper, a new automated technique, the 3D Improved Hough Transform (3DIHT) algorithm, which is potentially fast, accurate, and robust to provide needle segmentation in 3D US image for use of 3D US imaging guidance, was presented. Based on the coarse-fine search strategy and a four parameter representation of lines in 3D space, 3DIHT algorithm can segment needles quickly, accurately and robustly. The technique was evaluated using the 3D US images acquired by scanning a water phantom. The segmentation position deviation of the line was less than 2mm and angular deviation was much less than 2°. The average computational time measured on a Pentium IV 2.80GHz PC computer with a 381×381×250 image was less than 2s.

  1. Accurate reconstruction of hyperspectral images from compressive sensing measurements

    NASA Astrophysics Data System (ADS)

    Greer, John B.; Flake, J. C.

    2013-05-01

    The emerging field of Compressive Sensing (CS) provides a new way to capture data by shifting the heaviest burden of data collection from the sensor to the computer on the user-end. This new means of sensing requires fewer measurements for a given amount of information than traditional sensors. We investigate the efficacy of CS for capturing HyperSpectral Imagery (HSI) remotely. We also introduce a new family of algorithms for constructing HSI from CS measurements with Split Bregman Iteration [Goldstein and Osher,2009]. These algorithms combine spatial Total Variation (TV) with smoothing in the spectral dimension. We examine models for three different CS sensors: the Coded Aperture Snapshot Spectral Imager-Single Disperser (CASSI-SD) [Wagadarikar et al.,2008] and Dual Disperser (CASSI-DD) [Gehm et al.,2007] cameras, and a hypothetical random sensing model closer to CS theory, but not necessarily implementable with existing technology. We simulate the capture of remotely sensed images by applying the sensor forward models to well-known HSI scenes - an AVIRIS image of Cuprite, Nevada and the HYMAP Urban image. To measure accuracy of the CS models, we compare the scenes constructed with our new algorithm to the original AVIRIS and HYMAP cubes. The results demonstrate the possibility of accurately sensing HSI remotely with significantly fewer measurements than standard hyperspectral cameras.

  2. Automatic pulmonary vessel segmentation in 3D computed tomographic pulmonary angiographic (CTPA) images

    NASA Astrophysics Data System (ADS)

    Zhou, Chuan; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Patel, Smita; Cascade, Philip N.; Sahiner, Berkman; Wei, Jun; Ge, Jun; Kazerooni, Ella A.

    2006-03-01

    Automatic and accurate segmentation of the pulmonary vessels in 3D computed tomographic angiographic images (CTPA) is an essential step for computerized detection of pulmonary embolism (PE) because PEs only occur inside the pulmonary arteries. We are developing an automated method to segment the pulmonary vessels in 3D CTPA images. The lung region is first extracted using thresholding and morphological operations. 3D multiscale filters in combination with a newly developed response function derived from the eigenvalues of Hessian matrices are used to enhance all vascular structures including the vessel bifurcations and suppress non-vessel structures such as the lymphoid tissues surrounding the vessels. At each scale, a volume of interest (VOI) containing the response function value at each voxel is defined. The voxels with a high response indicate that there is an enhanced vessel whose size matches the given filter scale. A hierarchical expectation-maximization (EM) estimation is then applied to the VOI to segment the vessel by extracting the high response voxels at this single scale. The vessel tree is finally reconstructed by combining the segmented vessels at all scales based on a "connected component" analysis. Two experienced thoracic radiologists provided the gold standard of pulmonary arteries by manually tracking the arterial tree and marking the center of the vessels using a computer graphical user interface. Two CTPA cases containing PEs were used to evaluate the performance. One of these two cases also contained other lung diseases. The accuracy of vessel tree segmentation was evaluated by the percentage of the "gold standard" vessel center points overlapping with the segmented vessels. The result shows that 97.3% (1868/1920) and 92.0% (2277/2476) of the manually marked center points overlapped with the segmented vessels for the cases without and with other lung disease, respectively. The results demonstrate that vessel segmentation using our method is

  3. Bladder segmentation in MR images with watershed segmentation and graph cut algorithm

    NASA Astrophysics Data System (ADS)

    Blaffert, Thomas; Renisch, Steffen; Schadewaldt, Nicole; Schulz, Heinrich; Wiemker, Rafael

    2014-03-01

    Prostate and cervix cancer diagnosis and treatment planning that is based on MR images benefit from superior soft tissue contrast compared to CT images. For these images an automatic delineation of the prostate or cervix and the organs at risk such as the bladder is highly desirable. This paper describes a method for bladder segmentation that is based on a watershed transform on high image gradient values and gray value valleys together with the classification of watershed regions into bladder contents and tissue by a graph cut algorithm. The obtained results are superior if compared to a simple region-after-region classification.

  4. Improvement and Extension of Shape Evaluation Criteria in Multi-Scale Image Segmentation

    NASA Astrophysics Data System (ADS)

    Sakamoto, M.; Honda, Y.; Kondo, A.

    2016-06-01

    From the last decade, the multi-scale image segmentation is getting a particular interest and practically being used for object-based image analysis. In this study, we have addressed the issues on multi-scale image segmentation, especially, in improving the performances for validity of merging and variety of derived region's shape. Firstly, we have introduced constraints on the application of spectral criterion which could suppress excessive merging between dissimilar regions. Secondly, we have extended the evaluation for smoothness criterion by modifying the definition on the extent of the object, which was brought for controlling the shape's diversity. Thirdly, we have developed new shape criterion called aspect ratio. This criterion helps to improve the reproducibility on the shape of object to be matched to the actual objectives of interest. This criterion provides constraint on the aspect ratio in the bounding box of object by keeping properties controlled with conventional shape criteria. These improvements and extensions lead to more accurate, flexible, and diverse segmentation results according to the shape characteristics of the target of interest. Furthermore, we also investigated a technique for quantitative and automatic parameterization in multi-scale image segmentation. This approach is achieved by comparing segmentation result with training area specified in advance by considering the maximization of the average area in derived objects or satisfying the evaluation index called F-measure. Thus, it has been possible to automate the parameterization that suited the objectives especially in the view point of shape's reproducibility.

  5. A probability tracking approach to segmentation of ultrasound prostate images using weak shape priors

    NASA Astrophysics Data System (ADS)

    Xu, Robert S.; Michailovich, Oleg V.; Solovey, Igor; Salama, Magdy M. A.

    2010-03-01

    Prostate specific antigen density is an established parameter for indicating the likelihood of prostate cancer. To this end, the size and volume of the gland have become pivotal quantities used by clinicians during the standard cancer screening process. As an alternative to manual palpation, an increasing number of volume estimation methods are based on the imagery data of the prostate. The necessity to process large volumes of such data requires automatic segmentation algorithms, which can accurately and reliably identify the true prostate region. In particular, transrectal ultrasound (TRUS) imaging has become a standard means of assessing the prostate due to its safe nature and high benefit-to-cost ratio. Unfortunately, modern TRUS images are still plagued by many ultrasound imaging artifacts such as speckle noise and shadowing, which results in relatively low contrast and reduced SNR of the acquired images. Consequently, many modern segmentation methods incorporate prior knowledge about the prostate geometry to enhance traditional segmentation techniques. In this paper, a novel approach to the problem of TRUS segmentation, particularly the definition of the prostate shape prior, is presented. The proposed approach is based on the concept of distribution tracking, which provides a unified framework for tracking both photometric and morphological features of the prostate. In particular, the tracking of morphological features defines a novel type of "weak" shape priors. The latter acts as a regularization force, which minimally bias the segmentation procedure, while rendering the final estimate stable and robust. The value of the proposed methodology is demonstrated in a series of experiments.

  6. Segmentation Method of Time-Lapse Microscopy Images with the Focus on Biocompatibility Assessment.

    PubMed

    Soukup, Jindřich; Císař, Petr; Šroubek, Filip

    2016-06-01

    Biocompatibility testing of new materials is often performed in vitro by measuring the growth rate of mammalian cancer cells in time-lapse images acquired by phase contrast microscopes. The growth rate is measured by tracking cell coverage, which requires an accurate automatic segmentation method. However, cancer cells have irregular shapes that change over time, the mottled background pattern is partially visible through the cells and the images contain artifacts such as halos. We developed a novel algorithm for cell segmentation that copes with the mentioned challenges. It is based on temporal differences of consecutive images and a combination of thresholding, blurring, and morphological operations. We tested the algorithm on images of four cell types acquired by two different microscopes, evaluated the precision of segmentation against manual segmentation performed by a human operator, and finally provided comparison with other freely available methods. We propose a new, fully automated method for measuring the cell growth rate based on fitting a coverage curve with the Verhulst population model. The algorithm is fast and shows accuracy comparable with manual segmentation. Most notably it can correctly separate live from dead cells. PMID:27132464

  7. THE LAYERED NET SURFACE PROBLEMS IN DISCRETE GEOMETRY AND MEDICAL IMAGE SEGMENTATION.

    PubMed

    Wu, Xiaodong; Chen, Danny Z; Li, Kang; Sonka, Milan

    2007-01-01

    Efficient detection of multiple inter-related surfaces representing the boundaries of objects of interest in d-D images (d >/= 3) is important and remains challenging in many medical image analysis applications. In this paper, we study several layered net surface (LNS) problems captured by an interesting type of geometric graphs called ordered multi-column graphs in the d-D discrete space (d >/= 3 is any constant integer). The LNS problems model the simultaneous detection of multiple mutually related surfaces in three or higher dimensional medical images. Although we prove that the d-D LNS problem (d >/= 3) on a general ordered multi-column graph is NP-hard, the (special) ordered multi-column graphs that model medical image segmentation have the self-closure structures and thus admit polynomial time exact algorithms for solving the LNS problems. Our techniques also solve the related net surface volume (NSV) problems of computing well-shaped geometric regions of an optimal total volume in a d-D weighted voxel grid. The NSV problems find applications in medical image segmentation and data mining. Our techniques yield the first polynomial time exact algorithms for several high dimensional medical image segmentation problems. Experiments and comparisons based on real medical data showed that our LNS algorithms and software are computationally efficient and produce highly accurate and consistent segmentation results. PMID:20221409

  8. Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning

    SciTech Connect

    El Naqa, Issam; Yang Deshan; Apte, Aditya; Khullar, Divya; Mutic, Sasa; Zheng Jie; Bradley, Jeffrey D.; Grigsby, Perry; Deasy, Joseph O.

    2007-12-15

    Multimodality imaging information is regularly used now in radiotherapy treatment planning for cancer patients. The authors are investigating methods to take advantage of all the imaging information available for joint target registration and segmentation, including multimodality images or multiple image sets from the same modality. In particular, the authors have developed variational methods based on multivalued level set deformable models for simultaneous 2D or 3D segmentation of multimodality images consisting of combinations of coregistered PET, CT, or MR data sets. The combined information is integrated to define the overall biophysical structure volume. The authors demonstrate the methods on three patient data sets, including a nonsmall cell lung cancer case with PET/CT, a cervix cancer case with PET/CT, and a prostate patient case with CT and MRI. CT, PET, and MR phantom data were also used for quantitative validation of the proposed multimodality segmentation approach. The corresponding Dice similarity coefficient (DSC) was 0.90{+-}0.02 (p<0.0001) with an estimated target volume error of 1.28{+-}1.23% volume. Preliminary results indicate that concurrent multimodality segmentation methods can provide a feasible and accurate framework for combining imaging data from different modalities and are potentially useful tools for the delineation of biophysical structure volumes in radiotherapy treatment planning.

  9. Automatic needle segmentation in 3D ultrasound images using 3D Hough transform

    NASA Astrophysics Data System (ADS)

    Zhou, Hua; Qiu, Wu; Ding, Mingyue; Zhang, Songgeng

    2007-12-01

    3D ultrasound (US) is a new technology that can be used for a variety of diagnostic applications, such as obstetrical, vascular, and urological imaging, and has been explored greatly potential in the applications of image-guided surgery and therapy. Uterine adenoma and uterine bleeding are the two most prevalent diseases in Chinese woman, and a minimally invasive ablation system using an RF button electrode which is needle-like is being used to destroy tumor cells or stop bleeding currently. Now a 3D US guidance system has been developed to avoid accidents or death of the patient by inaccurate localizations of the electrode and the tumor position during treatment. In this paper, we described two automated techniques, the 3D Hough Transform (3DHT) and the 3D Randomized Hough Transform (3DRHT), which is potentially fast, accurate, and robust to provide needle segmentation in 3D US image for use of 3D US imaging guidance. Based on the representation (Φ , θ , ρ , α ) of straight lines in 3D space, we used the 3DHT algorithm to segment needles successfully assumed that the approximate needle position and orientation are known in priori. The 3DRHT algorithm was developed to detect needles quickly without any information of the 3D US images. The needle segmentation techniques were evaluated using the 3D US images acquired by scanning water phantoms. The experiments demonstrated the feasibility of two 3D needle segmentation algorithms described in this paper.

  10. Segmentation of bone pixels from EROI Image using clustering method for bone age assessment

    NASA Astrophysics Data System (ADS)

    Bakthula, Rajitha; Agarwal, Suneeta

    2016-03-01

    The bone age of a human can be identified using carpal and epiphysis bones ossification, which is limited to teen age. The accurate age estimation depends on best separation of bone pixels and soft tissue pixels in the ROI image. The traditional approaches like canny, sobel, clustering, region growing and watershed can be applied, but these methods requires proper pre-processing and accurate initial seed point estimation to provide accurate results. Therefore this paper proposes new approach to segment the bone from soft tissue and background pixels. First pixels are enhanced using BPE and the edges are identified by HIPI. Later a K-Means clustering is applied for segmentation. The performance of the proposed approach has been evaluated and compared with the existing methods.

  11. Robust real time extraction of plane segments from time-of-flight camera images

    NASA Astrophysics Data System (ADS)

    Dalbah, Yosef; Koltermann, Dirk; Wahl, Friedrich M.

    2014-04-01

    We present a method that extracts plane segments from images of a time-of-flight camera. Future driver assistance systems rely on an accurate description of the vehicle's environment. Time-of-flight cameras can be used for environment perception and for the reconstruction of the environment. Since most structures in urban environments are planar, extracted plane segments from single camera images can be used for the creation of a global map. We present a method for real time detection of planar surface structures from time-of-flight camera data. The concept is based on a planar surface segmentation that serves as the fundament for a subsequent global planar surface extraction. The evaluation demonstrates the ability of the described algorithm to detect planar surfaces form depth data of complex scenarios in real time. We compare our methods to state of the art planar surface extraction algorithms.

  12. Image Segmentation of Historical Handwriting from Palm Leaf Manuscripts

    NASA Astrophysics Data System (ADS)

    Surinta, Olarik; Chamchong, Rapeeporn

    Palm leaf manuscripts were one of the earliest forms of written media and were used in Southeast Asia to store early written knowledge about subjects such as medicine, Buddhist doctrine and astrology. Therefore, historical handwritten palm leaf manuscripts are important for people who like to learn about historical documents, because we can learn more experience from them. This paper presents an image segmentation of historical handwriting from palm leaf manuscripts. The process is composed of three steps: 1) background elimination to separate text and background by Otsu's algorithm 2) line segmentation and 3) character segmentation by histogram of image. The end result is the character's image. The results from this research may be applied to optical character recognition (OCR) in the future.

  13. An adaptive multi-feature segmentation model for infrared image

    NASA Astrophysics Data System (ADS)

    Zhang, Tingting; Han, Jin; Zhang, Yi; Bai, Lianfa

    2016-04-01

    Active contour models (ACM) have been extensively applied to image segmentation, conventional region-based active contour models only utilize global or local single feature information to minimize the energy functional to drive the contour evolution. Considering the limitations of original ACMs, an adaptive multi-feature segmentation model is proposed to handle infrared images with blurred boundaries and low contrast. In the proposed model, several essential local statistic features are introduced to construct a multi-feature signed pressure function (MFSPF). In addition, we draw upon the adaptive weight coefficient to modify the level set formulation, which is formed by integrating MFSPF with local statistic features and signed pressure function with global information. Experimental results demonstrate that the proposed method can make up for the inadequacy of the original method and get desirable results in segmenting infrared images.

  14. Novel technique in the segmentation of magnetic resonance image

    NASA Astrophysics Data System (ADS)

    Chan, Kwok-Leung

    1996-04-01

    In this investigation, automatic image segmentation is carried out on magnetic resonance image (MRI). A novel technique based on the maximum minimum measure is devised. The measure is improved by combining the smoothing and counting processes, and then normalizing the number of maximum and minimum positions over the region of interest (ROI). Two parameters (MM_H and MM_V) are generated and used for the segmentation. The technique is tested on some brain MRIs of a human male from the Visible Human Project of the National Library of Medicine, National Institutes of Health, USA. Preliminary results indicate that the maximum minimum measure can provide effective parameters for human tissue characterization and image segmentation with an added advantage of faster computation.

  15. Classifying and segmenting microscopy images with deep multiple instance learning

    PubMed Central

    Kraus, Oren Z.; Ba, Jimmy Lei; Frey, Brendan J.

    2016-01-01

    Motivation: High-content screening (HCS) technologies have enabled large scale imaging experiments for studying cell biology and for drug screening. These systems produce hundreds of thousands of microscopy images per day and their utility depends on automated image analysis. Recently, deep learning approaches that learn feature representations directly from pixel intensity values have dominated object recognition challenges. These tasks typically have a single centered object per image and existing models are not directly applicable to microscopy datasets. Here we develop an approach that combines deep convolutional neural networks (CNNs) with multiple instance learning (MIL) in order to classify and segment microscopy images using only whole image level annotations. Results: We introduce a new neural network architecture that uses MIL to simultaneously classify and segment microscopy images with populations of cells. We base our approach on the similarity between the aggregation function used in MIL and pooling layers used in CNNs. To facilitate aggregating across large numbers of instances in CNN feature maps we present the Noisy-AND pooling function, a new MIL operator that is robust to outliers. Combining CNNs with MIL enables training CNNs using whole microscopy images with image level labels. We show that training end-to-end MIL CNNs outperforms several previous methods on both mammalian and yeast datasets without requiring any segmentation steps. Availability and implementation: Torch7 implementation available upon request. Contact: oren.kraus@mail.utoronto.ca PMID:27307644

  16. Phase contrast image segmentation using a Laue analyser crystal

    NASA Astrophysics Data System (ADS)

    Kitchen, Marcus J.; Paganin, David M.; Uesugi, Kentaro; Allison, Beth J.; Lewis, Robert A.; Hooper, Stuart B.; Pavlov, Konstantin M.

    2011-02-01

    Dual-energy x-ray imaging is a powerful tool enabling two-component samples to be separated into their constituent objects from two-dimensional images. Phase contrast x-ray imaging can render the boundaries between media of differing refractive indices visible, despite them having similar attenuation properties; this is important for imaging biological soft tissues. We have used a Laue analyser crystal and a monochromatic x-ray source to combine the benefits of both techniques. The Laue analyser creates two distinct phase contrast images that can be simultaneously acquired on a high-resolution detector. These images can be combined to separate the effects of x-ray phase, absorption and scattering and, using the known complex refractive indices of the sample, to quantitatively segment its component materials. We have successfully validated this phase contrast image segmentation (PCIS) using a two-component phantom, containing an iodinated contrast agent, and have also separated the lungs and ribcage in images of a mouse thorax. Simultaneous image acquisition has enabled us to perform functional segmentation of the mouse thorax throughout the respiratory cycle during mechanical ventilation.

  17. Convex-relaxed kernel mapping for image segmentation.

    PubMed

    Ben Salah, Mohamed; Ben Ayed, Ismail; Jing Yuan; Hong Zhang

    2014-03-01

    This paper investigates a convex-relaxed kernel mapping formulation of image segmentation. We optimize, under some partition constraints, a functional containing two characteristic terms: 1) a data term, which maps the observation space to a higher (possibly infinite) dimensional feature space via a kernel function, thereby evaluating nonlinear distances between the observations and segments parameters and 2) a total-variation term, which favors smooth segment surfaces (or boundaries). The algorithm iterates two steps: 1) a convex-relaxation optimization with respect to the segments by solving an equivalent constrained problem via the augmented Lagrange multiplier method and 2) a convergent fixed-point optimization with respect to the segments parameters. The proposed algorithm can bear with a variety of image types without the need for complex and application-specific statistical modeling, while having the computational benefits of convex relaxation. Our solution is amenable to parallelized implementations on graphics processing units (GPUs) and extends easily to high dimensions. We evaluated the proposed algorithm with several sets of comprehensive experiments and comparisons, including: 1) computational evaluations over 3D medical-imaging examples and high-resolution large-size color photographs, which demonstrate that a parallelized implementation of the proposed method run on a GPU can bring a significant speed-up and 2) accuracy evaluations against five state-of-the-art methods over the Berkeley color-image database and a multimodel synthetic data set, which demonstrates competitive performances of the algorithm. PMID:24723519

  18. Graph - Based High Resolution Satellite Image Segmentation for Object Recognition

    NASA Astrophysics Data System (ADS)

    Ravali, K.; Kumar, M. V. Ravi; Venugopala Rao, K.

    2014-11-01

    Object based image processing and analysis is challenging research in very high resolution satellite utilisation. Commonly ei ther pixel based classification or visual interpretation is used to recognize and delineate land cover categories. The pixel based classification techniques use rich spectral content of satellite images and fail to utilise spatial relations. To overcome th is drawback, traditional time consuming visual interpretation methods are being used operational ly for preparation of thematic maps. This paper addresses computational vision principles to object level image segmentation. In this study, computer vision algorithms are developed to define the boundary between two object regions and segmentation by representing image as graph. Image is represented as a graph G (V, E), where nodes belong to pixels and, edges (E) connect nodes belonging to neighbouring pixels. The transformed Mahalanobis distance has been used to define a weight function for partition of graph into components such that each component represents the region of land category. This implies that edges between two vertices in the same component have relatively low weights and edges between vertices in different components should have higher weights. The derived segments are categorised to different land cover using supervised classification. The paper presents the experimental results on real world multi-spectral remote sensing images of different landscapes such as Urban, agriculture and mixed land cover. Graph construction done in C program and list the run time for both graph construction and segmentation calculation on dual core Intel i7 system with 16 GB RAM, running 64bit window 7.

