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Sample records for accurate segmentation results

  1. LSM: perceptually accurate line segment merging

    NASA Astrophysics Data System (ADS)

    Hamid, Naila; Khan, Nazar

    2016-11-01

    Existing line segment detectors tend to break up perceptually distinct line segments into multiple segments. We propose an algorithm for merging such broken segments to recover the original perceptually accurate line segments. The algorithm proceeds by grouping line segments on the basis of angular and spatial proximity. Then those line segment pairs within each group that satisfy unique, adaptive mergeability criteria are successively merged to form a single line segment. This process is repeated until no more line segments can be merged. We also propose a method for quantitative comparison of line segment detection algorithms. Results on the York Urban dataset show that our merged line segments are closer to human-marked ground-truth line segments compared to state-of-the-art line segment detection algorithms.

  2. Visual Mapping of Sedimentary Facies Can Yield Accurate And Geomorphically Meaningful Results at Morphological Unit to River Segment Scales

    NASA Astrophysics Data System (ADS)

    Pasternack, G. B.; Wyrick, J. R.; Jackson, J. R.

    2014-12-01

    Long practiced in fisheries, visual substrate mapping of coarse-bedded rivers is eschewed by geomorphologists for inaccuracy and limited sizing data. Geomorphologists perform time-consuming measurements of surficial grains, with the few locations precluding spatially explicit mapping and analysis of sediment facies. Remote sensing works for bare land, but not vegetated or subaqueous sediments. As visual systems apply the log2 Wentworth scale made for sieving, they suffer from human inability to readily discern those classes. We hypothesized that size classes centered on the PDF of the anticipated sediment size distribution would enable field crews to accurately (i) identify presence/absence of each class in a facies patch and (ii) estimate the relative amount of each class to within 10%. We first tested 6 people using 14 measured samples with different mixtures. Next, we carried out facies mapping for ~ 37 km of the lower Yuba River in California. Finally, we tested the resulting data to see if it produced statistically significant hydraulic-sedimentary-geomorphic results. Presence/absence performance error was 0-4% for four people, 13% for one person, and 33% for one person. The last person was excluded from further effort. For the abundance estimation performance error was 1% for one person, 7-12% for three people, and 33% for one person. This last person was further trained and re-tested. We found that the samples easiest to visually quantify were unimodal and bimodal, while those most difficult had nearly equal amounts of each size. This confirms psychological studies showing that humans have a more difficult time quantifying abundances of subgroups when confronted with well-mixed groups. In the Yuba, mean grain size decreased downstream, as is typical for an alluvial river. When averaged by reach, mean grain size and bed slope were correlated with an r2 of 0.95. At the morphological unit (MU) scale, eight in-channel bed MU types had an r2 of 0.90 between mean

  3. Accurate vessel segmentation with constrained B-snake.

    PubMed

    Yuanzhi Cheng; Xin Hu; Ji Wang; Yadong Wang; Tamura, Shinichi

    2015-08-01

    We describe an active contour framework with accurate shape and size constraints on the vessel cross-sectional planes to produce the vessel segmentation. It starts with a multiscale vessel axis tracing in a 3D computed tomography (CT) data, followed by vessel boundary delineation on the cross-sectional planes derived from the extracted axis. The vessel boundary surface is deformed under constrained movements on the cross sections and is voxelized to produce the final vascular segmentation. The novelty of this paper lies in the accurate contour point detection of thin vessels based on the CT scanning model, in the efficient implementation of missing contour points in the problematic regions and in the active contour model with accurate shape and size constraints. The main advantage of our framework is that it avoids disconnected and incomplete segmentation of the vessels in the problematic regions that contain touching vessels (vessels in close proximity to each other), diseased portions (pathologic structure attached to a vessel), and thin vessels. It is particularly suitable for accurate segmentation of thin and low contrast vessels. Our method is evaluated and demonstrated on CT data sets from our partner site, and its results are compared with three related methods. Our method is also tested on two publicly available databases and its results are compared with the recently published method. The applicability of the proposed method to some challenging clinical problems, the segmentation of the vessels in the problematic regions, is demonstrated with good results on both quantitative and qualitative experimentations; our segmentation algorithm can delineate vessel boundaries that have level of variability similar to those obtained manually.