  19. Refinement of ground reference data with segmented image data

    NASA Technical Reports Server (NTRS)

    Robinson, Jon W.; Tilton, James C.

    1991-01-01

    One of the ways to determine ground reference data (GRD) for satellite remote sensing data is to photo-interpret low altitude aerial photographs and then digitize the cover types on a digitized tablet and register them to 7.5 minute U.S.G.S. maps (that were themselves digitized). The resulting GRD can be registered to the satellite image or, vice versa. Unfortunately, there are many opportunities for error when using digitizing tablet and the resolution of the edges for the GRD depends on the spacing of the points selected on the digitizing tablet. One of the consequences of this is that when overlaid on the image, errors and missed detail in the GRD become evident. An approach is discussed for correcting these errors and adding detail to the GRD through the use of a highly interactive, visually oriented process. This process involves the use of overlaid visual displays of the satellite image data, the GRD, and a segmentation of the satellite image data. Several prototype programs were implemented which provide means of taking a segmented image and using the edges from the reference data to mask out these segment edges that are beyond a certain distance from the reference data edges. Then using the reference data edges as a guide, those segment edges that remain and that are judged not to be image versions of the reference edges are manually marked and removed. The prototype programs that were developed and the algorithmic refinements that facilitate execution of this task are described.

  20. Automated segmentation of the lungs from high resolution CT images for quantitative study of chronic obstructive pulmonary diseases

    NASA Astrophysics Data System (ADS)

    Garg, Ishita; Karwoski, Ronald A.; Camp, Jon J.; Bartholmai, Brian J.; Robb, Richard A.

    2005-04-01

    Chronic obstructive pulmonary diseases (COPD) are debilitating conditions of the lung and are the fourth leading cause of death in the United States. Early diagnosis is critical for timely intervention and effective treatment. The ability to quantify particular imaging features of specific pathology and accurately assess progression or response to treatment with current imaging tools is relatively poor. The goal of this project was to develop automated segmentation techniques that would be clinically useful as computer assisted diagnostic tools for COPD. The lungs were segmented using an optimized segmentation threshold and the trachea was segmented using a fixed threshold characteristic of air. The segmented images were smoothed by a morphological close operation using spherical elements of different sizes. The results were compared to other segmentation approaches using an optimized threshold to segment the trachea. Comparison of the segmentation results from 10 datasets showed that the method of trachea segmentation using a fixed air threshold followed by morphological closing with spherical element of size 23x23x5 yielded the best results. Inclusion of greater number of pulmonary vessels in the lung volume is important for the development of computer assisted diagnostic tools because the physiological changes of COPD can result in quantifiable anatomic changes in pulmonary vessels. Using a fixed threshold to segment the trachea removed airways from the lungs to a better extent as compared to using an optimized threshold. Preliminary measurements gathered from patient"s CT scans suggest that segmented images can be used for accurate analysis of total lung volume and volumes of regional lung parenchyma. Additionally, reproducible segmentation allows for quantification of specific pathologic features, such as lower intensity pixels, which are characteristic of abnormal air spaces in diseases like emphysema.

  1. Hyperspectral image segmentation using a cooperative nonparametric approach

    NASA Astrophysics Data System (ADS)

    Taher, Akar; Chehdi, Kacem; Cariou, Claude

    2013-10-01

    In this paper a new unsupervised nonparametric cooperative and adaptive hyperspectral image segmentation approach is presented. The hyperspectral images are partitioned band by band in parallel and intermediate classification results are evaluated and fused, to get the final segmentation result. Two unsupervised nonparametric segmentation methods are used in parallel cooperation, namely the Fuzzy C-means (FCM) method, and the Linde-Buzo-Gray (LBG) algorithm, to segment each band of the image. The originality of the approach relies firstly on its local adaptation to the type of regions in an image (textured, non-textured), and secondly on the introduction of several levels of evaluation and validation of intermediate segmentation results before obtaining the final partitioning of the image. For the management of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment steps that exploit the information of each spectral band and its adjacent bands, and finally the information of all the spectral bands. In our approach, the detected textured and non-textured regions are treated separately from feature extraction step, up to the final classification results. This approach was first evaluated on a large number of monocomponent images constructed from the Brodatz album. Then it was evaluated on two real applications using a respectively multispectral image for Cedar trees detection in the region of Baabdat (Lebanon) and a hyperspectral image for identification of invasive and non invasive vegetation in the region of Cieza (Spain). A correct classification rate (CCR) for the first application is over 97% and for the second application the average correct classification rate (ACCR) is over 99%.

  2. Variable-rate colour image quantization based on quadtree segmentation

    NASA Astrophysics Data System (ADS)

    Hu, Y. C.; Li, C. Y.; Chuang, J. C.; Lo, C. C.

    2011-09-01

    A novel variable-sized block encoding with threshold control for colour image quantization (CIQ) is presented in this paper. In CIQ, the colour palette used has a great influence on the reconstructed image quality. Typically, a higher image quality and a larger storage cost are obtained when a larger-sized palette is used in CIQ. To cut down the storage cost while preserving quality of the reconstructed images, the threshold control policy for quadtree segmentation is used in this paper. Experimental results show that the proposed method adaptively provides desired bit rates while having better image qualities comparing to CIQ with the usage of multiple palettes of different sizes.

  3. Image segmentation by nonlinear filtering of optical Hough transform.

    PubMed

    Fernández, Ariel; Flores, Jorge L; Alonso, Julia R; Ferrari, José A

    2016-05-01

    The identification and extraction (i.e., segmentation) of geometrical features is crucial in many tasks requiring image analysis. We present a method for the optical segmentation of features of interest from an edge enhanced image. The proposed method is based on the nonlinear filtering (implemented by the use of a spatial light modulator) of the generalized optical Hough transform and is capable of discriminating features by shape and by size. The robustness of the method against noise in the input, low contrast, or overlapping of geometrical features is assessed, and experimental validation of the working principle is presented. PMID:27140381

  4. Automated bone segmentation from large field of view 3D MR images of the hip joint.

    PubMed

    Xia, Ying; Fripp, Jurgen; Chandra, Shekhar S; Schwarz, Raphael; Engstrom, Craig; Crozier, Stuart

    2013-10-21

    Accurate bone segmentation in the hip joint region from magnetic resonance (MR) images can provide quantitative data for examining pathoanatomical conditions such as femoroacetabular impingement through to varying stages of osteoarthritis to monitor bone and associated cartilage morphometry. We evaluate two state-of-the-art methods (multi-atlas and active shape model (ASM) approaches) on bilateral MR images for automatic 3D bone segmentation in the hip region (proximal femur and innominate bone). Bilateral MR images of the hip joints were acquired at 3T from 30 volunteers. Image sequences included water-excitation dual echo stead state (FOV 38.6 × 24.1 cm, matrix 576 × 360, thickness 0.61 mm) in all subjects and multi-echo data image combination (FOV 37.6 × 23.5 cm, matrix 576 × 360, thickness 0.70 mm) for a subset of eight subjects. Following manual segmentation of femoral (head-neck, proximal-shaft) and innominate (ilium+ischium+pubis) bone, automated bone segmentation proceeded via two approaches: (1) multi-atlas segmentation incorporating non-rigid registration and (2) an advanced ASM-based scheme. Mean inter- and intra-rater reliability Dice's similarity coefficients (DSC) for manual segmentation of femoral and innominate bone were (0.970, 0.963) and (0.971, 0.965). Compared with manual data, mean DSC values for femoral and innominate bone volumes using automated multi-atlas and ASM-based methods were (0.950, 0.922) and (0.946, 0.917), respectively. Both approaches delivered accurate (high DSC values) segmentation results; notably, ASM data were generated in substantially less computational time (12 min versus 10 h). Both automated algorithms provided accurate 3D bone volumetric descriptions for MR-based measures in the hip region. The highly computational efficient ASM-based approach is more likely suitable for future clinical applications such as extracting bone-cartilage interfaces for potential cartilage segmentation. PMID:24077264

  5. Automated bone segmentation from large field of view 3D MR images of the hip joint

    NASA Astrophysics Data System (ADS)

    Xia, Ying; Fripp, Jurgen; Chandra, Shekhar S.; Schwarz, Raphael; Engstrom, Craig; Crozier, Stuart

    2013-10-01

    Accurate bone segmentation in the hip joint region from magnetic resonance (MR) images can provide quantitative data for examining pathoanatomical conditions such as femoroacetabular impingement through to varying stages of osteoarthritis to monitor bone and associated cartilage morphometry. We evaluate two state-of-the-art methods (multi-atlas and active shape model (ASM) approaches) on bilateral MR images for automatic 3D bone segmentation in the hip region (proximal femur and innominate bone). Bilateral MR images of the hip joints were acquired at 3T from 30 volunteers. Image sequences included water-excitation dual echo stead state (FOV 38.6 × 24.1 cm, matrix 576 × 360, thickness 0.61 mm) in all subjects and multi-echo data image combination (FOV 37.6 × 23.5 cm, matrix 576 × 360, thickness 0.70 mm) for a subset of eight subjects. Following manual segmentation of femoral (head-neck, proximal-shaft) and innominate (ilium+ischium+pubis) bone, automated bone segmentation proceeded via two approaches: (1) multi-atlas segmentation incorporating non-rigid registration and (2) an advanced ASM-based scheme. Mean inter- and intra-rater reliability Dice's similarity coefficients (DSC) for manual segmentation of femoral and innominate bone were (0.970, 0.963) and (0.971, 0.965). Compared with manual data, mean DSC values for femoral and innominate bone volumes using automated multi-atlas and ASM-based methods were (0.950, 0.922) and (0.946, 0.917), respectively. Both approaches delivered accurate (high DSC values) segmentation results; notably, ASM data were generated in substantially less computational time (12 min versus 10 h). Both automated algorithms provided accurate 3D bone volumetric descriptions for MR-based measures in the hip region. The highly computational efficient ASM-based approach is more likely suitable for future clinical applications such as extracting bone-cartilage interfaces for potential cartilage segmentation.

  6. A dendritic lattice neural network for color image segmentation

    NASA Astrophysics Data System (ADS)

    Urcid, Gonzalo; Lara-Rodríguez, Luis David; López-Meléndez, Elizabeth

    2015-09-01

    A two-layer dendritic lattice neural network is proposed to segment color images in the Red-Green-Blue (RGB) color space. The two layer neural network is a fully interconnected feed forward net consisting of an input layer that receives color pixel values, an intermediate layer that computes pixel interdistances, and an output layer used to classify colors by hetero-association. The two-layer net is first initialized with a finite small subset of the colors present in the input image. These colors are obtained by means of an automatic clustering procedure such as k-means or fuzzy c-means. In the second stage, the color image is scanned on a pixel by pixel basis where each picture element is treated as a vector and feeded into the network. For illustration purposes we use public domain color images to show the performance of our proposed image segmentation technique.

  7. Segmented infrared image analysis for rotating machinery fault diagnosis

    NASA Astrophysics Data System (ADS)

    Duan, Lixiang; Yao, Mingchao; Wang, Jinjiang; Bai, Tangbo; Zhang, Laibin

    2016-07-01

    As a noncontact and non-intrusive technique, infrared image analysis becomes promising for machinery defect diagnosis. However, the insignificant information and strong noise in infrared image limit its performance. To address this issue, this paper presents an image segmentation approach to enhance the feature extraction in infrared image analysis. A region selection criterion named dispersion degree is also formulated to discriminate fault representative regions from unrelated background information. Feature extraction and fusion methods are then applied to obtain features from selected regions for further diagnosis. Experimental studies on a rotor fault simulator demonstrate that the presented segmented feature enhancement approach outperforms the one from the original image using both Naïve Bayes classifier and support vector machine.

  8. Optimal feature extraction for segmentation of Diesel spray images.

    PubMed

    Payri, Francisco; Pastor, José V; Palomares, Alberto; Juliá, J Enrique

    2004-04-01

    A one-dimensional simplification, based on optimal feature extraction, of the algorithm based on the likelihood-ratio test method (LRT) for segmentation in colored Diesel spray images is presented. If the pixel values of the Diesel spray and the combustion images are represented in RGB space, in most cases they are distributed in an area with a given so-called privileged direction. It is demonstrated that this direction permits optimal feature extraction for one-dimensional segmentation in the Diesel spray images, and some of its advantages compared with more-conventional one-dimensional simplification methods, including considerably reduced computational cost while accuracy is maintained within more than reasonable limits, are presented. The method has been successfully applied to images of Diesel sprays injected at room temperature as well as to images of sprays with evaporation and combustion. It has proved to be valid for several cameras and experimental arrangements. PMID:15074419

  9. Image segmentation by iterative parallel region growing and splitting

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    1989-01-01

    The spatially constrained clustering (SCC) iterative parallel region-growing technique is applied to image analysis. The SCC algorithm is implemented on the massively parallel processor at NASA Goddard. Most previous region-growing approaches have the drawback that the segmentation produced depends on the order in which portions of the image are processed. The ideal solution to this problem (merging only the single most similar pair of spatially adjacent regions in the image in each iteration) becomes impractical except for very small images, even on a massively parallel computer. The SCC algorithm overcomes these problems by performing, in parallel, the best merge within each of a set of local, possibly overlapping, subimages. A region-splitting stage is also incorporated into the algorithm, but experiments show that region splitting generally does not improve segmentation results. The SCC algorithm has been tested on various imagery data, and test results for a Landsat TM image are summarized.

  10. Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications

    SciTech Connect

    Zhou Chuan; Chan, H.-P.; Sahiner, Berkman; Hadjiiski, Lubomir M.; Chughtai, Aamer; Patel, Smita; Wei Jun; Ge Jun; Cascade, Philip N.; Kazerooni, Ella A.

    2007-12-15

    The authors are developing a computerized pulmonary vessel segmentation method for a computer-aided pulmonary embolism (PE) detection system on computed tomographic pulmonary angiography (CTPA) images. Because PE only occurs inside pulmonary arteries, an automatic and accurate segmentation of the pulmonary vessels in 3D CTPA images is an essential step for the PE CAD system. To segment the pulmonary vessels within the lung, the lung regions are first extracted using expectation-maximization (EM) analysis and morphological operations. The authors developed a 3D multiscale filtering technique to enhance the pulmonary vascular structures based on the analysis of eigenvalues of the Hessian matrix at multiple scales. A new response function of the filter was designed to enhance all vascular structures including the vessel bifurcations and suppress nonvessel structures such as the lymphoid tissues surrounding the vessels. An EM estimation is then used to segment the vascular structures by extracting the high response voxels at each scale. The vessel tree is finally reconstructed by integrating the segmented vessels at all scales based on a 'connected component' analysis. Two CTPA cases containing PEs were used to evaluate the performance of the system. One of these two cases also contained pleural effusion disease. Two experienced thoracic radiologists provided the gold standard of pulmonary vessels including both arteries and veins by manually tracking the arterial tree and marking the center of the vessels using a computer graphical user interface. The accuracy of vessel tree segmentation was evaluated by the percentage of the 'gold standard' vessel center points overlapping with the segmented vessels. The results show that 96.2% (2398/2494) and 96.3% (1910/1984) of the manually marked center points in the arteries overlapped with segmented vessels for the case without and with other lung diseases. For the manually marked center points in all vessels including arteries

  11. Segmentation of complementary DNA microarray images by wavelet-based Markov random field model.

    PubMed

    Athanasiadis, Emmanouil I; Cavouras, Dionisis A; Glotsos, Dimitris Th; Georgiadis, Pantelis V; Kalatzis, Ioannis K; Nikiforidis, George C

    2009-11-01

    A wavelet-based modification of the Markov random field (WMRF) model is proposed for segmenting complementary DNA (cDNA) microarray images. For evaluation purposes, five simulated and a set of five real microarray images were used. The one-level stationary wavelet transform (SWT) of each microarray image was used to form two images, a denoised image, using hard thresholding filter, and a magnitude image, from the amplitudes of the horizontal and vertical components of SWT. Elements from these two images were suitably combined to form the WMRF model for segmenting spots from their background. The WMRF was compared against the conventional MRF and the Fuzzy C means (FCM) algorithms on simulated and real microarray images and their performances were evaluated by means of the segmentation matching factor (SMF) and the coefficient of determination (r2). Additionally, the WMRF was compared against the SPOT and SCANALYZE, and performances were evaluated by the mean absolute error (MAE) and the coefficient of variation (CV). The WMRF performed more accurately than the MRF and FCM (SMF: 92.66, 92.15, and 89.22, r2 : 0.92, 0.90, and 0.84, respectively) and achieved higher reproducibility than the MRF, SPOT, and SCANALYZE (MAE: 497, 1215, 1180, and 503, CV: 0.88, 1.15, 0.93, and 0.90, respectively). PMID:19783509

  12. Image Segmentation Analysis for NASA Earth Science Applications

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    2010-01-01

    NASA collects large volumes of imagery data from satellite-based Earth remote sensing sensors. Nearly all of the computerized image analysis of this data is performed pixel-by-pixel, in which an algorithm is applied directly to individual image pixels. While this analysis approach is satisfactory in many cases, it is usually not fully effective in extracting the full information content from the high spatial resolution image data that s now becoming increasingly available from these sensors. The field of object-based image analysis (OBIA) has arisen in recent years to address the need to move beyond pixel-based analysis. The Recursive Hierarchical Segmentation (RHSEG) software developed by the author is being used to facilitate moving from pixel-based image analysis to OBIA. The key unique aspect of RHSEG is that it tightly intertwines region growing segmentation, which produces spatially connected region objects, with region object classification, which groups sets of region objects together into region classes. No other practical, operational image segmentation approach has this tight integration of region growing object finding with region classification This integration is made possible by the recursive, divide-and-conquer implementation utilized by RHSEG, in which the input image data is recursively subdivided until the image data sections are small enough to successfully mitigat the combinatorial explosion caused by the need to compute the dissimilarity between each pair of image pixels. RHSEG's tight integration of region growing object finding and region classification is what enables the high spatial fidelity of the image segmentations produced by RHSEG. This presentation will provide an overview of the RHSEG algorithm and describe how it is currently being used to support OBIA or Earth Science applications such as snow/ice mapping and finding archaeological sites from remotely sensed data.

  13. Automatic segmentation and classification of seven-segment display digits on auroral images

    NASA Astrophysics Data System (ADS)

    Savolainen, Tuomas; Whiter, Daniel Keith; Partamies, Noora

    2016-07-01

    In this paper we describe a new and fully automatic method for segmenting and classifying digits in seven-segment displays. The method is applied to a dataset consisting of about 7 million auroral all-sky images taken during the time period of 1973-1997 at camera stations centred around Sodankylä observatory in northern Finland. In each image there is a clock display for the date and time together with the reflection of the whole night sky through a spherical mirror. The digitised film images of the night sky contain valuable scientific information but are impractical to use without an automatic method for extracting the date-time from the display. We describe the implementation and the results of such a method in detail in this paper.

  14. Image Segmentation Using Higher-Order Correlation Clustering.

    PubMed

    Kim, Sungwoong; Yoo, Chang D; Nowozin, Sebastian; Kohli, Pushmeet

    2014-09-01

    In this paper, a hypergraph-based image segmentation framework is formulated in a supervised manner for many high-level computer vision tasks. To consider short- and long-range dependency among various regions of an image and also to incorporate wider selection of features, a higher-order correlation clustering (HO-CC) is incorporated in the framework. Correlation clustering (CC), which is a graph-partitioning algorithm, was recently shown to be effective in a number of applications such as natural language processing, document clustering, and image segmentation. It derives its partitioning result from a pairwise graph by optimizing a global objective function such that it simultaneously maximizes both intra-cluster similarity and inter-cluster dissimilarity. In the HO-CC, the pairwise graph which is used in the CC is generalized to a hypergraph which can alleviate local boundary ambiguities that can occur in the CC. Fast inference is possible by linear programming relaxation, and effective parameter learning by structured support vector machine is also possible by incorporating a decomposable structured loss function. Experimental results on various data sets show that the proposed HO-CC outperforms other state-of-the-art image segmentation algorithms. The HO-CC framework is therefore an efficient and flexible image segmentation framework. PMID:26352230

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

    NASA Astrophysics Data System (ADS)

    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.

  16. AIRS: The Medical Imaging Software for Segmentation and Registration in SPECT/CT

    NASA Astrophysics Data System (ADS)

    Widita, R.; Kurniadi, R.; Haryanto, F.; Darma, Y.; Perkasa, Y. S.; Zasneda, S. S.

    2010-06-01

    We have been successfully developed a new software, Automated Image Registration and Segmentation (AIRS), to fuse the CT and SPECT images. It is designed to solve different registration and segmentation problems that arises in tomographic data sets. AIRS is addressed to obtain anatomic information to be applied to NanoSpect system which is imaging for nano-tissues or small animals. It will be demonstrated that the information obtained by SPECT/CT is more accurate in evaluating patients/objects than that obtained from either SPECT or CT alone. The registration methods developed here are for both two-dimensional and three-dimensional registration. We used normalized mutual information (NMI) which is amenable for images produced by different modalities and having unclear boundaries between tissues. The segmentation components used in this software is region growing algorithms which have proven to be an effective approach for image segmentation. The implementations of region growing developed here are connected threshold and neighborhood connected. Our method is designed to perform with clinically acceptable speed, using accelerated techniques (multiresolution).

  17. A game-theoretic framework for landmark-based image segmentation.

    PubMed

    Ibragimov, Bulat; Likar, Boštjan; Pernus, Franjo; Vrtovec, Tomaz

    2012-09-01

    A novel game-theoretic framework for landmark-based image segmentation is presented. Landmark detection is formulated as a game, in which landmarks are players, landmark candidate points are strategies, and likelihoods that candidate points represent landmarks are payoffs, determined according to the similarity of image intensities and spatial relationships between the candidate points in the target image and their corresponding landmarks in images from the training set. The solution of the formulated game-theoretic problem is the equilibrium of candidate points that represent landmarks in the target image and is obtained by a novel iterative scheme that solves the segmentation problem in polynomial time. The object boundaries are finally extracted by applying dynamic programming to the optimal path searching problem between the obtained adjacent landmarks. The performance of the proposed framework was evaluated for segmentation of lung fields from chest radiographs and heart ventricles from cardiac magnetic resonance cross sections. The comparison to other landmark-based segmentation techniques shows that the results obtained by the proposed game-theoretic framework are highly accurate and precise in terms of mean boundary distance and area overlap. Moreover, the framework overcomes several shortcomings of the existing techniques, such as sensitivity to initialization and convergence to local optima. PMID:22692901

  18. Automatic segmentation of heart cavities in multidimensional ultrasound images

    NASA Astrophysics Data System (ADS)

    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.

  19. Filter Design and Performance Evaluation for Fingerprint Image Segmentation

    PubMed Central

    Thai, Duy Hoang; Huckemann, Stephan; Gottschlich, Carsten

    2016-01-01

    Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available. PMID:27171150

  20. Multiple LREK active contours for knee meniscus ultrasound image segmentation.

    PubMed

    Faisal, Amir; Ng, Siew-Cheok; Goh, Siew-Li; George, John; Supriyanto, Eko; Lai, Khin W

    2015-10-01

    Quantification of knee meniscus degeneration and displacement in an ultrasound image requires simultaneous segmentation of femoral condyle, meniscus, and tibial plateau in order to determine the area and the position of the meniscus. In this paper, we present an active contour for image segmentation that uses scalable local regional information on expandable kernel (LREK). It includes using a strategy to adapt the size of a local window in order to avoid being confined locally in a homogeneous region during the segmentation process. We also provide a multiple active contours framework called multiple LREK (MLREK) to deal with multiple object segmentation without merging and overlapping between the neighboring contours in the shared boundaries of separate regions. We compare its performance to other existing active contour models and show an improvement offered by our model. We then investigate the choice of various parameters in the proposed framework in response to the segmentation outcome. Dice coefficient and Hausdorff distance measures over a set of real knee meniscus ultrasound images indicate a potential application of MLREK for assessment of knee meniscus degeneration and displacement. PMID:25910057

  1. Filter Design and Performance Evaluation for Fingerprint Image Segmentation.

    PubMed

    Thai, Duy Hoang; Huckemann, Stephan; Gottschlich, Carsten

    2016-01-01

    Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: 'true' foreground can be labeled as background and features like minutiae can be lost, or conversely 'true' background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available. PMID:27171150

  2. Optimal graph search based image segmentation for objects with complex topologies

    NASA Astrophysics Data System (ADS)

    Liu, Xiaomin; Chen, Danny Z.; Wu, Xiaodong; Sonka, Milan

    2009-02-01

    Segmenting objects with complicated topologies in 3D images is a challenging problem in medical image processing, especially for objects with multiple interrelated surfaces. In this paper, we extend a graph search based technique to simultaneously identifying multiple interrelated surfaces for objects that have complex topologies (e.g., with tree-like structures) in 3D. We first perform a pre-segmentation on the input image to obtain basic information of the objects' topologies. Based on the initial pre-segmentation, the original image is resampled along judiciously determined directions to produce a set of vectors of voxels (called voxel columns). The resampling process utilizes medial axes to ensure that voxel columns of appropriate lengths are used to capture the sought object surfaces. Then a geometric graph is constructed whose edges connect voxels in the resampled voxel columns and enforce the smoothness constraint and separation constraint on the sought surfaces. Validation of our algorithm was performed on the segmentation of airway trees and lung vascular trees in human in-vivo CT scans. Cost functions with directional information are applied to distinguish the airway inner wall and outer wall. We succeed in extracting the outer airway wall and optimizing the location of the inner wall in all cases, while the vascular trees are optimized as well. Comparing with the pre-segmentation results, our approach captures the wall surfaces more accurately, especially across bifurcations. The statistical evaluation on a double wall phantom derived from in-vivo CT images yields highly accurate results of the wall thickness measurement on the whole tree (with mean unsigned error 0.16 +/- 0.16mm).

  3. User-agent cooperation in multiagent IVUS image segmentation.

    PubMed

    Bovenkamp, E G P; Dijkstra, J; Bosch, J G; Reiber, J H C

    2009-01-01

    Automated interpretation of complex images requires elaborate knowledge and model-based image analysis, but often needs interaction with an expert as well. This research describes expert interaction with a multiagent image interpretation system using only a restricted vocabulary of high-level user interactions. The aim is to minimize inter- and intra-observer variability by keeping the total number of interactions as low and simple as possible. The multiagent image interpretation system has elaborate high-level knowledge-based control over low-level image segmentation algorithms. Agents use contextual knowledge to keep the number of interactions low but, when in doubt, present the user with the most likely interpretation of the situation. The user, in turn, can correct, supplement, and/or confirm the results of image-processing agents. This is done at a very high level of abstraction such that no knowledge of the underlying segmentation methods, parameters or agent functioning is needed. High-level interaction thereby replaces more traditional contour correction methods like inserting points and/or (re)drawing contours. This makes it easier for the user to obtain good results, while inter- and intra-observer variability are kept minimal, since the image segmentation itself remains under control of image-processing agents. The system has been applied to intravascular ultrasound (IVUS) images. Experiments show that with an average of 2-3 high-level user interactions per correction, segmentation results substantially improve while the variation is greatly reduced. The achieved level of accuracy and repeatability is equivalent to that of manual drawing by an expert. PMID:19116192

  4. In Vivo Imaging of Human Cone Photoreceptor Inner Segments

    PubMed Central

    Scoles, Drew; Sulai, Yusufu N.; Langlo, Christopher S.; Fishman, Gerald A.; Curcio, Christine A.; Carroll, Joseph; Dubra, Alfredo

    2014-01-01

    Purpose. An often overlooked prerequisite to cone photoreceptor gene therapy development is residual photoreceptor structure that can be rescued. While advances in adaptive optics (AO) retinal imaging have recently enabled direct visualization of individual cone and rod photoreceptors in the living human retina, these techniques largely detect strongly directionally-backscattered (waveguided) light from normal intact photoreceptors. This represents a major limitation in using existing AO imaging to quantify structure of remnant cones in degenerating retina. Methods. Photoreceptor inner segment structure was assessed with a novel AO scanning light ophthalmoscopy (AOSLO) differential phase technique, that we termed nonconfocal split-detector, in two healthy subjects and four subjects with achromatopsia. Ex vivo preparations of five healthy donor eyes were analyzed for comparison of inner segment diameter to that measured in vivo with split-detector AOSLO. Results. Nonconfocal split-detector AOSLO reveals the photoreceptor inner segment with or without the presence of a waveguiding outer segment. The diameter of inner segments measured in vivo is in good agreement with histology. A substantial number of foveal and parafoveal cone photoreceptors with apparently intact inner segments were identified in patients with the inherited disease achromatopsia. Conclusions. The application of nonconfocal split-detector to emerging human gene therapy trials will improve the potential of therapeutic success, by identifying patients with sufficient retained photoreceptor structure to benefit the most from intervention. Additionally, split-detector imaging may be useful for studies of other retinal degenerations such as AMD, retinitis pigmentosa, and choroideremia where the outer segment is lost before the remainder of the photoreceptor cell. PMID:24906859

  5. Segmentation and separation of venous vasculatures in liver CT images

    NASA Astrophysics Data System (ADS)

    Wang, Lei; Hansen, Christian; Zidowitz, Stephan; Hahn, Horst K.

    2014-03-01

    Computer-aided analysis of venous vasculatures including hepatic veins and portal veins is important in liver surgery planning. The analysis normally consists of two important pre-processing tasks: segmenting both vasculatures and separating them from each other by assigning different labels. During the acquisition of multi-phase CT images, both of the venous vessels are enhanced by injected contrast agent and acquired either in a common phase or in two individual phases. The enhanced signals established by contrast agent are often not stably acquired due to non-optimal acquisition time. Inadequate contrast and the presence of large lesions in oncological patients, make the segmentation task quite challenging. To overcome these diffculties, we propose a framework with minimal user interactions to analyze venous vasculatures in multi-phase CT images. Firstly, presented vasculatures are automatically segmented adopting an efficient multi-scale Hessian-based vesselness filter. The initially segmented vessel trees are then converted to a graph representation, on which a series of graph filters are applied in post-processing steps to rule out irrelevant structures. Eventually, we develop a semi-automatic workow to refine the segmentation in the areas of inferior vena cava and entrance of portal veins, and to simultaneously separate hepatic veins from portal veins. Segmentation quality was evaluated with intensive tests enclosing 60 CT images from both healthy liver donors and oncological patients. To quantitatively measure the similarities between segmented and reference vessel trees, we propose three additional metrics: skeleton distance, branch coverage, and boundary surface distance, which are dedicated to quantifying the misalignment induced by both branching patterns and radii of two vessel trees.

  6. Effect of image scaling and segmentation in digital rock characterisation

    NASA Astrophysics Data System (ADS)

    Jones, B. D.; Feng, Y. T.

    2016-04-01

    Digital material characterisation from microstructural geometry is an emerging field in computer simulation. For permeability characterisation, a variety of studies exist where the lattice Boltzmann method (LBM) has been used in conjunction with computed tomography (CT) imaging to simulate fluid flow through microscopic rock pores. While these previous works show that the technique is applicable, the use of binary image segmentation and the bounceback boundary condition results in a loss of grain surface definition when the modelled geometry is compared to the original CT image. We apply the immersed moving boundary (IMB) condition of Noble and Torczynski as a partial bounceback boundary condition which may be used to better represent the geometric definition provided by a CT image. The IMB condition is validated against published work on idealised porous geometries in both 2D and 3D. Following this, greyscale image segmentation is applied to a CT image of Diemelstadt sandstone. By varying the mapping of CT voxel densities to lattice sites, it is shown that binary image segmentation may underestimate the true permeability of the sample. A CUDA-C-based code, LBM-C, was developed specifically for this work and leverages GPU hardware in order to carry out computations.

  7. Automatic sputum color image segmentation for tuberculosis diagnosis

    NASA Astrophysics Data System (ADS)

    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.

  8. Nested-hierarchical scene models and image segmentation

    NASA Technical Reports Server (NTRS)

    Woodcock, C.; Harward, V. J.

    1992-01-01

    An improved model of scenes for image analysis purposes, a nested-hierarchical approach which explicitly acknowledges multiple scales of objects or categories of objects, is presented. A multiple-pass, region-based segmentation algorithm improves the segmentation of images from scenes better modeled as a nested hierarchy. A multiple-pass approach allows slow and careful growth of regions while interregion distances are below a global threshold. Past the global threshold, a minimum region size parameter forces development of regions in areas of high local variance. Maximum and viable region size parameters limit the development of undesirably large regions. Application of the segmentation algorithm for forest stand delineation in TM imagery yields regions corresponding to identifiable features in the landscape. The use of a local variance, adaptive-window texture channel in conjunction with spectral bands improves the ability to define regions corresponding to sparsely stocked forest stands which have high internal variance.

  9. Medical image segmentation using object atlas versus object cloud models

    NASA Astrophysics Data System (ADS)

    Phellan, Renzo; Falcão, Alexandre X.; Udupa, Jayaram K.

    2015-03-01

    Medical image segmentation is crucial for quantitative organ analysis and surgical planning. Since interactive segmentation is not practical in a production-mode clinical setting, automatic methods based on 3D object appearance models have been proposed. Among them, approaches based on object atlas are the most actively investigated. A key drawback of these approaches is that they require a time-costly image registration process to build and deploy the atlas. Object cloud models (OCM) have been introduced to avoid registration, considerably speeding up the whole process, but they have not been compared to object atlas models (OAM). The present paper fills this gap by presenting a comparative analysis of the two approaches in the task of individually segmenting nine anatomical structures of the human body. Our results indicate that OCM achieve a statistically significant better accuracy for seven anatomical structures, in terms of Dice Similarity Coefficient and Average Symmetric Surface Distance.

  10. Segmented images and 3D images for studying the anatomical structures in MRIs

    NASA Astrophysics Data System (ADS)

    Lee, Yong Sook; Chung, Min Suk; Cho, Jae Hyun

    2004-05-01

    For identifying the pathological findings in MRIs, the anatomical structures in MRIs should be identified in advance. For studying the anatomical structures in MRIs, an education al tool that includes the horizontal, coronal, sagittal MRIs of entire body, corresponding segmented images, 3D images, and browsing software is necessary. Such an educational tool, however, is hard to obtain. Therefore, in this research, such an educational tool which helps medical students and doctors study the anatomical structures in MRIs was made as follows. A healthy, young Korean male adult with standard body shape was selected. Six hundred thirteen horizontal MRIs of the entire body were scanned and inputted to the personal computer. Sixty anatomical structures in the horizontal MRIs were segmented to make horizontal segmented images. Coronal, sagittal MRIs and coronal, sagittal segmented images were made. 3D images of anatomical structures in the segmented images were reconstructed by surface rendering method. Browsing software of the MRIs, segmented images, and 3D images was composed. This educational tool that includes horizontal, coronal, sagittal MRIs of entire body, corresponding segmented images, 3D images, and browsing software is expected to help medical students and doctors study anatomical structures in MRIs.

  11. Hierarchical nucleus segmentation in digital pathology images

    PubMed Central

    Gao, Yi; Ratner, Vadim; Zhu, Liangjia; Diprima, Tammy; Kurc, Tahsin; Tannenbaum, Allen; Saltz, Joel

    2016-01-01

    Extracting nuclei is one of the most actively studied topic in the digital pathology researches. Most of the studies directly search the nuclei (or seeds for the nuclei) from the finest resolution available. While the richest information has been utilized by such approaches, it is sometimes difficult to address the heterogeneity of nuclei in different tissues. In this work, we propose a hierarchical approach which starts from the lower resolution level and adaptively adjusts the parameters while progressing into finer and finer resolution. The algorithm is tested on brain and lung cancers images from The Cancer Genome Atlas data set. PMID:27375315

  12. Statistical Characterization and Segmentation of Drusen in Fundus Images

    SciTech Connect

    Santos-Villalobos, Hector J; Karnowski, Thomas Paul; Aykac, Deniz; Giancardo, Luca; Li, Yaquin; Nichols, Trent L; Tobin Jr, Kenneth William; Chaum, Edward

    2011-01-01

    Age related Macular Degeneration (AMD) is a disease of the retina associated with aging. AMD progression in patients is characterized by drusen, pigmentation changes, and geographic atrophy, which can be seen using fundus imagery. The level of AMD is characterized by standard scaling methods, which can be somewhat subjective in practice. In this work we propose a statistical image processing approach to segment drusen with the ultimate goal of characterizing the AMD progression in a data set of longitudinal images. The method characterizes retinal structures with a statistical model of the colors in the retina image. When comparing the segmentation results of the method between longitudinal images with known AMD progression and those without, the method detects progression in our longitudinal data set with an area under the receiver operating characteristics curve of 0.99.

  13. Cervigram image segmentation based on reconstructive sparse representations

    NASA Astrophysics Data System (ADS)

    Zhang, Shaoting; Huang, Junzhou; Wang, Wei; Huang, Xiaolei; Metaxas, Dimitris

    2010-03-01

    We proposed an approach based on reconstructive sparse representations to segment tissues in optical images of the uterine cervix. Because of large variations in image appearance caused by the changing of the illumination and specular reflection, the color and texture features in optical images often overlap with each other and are not linearly separable. By leveraging sparse representations the data can be transformed to higher dimensions with sparse constraints and become more separated. K-SVD algorithm is employed to find sparse representations and corresponding dictionaries. The data can be reconstructed from its sparse representations and positive and/or negative dictionaries. Classification can be achieved based on comparing the reconstructive errors. In the experiments we applied our method to automatically segment the biomarker AcetoWhite (AW) regions in an archive of 60,000 images of the uterine cervix. Compared with other general methods, our approach showed lower space and time complexity and higher sensitivity.

  14. An Investigation of Implicit Active Contours for Scientific Image Segmentation

    SciTech Connect

    Weeratunga, S K; Kamath, C

    2003-10-29

    The use of partial differential equations in image processing has become an active area of research in the last few years. In particular, active contours are being used for image segmentation, either explicitly as snakes, or implicitly through the level set approach. In this paper, we consider the use of the implicit active contour approach for segmenting scientific images of pollen grains obtained using a scanning electron microscope. Our goal is to better understand the pros and cons of these techniques and to compare them with the traditional approaches such as the Canny and SUSAN edge detectors. The preliminary results of our study show that the level set method is computationally expensive and requires the setting of several different parameters. However, it results in closed contours, which may be useful in separating objects from the background in an image.

  15. Segmentation of mosaicism in cervicographic images using support vector machines

    NASA Astrophysics Data System (ADS)

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

    2009-02-01

    The National Library of Medicine (NLM), in collaboration with the National Cancer Institute (NCI), is creating a large digital repository of cervicographic images for the study of uterine cervix cancer prevention. One of the research goals is to automatically detect diagnostic bio-markers in these images. Reliable bio-marker segmentation in large biomedical image collections is a challenging task due to the large variation in image appearance. Methods described in this paper focus on segmenting mosaicism, which is an important vascular feature used to visually assess the degree of cervical intraepithelial neoplasia. The proposed approach uses support vector machines (SVM) trained on a ground truth dataset annotated by medical experts (which circumvents the need for vascular structure extraction). We have evaluated the performance of the proposed algorithm and experimentally demonstrated its feasibility.

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

    PubMed Central

    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

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

    PubMed

    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

  18. Automatic tissue segmentation of breast biopsies imaged by QPI

    NASA Astrophysics Data System (ADS)

    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.

  19. Fast, Accurate and Precise Mid-Sagittal Plane Location in 3D MR Images of the Brain

    NASA Astrophysics Data System (ADS)

    Bergo, Felipe P. G.; Falcão, Alexandre X.; Yasuda, Clarissa L.; Ruppert, Guilherme C. S.

    Extraction of the mid-sagittal plane (MSP) is a key step for brain image registration and asymmetry analysis. We present a fast MSP extraction method for 3D MR images, based on automatic segmentation of the brain and on heuristic maximization of the cerebro-spinal fluid within the MSP. The method is robust to severe anatomical asymmetries between the hemispheres, caused by surgical procedures and lesions. The method is also accurate with respect to MSP delineations done by a specialist. The method was evaluated on 64 MR images (36 pathological, 20 healthy, 8 synthetic), and it found a precise and accurate approximation of the MSP in all of them with a mean time of 60.0 seconds per image, mean angular variation within a same image (precision) of 1.26o and mean angular difference from specialist delineations (accuracy) of 1.64o.

  20. Iterative feature refinement for accurate undersampled MR image reconstruction.

    PubMed

    Wang, Shanshan; Liu, Jianbo; Liu, Qiegen; Ying, Leslie; Liu, Xin; Zheng, Hairong; Liang, Dong

    2016-05-01

    Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches. PMID:27032527

  1. Iterative feature refinement for accurate undersampled MR image reconstruction

    NASA Astrophysics Data System (ADS)

    Wang, Shanshan; Liu, Jianbo; Liu, Qiegen; Ying, Leslie; Liu, Xin; Zheng, Hairong; Liang, Dong

    2016-05-01

    Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches.

  2. A geometric deformable model for echocardiographic image segmentation

    NASA Technical Reports Server (NTRS)

    Hang, X.; Greenberg, N. L.; Thomas, J. D.

    2002-01-01

    Gradient vector flow (GVF), an elegant external force for parametric deformable models, can capture object boundaries from both sides. A new geometric deformable model is proposed that combines GVF and the geodesic active contour model. The level set method is used as the numerical method of this model. The model is applied for echocardiographic image segmentation.

  3. Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images.

    PubMed

    Berton, Florian; Cheriet, Farida; Miron, Marie-Claude; Laporte, Catherine

    2016-05-01

    Spinal ultrasound imaging is emerging as a low-cost, radiation-free alternative to conventional X-ray imaging for the clinical follow-up of patients with scoliosis. Currently, deformity measurement relies almost entirely on manual identification of key vertebral landmarks. However, the interpretation of vertebral ultrasound images is challenging, primarily because acoustic waves are entirely reflected by bone. To alleviate this problem, we propose an algorithm to segment these images into three regions: the spinous process, its acoustic shadow and other tissues. This method consists, first, in the extraction of several image features and the selection of the most relevant ones for the discrimination of the three regions. Then, using this set of features and linear discriminant analysis, each pixel of the image is classified as belonging to one of the three regions. Finally, the image is segmented by regularizing the pixel-wise classification results to account for some geometrical properties of vertebrae. The feature set was first validated by analyzing the classification results across a learning database. The database contained 107 vertebral ultrasound images acquired with convex and linear probes. Classification rates of 84%, 92% and 91% were achieved for the spinous process, the acoustic shadow and other tissues, respectively. Dice similarity coefficients of 0.72 and 0.88 were obtained respectively for the spinous process and acoustic shadow, confirming that the proposed method accurately segments the spinous process and its acoustic shadow in vertebral ultrasound images. Furthermore, the centroid of the automatically segmented spinous process was located at an average distance of 0.38 mm from that of the manually labeled spinous process, which is on the order of image resolution. This suggests that the proposed method is a promising tool for the measurement of the Spinous Process Angle and, more generally, for assisting ultrasound-based assessment of scoliosis

  4. Lung vessel segmentation in CT images using graph-cuts

    NASA Astrophysics Data System (ADS)

    Zhai, Zhiwei; Staring, Marius; Stoel, Berend C.