  4. Accurate colon residue detection algorithm with partial volume segmentation

    NASA Astrophysics Data System (ADS)

    Li, Xiang; Liang, Zhengrong; Zhang, PengPeng; Kutcher, Gerald J.

    2004-05-01

    Colon cancer is the second leading cause of cancer-related death in the United States. Earlier detection and removal of polyps can dramatically reduce the chance of developing malignant tumor. Due to some limitations of optical colonoscopy used in clinic, many researchers have developed virtual colonoscopy as an alternative technique, in which accurate colon segmentation is crucial. However, partial volume effect and existence of residue make it very challenging. The electronic colon cleaning technique proposed by Chen et al is a very attractive method, which is also kind of hard segmentation method. As mentioned in their paper, some artifacts were produced, which might affect the accurate colon reconstruction. In our paper, instead of labeling each voxel with a unique label or tissue type, the percentage of different tissues within each voxel, which we call a mixture, was considered in establishing a maximum a posterior probability (MAP) image-segmentation framework. A Markov random field (MRF) model was developed to reflect the spatial information for the tissue mixtures. The spatial information based on hard segmentation was used to determine which tissue types are in the specific voxel. Parameters of each tissue class were estimated by the expectation-maximization (EM) algorithm during the MAP tissue-mixture segmentation. Real CT experimental results demonstrated that the partial volume effects between four tissue types have been precisely detected. Meanwhile, the residue has been electronically removed and very smooth and clean interface along the colon wall has been obtained.

  5. Method for Accurate Unsupervised Cell Nucleus Segmentation

    DTIC Science & Technology

    2007-11-02

    development of a cervical cancer screening ma- chine despite projects being initiated in the 1950’s is perhaps a good indication of the magnitude of the...classi- fication processes can only become more robust. REFERENCES [1] P. Bamford. The Segmentation of Cell Images with Applica- tion to Cervical Cancer Screening... Cervical Cancer : Algorithms and Implementation. PhD thesis, Uppsala University, 1989.

  6. Interacting with image hierarchies for fast and accurate object segmentation

    NASA Astrophysics Data System (ADS)

    Beard, David V.; Eberly, David H.; Hemminger, Bradley M.; Pizer, Stephen M.; Faith, R. E.; Kurak, Charles; Livingston, Mark

    1994-05-01

    Object definition is an increasingly important area of medical image research. Accurate and fairly rapid object definition is essential for measuring the size and, perhaps more importantly, the change in size of anatomical objects such as kidneys and tumors. Rapid and fairly accurate object definition is essential for 3D real-time visualization including both surgery planning and Radiation oncology treatment planning. One approach to object definition involves the use of 3D image hierarchies, such as Eberly's Ridge Flow. However, the image hierarchy segmentation approach requires user interaction in selecting regions and subtrees. Further, visualizing and comprehending the anatomy and the selected portions of the hierarchy can be problematic. In this paper we will describe the Magic Crayon tool which allows a user to define rapidly and accurately various anatomical objects by interacting with image hierarchies such as those generated with Eberly's Ridge Flow algorithm as well as other 3D image hierarchies. Preliminary results suggest that fairly complex anatomical objects can be segmented in under a minute with sufficient accuracy for 3D surgery planning, 3D radiation oncology treatment planning, and similar applications. Potential modifications to the approach for improved accuracy are summarized.

  7. 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.

  8. MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images.

    PubMed

    Chu, Chengwen; Bai, Junjie; Wu, Xiaodong; Zheng, Guoyan

    2015-12-01

    This paper addresses the issue of fully automatic segmentation of a hip CT image with the goal to preserve the joint structure for clinical applications in hip disease diagnosis and treatment. For this purpose, we propose a Multi-Atlas Segmentation Constrained Graph (MASCG) method. The MASCG method uses multi-atlas based mesh fusion results to initialize a bone sheetness based multi-label graph cut for an accurate hip CT segmentation which has the inherent advantage of automatic separation of the pelvic region from the bilateral proximal femoral regions. We then introduce a graph cut constrained graph search algorithm to further improve the segmentation accuracy around the bilateral hip joint regions. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 15-fold cross validation. When the present approach was compared to manual segmentation, an average surface distance error of 0.30 mm, 0.29 mm, and 0.30 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. A further look at the bilateral hip joint regions demonstrated an average surface distance error of 0.16 mm, 0.21 mm and 0.20 mm for the acetabulum, the left femoral head, and the right femoral head, respectively.