    2016-03-01

    Accurate lung vessel segmentation is an important operation for lung CT analysis. Filters that are based on analyzing the eigenvalues of the Hessian matrix are popular for pulmonary vessel enhancement. However, due to their low response at vessel bifurcations and vessel boundaries, extracting lung vessels by thresholding the vesselness is not sufficiently accurate. Some methods turn to graph-cuts for more accurate segmentation, as it incorporates neighbourhood information. In this work, we propose a new graph-cuts cost function combining appearance and shape, where CT intensity represents appearance and vesselness from a Hessian-based filter represents shape. Due to the amount of voxels in high resolution CT scans, the memory requirement and time consumption for building a graph structure is very high. In order to make the graph representation computationally tractable, those voxels that are considered clearly background are removed from the graph nodes, using a threshold on the vesselness map. The graph structure is then established based on the remaining voxel nodes, source/sink nodes and the neighbourhood relationship of the remaining voxels. Vessels are segmented by minimizing the energy cost function with the graph-cuts optimization framework. We optimized the parameters used in the graph-cuts cost function and evaluated the proposed method with two manually labeled sub-volumes. For independent evaluation, we used 20 CT scans of the VESSEL12 challenge. The evaluation results of the sub-volume data show that the proposed method produced a more accurate vessel segmentation compared to the previous methods, with F1 score 0.76 and 0.69. In the VESSEL12 data-set, our method obtained a competitive performance with an area under the ROC curve of 0.975, especially among the binary submissions.

  5. Anatomical Brain Images Alone Can Accurately Diagnose Chronic Neuropsychiatric Illnesses

    PubMed Central

    Bansal, Ravi; Staib, Lawrence H.; Laine, Andrew F.; Hao, Xuejun; Xu, Dongrong; Liu, Jun; Weissman, Myrna; Peterson, Bradley S.

    2012-01-01

    Objective Diagnoses using imaging-based measures alone offer the hope of improving the accuracy of clinical diagnosis, thereby reducing the costs associated with incorrect treatments. Previous attempts to use brain imaging for diagnosis, however, have had only limited success in diagnosing patients who are independent of the samples used to derive the diagnostic algorithms. We aimed to develop a classification algorithm that can accurately diagnose chronic, well-characterized neuropsychiatric illness in single individuals, given the availability of sufficiently precise delineations of brain regions across several neural systems in anatomical MR images of the brain. Methods We have developed an automated method to diagnose individuals as having one of various neuropsychiatric illnesses using only anatomical MRI scans. The method employs a semi-supervised learning algorithm that discovers natural groupings of brains based on the spatial patterns of variation in the morphology of the cerebral cortex and other brain regions. We used split-half and leave-one-out cross-validation analyses in large MRI datasets to assess the reproducibility and diagnostic accuracy of those groupings. Results In MRI datasets from persons with Attention-Deficit/Hyperactivity Disorder, Schizophrenia, Tourette Syndrome, Bipolar Disorder, or persons at high or low familial risk for Major Depressive Disorder, our method discriminated with high specificity and nearly perfect sensitivity the brains of persons who had one specific neuropsychiatric disorder from the brains of healthy participants and the brains of persons who had a different neuropsychiatric disorder. Conclusions Although the classification algorithm presupposes the availability of precisely delineated brain regions, our findings suggest that patterns of morphological variation across brain surfaces, extracted from MRI scans alone, can successfully diagnose the presence of chronic neuropsychiatric disorders. Extensions of these

  6. Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm.

    PubMed

    Yang, Zhang; Shufan, Ye; Li, Guo; Weifeng, Ding

    2016-01-01

    The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. PMID:27403428

  7. Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

    PubMed Central

    Yang, Zhang; Li, Guo; Weifeng, Ding

    2016-01-01

    The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. PMID:27403428

  8. SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction

    PubMed Central

    Zhao, Quanhua; Li, Yu; Liu, Zhenggang

    2013-01-01

    This paper presents a new segmentation-based algorithm for oil spill feature extraction from Synthetic Aperture Radar (SAR) intensity images. The proposed algorithm combines a Voronoi tessellation, Bayesian inference and Markov Chain Monte Carlo (MCMC) scheme. The shape and distribution features of dark spots can be obtained by segmenting a scene covering an oil spill and/or look-alikes into two homogenous regions: dark spots and their marine surroundings. The proposed algorithm is applied simultaneously to several real SAR intensity images and simulated SAR intensity images which are used for accurate evaluation. The results show that the proposed algorithm can extract the shape and distribution parameters of dark spot areas, which are useful for recognizing oil spills in a further classification stage. PMID:24233074

  9. Shape “Break-and-Repair” Strategy and Its Application to Automated Medical Image Segmentation

    PubMed Central

    Pu, Jiantao; Paik, David S.; Meng, Xin; Roos, Justus E.; Rubin, Geoffrey D.

    2011-01-01

    In three-dimensional medical imaging, segmentation of specific anatomy structure is often a preprocessing step for computer-aided detection/diagnosis (CAD) purposes, and its performance has a significant impact on diagnosis of diseases as well as objective quantitative assessment of therapeutic efficacy. However, the existence of various diseases, image noise or artifacts, and individual anatomical variety generally impose a challenge for accurate segmentation of specific structures. To address these problems, a shape analysis strategy termed “break-and-repair” is presented in this study to facilitate automated medical image segmentation. Similar to surface approximation using a limited number of control points, the basic idea is to remove problematic regions and then estimate a smooth and complete surface shape by representing the remaining regions with high fidelity as an implicit function. The innovation of this shape analysis strategy is the capability of solving challenging medical image segmentation problems in a unified framework, regardless of the variability of anatomical structures in question. In our implementation, principal curvature analysis is used to identify and remove the problematic regions and radial basis function (RBF) based implicit surface fitting is used to achieve a closed (or complete) surface boundary. The feasibility and performance of this strategy are demonstrated by applying it to automated segmentation of two completely different anatomical structures depicted on CT examinations, namely human lungs and pulmonary nodules. Our quantitative experiments on a large number of clinical CT examinations collected from different sources demonstrate the accuracy, robustness, and generality of the shape “break-and-repair” strategy in medical image segmentation. PMID:21071791

  10. A Simulation Study on Segmentation Methods of the Soil Aggregate Microtomographic Images

    NASA Astrophysics Data System (ADS)

    Wang, W.; Kravchenko, A.; Ananyeva, K.; Smucker, A.; Lim, C.; Rivers, M.

    2009-05-01

    Advances in X-ray microtomography open up a new way for examining the internal structures of soil aggregates in 3D space with a resolution of only several microns. However, processing of X-ray soil images in order to obtain reliable representations of pore geometries within aggregate pore remain to be established. Multiple approaches to the segmentation algorithms used to best separate gray-scale images into pores and solid material. Segmentation of soil volumes requires a combination of multiple interactive algorithms that identify specific properties of the studied features of each volume. Additionally, similar 3D objects with known pore geometries and connectivities are needed to provide specific information that identifies the most accurate segmentation of microtomographic images. The objective of this study was to compare the performance of segmentation methods on simulated soil aggregate images with various porosities as scenarios of the ground-truth standards. Simulations of the soil aggregate images were conducted on pore and solid spaces respectively. For the pore space, taking into consideration of partial volume and other pronounced artifacts, several layers of the pores at different scales were created and overlaid and random Gaussian noises were added. For the solid space, LU decomposition technique on a Gaussian random field with a specified mean and covariance structure was applied on a conditional data set of the known pore space. Several different kinds of segmentation methods, namely, entropy-based methods, indicator kriging methods and clustering methods, were examined and compared based on thresholding criterion such as non-uniformity measure and misclassification error. Majority filtering was applied to smooth the resulting images. We found that clustering methods uniformly outperformed two other methods, especially in the relatively low porosity cases. Moreover, the indicator kriging method performs better in high porosity cases, however, its

  11. Automatic segmentation of chromatographic images for region of interest delineation

    NASA Astrophysics Data System (ADS)

    Mendonça, Ana M.; Sousa, António V.; Sá-Miranda, M. Clara; Campilho, Aurélio C.

    2011-03-01

    This paper describes a segmentation method for automating the region of interest (ROI) delineation in chromatographic images, thus allowing the definition of the image area that contains the fundamental information for further processing while excluding the frame of the chromatographic plate that does not contain relevant data for disease identification. This is the first component of a screening tool for Fabry disease, which will be based on the automatic analysis of the chromatographic patterns extracted from the image ROI. Image segmentation is performed in two phases, where each individual pixel is finally considered as frame or ROI. In the first phase, an unsupervised learning method is used for classifying image pixels into three classes: frame, ROI or unknown. In the second phase, distance features are used for deciding which class the unknown pixels belong to. The segmentation result is post-processed using a sequence of morphological operators in order to obtain the final ROI rectangular area. The proposed methodology was successfully evaluated in a dataset of 41 chromatographic images.

  12. Automated vessel shadow segmentation of fovea-centered spectral-domain images from multiple OCT devices

    NASA Astrophysics Data System (ADS)

    Wu, Jing; Gerendas, Bianca S.; Waldstein, Sebastian M.; Simader, Christian; Schmidt-Erfurth, Ursula

    2014-03-01

    Spectral-domain Optical Coherence Tomography (SD-OCT) is a non-invasive modality for acquiring high reso- lution, three-dimensional (3D) cross sectional volumetric images of the retina and the subretinal layers. SD-OCT also allows the detailed imaging of retinal pathology, aiding clinicians in the diagnosis of sight degrading diseases such as age-related macular degeneration (AMD) and glaucoma.1 Disease diagnosis, assessment, and treatment requires a patient to undergo multiple OCT scans, possibly using different scanning devices, to accurately and precisely gauge disease activity, progression and treatment success. However, the use of OCT imaging devices from different vendors, combined with patient movement may result in poor scan spatial correlation, potentially leading to incorrect patient diagnosis or treatment analysis. Image registration can be used to precisely compare disease states by registering differing 3D scans to one another. In order to align 3D scans from different time- points and vendors using registration, landmarks are required, the most obvious being the retinal vasculature. Presented here is a fully automated cross-vendor method to acquire retina vessel locations for OCT registration from fovea centred 3D SD-OCT scans based on vessel shadows. Noise filtered OCT scans are flattened based on vendor retinal layer segmentation, to extract the retinal pigment epithelium (RPE) layer of the retina. Voxel based layer profile analysis and k-means clustering is used to extract candidate vessel shadow regions from the RPE layer. In conjunction, the extracted RPE layers are combined to generate a projection image featuring all candidate vessel shadows. Image processing methods for vessel segmentation of the OCT constructed projection image are then applied to optimize the accuracy of OCT vessel shadow segmentation through the removal of false positive shadow regions such as those caused by exudates and cysts. Validation of segmented vessel shadows uses

  13. A general approach to liver lesion segmentation in CT images

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

    Lesion segmentation has remained a challenge in different body regions. Generalizability is lacking in published methods as variability in results is common, even for a given organ and modality, such that it becomes difficult to establish standardized methods of disease quantification and reporting. This paper makes an attempt at a generalizable method based on classifying lesions along with their background into groups using clinically used visual attributes. Using an Iterative Relative Fuzzy Connectedness (IRFC) delineation engine, the ideas are implemented for the task of liver lesion segmentation in computed tomography (CT) images. For lesion groups with the same background properties, a few subjects are chosen as the training set to obtain the optimal IRFC parameters for the background tissue components. For lesion groups with similar foreground properties, optimal foreground parameters for IRFC are set as the median intensity value of the training lesion subset. To segment liver lesions belonging to a certain group, the devised method requires manual loading of the corresponding parameters, and correct setting of the foreground and background seeds. The segmentation is then completed in seconds. Segmentation accuracy and repeatability with respect to seed specification are evaluated. Accuracy is assessed by the assignment of a delineation quality score (DQS) to each case. Inter-operator repeatability is assessed by the difference between segmentations carried out independently by two operators. Experiments on 80 liver lesion cases show that the proposed method achieves a mean DQS score of 4.03 and inter-operator repeatability of 92.3%.

  14. Automated Drusen Segmentation and Quantification in SD-OCT Images

    PubMed Central

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

    2013-01-01

    Spectral domain optical coherence tomography (SD-OCT) is a useful tool for the visualization of drusen, a retinal abnormality seen in patients with age-related macular degeneration (AMD); however, objective assessment of drusen is thwarted by the lack of a method to robustly quantify these lesions on serial OCT images. Here, we describe an automatic drusen segmentation method for SD-OCT retinal images, which leverages a priori knowledge of normal retinal morphology and anatomical features. The highly reflective and locally connected pixels located below the retinal nerve fiber layer (RNFL) are used to generate a segmentation of the retinal pigment epithelium (RPE) layer. The observed and expected contours of the RPE layer are obtained by interpolating and fitting the shape of the segmented RPE layer, respectively. The areas located between the interpolated and fitted RPE shapes (which have nonzero area when drusen occurs) are marked as drusen. To enhance drusen quantification, we also developed a novel method of retinal projection to generate an en face retinal image based on the RPE extraction, which improves the quality of drusen visualization over the current approach to producing retinal projections from SD-OCT images based on a summed-voxel projection (SVP), and it provides a means of obtaining quantitative features of drusen in the en face projection. Visualization of the segmented drusen is refined through several post-processing steps, drusen detection to eliminate false positive detections on consecutive slices, drusen refinement on a projection view of drusen, and drusen smoothing. Experimental evaluation results demonstrate that our method is effective for drusen segmentation. In a preliminary analysis of the potential clinical utility of our methods, quantitative drusen measurements, such as area and volume, can be correlated with the drusen progression in non-exudative AMD, suggesting that our approach may produce useful quantitative imaging biomarkers

  15. Automated 3D renal segmentation based on image partitioning

    NASA Astrophysics Data System (ADS)

    Yeghiazaryan, Varduhi; Voiculescu, Irina D.

    2016-03-01

    Despite several decades of research into segmentation techniques, automated medical image segmentation is barely usable in a clinical context, and still at vast user time expense. This paper illustrates unsupervised organ segmentation through the use of a novel automated labelling approximation algorithm followed by a hypersurface front propagation method. The approximation stage relies on a pre-computed image partition forest obtained directly from CT scan data. We have implemented all procedures to operate directly on 3D volumes, rather than slice-by-slice, because our algorithms are dimensionality-independent. The results picture segmentations which identify kidneys, but can easily be extrapolated to other body parts. Quantitative analysis of our automated segmentation compared against hand-segmented gold standards indicates an average Dice similarity coefficient of 90%. Results were obtained over volumes of CT data with 9 kidneys, computing both volume-based similarity measures (such as the Dice and Jaccard coefficients, true positive volume fraction) and size-based measures (such as the relative volume difference). The analysis considered both healthy and diseased kidneys, although extreme pathological cases were excluded from the overall count. Such cases are difficult to segment both manually and automatically due to the large amplitude of Hounsfield unit distribution in the scan, and the wide spread of the tumorous tissue inside the abdomen. In the case of kidneys that have maintained their shape, the similarity range lies around the values obtained for inter-operator variability. Whilst the procedure is fully automated, our tools also provide a light level of manual editing.

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

    PubMed Central

    Gribble, Megan; Pertsov, Arkady M.; Shi, Pengcheng

    2013-01-01

    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. PMID:24454530

  17. Graph cut based co-segmentation of lung tumor in PET-CT images

    NASA Astrophysics Data System (ADS)

    Ju, Wei; Xiang, Dehui; Zhang, Bin; Chen, Xinjian

    2015-03-01

    Accurate segmentation of pulmonary tumor is important for clinicians to make appropriate diagnosis and treatment. Positron Emission Tomography (PET) and Computed Tomography (CT) are two commonly used imaging technologies for image-guided radiation therapy. In this study, we present a graph-based method to integrate the two modalities to segment the tumor simultaneously on PET and CT images. The co-segmentation problem is formulated as an energy minimization problem. Two weighted sub-graphs are constructed for PET and CT. The characteristic information of the two modalities is encoded on the edges of the graph. A context cost is enforced by adding context arcs to achieve consistent results between the two modalities. An optimal solution can be achieved by solving a maximum flow problem. The proposed segmentation method was validated on 18 sets of PET-CT images from different patients with non-small cell lung cancer (NSCLC). The quantitative results show significant improvement of our method with a mean DSC value 0.82.

  18. Automatic cytoplasm and nuclei segmentation for color cervical smear image using an efficient gap-search MRF.

    PubMed

    Zhao, Lili; Li, Kuan; Wang, Mao; Yin, Jianping; Zhu, En; Wu, Chengkun; Wang, Siqi; Zhu, Chengzhang

    2016-04-01

    Accurate and effective cervical smear image segmentation is required for automated cervical cell analysis systems. Thus, we proposed a novel superpixel-based Markov random field (MRF) segmentation framework to acquire the nucleus, cytoplasm and image background of cell images. We seek to classify color non-overlapping superpixel-patches on one image for image segmentation. This model describes the whole image as an undirected probabilistic graphical model and was developed using an automatic label-map mechanism for determining nuclear, cytoplasmic and background regions. A gap-search algorithm was designed to enhance the model efficiency. Data show that the algorithms of our framework provide better accuracy for both real-world and the public Herlev datasets. Furthermore, the proposed gap-search algorithm of this model is much more faster than pixel-based and superpixel-based algorithms. PMID:26874832

  19. Correlation-based discrimination between cardiac tissue and blood for segmentation of 3D echocardiographic images

    NASA Astrophysics Data System (ADS)

    Saris, Anne E. C. M.; Nillesen, Maartje M.; Lopata, Richard G. P.; de Korte, Chris L.

    2013-03-01

    Automated segmentation of 3D echocardiographic images in patients with congenital heart disease is challenging, because the boundary between blood and cardiac tissue is poorly defined in some regions. Cardiologists mentally incorporate movement of the heart, using temporal coherence of structures to resolve ambiguities. Therefore, we investigated the merit of temporal cross-correlation for automated segmentation over the entire cardiac cycle. Optimal settings for maximum cross-correlation (MCC) calculation, based on a 3D cross-correlation based displacement estimation algorithm, were determined to obtain the best contrast between blood and myocardial tissue over the entire cardiac cycle. Resulting envelope-based as well as RF-based MCC values were used as additional external force in a deformable model approach, to segment the left-ventricular cavity in entire systolic phase. MCC values were tested against, and combined with, adaptive filtered, demodulated RF-data. Segmentation results were compared with manually segmented volumes using a 3D Dice Similarity Index (3DSI). Results in 3D pediatric echocardiographic images sequences (n = 4) demonstrate that incorporation of temporal information improves segmentation. The use of MCC values, either alone or in combination with adaptive filtered, demodulated RF-data, resulted in an increase of the 3DSI in 75% of the cases (average 3DSI increase: 0.71 to 0.82). Results might be further improved by optimizing MCC-contrast locally, in regions with low blood-tissue contrast. Reducing underestimation of the endocardial volume due to MCC processing scheme (choice of window size) and consequential border-misalignment, could also lead to more accurate segmentations. Furthermore, increasing the frame rate will also increase MCC-contrast and thus improve segmentation.

  20. Building Roof Segmentation from Aerial Images Using a Line-and Region-Based Watershed Segmentation Technique

    PubMed Central

    Merabet, Youssef El; Meurie, Cyril; Ruichek, Yassine; Sbihi, Abderrahmane; Touahni, Raja

    2015-01-01

    In this paper, we present a novel strategy for roof segmentation from aerial images (orthophotoplans) based on the cooperation of edge- and region-based segmentation methods. The proposed strategy is composed of three major steps. The first one, called the pre-processing step, consists of simplifying the acquired image with an appropriate couple of invariant and gradient, optimized for the application, in order to limit illumination changes (shadows, brightness, etc.) affecting the images. The second step is composed of two main parallel treatments: on the one hand, the simplified image is segmented by watershed regions. Even if the first segmentation of this step provides good results in general, the image is often over-segmented. To alleviate this problem, an efficient region merging strategy adapted to the orthophotoplan particularities, with a 2D modeling of roof ridges technique, is applied. On the other hand, the simplified image is segmented by watershed lines. The third step consists of integrating both watershed segmentation strategies into a single cooperative segmentation scheme in order to achieve satisfactory segmentation results. Tests have been performed on orthophotoplans containing 100 roofs with varying complexity, and the results are evaluated with the VINETcriterion using ground-truth image segmentation. A comparison with five popular segmentation techniques of the literature demonstrates the effectiveness and the reliability of the proposed approach. Indeed, we obtain a good segmentation rate of 96% with the proposed method compared to 87.5% with statistical region merging (SRM), 84% with mean shift, 82% with color structure code (CSC), 80% with efficient graph-based segmentation algorithm (EGBIS) and 71% with JSEG. PMID:25648706

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

    NASA Astrophysics Data System (ADS)

    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.

  2. Unsupervised segmentation of MRI knees using image partition forests

    NASA Astrophysics Data System (ADS)

    Marčan, Marija; Voiculescu, Irina

    2016-03-01

    Nowadays many people are affected by arthritis, a condition of the joints with limited prevention measures, but with various options of treatment the most radical of which is surgical. In order for surgery to be successful, it can make use of careful analysis of patient-based models generated from medical images, usually by manual segmentation. In this work we show how to automate the segmentation of a crucial and complex joint -- the knee. To achieve this goal we rely on our novel way of representing a 3D voxel volume as a hierarchical structure of partitions which we have named Image Partition Forest (IPF). The IPF contains several partition layers of increasing coarseness, with partitions nested across layers in the form of adjacency graphs. On the basis of a set of properties (size, mean intensity, coordinates) of each node in the IPF we classify nodes into different features. Values indicating whether or not any particular node belongs to the femur or tibia are assigned through node filtering and node-based region growing. So far we have evaluated our method on 15 MRI knee images. Our unsupervised segmentation compared against a hand-segmented gold standard has achieved an average Dice similarity coefficient of 0.95 for femur and 0.93 for tibia, and an average symmetric surface distance of 0.98 mm for femur and 0.73 mm for tibia. The paper also discusses ways to introduce stricter morphological and spatial conditioning in the bone labelling process.

  3. Superpixel Cut for Figure-Ground Image Segmentation

    NASA Astrophysics Data System (ADS)

    Yang, Michael Ying; Rosenhahn, Bodo

    2016-06-01

    Figure-ground image segmentation has been a challenging problem in computer vision. Apart from the difficulties in establishing an effective framework to divide the image pixels into meaningful groups, the notions of figure and ground often need to be properly defined by providing either user inputs or object models. In this paper, we propose a novel graph-based segmentation framework, called superpixel cut. The key idea is to formulate foreground segmentation as finding a subset of superpixels that partitions a graph over superpixels. The problem is formulated as Min-Cut. Therefore, we propose a novel cost function that simultaneously minimizes the inter-class similarity while maximizing the intra-class similarity. This cost function is optimized using parametric programming. After a small learning step, our approach is fully automatic and fully bottom-up, which requires no high-level knowledge such as shape priors and scene content. It recovers coherent components of images, providing a set of multiscale hypotheses for high-level reasoning. We evaluate our proposed framework by comparing it to other generic figure-ground segmentation approaches. Our method achieves improved performance on state-of-the-art benchmark databases.

  4. Comparison of perceptual color spaces for natural image segmentation tasks

    NASA Astrophysics Data System (ADS)

    Correa-Tome, Fernando E.; Sanchez-Yanez, Raul E.; Ayala-Ramirez, Victor

    2011-11-01

    Color image segmentation largely depends on the color space chosen. Furthermore, spaces that show perceptual uniformity seem to outperform others due to their emulation of the human perception of color. We evaluate three perceptual color spaces, CIELAB, CIELUV, and RLAB, in order to determine their contribution to natural image segmentation and to identify the space that obtains the best results over a test set of images. The nonperceptual color space RGB is also included for reference purposes. In order to quantify the quality of resulting segmentations, an empirical discrepancy evaluation methodology is discussed. The Berkeley Segmentation Dataset and Benchmark is used in test series, and two approaches are taken to perform the experiments: supervised pixelwise classification using reference colors, and unsupervised clustering using k-means. A majority filter is used as a postprocessing stage, in order to determine its contribution to the result. Furthermore, a comparison of elapsed times taken by the required transformations is included. The main finding of our study is that the CIELUV color space outperforms the other color spaces in both discriminatory performance and computational speed, for the average case.

  5. 3D segmentation of prostate ultrasound images using wavelet transform

    NASA Astrophysics Data System (ADS)

    Akbari, Hamed; Yang, Xiaofeng; Halig, Luma V.; Fei, Baowei

    2011-03-01

    The current definitive diagnosis of prostate cancer is transrectal ultrasound (TRUS) guided biopsy. However, the current procedure is limited by using 2D biopsy tools to target 3D biopsy locations. This paper presents a new method for automatic segmentation of the prostate in three-dimensional transrectal ultrasound images, by extracting texture features and by statistically matching geometrical shape of the prostate. A set of Wavelet-based support vector machines (WSVMs) are located and trained at different regions of the prostate surface. The WSVMs capture texture priors of ultrasound images for classification of the prostate and non-prostate tissues in different zones around the prostate boundary. In the segmentation procedure, these W-SVMs are trained in three sagittal, coronal, and transverse planes. The pre-trained W-SVMs are employed to tentatively label each voxel around the surface of the model as a prostate or non-prostate voxel by the texture matching. The labeled voxels in three planes after post-processing is overlaid on a prostate probability model. The probability prostate model is created using 10 segmented prostate data. Consequently, each voxel has four labels: sagittal, coronal, and transverse planes and one probability label. By defining a weight function for each labeling in each region, each voxel is labeled as a prostate or non-prostate voxel. Experimental results by using real patient data show the good performance of the proposed model in segmenting the prostate from ultrasound images.

  6. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).

    PubMed

    Menze, Bjoern H; Jakab, Andras; Bauer, Stefan; Kalpathy-Cramer, Jayashree; Farahani, Keyvan; Kirby, Justin; Burren, Yuliya; Porz, Nicole; Slotboom, Johannes; Wiest, Roland; Lanczi, Levente; Gerstner, Elizabeth; Weber, Marc-André; Arbel, Tal; Avants, Brian B; Ayache, Nicholas; Buendia, Patricia; Collins, D Louis; Cordier, Nicolas; Corso, Jason J; Criminisi, Antonio; Das, Tilak; Delingette, Hervé; Demiralp, Çağatay; Durst, Christopher R; Dojat, Michel; Doyle, Senan; Festa, Joana; Forbes, Florence; Geremia, Ezequiel; Glocker, Ben; Golland, Polina; Guo, Xiaotao; Hamamci, Andac; Iftekharuddin, Khan M; Jena, Raj; John, Nigel M; Konukoglu, Ender; Lashkari, Danial; Mariz, José Antonió; Meier, Raphael; Pereira, Sérgio; Precup, Doina; Price, Stephen J; Raviv, Tammy Riklin; Reza, Syed M S; Ryan, Michael; Sarikaya, Duygu; Schwartz, Lawrence; Shin, Hoo-Chang; Shotton, Jamie; Silva, Carlos A; Sousa, Nuno; Subbanna, Nagesh K; Szekely, Gabor; Taylor, Thomas J; Thomas, Owen M; Tustison, Nicholas J; Unal, Gozde; Vasseur, Flor; Wintermark, Max; Ye, Dong Hye; Zhao, Liang; Zhao, Binsheng; Zikic, Darko; Prastawa, Marcel; Reyes, Mauricio; Van Leemput, Koen

    2015-10-01

    In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource. PMID:25494501

  7. Segmentation of intensity inhomogeneous brain MR images using active contours.

    PubMed

    Akram, Farhan; Kim, Jeong Heon; Lim, Han Ul; Choi, Kwang Nam

    2014-01-01

    Segmentation of intensity inhomogeneous regions is a well-known problem in image analysis applications. This paper presents a region-based active contour method for image segmentation, which properly works in the context of intensity inhomogeneity problem. The proposed region-based active contour method embeds both region and gradient information unlike traditional methods. It contains mainly two terms, area and length, in which the area term practices a new region-based signed pressure force (SPF) function, which utilizes mean values from a certain neighborhood using the local binary fitted (LBF) energy model. In turn, the length term uses gradient information. The novelty of our method is to locally compute new SPF function, which uses local mean values and is able to detect boundaries of the homogenous regions. Finally, a truncated Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need of computationally expensive reinitialization. The proposed method targets the segmentation problem of intensity inhomogeneous images and reduces the time complexity among locally computed active contour methods. The experimental results show that the proposed method yields better segmentation result as well as less time complexity compared with the state-of-the-art active contour methods. PMID:25143780

  8. Cryo-EM Structure Determination Using Segmented Helical Image Reconstruction.

    PubMed

    Fromm, S A; Sachse, C

    2016-01-01

    Treating helices as single-particle-like segments followed by helical image reconstruction has become the method of choice for high-resolution structure determination of well-ordered helical viruses as well as flexible filaments. In this review, we will illustrate how the combination of latest hardware developments with optimized image processing routines have led to a series of near-atomic resolution structures of helical assemblies. Originally, the treatment of helices as a sequence of segments followed by Fourier-Bessel reconstruction revealed the potential to determine near-atomic resolution structures from helical specimens. In the meantime, real-space image processing of helices in a stack of single particles was developed and enabled the structure determination of specimens that resisted classical Fourier helical reconstruction and also facilitated high-resolution structure determination. Despite the progress in real-space analysis, the combination of Fourier and real-space processing is still commonly used to better estimate the symmetry parameters as the imposition of the correct helical symmetry is essential for high-resolution structure determination. Recent hardware advancement by the introduction of direct electron detectors has significantly enhanced the image quality and together with improved image processing procedures has made segmented helical reconstruction a very productive cryo-EM structure determination method. PMID:27572732

  9. Object Segmentation and Ground Truth in 3D Embryonic Imaging

    PubMed Central

    Rajasekaran, Bhavna; Uriu, Koichiro; Valentin, Guillaume; Tinevez, Jean-Yves; Oates, Andrew C.

    2016-01-01

    Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets. PMID:27332860

  10. Semi-Automated Segmentation of Microbes in Color Images

    NASA Astrophysics Data System (ADS)

    Reddy, Chandankumar K.; Liu, Feng-I.; Dazzo, Frank B.

    2003-01-01

    The goal of this work is to develop a system that can semi-automate the detection of multicolored foreground objects in digitized color images that also contain complex and very noisy backgrounds. Although considered a general problem of color image segmentation, our application is microbiology where various colored stains are used to reveal information on the microbes without cultivation. Instead of providing a simple threshold, the proposed system offers an interactive environment whereby the user chooses multiple sample points to define the range of color pixels comprising the foreground microbes of interest. The system then uses the color and spatial distances of these target points to segment the microbes from the confusing background of pixels whose RGB values lie outside the newly defined range and finally finds each cell's boundary using region-growing and mathematical morphology. Some other image processing methods are also applied to enhance the resultant image containing the colored microbes against a noise-free background. The prototype performs with 98% accuracy on a test set compared to ground truth data. The system described here will have many applications in image processing and analysis where one needs to segment typical pixel regions of similar but non-identical colors.

  11. Robust supervised segmentation of neuropathology whole-slide microscopy images.

    PubMed

    Vandenberghe, Michel E; Balbastre, Yaël; Souedet, Nicolas; Hérard, Anne-Sophie; Dhenain, Marc; Frouin, Frédérique; Delzescaux, Thierry

    2015-08-01

    Alzheimer's disease is characterized by brain pathological aggregates such as Aβ plaques and neurofibrillary tangles which trigger neuroinflammation and participate to neuronal loss. Quantification of these pathological markers on histological sections is widely performed to study the disease and to evaluate new therapies. However, segmentation of neuropathology images presents difficulties inherent to histology (presence of debris, tissue folding, non-specific staining) as well as specific challenges (sparse staining, irregular shape of the lesions). Here, we present a supervised classification approach for the robust pixel-level classification of large neuropathology whole slide images. We propose a weighted form of Random Forest in order to fit nonlinear decision boundaries that take into account class imbalance. Both color and texture descriptors were used as predictors and model selection was performed via a leave-one-image-out cross-validation scheme. Our method showed superior results compared to the current state of the art method when applied to the segmentation of Aβ plaques and neurofibrillary tangles in a human brain sample. Furthermore, using parallel computing, our approach easily scales-up to large gigabyte-sized images. To show this, we segmented a whole brain histology dataset of a mouse model of Alzheimer's disease. This demonstrates our method relevance as a routine tool for whole slide microscopy images analysis in clinical and preclinical research settings. PMID:26737134

  12. Automatic segmentation of lung fields on chest radiographic images.

    PubMed

    Carreira, M J; Cabello, D; Mosquera, A

    1999-06-01

    In this work we have implemented a system for the automatic segmentation of lung fields in chest radiographic images. The image analysis process is carried out in three levels. In the first one we perform operations on the image that are independent from domain knowledge. This knowledge is implicitly and not very elaborately used in the intermediate level and used in an explicit manner in the high level block, globally corresponding to the idea of progressive segmentation. The representation of knowledge in the high level block is in the form of production rules. The control structure is in general bottom-up but there are certain hybrid control stages, in which the control is driven by the region model (main organs) we are seeking. We have applied the global system to a set of 45 posteroanterior (PA) chest radiographs, obtaining a mean degree of overlap with contours drawn by radiologists of 87%. PMID:10356306

  13. Unsupervised color image segmentation using a lattice algebra clustering technique

    NASA Astrophysics Data System (ADS)

    Urcid, Gonzalo; Ritter, Gerhard X.

    2011-08-01

    In this paper we introduce a lattice algebra clustering technique for segmenting digital images in the Red-Green- Blue (RGB) color space. The proposed technique is a two step procedure. Given an input color image, the first step determines the finite set of its extreme pixel vectors within the color cube by means of the scaled min-W and max-M lattice auto-associative memory matrices, including the minimum and maximum vector bounds. In the second step, maximal rectangular boxes enclosing each extreme color pixel are found using the Chebychev distance between color pixels; afterwards, clustering is performed by assigning each image pixel to its corresponding maximal box. The two steps in our proposed method are completely unsupervised or autonomous. Illustrative examples are provided to demonstrate the color segmentation results including a brief numerical comparison with two other non-maximal variations of the same clustering technique.

  14. Level set method for image segmentation based on moment competition

    NASA Astrophysics Data System (ADS)

    Min, Hai; Wang, Xiao-Feng; Huang, De-Shuang; Jin, Jing; Wang, Hong-Zhi; Li, Hai

    2015-05-01

    We propose a level set method for image segmentation which introduces the moment competition and weakly supervised information into the energy functional construction. Different from the region-based level set methods which use force competition, the moment competition is adopted to drive the contour evolution. Here, a so-called three-point labeling scheme is proposed to manually label three independent points (weakly supervised information) on the image. Then the intensity differences between the three points and the unlabeled pixels are used to construct the force arms for each image pixel. The corresponding force is generated from the global statistical information of a region-based method and weighted by the force arm. As a result, the moment can be constructed and incorporated into the energy functional to drive the evolving contour to approach the object boundary. In our method, the force arm can take full advantage of the three-point labeling scheme to constrain the moment competition. Additionally, the global statistical information and weakly supervised information are successfully integrated, which makes the proposed method more robust than traditional methods for initial contour placement and parameter setting. Experimental results with performance analysis also show the superiority of the proposed method on segmenting different types of complicated images, such as noisy images, three-phase images, images with intensity inhomogeneity, and texture images.

  15. Automated multimodality concurrent classification for segmenting vessels in 3D spectral OCT and color fundus images

    NASA Astrophysics Data System (ADS)

    Hu, Zhihong; Abràmoff, Michael D.; Niemeijer, Meindert; Garvin, Mona K.

    2011-03-01

    Segmenting vessels in spectral-domain optical coherence tomography (SD-OCT) volumes is particularly challenging in the region near and inside the neural canal opening (NCO). Furthermore, accurately segmenting them in color fundus photographs also presents a challenge near the projected NCO. However, both modalities also provide complementary information to help indicate vessels, such as a better NCO contrast from the NCO-aimed OCT projection image and a better vessel contrast inside the NCO from fundus photographs. We thus present a novel multimodal automated classification approach for simultaneously segmenting vessels in SD-OCT volumes and fundus photographs, with a particular focus on better segmenting vessels near and inside the NCO by using a combination of their complementary features. In particular, in each SD-OCT volume, the algorithm pre-segments the NCO using a graph-theoretic approach and then applies oriented Gabor wavelets with oriented NCO-based templates to generate OCT image features. After fundus-to-OCT registration, the fundus image features are computed using Gaussian filter banks and combined with OCT image features. A k-NN classifier is trained on 5 and tested on 10 randomly chosen independent image pairs of SD-OCT volumes and fundus images from 15 subjects with glaucoma. Using ROC analysis, we demonstrate an improvement over two closest previous works performed in single modal SD-OCT volumes with an area under the curve (AUC) of 0.87 (0.81 for our and 0.72 for Niemeijer's single modal approach) in the region around the NCO and 0.90 outside the NCO (0.84 for our and 0.81 for Niemeijer's single modal approach).

  16. 3D automatic liver segmentation using feature-constrained Mahalanobis distance in CT images.

    PubMed

    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

  17. Automated segmentation of serous pigment epithelium detachment in SD-OCT images

    NASA Astrophysics Data System (ADS)

    Sun, Zhuli; Shi, Fei; Xiang, Dehui; Chen, Haoyu; Chen, Xinjian

    2015-03-01

    Pigment epithelium detachment (PED) is an important clinical manifestation of multiple chorio-retinal disease processes, which can cause the loss of central vision. A 3-D method is proposed to automatically segment serous PED in SD-OCT images. The proposed method consists of five steps: first, a curvature anisotropic diffusion filter is applied to remove speckle noise. Second, the graph search method is applied for abnormal retinal layer segmentation associated with retinal pigment epithelium (RPE) deformation. During this process, Bruch's membrane, which doesn't show in the SD-OCT images, is estimated with the convex hull algorithm. Third, the foreground and background seeds are automatically obtained from retinal layer segmentation result. Fourth, the serous PED is segmented based on the graph cut method. Finally, a post-processing step is applied to remove false positive regions based on mathematical morphology. The proposed method was tested on 20 SD-OCT volumes from 20 patients diagnosed with serous PED. The average true positive volume fraction (TPVF), false positive volume fraction (FPVF), dice similarity coefficient (DSC) and positive predictive value (PPV) are 97.19%, 0.03%, 96.34% and 95.59%, respectively. Linear regression analysis shows a strong correlation (r = 0.975) comparing the segmented PED volumes with the ground truth labeled by an ophthalmology expert. The proposed method can provide clinicians with accurate quantitative information, including shape, size and position of the PED regions, which can assist diagnose and treatment.

  18. Survey of contemporary trends in color image segmentation

    NASA Astrophysics Data System (ADS)

    Vantaram, Sreenath Rao; Saber, Eli

    2012-10-01

    In recent years, the acquisition of image and video information for processing, analysis, understanding, and exploitation of the underlying content in various applications, ranging from remote sensing to biomedical imaging, has grown at an unprecedented rate. Analysis by human observers is quite laborious, tiresome, and time consuming, if not infeasible, given the large and continuously rising volume of data. Hence the need for systems capable of automatically and effectively analyzing the aforementioned imagery for a variety of uses that span the spectrum from homeland security to elderly care. In order to achieve the above, tools such as image segmentation provide the appropriate foundation for expediting and improving the effectiveness of subsequent high-level tasks by providing a condensed and pertinent representation of image information. We provide a comprehensive survey of color image segmentation strategies adopted over the last decade, though notable contributions in the gray scale domain will also be discussed. Our taxonomy of segmentation techniques is sampled from a wide spectrum of spatially blind (or feature-based) approaches such as clustering and histogram thresholding as well as spatially guided (or spatial domain-based) methods such as region growing/splitting/merging, energy-driven parametric/geometric active contours, supervised/unsupervised graph cuts, and watersheds, to name a few. In addition, qualitative and quantitative results of prominent algorithms on several images from the Berkeley segmentation dataset are shown in order to furnish a fair indication of the current quality of the state of the art. Finally, we provide a brief discussion on our current perspective of the field as well as its associated future trends.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  20. Image enhancement and segmentation of fluid-filled structures in 3D ultrasound images

    NASA Astrophysics Data System (ADS)

    Chalana, Vikram; Dudycha, Stephen; McMorrow, Gerald

    2003-05-01

    Segmentation of fluid-filled structures, such as the urinary bladder, from three-dimensional ultrasound images is necessary for measuring their volume. This paper describes a system for image enhancement, segmentation and volume measurement of fluid-filled structures on 3D ultrasound images. The system was applied for the measurement of urinary bladder volume. Results show an average error of less than 10% in the estimation of the total bladder volume.

  1. Control of multiple excited image states around segmented carbon nanotubes.

    PubMed

    Knörzer, J; Fey, C; Sadeghpour, H R; Schmelcher, P

    2015-11-28

    Electronic image states around segmented carbon nanotubes can be confined and shaped along the nanotube axis by engineering the image potential. We show how several such image states can be prepared simultaneously along the same nanotube. The inter-electronic distance can be controlled a priori by engineering tubes of specific geometries. High sensitivity to external electric and magnetic fields can be exploited to manipulate these states and their mutual long-range interactions. These building blocks provide access to a new kind of tailored interacting quantum systems. PMID:26627961

  2. Control of multiple excited image states around segmented carbon nanotubes

    SciTech Connect

    Knörzer, J. Fey, C.; Sadeghpour, H. R.; Schmelcher, P.

    2015-11-28

    Electronic image states around segmented carbon nanotubes can be confined and shaped along the nanotube axis by engineering the image potential. We show how several such image states can be prepared simultaneously along the same nanotube. The inter-electronic distance can be controlled a priori by engineering tubes of specific geometries. High sensitivity to external electric and magnetic fields can be exploited to manipulate these states and their mutual long-range interactions. These building blocks provide access to a new kind of tailored interacting quantum systems.

  3. Accurate scatter compensation using neural networks in radionuclide imaging

    SciTech Connect

    Ogawa, Koichi; Nishizaki, N. . Dept. of Electrical Engineering)

    1993-08-01

    The paper presents a new method to estimate primary photons using an artificial neural network in radionuclide imaging. The neural network for [sup 99m]Tc had three layers, i.e., one input layer with five units, one hidden layer with five units, and one output layer with two units. As input values to the input units, the authors used count ratios which were the ratios of the counts acquired by narrow windows to the total count acquired by a broad window with the energy range from 125 to 154 keV. The outputs were a scatter count ratio and a primary count ratio. Using the primary count ratio and the total count they calculated the primary count of the pixel directly. The neural network was trained with a back-propagation algorithm using calculated true energy spectra obtained by a Monte Carlo method. The simulation showed that an accurate estimation of primary photons was accomplished within an error ratio of 5% for primary photons.

  4. A hierarchical method based on active shape models and directed Hough transform for segmentation of noisy biomedical images; application in segmentation of pelvic X-ray images

    PubMed Central

    Smith, Rebecca; Najarian, Kayvan; Ward, Kevin

    2009-01-01

    Background Traumatic pelvic injuries are often associated with severe, life-threatening hemorrhage, and immediate medical treatment is therefore vital. However, patient prognosis depends heavily on the type, location and severity of the bone fracture, and the complexity of the pelvic structure presents diagnostic challenges. Automated fracture detection from initial patient X-ray images can assist physicians in rapid diagnosis and treatment, and a first and crucial step of such a method is to segment key bone structures within the pelvis; these structures can then be analyzed for specific fracture characteristics. Active Shape Model has been applied for this task in other bone structures but requires manual initialization by the user. This paper describes a algorithm for automatic initialization and segmentation of key pelvic structures - the iliac crests, pelvic ring, left and right pubis and femurs - using a hierarchical approach that combines directed Hough transform and Active Shape Models. Results Performance of the automated algorithm is compared with results obtained via manual initialization. An error measures is calculated based on the shapes detected with each method and the gold standard shapes. ANOVA results on these error measures show that the automated algorithm performs at least as well as the manual method. Visual inspection by two radiologists and one trauma surgeon also indicates generally accurate performance. Conclusion The hierarchical algorithm described in this paper automatically detects and segments key structures from pelvic X-rays. Unlike various other x-ray segmentation methods, it does not require manual initialization or input. Moreover, it handles the inconsistencies between x-ray images in a clinical environment and performs successfully in the presence of fracture. This method and the segmentation results provide a valuable base for future work in fracture detection. PMID:19891796

  5. Fingerprint image segmentation based on multi-features histogram analysis

    NASA Astrophysics Data System (ADS)

    Wang, Peng; Zhang, Youguang

    2007-11-01

    An effective fingerprint image segmentation based on multi-features histogram analysis is presented. We extract a new feature, together with three other features to segment fingerprints. Two of these four features, each of which is related to one of the other two, are reciprocals with each other, so features are divided into two groups. These two features' histograms are calculated respectively to determine which feature group is introduced to segment the aim-fingerprint. The features could also divide fingerprints into two classes with high and low quality. Experimental results show that our algorithm could classify foreground and background effectively with lower computational cost, and it can also reduce pseudo-minutiae detected and improve the performance of AFIS.