  9. 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.

  10. Robust and accurate star segmentation algorithm based on morphology

    NASA Astrophysics Data System (ADS)

    Jiang, Jie; Lei, Liu; Guangjun, Zhang

    2016-06-01

    Star tracker is an important instrument of measuring a spacecraft's attitude; it measures a spacecraft's attitude by matching the stars captured by a camera and those stored in a star database, the directions of which are known. Attitude accuracy of star tracker is mainly determined by star centroiding accuracy, which is guaranteed by complete star segmentation. Current algorithms of star segmentation cannot suppress different interferences in star images and cannot segment stars completely because of these interferences. To solve this problem, a new star target segmentation algorithm is proposed on the basis of mathematical morphology. The proposed algorithm utilizes the margin structuring element to detect small targets and the opening operation to suppress noises, and a modified top-hat transform is defined to extract stars. A combination of three different structuring elements is utilized to define a new star segmentation algorithm, and the influence of three different structural elements on the star segmentation results is analyzed. Experimental results show that the proposed algorithm can suppress different interferences and segment stars completely, thus providing high star centroiding accuracy.

  11. An accurate and efficient bayesian method for automatic segmentation of brain MRI.

    PubMed

    Marroquin, J L; Vemuri, B C; Botello, S; Calderon, F; Fernandez-Bouzas, A

    2002-08-01

    Automatic three-dimensional (3-D) segmentation of the brain from magnetic resonance (MR) scans is a challenging problem that has received an enormous amount of attention lately. Of the techniques reported in the literature, very few are fully automatic. In this paper, we present an efficient and accurate, fully automatic 3-D segmentation procedure for brain MR scans. It has several salient features; namely, the following. 1) Instead of a single multiplicative bias field that affects all tissue intensities, separate parametric smooth models are used for the intensity of each class. 2) A brain atlas is used in conjunction with a robust registration procedure to find a nonrigid transformation that maps the standard brain to the specimen to be segmented. This transformation is then used to: segment the brain from nonbrain tissue; compute prior probabilities for each class at each voxel location and find an appropriate automatic initialization. 3) Finally, a novel algorithm is presented which is a variant of the expectation-maximization procedure, that incorporates a fast and accurate way to find optimal segmentations, given the intensity models along with the spatial coherence assumption. Experimental results with both synthetic and real data are included, as well as comparisons of the performance of our algorithm with that of other published methods.

  12. 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

  13. [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

  14. 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.

  15. 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

  16. 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.

  17. 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.

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

    PubMed

    Browning, Sharon R; Browning, Brian L

    2015-09-03

    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.

  19. 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

  20. An automatic method for fast and accurate liver segmentation in CT images using a shape detection level set method

    NASA Astrophysics Data System (ADS)

    Lee, Jeongjin; Kim, Namkug; Lee, Ho; Seo, Joon Beom; Won, Hyung Jin; Shin, Yong Moon; Shin, Yeong Gil

    2007-03-01

    Automatic liver segmentation is still a challenging task due to the ambiguity of liver boundary and the complex context of nearby organs. In this paper, we propose a faster and more accurate way of liver segmentation in CT images with an enhanced level set method. The speed image for level-set propagation is smoothly generated by increasing number of iterations in anisotropic diffusion filtering. This prevents the level-set propagation from stopping in front of local minima, which prevails in liver CT images due to irregular intensity distributions of the interior liver region. The curvature term of shape modeling level-set method captures well the shape variations of the liver along the slice. Finally, rolling ball algorithm is applied for including enhanced vessels near the liver boundary. Our approach are tested and compared to manual segmentation results of eight CT scans with 5mm slice distance using the average distance and volume error. The average distance error between corresponding liver boundaries is 1.58 mm and the average volume error is 2.2%. The average processing time for the segmentation of each slice is 5.2 seconds, which is much faster than the conventional ones. Accurate and fast result of our method will expedite the next stage of liver volume quantification for liver transplantations.

  1. Accurate stress resultants equations for laminated composite deep thick shells

    SciTech Connect

    Qatu, M.S.