  6. Colour image segmentation using unsupervised clustering technique for acute leukemia images

    NASA Astrophysics Data System (ADS)

    Halim, N. H. Abd; Mashor, M. Y.; Nasir, A. S. Abdul; Mustafa, N.; Hassan, R.

    2015-05-01

    Colour image segmentation has becoming more popular for computer vision due to its important process in most medical analysis tasks. This paper proposes comparison between different colour components of RGB(red, green, blue) and HSI (hue, saturation, intensity) colour models that will be used in order to segment the acute leukemia images. First, partial contrast stretching is applied on leukemia images to increase the visual aspect of the blast cells. Then, an unsupervised moving k-means clustering algorithm is applied on the various colour components of RGB and HSI colour models for the purpose of segmentation of blast cells from the red blood cells and background regions in leukemia image. Different colour components of RGB and HSI colour models have been analyzed in order to identify the colour component that can give the good segmentation performance. The segmented images are then processed using median filter and region growing technique to reduce noise and smooth the images. The results show that segmentation using saturation component of HSI colour model has proven to be the best in segmenting nucleus of the blast cells in acute leukemia image as compared to the other colour components of RGB and HSI colour models.

  7. Watertight modeling and segmentation of bifurcated Coronary arteries for blood flow simulation using CT imaging.

    PubMed

    Zhou, Haoyin; Sun, Peng; Ha, Seongmin; Lundine, Devon; Xiong, Guanglei

    2016-10-01

    Image-based simulation of blood flow using computational fluid dynamics has been shown to play an important role in the diagnosis of ischemic coronary artery disease. Accurate extraction of complex coronary artery structures in a watertight geometry is a prerequisite, but manual segmentation is both tedious and subjective. Several semi- and fully automated coronary artery extraction approaches have been developed but have faced several challenges. Conventional voxel-based methods allow for watertight segmentation but are slow and difficult to incorporate expert knowledge. Machine learning based methods are relatively fast and capture rich information embedded in manual annotations. Although sufficient for visualization and analysis of coronary anatomy, these methods cannot be used directly for blood flow simulation if the coronary vasculature is represented as a loose combination of tubular structures and the bifurcation geometry is improperly modeled. In this paper, we propose a novel method to extract branching coronary arteries from CT imaging with a focus on explicit bifurcation modeling and application of machine learning. A bifurcation lumen is firstly modeled by generating the convex hull to join tubular vessel branches. Guided by the pre-determined centerline, machine learning based segmentation is performed to adapt the bifurcation lumen model to target vessel boundaries and smoothed by subdivision surfaces. Our experiments show the constructed coronary artery geometry from CT imaging is accurate by comparing results against the manually annotated ground-truths, and can be directly applied to coronary blood flow simulation. PMID:27490317

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

    NASA Astrophysics Data System (ADS)

    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.

  9. A fully automatic multi-atlas based segmentation method for prostate MR images

    NASA Astrophysics Data System (ADS)

    Tian, Zhiqiang; Liu, LiZhi; Fei, Baowei

    2015-03-01

    Most of multi-atlas segmentation methods focus on the registration between the full-size volumes of the data set. Although the transformations obtained from these registrations may be accurate for the global field of view of the images, they may not be accurate for the local prostate region. This is because different magnetic resonance (MR) images have different fields of view and may have large anatomical variability around the prostate. To overcome this limitation, we proposed a two-stage prostate segmentation method based on a fully automatic multi-atlas framework, which includes the detection stage i.e. locating the prostate, and the segmentation stage i.e. extracting the prostate. The purpose of the first stage is to find a cuboid that contains the whole prostate as small cubage as possible. In this paper, the cuboid including the prostate is detected by registering atlas edge volumes to the target volume while an edge detection algorithm is applied to every slice in the volumes. At the second stage, the proposed method focuses on the registration in the region of the prostate vicinity, which can improve the accuracy of the prostate segmentation. We evaluated the proposed method on 12 patient MR volumes by performing a leave-one-out study. Dice similarity coefficient (DSC) and Hausdorff distance (HD) are used to quantify the difference between our method and the manual ground truth. The proposed method yielded a DSC of 83.4%+/-4.3%, and a HD of 9.3 mm+/-2.6 mm. The fully automated segmentation method can provide a useful tool in many prostate imaging applications.

  10. An efficient MRF embedded level set method for image segmentation.

    PubMed

    Yang, Xi; Gao, Xinbo; Tao, Dacheng; Li, Xuelong; Li, Jie

    2015-01-01

    This paper presents a fast and robust level set method for image segmentation. To enhance the robustness against noise, we embed a Markov random field (MRF) energy function to the conventional level set energy function. This MRF energy function builds the correlation of a pixel with its neighbors and encourages them to fall into the same region. To obtain a fast implementation of the MRF embedded level set model, we explore algebraic multigrid (AMG) and sparse field method (SFM) to increase the time step and decrease the computation domain, respectively. Both AMG and SFM can be conducted in a parallel fashion, which facilitates the processing of our method for big image databases. By comparing the proposed fast and robust level set method with the standard level set method and its popular variants on noisy synthetic images, synthetic aperture radar (SAR) images, medical images, and natural images, we comprehensively demonstrate the new method is robust against various kinds of noises. In particular, the new level set method can segment an image of size 500 × 500 within 3 s on MATLAB R2010b installed in a computer with 3.30-GHz CPU and 4-GB memory. PMID:25420261

  11. Crowdsourcing the creation of image segmentation algorithms for connectomics

    PubMed Central

    Arganda-Carreras, Ignacio; Turaga, Srinivas C.; Berger, Daniel R.; Cireşan, Dan; Giusti, Alessandro; Gambardella, Luca M.; Schmidhuber, Jürgen; Laptev, Dmitry; Dwivedi, Sarvesh; Buhmann, Joachim M.; Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga; Kamentsky, Lee; Burget, Radim; Uher, Vaclav; Tan, Xiao; Sun, Changming; Pham, Tuan D.; Bas, Erhan; Uzunbas, Mustafa G.; Cardona, Albert; Schindelin, Johannes; Seung, H. Sebastian

    2015-01-01

    To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This “deep learning” approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge. PMID:26594156

  12. Cerebral magnetic resonance image segmentation using data fusion

    SciTech Connect

    Rajapakse, J.C.; Giedd, J.N.; Krain, A.L.; Hamburger, S.D.; Rapoport, J.L.; DeCarli, C.

    1996-03-01

    A semiautomated method is described for segmenting dual echo MR head scans into gray and white matter and CSF. The method is applied to brain scans of 80 healthy children and adolescents. A probabilistic data fusion equation was used to combine simultaneously acquired T2-weighted and proton density head scans for tissue segmentation. The fusion equation optimizes the probability of a voxel being a particular tissue type, given the corresponding probabilities from both images. The algorithm accounts for the intensity inhomogeneities present in the images by fusion of local regions of the images. The method was validated using a phantom (agarose gel with iron oxide particles) and hand-segmented imager. Gray and white matter volumes for subjects aged 20-30 years were close to those previously published. White matter and CSF volume increased and gray matter volume decreased significantly across ages 4-18 years. White matter, gray matter, and CSF volumes were larger for males than for females. Males and females showed similar change of gray and white matter volumes with age. This simple, reliable, and valid method can be employed in clinical research for quantification of gray and white matter and CSF volumes in MR head scans. Increase in white matter volume may reflect ongoing axonal growth and myelination, and gray matter reductions may reflect synaptic pruning or cell death in the age span of 4-18 years. 41 refs., 5 figs., 3 tabs.

  13. Crowdsourcing the creation of image segmentation algorithms for connectomics.

    PubMed

    Arganda-Carreras, Ignacio; Turaga, Srinivas C; Berger, Daniel R; Cireşan, Dan; Giusti, Alessandro; Gambardella, Luca M; Schmidhuber, Jürgen; Laptev, Dmitry; Dwivedi, Sarvesh; Buhmann, Joachim M; Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga; Kamentsky, Lee; Burget, Radim; Uher, Vaclav; Tan, Xiao; Sun, Changming; Pham, Tuan D; Bas, Erhan; Uzunbas, Mustafa G; Cardona, Albert; Schindelin, Johannes; Seung, H Sebastian

    2015-01-01

    To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This "deep learning" approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge. PMID:26594156

  14. Joint graph cut and relative fuzzy connectedness image segmentation algorithm.

    PubMed

    Ciesielski, Krzysztof Chris; Miranda, Paulo A V; Falcão, Alexandre X; Udupa, Jayaram K

    2013-12-01

    We introduce an image segmentation algorithm, called GC(sum)(max), which combines, in novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness (RFC) and (standard) Graph Cut (GC). We show, both theoretically and experimentally, that GC(sum)(max) preserves robustness of RFC with respect to the seed choice (thus, avoiding "shrinking problem" of GC), while keeping GC's stronger control over the problem of "leaking though poorly defined boundary segments." The analysis of GC(sum)(max) is greatly facilitated by our recent theoretical results that RFC can be described within the framework of Generalized GC (GGC) segmentation algorithms. In our implementation of GC(sum)(max) we use, as a subroutine, a version of RFC algorithm (based on Image Forest Transform) that runs (provably) in linear time with respect to the image size. This results in GC(sum)(max) running in a time close to linear. Experimental comparison of GC(sum)(max) to GC, an iterative version of RFC (IRFC), and power watershed (PW), based on a variety medical and non-medical images, indicates superior accuracy performance of GC(sum)(max) over these other methods, resulting in a rank ordering of GC(sum)(max)>PW∼IRFC>GC. PMID:23880374

  15. Detection of windthrow areas by object based image segmentation

    NASA Astrophysics Data System (ADS)

    Schmoeckel, J.; Kauffmann, M.

    2003-04-01

    In high resolution aerial images, areas that are uniform from the view of the application are not represented by an average spectral pattern, but are resolved into their components. While this enhanced information content offers the possibility of a more differentiating and correct classification, the classical spectral classification of single pixels comes up against its limits. Image analysis methods that take into account local neighborhood characteristics (edges, textures) can help to some extent, but deliver crumbled information that needs additional treatment. The new method of object based multispectral image segmentation (software "eCognition") promises a sulution. In a first step, the image is segmented into areas that are "looking" uniform, with respect to spectral, textural and shape properties. For each area, some characteristic values are calculated. In the second step, the segments are classified according to these attributes. The classification can be refined by giving training areas and previous knowledge (fuzzy class membership functions). In a third step, the classification can be improved by iterative application of neighbourhood criteria. In this work, the object based segmentation approach is applied to the detection of windthrow areas in multispectral images gained by an airborne survey with a digital line scanner. The characteristic pattern of lying trees, that is obvious to the human observer, can be detected in this way. Additionally, foreground objects (clouds) and settelement areas, which must be excluded, can be found. The derivated damage pattern can be used for an analysis of orographical influence on storm damage to forests in mountain areas (contribution of J. Schmoeckel and Ch. Kottmeier).

  16. GPU-based relative fuzzy connectedness image segmentation

    SciTech Connect

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

    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.

  17. Evaluation of automated brain MR image segmentation and volumetry methods.

    PubMed

    Klauschen, Frederick; Goldman, Aaron; Barra, Vincent; Meyer-Lindenberg, Andreas; Lundervold, Arvid

    2009-04-01

    We compare three widely used brain volumetry methods available in the software packages FSL, SPM5, and FreeSurfer and evaluate their performance using simulated and real MR brain data sets. We analyze the accuracy of gray and white matter volume measurements and their robustness against changes of image quality using the BrainWeb MRI database. These images are based on "gold-standard" reference brain templates. This allows us to assess between- (same data set, different method) and also within-segmenter (same method, variation of image quality) comparability, for both of which we find pronounced variations in segmentation results for gray and white matter volumes. The calculated volumes deviate up to >10% from the reference values for gray and white matter depending on method and image quality. Sensitivity is best for SPM5, volumetric accuracy for gray and white matter was similar in SPM5 and FSL and better than in FreeSurfer. FSL showed the highest stability for white (<5%), FreeSurfer (6.2%) for gray matter for constant image quality BrainWeb data. Between-segmenter comparisons show discrepancies of up to >20% for the simulated data and 24% on average for the real data sets, whereas within-method performance analysis uncovered volume differences of up to >15%. Since the discrepancies between results reach the same order of magnitude as volume changes observed in disease, these effects limit the usability of the segmentation methods for following volume changes in individual patients over time and should be taken into account during the planning and analysis of brain volume studies. PMID:18537111

  18. GPU-based relative fuzzy connectedness image segmentation

    PubMed Central

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

    2013-01-01

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

  19. Continuously live image processor for drift chamber track segment triggering

    SciTech Connect

    Berenyi, A.; Chen, H.K.; Dao, K.

    1999-06-01

    The first portion of the BaBar experiment Level 1 Drift Chamber Trigger pipeline is the Track Segment Finder (TSF). Using a novel method incorporating both occupancy and drift-time information, the TSF system continually searches for segments in the supercells of the full 7104-wire Drift Chamber hit image at 3.7 MHz. The TSF was constructed to operate in a potentially high beam-background environment while achieving high segment-finding efficiency, deadtime-free operation, a spatial resolution of <0.7 mm and a per-segment event time resolution of <70 ns. The TSF system consists of 24 hardware-identical TSF modules. These are the most complex modules in the BaBar trigger. On each module, fully parallel segment finding proceeds in 20 pipeline steps. Each module consists of a 9U algorithm board and a 6U interface board. The 9U printed circuit board has 10 layers and contains 0.9 million gates implemented in 25 FPGAs, which were synthesized from a total of 50,000 lines of VHDL. The boards were designed from the top-down with state-of-the-art CAD tools, which included gate-level board simulation. This methodology enabled production of a flawless board with no intermediate prototypes. It was fully tested with basic test patterns and 10{sup 5} simulated physics events.

  20. Segmentation and classification of breast tumor using dynamic contrast-enhanced MR images.

    PubMed

    Zheng, Yuanjie; Baloch, Sajjad; Englander, Sarah; Schnall, Mitchell D; Shen, Dinggang

    2007-01-01

    Accuracy of automatic cancer diagnosis is largely determined by two factors, namely, the precision of tumor segmentation, and the suitability of extracted features for discrimination between malignancy and benignancy. In this paper, we propose a new framework for accurate characterization of tumors in contrast enhanced MR images. First, a new graph cut based segmentation algorithm is developed for refining coarse manual segmentation, which allows precise identification of tumor regions. Second, by considering serial contrast-enhanced images as a single spatio-temporal image, a spatio-temporal model of segmented tumor is constructed to extract Spatio-Temporal Enhancement Patterns (STEPs). STEPs are designed to capture not only dynamic enhancement and architectural features, but also spatial variations of pixel-wise temporal enhancement of the tumor. While temporal enhancement features are extracted through Fourier transform, the resulting STEP framework captures spatial patterns of temporal enhancement features via moment invariants and rotation invariant Gabor textures. High accuracy of the proposed framework is a direct consequence of this two pronged approach, which is validated through experiments yielding, for instance, an area of 0.97 under the ROC curve. PMID:18044593

  1. 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Castro-Mateos, Isaac; Pozo, Jose M.; Eltes, Peter E.; Del Rio, Luis; Lazary, Aron; Frangi, Alejandro F.

    2014-12-01

    Computational medicine aims at employing personalised computational models in diagnosis and treatment planning. The use of such models to help physicians in finding the best treatment for low back pain (LBP) is becoming popular. One of the challenges of creating such models is to derive patient-specific anatomical and tissue models of the lumbar intervertebral discs (IVDs), as a prior step. This article presents a segmentation scheme that obtains accurate results irrespective of the degree of IVD degeneration, including pathological discs with protrusion or herniation. The segmentation algorithm, employing a novel feature selector, iteratively deforms an initial shape, which is projected into a statistical shape model space at first and then, into a B-Spline space to improve accuracy. The method was tested on a MR dataset of 59 patients suffering from LBP. The images follow a standard T2-weighted protocol in coronal and sagittal acquisitions. These two image volumes were fused in order to overcome large inter-slice spacing. The agreement between expert-delineated structures, used here as gold-standard, and our automatic segmentation was evaluated using Dice Similarity Index and surface-to-surface distances, obtaining a mean error of 0.68 mm in the annulus segmentation and 1.88 mm in the nucleus, which are the best results with respect to the image resolution in the current literature.

  2. Fast CEUS image segmentation based on self organizing maps

    NASA Astrophysics Data System (ADS)

    Paire, Julie; Sauvage, Vincent; Albouy-Kissi, Adelaïde; Ladam Marcus, Viviane; Marcus, Claude; Hoeffel, Christine

    2014-03-01

    Contrast-enhanced ultrasound (CEUS) has recently become an important technology for lesion detection and characterization. CEUS is used to investigate the perfusion kinetics in tissue over time, which relates to tissue vascularization. In this paper, we present an interactive segmentation method based on the neural networks, which enables to segment malignant tissue over CEUS sequences. We use Self-Organizing-Maps (SOM), an unsupervised neural network, to project high dimensional data to low dimensional space, named a map of neurons. The algorithm gathers the observations in clusters, respecting the topology of the observations space. This means that a notion of neighborhood between classes is defined. Adjacent observations in variables space belong to the same class or related classes after classification. Thanks to this neighborhood conservation property and associated with suitable feature extraction, this map provides user friendly segmentation tool. It will assist the expert in tumor segmentation with fast and easy intervention. We implement SOM on a Graphics Processing Unit (GPU) to accelerate treatment. This allows a greater number of iterations and the learning process to converge more precisely. We get a better quality of learning so a better classification. Our approach allows us to identify and delineate lesions accurately. Our results show that this method improves markedly the recognition of liver lesions and opens the way for future precise quantification of contrast enhancement.

  3. A multi-scale segmentation approach to filling gaps in Landsat ETM+ SLC-off images

    USGS Publications Warehouse

    Maxwell, S.K.; Schmidt, G.L.; Storey, J.C.

    2007-01-01

    On 31 May 2003, the Landsat Enhanced Thematic Plus (ETM+) Scan Line Corrector (SLC) failed, causing the scanning pattern to exhibit wedge-shaped scan-to-scan gaps. We developed a method that uses coincident spectral data to fill the image gaps. This method uses a multi-scale segment model, derived from a previous Landsat SLC-on image (image acquired prior to the SLC failure), to guide the spectral interpolation across the gaps in SLC-off images (images acquired after the SLC failure). This paper describes the process used to generate the segment model, provides details of the gap-fill algorithm used in deriving the segment-based gap-fill product, and presents the results of the gap-fill process applied to grassland, cropland, and forest landscapes. Our results indicate this product will be useful for a wide variety of applications, including regional-scale studies, general land cover mapping (e.g. forest, urban, and grass), crop-specific mapping and monitoring, and visual assessments. Applications that need to be cautious when using pixels in the gap areas include any applications that require per-pixel accuracy, such as urban characterization or impervious surface mapping, applications that use texture to characterize landscape features, and applications that require accurate measurements of small or narrow landscape features such as roads, farmsteads, and riparian areas.

  4. Binary image segmentation based on optimized parallel K-means

    NASA Astrophysics Data System (ADS)

    Qiu, Xiao-bing; Zhou, Yong; Lin, Li

    2015-07-01

    K-means is a classic unsupervised learning clustering algorithm. In theory, it can work well in the field of image segmentation. But compared with other segmentation algorithms, this algorithm needs much more computation, and segmentation speed is slow. This limits its application. With the emergence of general-purpose computing on the GPU and the release of CUDA, some scholars try to implement K-means algorithm in parallel on the GPU, and applied to image segmentation at the same time. They have achieved some results, but the approach they use is not completely parallel, not take full advantage of GPU's super computing power. K-means algorithm has two core steps: label and update, in current parallel realization of K-means, only labeling is parallel, update operation is still serial. In this paper, both of the two steps in K-means will be parallel to improve the degree of parallelism and accelerate this algorithm. Experimental results show that this improvement has reached a much quicker speed than the previous research.

  5. Segmentation of anatomical structures in x-ray computed tomography images using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Zhang, Di; Valentino, Daniel J.

    2002-05-01

    Hierarchies of artificial neural networks(ANN's) were trained to segment regularly-shaped and constantly-located anatomical structures in x-ray computed tomography (CT) images. These neural networks learned to associate a point in an image with the anatomical structure containing the point using the image pixel intensity values located in a pattern around the point. The single layer ANN and the bilayer and multi-layer hierarchies of neural networks were developed and evaluated. The hierarchical Artificial Neural Networks(HANN's) consisted of a high-level ANN that identified large-scale anatomical structures (e.g., the head or chest), whose result was passed to a group of neural networks that identified smaller structures (e.g., the brain, sinus, soft tissue, skull, bone, or lung) within the large-scale structures. The ANN's were trained to segment and classify images based on different numbers of training images, numbers of sampling points per image, pixel intensity sampling patterns, hidden layer configuration. The experimental results indicate that multi-layer hierarchy of ANN's trained with data collected from multiple image series accurately classified anatomical structures in unknown chest and head CT images.

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

    PubMed Central

    Jain, Saurabh; Sima, Diana M.; Ribbens, Annemie; Cambron, Melissa; Maertens, Anke; Van Hecke, Wim; De Mey, Johan; Barkhof, Frederik; Steenwijk, Martijn D.; Daams, Marita; Maes, Frederik; Van Huffel, Sabine; Vrenken, Hugo; Smeets, Dirk

    2015-01-01

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

  7. Using 3-D shape models to guide segmentation of MR brain images.

    PubMed Central

    Hinshaw, K. P.; Brinkley, J. F.

    1997-01-01

    Accurate segmentation of medical images poses one of the major challenges in computer vision. Approaches that rely solely on intensity information frequently fail because similar intensity values appear in multiple structures. This paper presents a method for using shape knowledge to guide the segmentation process, applying it to the task of finding the surface of the brain. A 3-D model that includes local shape constraints is fitted to an MR volume dataset. The resulting low-resolution surface is used to mask out regions far from the cortical surface, enabling an isosurface extraction algorithm to isolate a more detailed surface boundary. The surfaces generated by this technique are comparable to those achieved by other methods, without requiring user adjustment of a large number of ad hoc parameters. Images Figure 1 Figure 2 Figure 3 Figure 4 PMID:9357670

  8. An evolutionary tabu search for cell image segmentation.

    PubMed

    Jiang, Tianzi; Yang, Faguo

    2002-01-01

    Many engineering problems can be formulated as optimization problems. It has become more and more important to develop an efficient global optimization technique for solving these problems. In this paper, we propose an evolutionary tabu search (ETS) for cell image segmentation. The advantages of genetic algorithms (GA) and TS algorithms are incorporated into the proposed method. More precisely, we incorporate "the survival of the fittest" from evolutionary algorithms into TS. The method has been applied to the segmentation of several kinds of cell images. The experimental results show that the new algorithm is a practical and effective one for global optimization; it can yield good, near-optimal solutions and has better convergence and robustness than other global optimization approaches. PMID:18244872

  9. [Polar coordinates representation based leukocyte segmentation of microscopic cell images].

    PubMed

    Gu, Guanghua; Cui, Dong; Hao, Lianwang

    2010-12-01

    We propose an algorithm for segmentation of the overlapped leukocyte in the microscopic cell image. The histogram of the saturation channel in the cell image is smoothed to obtain the meaningful global valley point by the fingerprint smoothing method, and then the nucleus can be segmented. A circular region, containing the entire regions of the leukocyte, is marked off according to the equivalent sectional radius of the nucleus. Then, the edge of the overlapped leukocyte is represented by polar coordinates. The overlapped region by the change of the polar angle of the edge pixels is determined, and the closed edge of the leukocyte integrating the gradient information of the overlapped region is reconstructed. Finally, the leukocyte is exactly extracted. The experimental results show that our method has good performance in terms of recall ratio, precision ratio and pixel error ratio. PMID:21374971

  10. Hyperspectral image segmentation using spatial-spectral graphs

    NASA Astrophysics Data System (ADS)

    Gillis, David B.; Bowles, Jeffrey H.

    2012-06-01

    Spectral graph theory has proven to be a useful tool in the analysis of high-dimensional data sets. Recall that, mathematically, a graph is a collection of objects (nodes) and connections between them (edges); a weighted graph additionally assigns numerical values (weights) to the edges. Graphs are represented by their adjacency whose elements are the weights between the nodes. Spectral graph theory uses the eigendecomposition of the adjacency matrix (or, more generally, the Laplacian of the graph) to derive information about the underlying graph. In this paper, we develop a spectral method based on the 'normalized cuts' algorithm to segment hyperspectral image data (HSI). In particular, we model an image as a weighted graph whose nodes are the image pixels, and edges defined as connecting spatial neighbors; the edge weights are given by a weighted combination of the spatial and spectral distances between nodes. We then use the Laplacian of the graph to recursively segment the image. The advantages of our approach are that, first, the graph structure naturally incorporates both the spatial and spectral information present in HSI; also, by using only spatial neighbors, the adjacency matrix is highly sparse; as a result, it is possible to apply our technique to much larger images than previous techniques. In the paper, we present the details of our algorithm, and include experimental results from a variety of hyperspectral images.

  11. The cascaded moving k-means and fuzzy c-means clustering algorithms for unsupervised segmentation of malaria images

    NASA Astrophysics Data System (ADS)

    Abdul-Nasir, Aimi Salihah; Mashor, Mohd Yusoff; Halim, Nurul Hazwani Abd; Mohamed, Zeehaida

    2015-05-01

    Malaria is a life-threatening parasitic infectious disease that corresponds for nearly one million deaths each year. Due to the requirement of prompt and accurate diagnosis of malaria, the current study has proposed an unsupervised pixel segmentation based on clustering algorithm in order to obtain the fully segmented red blood cells (RBCs) infected with malaria parasites based on the thin blood smear images of P. vivax species. In order to obtain the segmented infected cell, the malaria images are first enhanced by using modified global contrast stretching technique. Then, an unsupervised segmentation technique based on clustering algorithm has been applied on the intensity component of malaria image in order to segment the infected cell from its blood cells background. In this study, cascaded moving k-means (MKM) and fuzzy c-means (FCM) clustering algorithms has been proposed for malaria slide image segmentation. After that, median filter algorithm has been applied to smooth the image as well as to remove any unwanted regions such as small background pixels from the image. Finally, seeded region growing area extraction algorithm has been applied in order to remove large unwanted regions that are still appeared on the image due to their size in which cannot be cleaned by using median filter. The effectiveness of the proposed cascaded MKM and FCM clustering algorithms has been analyzed qualitatively and quantitatively by comparing the proposed cascaded clustering algorithm with MKM and FCM clustering algorithms. Overall, the results indicate that segmentation using the proposed cascaded clustering algorithm has produced the best segmentation performances by achieving acceptable sensitivity as well as high specificity and accuracy values compared to the segmentation results provided by MKM and FCM algorithms.

  12. Multi-atlas segmentation of biomedical images: A survey.

    PubMed

    Iglesias, Juan Eugenio; Sabuncu, Mert R

    2015-08-01

    Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al. (2004), Klein, et al. (2005), and Heckemann, et al. (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003-2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation. PMID:26201875

  13. Multi-Atlas Segmentation of Biomedical Images: A Survey

    PubMed Central

    Iglesias, Juan Eugenio; Sabuncu, Mert R.

    2015-01-01

    Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh, Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert and Hammers (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of “atlases” (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003 – 2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation. PMID:26201875

  14. Automatic segmentation method of striatum regions in quantitative susceptibility mapping images

    NASA Astrophysics Data System (ADS)

    Murakawa, Saki; Uchiyama, Yoshikazu; Hirai, Toshinori

    2015-03-01

    Abnormal accumulation of brain iron has been detected in various neurodegenerative diseases. Quantitative susceptibility mapping (QSM) is a novel contrast mechanism in magnetic resonance (MR) imaging and enables the quantitative analysis of local tissue susceptibility property. Therefore, automatic segmentation tools of brain regions on QSM images would be helpful for radiologists' quantitative analysis in various neurodegenerative diseases. The purpose of this study was to develop an automatic segmentation and classification method of striatum regions on QSM images. Our image database consisted of 22 QSM images obtained from healthy volunteers. These images were acquired on a 3.0 T MR scanner. The voxel size was 0.9×0.9×2 mm. The matrix size of each slice image was 256×256 pixels. In our computerized method, a template mating technique was first used for the detection of a slice image containing striatum regions. An image registration technique was subsequently employed for the classification of striatum regions in consideration of the anatomical knowledge. After the image registration, the voxels in the target image which correspond with striatum regions in the reference image were classified into three striatum regions, i.e., head of the caudate nucleus, putamen, and globus pallidus. The experimental results indicated that 100% (21/21) of the slice images containing striatum regions were detected accurately. The subjective evaluation of the classification results indicated that 20 (95.2%) of 21 showed good or adequate quality. Our computerized method would be useful for the quantitative analysis of Parkinson diseases in QSM images.

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

    SciTech Connect

    Feng, Y; Olsen, J.; Parikh, P.; Noel, C; Wooten, H; Du, D; Mutic, S; Hu, Y; Kawrakow, I; Dempsey, J

    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

  16. A holistic image segmentation framework for cloud detection and extraction

    NASA Astrophysics Data System (ADS)

    Shen, Dan; Xu, Haotian; Blasch, Erik; Horvath, Gregory; Pham, Khanh; Zheng, Yufeng; Ling, Haibin; Chen, Genshe

    2013-05-01

    Atmospheric clouds are commonly encountered phenomena affecting visual tracking from air-borne or space-borne sensors. Generally clouds are difficult to detect and extract because they are complex in shape and interact with sunlight in a complex fashion. In this paper, we propose a clustering game theoretic image segmentation based approach to identify, extract, and patch clouds. In our framework, the first step is to decompose a given image containing clouds. The problem of image segmentation is considered as a "clustering game". Within this context, the notion of a cluster is equivalent to a classical equilibrium concept from game theory, as the game equilibrium reflects both the internal and external (e.g., two-player) cluster conditions. To obtain the evolutionary stable strategies, we explore three evolutionary dynamics: fictitious play, replicator dynamics, and infection and immunization dynamics (InImDyn). Secondly, we use the boundary and shape features to refine the cloud segments. This step can lower the false alarm rate. In the third step, we remove the detected clouds and patch the empty spots by performing background recovery. We demonstrate our cloud detection framework on a video clip provides supportive results.

  17. Exploiting spectral content for image segmentation in GPR data

    NASA Astrophysics Data System (ADS)

    Wang, Patrick K.; Morton, Kenneth D., Jr.; Collins, Leslie M.; Torrione, Peter A.

    2011-06-01

    Ground-penetrating radar (GPR) sensors provide an effective means for detecting changes in the sub-surface electrical properties of soils, such as changes indicative of landmines or other buried threats. However, most GPR-based pre-screening algorithms only localize target responses along the surface of the earth, and do not provide information regarding an object's position in depth. As a result, feature extraction algorithms are forced to process data from entire cubes of data around pre-screener alarms, which can reduce feature fidelity and hamper performance. In this work, spectral analysis is investigated as a method for locating subsurface anomalies in GPR data. In particular, a 2-D spatial/frequency decomposition is applied to pre-screener flagged GPR B-scans. Analysis of these spatial/frequency regions suggests that aspects (e.g. moments, maxima, mode) of the frequency distribution of GPR energy can be indicative of the presence of target responses. After translating a GPR image to a function of the spatial/frequency distributions at each pixel, several image segmentation approaches can be applied to perform segmentation in this new transformed feature space. To illustrate the efficacy of the approach, a performance comparison between feature processing with and without the image segmentation algorithm is provided.

  18. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

    SciTech Connect

    Guo, Yanrong; Shao, Yeqin; Gao, Yaozong; Price, True; Oto, Aytekin; Shen, Dinggang

    2014-07-15

    patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

  19. SU-E-J-208: Fast and Accurate Auto-Segmentation of Abdominal Organs at Risk for Online Adaptive Radiotherapy

    SciTech Connect

    Gupta, V; Wang, Y; Romero, A; Heijmen, B; Hoogeman, M; Myronenko, A; Jordan, P

    2014-06-01

    Purpose: Various studies have demonstrated that online adaptive radiotherapy by real-time re-optimization of the treatment plan can improve organs-at-risk (OARs) sparing in the abdominal region. Its clinical implementation, however, requires fast and accurate auto-segmentation of OARs in CT scans acquired just before each treatment fraction. Autosegmentation is particularly challenging in the abdominal region due to the frequently observed large deformations. We present a clinical validation of a new auto-segmentation method that uses fully automated non-rigid registration for propagating abdominal OAR contours from planning to daily treatment CT scans. Methods: OARs were manually contoured by an expert panel to obtain ground truth contours for repeat CT scans (3 per patient) of 10 patients. For the non-rigid alignment, we used a new non-rigid registration method that estimates the deformation field by optimizing local normalized correlation coefficient with smoothness regularization. This field was used to propagate planning contours to repeat CTs. To quantify the performance of the auto-segmentation, we compared the propagated and ground truth contours using two widely used metrics- Dice coefficient (Dc) and Hausdorff distance (Hd). The proposed method was benchmarked against translation and rigid alignment based auto-segmentation. Results: For all organs, the auto-segmentation performed better than the baseline (translation) with an average processing time of 15 s per fraction CT. The overall improvements ranged from 2% (heart) to 32% (pancreas) in Dc, and 27% (heart) to 62% (spinal cord) in Hd. For liver, kidneys, gall bladder, stomach, spinal cord and heart, Dc above 0.85 was achieved. Duodenum and pancreas were the most challenging organs with both showing relatively larger spreads and medians of 0.79 and 2.1 mm for Dc and Hd, respectively. Conclusion: Based on the achieved accuracy and computational time we conclude that the investigated auto-segmentation

  20. A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching

    PubMed Central

    Chen, Cheng; Wang, Wei; Ozolek, John A.; Rohde, Gustavo K.

    2013-01-01

    We describe a new supervised learning-based template matching approach for segmenting cell nuclei from microscopy images. The method uses examples selected by a user for building a statistical model which captures the texture and shape variations of the nuclear structures from a given dataset to be segmented. Segmentation of subsequent, unlabeled, images is then performed by finding the model instance that best matches (in the normalized cross correlation sense) local neighborhood in the input image. We demonstrate the application of our method to segmenting nuclei from a variety of imaging modalities, and quantitatively compare our results to several other methods. Quantitative results using both simulated and real image data show that, while certain methods may work well for certain imaging modalities, our software is able to obtain high accuracy across several imaging modalities studied. Results also demonstrate that, relative to several existing methods, the template-based method we propose presents increased robustness in the sense of better handling variations in illumination, variations in texture from different imaging modalities, providing more smooth and accurate segmentation borders, as well as handling better cluttered nuclei. PMID:23568787

  1. Vascular Tree Segmentation in Medical Images Using Hessian-Based Multiscale Filtering and Level Set Method

    PubMed Central

    Jin, Jiaoying; Yang, Linjun; Zhang, Xuming

    2013-01-01

    Vascular segmentation plays an important role in medical image analysis. A novel technique for the automatic extraction of vascular trees from 2D medical images is presented, which combines Hessian-based multiscale filtering and a modified level set method. In the proposed algorithm, the morphological top-hat transformation is firstly adopted to attenuate background. Then Hessian-based multiscale filtering is used to enhance vascular structures by combining Hessian matrix with Gaussian convolution to tune the filtering response to the specific scales. Because Gaussian convolution tends to blur vessel boundaries, which makes scale selection inaccurate, an improved level set method is finally proposed to extract vascular structures by introducing an external constrained term related to the standard deviation of Gaussian function into the traditional level set. Our approach was tested on synthetic images with vascular-like structures and 2D slices extracted from real 3D abdomen magnetic resonance angiography (MRA) images along the coronal plane. The segmentation rates for synthetic images are above 95%. The results for MRA images demonstrate that the proposed method can extract most of the vascular structures successfully and accurately in visualization. Therefore, the proposed method is effective for the vascular tree extraction in medical images. PMID:24348738

  2. Segmentation of extreme ultraviolet solar images via multichannel fuzzy clustering

    NASA Astrophysics Data System (ADS)

    Barra, Vincent; Delouille, Véronique; Hochedez, Jean-François

    2008-09-01

    The study of the variability of the solar corona and the monitoring of its traditional regions (Coronal Holes, Quiet Sun and Active Regions) are of great importance in astrophysics as well as in view of the Space Weather and Space Climate applications. Here we propose a multichannel unsupervised spatially constrained fuzzy clustering algorithm that automatically segments EUV solar images into Coronal Holes, Quiet Sun and Active Regions. Fuzzy logic allows to manage the various noises present in the images and the imprecision in the definition of the above regions. The process is fast and automatic. It is applied to SoHO EIT images taken from February 1997 till May 2005, i.e. along almost a full solar cycle. Results in terms of areas and intensity estimations are consistent with previous knowledge. The method reveal the rotational and other mid-term periodicities in the extracted time series across solar cycle 23. Further, such an approach paves the way to bridging observations between spatially resolved data from imaging telescopes and time series from radiometers. Time series resulting form the segmentation of EUV coronal images can indeed provide an essential component in the process of reconstructing the solar spectrum.

  3. Unsupervised texture image segmentation by improved neural network ART2

    NASA Technical Reports Server (NTRS)

    Wang, Zhiling; Labini, G. Sylos; Mugnuolo, R.; Desario, Marco

    1994-01-01

    We here propose a segmentation algorithm of texture image for a computer vision system on a space robot. An improved adaptive resonance theory (ART2) for analog input patterns is adapted to classify the image based on a set of texture image features extracted by a fast spatial gray level dependence method (SGLDM). The nonlinear thresholding functions in input layer of the neural network have been constructed by two parts: firstly, to reduce the effects of image noises on the features, a set of sigmoid functions is chosen depending on the types of the feature; secondly, to enhance the contrast of the features, we adopt fuzzy mapping functions. The cluster number in output layer can be increased by an autogrowing mechanism constantly when a new pattern happens. Experimental results and original or segmented pictures are shown, including the comparison between this approach and K-means algorithm. The system written in C language is performed on a SUN-4/330 sparc-station with an image board IT-150 and a CCD camera.

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

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  5. Knowledge guided information fusion for segmentation of multiple sclerosis lesions in MRI images

    NASA Astrophysics Data System (ADS)

    Zhu, Chaozhe; Jiang, Tianzi

    2003-05-01

    In this work, T1-, T2- and PD-weighted MR images of multiple sclerosis (MS) patients, providing information on the properties of tissues from different aspects, are treated as three independent information sources for the detection and segmentation of MS lesions. Based on information fusion theory, a knowledge guided information fusion framework is proposed to accomplish 3-D segmentation of MS lesions. This framework consists of three parts: (1) information extraction, (2) information fusion, and (3) decision. Information provided by different spectral images is extracted and modeled separately in each spectrum using fuzzy sets, aiming at managing the uncertainty and ambiguity in the images due to noise and partial volume effect. In the second part, the possible fuzzy map of MS lesions in each spectral image is constructed from the extracted information under the guidance of experts' knowledge, and then the final fuzzy map of MS lesions is constructed through the fusion of the fuzzy maps obtained from different spectrum. Finally, 3-D segmentation of MS lesions is derived from the final fuzzy map. Experimental results show that this method is fast and accurate.

  6. An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images

    PubMed Central

    Sun, Zhuli; Chen, Haoyu; Shi, Fei; Wang, Lirong; Zhu, Weifang; Xiang, Dehui; Yan, Chenglin; Li, Liang; Chen, Xinjian

    2016-01-01

    Pigment epithelium detachment (PED) is an important clinical manifestation of multiple chorioretinal diseases, which can cause loss of central vision. In this paper, an automated framework is proposed to segment serous PED in SD-OCT images. The proposed framework consists of four main steps: first, a multi-scale graph search method is applied to segment abnormal retinal layers; second, an effective AdaBoost method is applied to refine the initial segmented regions based on 62 extracted features; third, a shape-constrained graph cut method is applied to segment serous PED, in which the foreground and background seeds are obtained automatically; finally, an adaptive structure elements based morphology method is applied to remove false positive segmented regions. The proposed framework was tested on 25 SD-OCT volumes from 25 patients diagnosed with serous PED. The average true positive volume fraction (TPVF), false positive volume fraction (FPVF), dice similarity coefficient (DSC) and positive predictive value (PPV) are 90.08%, 0.22%, 91.20% and 92.62%, respectively. The proposed framework can provide clinicians with accurate quantitative information, including shape, size and position of the PED region, which can assist clinical diagnosis and treatment. PMID:26899236

  7. Fully automatic prostate segmentation from transrectal ultrasound images based on radial bas-relief initialization and slice-based propagation.

    PubMed

    Yu, Yanyan; Chen, Yimin; Chiu, Bernard

    2016-07-01

    Prostate segmentation from transrectal ultrasound (TRUS) images plays an important role in the diagnosis and treatment planning of prostate cancer. In this paper, a fully automatic slice-based segmentation method was developed to segment TRUS prostate images. The initial prostate contour was determined using a novel method based on the radial bas-relief (RBR) method, and a false edge removal algorithm proposed here in. 2D slice-based propagation was used in which the contour on each image slice was deformed using a level-set evolution model, which was driven by edge-based and region-based energy fields generated by dyadic wavelet transform. The optimized contour on an image slice propagated to the adjacent slice, and subsequently deformed using the level-set model. The propagation continued until all image slices were segmented. To determine the initial slice where the propagation began, the initial prostate contour was deformed individually on each transverse image. A method was developed to self-assess the accuracy of the deformed contour based on the average image intensity inside and outside of the contour. The transverse image on which highest accuracy was attained was chosen to be the initial slice for the propagation process. Evaluation was performed for 336 transverse images from 15 prostates that include images acquired at mid-gland, base and apex regions of the prostates. The average mean absolute difference (MAD) between algorithm and manual segmentations was 0.79±0.26mm, which is comparable to results produced by previously published semi-automatic segmentation methods. Statistical evaluation shows that accurate segmentation was not only obtained at the mid-gland, but also at the base and apex regions. PMID:27208705

  8. Filler segmentation of SEM paper images based on mathematical morphology.

    PubMed

    Ait Kbir, M; Benslimane, Rachid; Princi, Elisabetta; Vicini, Silvia; Pedemonte, Enrico

    2007-07-01

    Recent developments in microscopy and image processing have made digital measurements on high-resolution images of fibrous materials possible. This helps to gain a better understanding of the structure and other properties of the material at micro level. In this paper SEM image segmentation based on mathematical morphology is proposed. In fact, paper models images (Whatman, Murillo, Watercolor, Newsprint paper) selected in the context of the Euro Mediterranean PaperTech Project have different distributions of fibers and fillers, caused by the presence of SiAl and CaCO3 particles. It is a microscopy challenge to make filler particles in the sheet distinguishable from the other components of the paper surface. This objectif is reached here by using switable strutural elements and mathematical morphology operators. PMID:17867540

  9. Mapping of photon distribution and imaging of MR-derived anatomically accurate optical models of the female breast

    NASA Astrophysics Data System (ADS)

    Barbour, San-Lian S.; Barbour, Randall L.; Koo, Ping C.; Graber, Harry L.; Chang, Jenghwa

    1995-05-01

    We have computed optical images of the female breast based on analysis of tomographic data obtained from simulated time-independent optical measurements of anatomically accurate maps derived from segmented 3D magnetic resonance (MR) images. Images were segmented according to the measured MR contrast levels for fat and parenchymal tissue from T1 weighted acquisitions. Computed images were obtained from analysis of solutions to the forward problem for breasts containing 'added pathologies', representing tumors, to breasts lacking these inclusions. Both breast size and its optical properties have been examined in tissue. In each case, two small simulated tumors were 'added' to the background issue. Values of absorption and scattering coefficients of the tumors have been examined that are both greater and less than the surrounding tissue. Detector responses and the required imaging operators were computed by numerically solving the diffusion equation for inhomogeneous media. Detectors were distributed uniformly, in a circular fashion, around the breast in a plane positioned parallel and half-way between the chest wall and the nipple. A total of 20 sources were used, and for each 20 detectors. Reconstructed images were obtained by solving a linear perturbation equation derived from transport theory. Three algorithms were tested to solve the perturbation equation and include, the methods of conjugate gradient decent (CGD), projection onto convex sets (POCS), and simultaneous algebraic reconstruction technique (SART). Results obtained showed that in each case, high quality reconstructions were obtained. The computed images correctly resolved and identified the spatial position of the two tumors. Additional studies showed that computed images were stable to large systematic errors in the imaging operators and to added noise. Further, examination of the computed detector readings indicate that images of tissue up to approximately 10 cm in thickness should be possible. The

  10. Morphological segmentation of multiprobe fluorescence images for immunophenotyping in melanoma tissue sections

    NASA Astrophysics Data System (ADS)

    Dow, Alasdair I.; Shafer, Steven A.; Waggoner, Alan S.

    1993-08-01

    A fundamental task in studying the action of cancer chemotherapy is to determine the quantity and spatial relationship of tumor-infiltrating lymphocyte populations. Classically this is performed by staining thin tissue sections with antibodies by immunoperoxidase amplification. The staining technique is practically limited to locating a single cell type per tissue section. Full immunophenotyping requires successive staining of serial sections, using statistical analysis to correlate the results. This paper describes a system that brings together multi- parameter fluorescence imaging and morphological segmentation techniques to provide a fast, accurate, and automatic analysis of the lymphocyte infiltrate in tissue sections. With fluorescence techniques a single section can be stained with up to four distinct fluorescently labelled antibodies to determine cell phenotypes. To harness this potential computer vision techniques are required to analyze the images. A routine based on the water shed algorithm has been developed that segments the nuclei image with an accuracy of greater than 90%. By matching the nuclei boundaries to the local peak fluorescence, cell boundary estimates are obtained in the antigen images. By then extracting two measurements from the boundary signal the cells can be classified according to their antigen expression. Determining cell expression of multiple antigens simultaneously provides a more detailed and accurate picture of the tumor infiltrate than single parameter analysis, and increases understanding of the immune response associated with the chemotherapy.

  11. Computerized segmentation and measurement of chronic wound images.

    PubMed

    Ahmad Fauzi, Mohammad Faizal; Khansa, Ibrahim; Catignani, Karen; Gordillo, Gayle; Sen, Chandan K; Gurcan, Metin N

    2015-05-01

    An estimated 6.5 million patients in the United States are affected by chronic wounds, with more than US$25 billion and countless hours spent annually for all aspects of chronic wound care. There is a need for an intelligent software tool to analyze wound images, characterize wound tissue composition, measure wound size, and monitor changes in wound in between visits. Performed manually, this process is very time-consuming and subject to intra- and inter-reader variability. In this work, our objective is to develop methods to segment, measure and characterize clinically presented chronic wounds from photographic images. The first step of our method is to generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the segmentation process using either optimal thresholding or region growing. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively; while the white probability map is to detect the white label card for measurement calibration purposes. The innovative aspects of this work include defining a four-dimensional probability map specific to wound characteristics, a computationally efficient method to segment wound images utilizing the probability map, and auto-calibration of wound measurements using the content of the image. These methods were applied to 80 wound images, captured in a clinical setting at the Ohio State University Comprehensive Wound Center, with the ground truth independently generated by the consensus of at least two clinicians. While the mean inter-reader agreement between the readers varied between 67.4% and 84.3%, the computer achieved an average accuracy of 75.1%. PMID:25756704

  12. Level set segmentation of bovine corpora lutea in ex situ ovarian ultrasound images

    PubMed Central

    Rusnell, Brennan J; Pierson, Roger A; Singh, Jaswant; Adams, Gregg P; Eramian, Mark G

    2008-01-01

    Background The objective of this study was to investigate the viability of level set image segmentation methods for the detection of corpora lutea (corpus luteum, CL) boundaries in ultrasonographic ovarian images. It was hypothesized that bovine CL boundaries could be located within 1–2 mm by a level set image segmentation methodology. Methods Level set methods embed a 2D contour in a 3D surface and evolve that surface over time according to an image-dependent speed function. A speed function suitable for segmentation of CL's in ovarian ultrasound images was developed. An initial contour was manually placed and contour evolution was allowed to proceed until the rate of change of the area was sufficiently small. The method was tested on ovarian ultrasonographic images (n = 8) obtained ex situ. A expert in ovarian ultrasound interpretation delineated CL boundaries manually to serve as a "ground truth". Accuracy of the level set segmentation algorithm was determined by comparing semi-automatically determined contours with ground truth contours using the mean absolute difference (MAD), root mean squared difference (RMSD), Hausdorff distance (HD), sensitivity, and specificity metrics. Results and discussion The mean MAD was 0.87 mm (sigma = 0.36 mm), RMSD was 1.1 mm (sigma = 0.47 mm), and HD was 3.4 mm (sigma = 2.0 mm) indicating that, on average, boundaries were accurate within 1–2 mm, however, deviations in excess of 3 mm from the ground truth were observed indicating under- or over-expansion of the contour. Mean sensitivity and specificity were 0.814 (sigma = 0.171) and 0.990 (sigma = 0.00786), respectively, indicating that CLs were consistently undersegmented but rarely did the contour interior include pixels that were judged by the human expert not to be part of the CL. It was observed that in localities where gradient magnitudes within the CL were strong due to high contrast speckle, contour expansion stopped too early. Conclusion The hypothesis that level set

  13. Seamless contiguity method for parallel segmentation of remote sensing image

    NASA Astrophysics Data System (ADS)

    Wang, Geng; Wang, Guanghui; Yu, Mei; Cui, Chengling

    2015-12-01

    Seamless contiguity is the key technology for parallel segmentation of remote sensing data with large quantities. It can be effectively integrate fragments of the parallel processing into reasonable results for subsequent processes. There are numerous methods reported in the literature for seamless contiguity, such as establishing buffer, area boundary merging and data sewing. et. We proposed a new method which was also based on building buffers. The seamless contiguity processes we adopt are based on the principle: ensuring the accuracy of the boundary, ensuring the correctness of topology. Firstly, block number is computed based on data processing ability, unlike establishing buffer on both sides of block line, buffer is established just on the right side and underside of the line. Each block of data is segmented respectively and then gets the segmentation objects and their label value. Secondly, choose one block(called master block) and do stitching on the adjacent blocks(called slave block), process the rest of the block in sequence. Through the above processing, topological relationship and boundaries of master block are guaranteed. Thirdly, if the master block polygons boundaries intersect with buffer boundary and the slave blocks polygons boundaries intersect with block line, we adopt certain rules to merge and trade-offs them. Fourthly, check the topology and boundary in the buffer area. Finally, a set of experiments were conducted and prove the feasibility of this method. This novel seamless contiguity algorithm provides an applicable and practical solution for efficient segmentation of massive remote sensing image.

  14. Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique

    SciTech Connect

    Wang Jiahui; Engelmann, Roger; Li Qiang

    2007-12-15

    Accurate segmentation of pulmonary nodules in computed tomography (CT) is an important and difficult task for computer-aided diagnosis of lung cancer. Therefore, the authors developed a novel automated method for accurate segmentation of nodules in three-dimensional (3D) CT. First, a volume of interest (VOI) was determined at the location of a nodule. To simplify nodule segmentation, the 3D VOI was transformed into a two-dimensional (2D) image by use of a key 'spiral-scanning' technique, in which a number of radial lines originating from the center of the VOI spirally scanned the VOI from the 'north pole' to the 'south pole'. The voxels scanned by the radial lines provided a transformed 2D image. Because the surface of a nodule in the 3D image became a curve in the transformed 2D image, the spiral-scanning technique considerably simplified the segmentation method and enabled reliable segmentation results to be obtained. A dynamic programming technique was employed to delineate the 'optimal' outline of a nodule in the 2D image, which corresponded to the surface of the nodule in the 3D image. The optimal outline was then transformed back into 3D image space to provide the surface of the nodule. An overlap between nodule regions provided by computer and by the radiologists was employed as a performance metric for evaluating the segmentation method. The database included two Lung Imaging Database Consortium (LIDC) data sets that contained 23 and 86 CT scans, respectively, with 23 and 73 nodules that were 3 mm or larger in diameter. For the two data sets, six and four radiologists manually delineated the outlines of the nodules as reference standards in a performance evaluation for nodule segmentation. The segmentation method was trained on the first and was tested on the second LIDC data sets. The mean overlap values were 66% and 64% for the nodules in the first and second LIDC data sets, respectively, which represented a higher performance level than those of two

  15. Differences in the Association between Segment and Language: Early Bilinguals Pattern with Monolinguals and Are Less Accurate than Late Bilinguals

    PubMed Central

    Blanco, Cynthia P.; Bannard, Colin; Smiljanic, Rajka

    2016-01-01

    Early bilinguals often show as much sensitivity to L2-specific contrasts as monolingual speakers of the L2, but most work on cross-language speech perception has focused on isolated segments, and typically only on neighboring vowels or stop contrasts. In tasks that include sounds in context, listeners’ success is more variable, so segment discrimination in isolation may not adequately represent the phonetic detail in stored representations. The current study explores the relationship between language experience and sensitivity to segmental cues in context by comparing the categorization patterns of monolingual English listeners and early and late Spanish–English bilinguals. Participants categorized nonce words containing different classes of English- and Spanish-specific sounds as being more English-like or more Spanish-like; target segments included phonemic cues, cues for which there is no analogous sound in the other language, or phonetic cues, cues for which English and Spanish share the category but for which each language varies in its phonetic implementation. Listeners’ language categorization accuracy and reaction times were analyzed. Our results reveal a largely uniform categorization pattern across listener groups: Spanish cues were categorized more accurately than English cues, and phonemic cues were easier for listeners to categorize than phonetic cues. There were no differences in the sensitivity of monolinguals and early bilinguals to language-specific cues, suggesting that the early bilinguals’ exposure to Spanish did not fundamentally change their representations of English phonology. However, neither did the early bilinguals show more sensitivity than the monolinguals to Spanish sounds. The late bilinguals however, were significantly more accurate than either of the other groups. These findings indicate that listeners with varying exposure to English and Spanish are able to use language-specific cues in a nonce-word language categorization

  16. Differences in the Association between Segment and Language: Early Bilinguals Pattern with Monolinguals and Are Less Accurate than Late Bilinguals.

    PubMed

    Blanco, Cynthia P; Bannard, Colin; Smiljanic, Rajka

    2016-01-01

    Early bilinguals often show as much sensitivity to L2-specific contrasts as monolingual speakers of the L2, but most work on cross-language speech perception has focused on isolated segments, and typically only on neighboring vowels or stop contrasts. In tasks that include sounds in context, listeners' success is more variable, so segment discrimination in isolation may not adequately represent the phonetic detail in stored representations. The current study explores the relationship between language experience and sensitivity to segmental cues in context by comparing the categorization patterns of monolingual English listeners and early and late Spanish-English bilinguals. Participants categorized nonce words containing different classes of English- and Spanish-specific sounds as being more English-like or more Spanish-like; target segments included phonemic cues, cues for which there is no analogous sound in the other language, or phonetic cues, cues for which English and Spanish share the category but for which each language varies in its phonetic implementation. Listeners' language categorization accuracy and reaction times were analyzed. Our results reveal a largely uniform categorization pattern across listener groups: Spanish cues were categorized more accurately than English cues, and phonemic cues were easier for listeners to categorize than phonetic cues. There were no differences in the sensitivity of monolinguals and early bilinguals to language-specific cues, suggesting that the early bilinguals' exposure to Spanish did not fundamentally change their representations of English phonology. However, neither did the early bilinguals show more sensitivity than the monolinguals to Spanish sounds. The late bilinguals however, were significantly more accurate than either of the other groups. These findings indicate that listeners with varying exposure to English and Spanish are able to use language-specific cues in a nonce-word language categorization task

  17. Evaluation of single-photon emission computed tomography images obtained with and without copper filter by segmentation

    PubMed Central

    Kheruka, Subhash Chand; Aggarwal, Lalit Mohan; Sharma, Neeraj; Naithani, Umesh Chand; Maurya, Anil Kumar; Gambhir, Sanjay

    2016-01-01

    Background: Measurement of accurate attenuation of photon flux in tissue is important to obtain reconstructed images using single-photon emission computed tomography (SPECT). Computed tomography (CT) scanner provides attenuation correction data for SPECT as well as anatomic information for diagnostic purposes. Segmentation is a process of dividing an image into regions having similar properties such as gray level, color, texture, brightness, and contrast. Image segmentation is an important tool for evaluation of medical images. X-ray beam used in CT scan is poly-energetic; therefore, we have used a copper filter to remove the low energy X-rays for obtaining correct attenuation factor. Images obtained with and without filters were quantitatively evaluated by segmentation method to avoid human error. Materials and Methods: Axial images of AAPM CT phantom were acquired with 3 mm copper filter (low intensity) and without copper filter (high intensity) using low-dose CT (140 kvp and 2.5 mA) of SPECT/CT system (Hawkeye, GE Healthcare). For segmentation Simulated Annealing Based Fuzzy c-means, algorithm is applied. Quantitative measurement of quality is done based on universal image quality index. Further, for the validation of attenuation correction map of filtered CT images, Jaszczak SPECT phantom was filled with 500 MBq of 99mTc and SPECT study was acquired. Low dose CT images were acquired for attenuation correction to be used for reconstruction of SPECT images. Another set of CT images were acquired after applying additional 3 mm copper filter. Two sets of axial SPECT images were reconstructed using attenuation map from both the CT images obtained without and with a filter. Results and Conclusions: When we applied Simulated Annealing Based Fuzzy c-means segmentation on both the CT images, the CT images with filter shows remarkable improvement and all the six section of the spheres in the Jaszczak SPECT phantom were clearly visualized. PMID:27095859

  18. Segmentation of the ovine lung in 3D CT Images

    NASA Astrophysics Data System (ADS)

    Shi, Lijun; Hoffman, Eric A.; Reinhardt, Joseph M.

    2004-04-01

    Pulmonary CT images can provide detailed information about the regional structure and function of the respiratory system. Prior to any of these analyses, however, the lungs must be identified in the CT data sets. A popular animal model for understanding lung physiology and pathophysiology is the sheep. In this paper we describe a lung segmentation algorithm for CT images of sheep. The algorithm has two main steps. The first step is lung extraction, which identifies the lung region using a technique based on optimal thresholding and connected components analysis. The second step is lung separation, which separates the left lung from the right lung by identifying the central fissure using an anatomy-based method incorporating dynamic programming and a line filter algorithm. The lung segmentation algorithm has been validated by comparing our automatic method to manual analysis for five pulmonary CT datasets. The RMS error between the computer-defined and manually-traced boundary is 0.96 mm. The segmentation requires approximately 10 minutes for a 512x512x400 dataset on a PC workstation (2.40 GHZ CPU, 2.0 GB RAM), while it takes human observer approximately two hours to accomplish the same task.

  19. Fuzzy Markov random fields versus chains for multispectral image segmentation.

    PubMed

    Salzenstein, Fabien; Collet, Christophe

    2006-11-01

    This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (Mode of Posterior Marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data. PMID:17063681

  20. Model-based segmentation of medical x-ray images

    NASA Astrophysics Data System (ADS)

    Hoare, Frederick; de Jager, Gerhard

    1994-03-01

    This paper discusses the methods used to model the structure of x-ray images of the human body and the individual organs within the body. A generic model of a region is built up from x-ray images to aid in automatic segmentation. By using the ribs from a chest x-ray image as an example, it is shown how models of the different organs can be generated. The generic model of the chest region is built up by using a priori knowledge of the physical structure of the human body. The models of the individual organs are built up by using knowledge of the structure of the organs as well as other information contained within each image. Each image is unique and therefore information from the region surrounding the organs in the image has to be taken into account when adapting the generic model to individual images. Results showing the application of these techniques to x-ray images of the chest region, the labelling of individual organs, and the generation of models of the ribs are presented.

  1. Automated target recognition technique for image segmentation and scene analysis

    NASA Astrophysics Data System (ADS)

    Baumgart, Chris W.; Ciarcia, Christopher A.

    1994-03-01

    Automated target recognition (ATR) software has been designed to perform image segmentation and scene analysis. Specifically, this software was developed as a package for the Army's Minefield and Reconnaissance and Detector (MIRADOR) program. MIRADOR is an on/off road, remote control, multisensor system designed to detect buried and surface- emplaced metallic and nonmetallic antitank mines. The basic requirements for this ATR software were the following: (1) an ability to separate target objects from the background in low signal-noise conditions; (2) an ability to handle a relatively high dynamic range in imaging light levels; (3) the ability to compensate for or remove light source effects such as shadows; and (4) the ability to identify target objects as mines. The image segmentation and target evaluation was performed using an integrated and parallel processing approach. Three basic techniques (texture analysis, edge enhancement, and contrast enhancement) were used collectively to extract all potential mine target shapes from the basic image. Target evaluation was then performed using a combination of size, geometrical, and fractal characteristics, which resulted in a calculated probability for each target shape. Overall results with this algorithm were quite good, though there is a tradeoff between detection confidence and the number of false alarms. This technology also has applications in the areas of hazardous waste site remediation, archaeology, and law enforcement.

  2. Semantic Segmentation of Aerial Images with AN Ensemble of Cnns

    NASA Astrophysics Data System (ADS)

    Marmanis, D.; Wegner, J. D.; Galliani, S.; Schindler, K.; Datcu, M.; Stilla, U.

    2016-06-01

    This paper describes a deep learning approach to semantic segmentation of very high resolution (aerial) images. Deep neural architectures hold the promise of end-to-end learning from raw images, making heuristic feature design obsolete. Over the last decade this idea has seen a revival, and in recent years deep convolutional neural networks (CNNs) have emerged as the method of choice for a range of image interpretation tasks like visual recognition and object detection. Still, standard CNNs do not lend themselves to per-pixel semantic segmentation, mainly because one of their fundamental principles is to gradually aggregate information over larger and larger image regions, making it hard to disentangle contributions from different pixels. Very recently two extensions of the CNN framework have made it possible to trace the semantic information back to a precise pixel position: deconvolutional network layers undo the spatial downsampling, and Fully Convolution Networks (FCNs) modify the fully connected classification layers of the network in such a way that the location of individual activations remains explicit. We design a FCN which takes as input intensity and range data and, with the help of aggressive deconvolution and recycling of early network layers, converts them into a pixelwise classification at full resolution. We discuss design choices and intricacies of such a network, and demonstrate that an ensemble of several networks achieves excellent results on challenging data such as the ISPRS semantic labeling benchmark, using only the raw data as input.

  3. Proximity graphs based multi-scale image segmentation

    SciTech Connect

    Skurikhin, Alexei N

    2008-01-01

    We present a novel multi-scale image segmentation approach based on irregular triangular and polygonal tessellations produced by proximity graphs. Our approach consists of two separate stages: polygonal seeds generation followed by an iterative bottom-up polygon agglomeration into larger chunks. We employ constrained Delaunay triangulation combined with the principles known from the visual perception to extract an initial ,irregular polygonal tessellation of the image. These initial polygons are built upon a triangular mesh composed of irregular sized triangles and their shapes are ad'apted to the image content. We then represent the image as a graph with vertices corresponding to the polygons and edges reflecting polygon relations. The segmentation problem is then formulated as Minimum Spanning Tree extraction. We build a successive fine-to-coarse hierarchy of irregular polygonal grids by an iterative graph contraction constructing Minimum Spanning Tree. The contraction uses local information and merges the polygons bottom-up based on local region-and edge-based characteristics.

  4. The level of detail required in a deformable phantom to accurately perform quality assurance of deformable image registration

    NASA Astrophysics Data System (ADS)

    Saenz, Daniel L.; Kim, Hojin; Chen, Josephine; Stathakis, Sotirios; Kirby, Neil

    2016-09-01

    The primary purpose of the study was to determine how detailed deformable image registration (DIR) phantoms need to adequately simulate human anatomy and accurately assess the quality of DIR algorithms. In particular, how many distinct tissues are required in a phantom to simulate complex human anatomy? Pelvis and head-and-neck patient CT images were used for this study as virtual phantoms. Two data sets from each site were analyzed. The virtual phantoms were warped to create two pairs consisting of undeformed and deformed images. Otsu’s method was employed to create additional segmented image pairs of n distinct soft tissue CT number ranges (fat, muscle, etc). A realistic noise image was added to each image. Deformations were applied in MIM Software (MIM) and Velocity deformable multi-pass (DMP) and compared with the known warping. Images with more simulated tissue levels exhibit more contrast, enabling more accurate results. Deformation error (magnitude of the vector difference between known and predicted deformation) was used as a metric to evaluate how many CT number gray levels are needed for a phantom to serve as a realistic patient proxy. Stabilization of the mean deformation error was reached by three soft tissue levels for Velocity DMP and MIM, though MIM exhibited