    1995-11-01

    This paper derives accurate equations for the normal and shear force as well as bending and twisting moment resultants for laminated composite deep, thick shells. The stress resultant equations for laminated composite thick shells are shown to be different from those of plates. This is due to the fact the stresses over the thickness of the shell have to be integrated on a trapezoidal-like shell element to obtain the stress resultants. Numerical results are obtained and showed that accurate stress resultants are needed for laminated composite deep thick shells, especially if the curvature is not spherical.

  2. A spectral element method with adaptive segmentation for accurately simulating extracellular electrical stimulation of neurons.

    PubMed

    Eiber, Calvin D; Dokos, Socrates; Lovell, Nigel H; Suaning, Gregg J

    2016-08-19

    The capacity to quickly and accurately simulate extracellular stimulation of neurons is essential to the design of next-generation neural prostheses. Existing platforms for simulating neurons are largely based on finite-difference techniques; due to the complex geometries involved, the more powerful spectral or differential quadrature techniques cannot be applied directly. This paper presents a mathematical basis for the application of a spectral element method to the problem of simulating the extracellular stimulation of retinal neurons, which is readily extensible to neural fibers of any kind. The activating function formalism is extended to arbitrary neuron geometries, and a segmentation method to guarantee an appropriate choice of collocation points is presented. Differential quadrature may then be applied to efficiently solve the resulting cable equations. The capacity for this model to simulate action potentials propagating through branching structures and to predict minimum extracellular stimulation thresholds for individual neurons is demonstrated. The presented model is validated against published values for extracellular stimulation threshold and conduction velocity for realistic physiological parameter values. This model suggests that convoluted axon geometries are more readily activated by extracellular stimulation than linear axon geometries, which may have ramifications for the design of neural prostheses.

  3. 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

  4. Reconstruction of the activity of point sources for the accurate characterization of nuclear waste drums by segmented gamma scanning.

    PubMed

    Krings, Thomas; Mauerhofer, Eric

    2011-06-01

    This work improves the reliability and accuracy in the reconstruction of the total isotope activity content in heterogeneous nuclear waste drums containing point sources. The method is based on χ(2)-fits of the angular dependent count rate distribution measured during a drum rotation in segmented gamma scanning. A new description of the analytical calculation of the angular count rate distribution is introduced based on a more precise model of the collimated detector. The new description is validated and compared to the old description using MCNP5 simulations of angular dependent count rate distributions of Co-60 and Cs-137 point sources. It is shown that the new model describes the angular dependent count rate distribution significantly more accurate compared to the old model. Hence, the reconstruction of the activity is more accurate and the errors are considerably reduced that lead to more reliable results. Furthermore, the results are compared to the conventional reconstruction method assuming a homogeneous matrix and activity distribution.

  5. Accurate segmentation of leukocyte in blood cell images using Atanassov's intuitionistic fuzzy and interval Type II fuzzy set theory.

    PubMed

    Chaira, Tamalika

    2014-06-01

    In this paper automatic leukocyte segmentation in pathological blood cell images is proposed using intuitionistic fuzzy and interval Type II fuzzy set theory. This is done to count different types of leukocytes for disease detection. Also, the segmentation should be accurate so that the shape of the leukocytes is preserved. So, intuitionistic fuzzy set and interval Type II fuzzy set that consider either more number of uncertainties or a different type of uncertainty as compared to fuzzy set theory are used in this work. As the images are considered fuzzy due to imprecise gray levels, advanced fuzzy set theories may be expected to give better result. A modified Cauchy distribution is used to find the membership function. In intuitionistic fuzzy method, non-membership values are obtained using Yager's intuitionistic fuzzy generator. Optimal threshold is obtained by minimizing intuitionistic fuzzy divergence. In interval type II fuzzy set, a new membership function is generated that takes into account the two levels in Type II fuzzy set using probabilistic T co norm. Optimal threshold is selected by minimizing a proposed Type II fuzzy divergence. Though fuzzy techniques were applied earlier but these methods failed to threshold multiple leukocytes in images. Experimental results show that both interval Type II fuzzy and intuitionistic fuzzy methods perform better than the existing non-fuzzy/fuzzy methods but interval Type II fuzzy thresholding method performs little bit better than intuitionistic fuzzy method. Segmented leukocytes in the proposed interval Type II fuzzy method are observed to be distinct and clear.

  6. 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.

  7. 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

  8. 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

  9. Blood Pool Segmentation Results in Superior Virtual Cardiac Models than Myocardial Segmentation for 3D Printing.

    PubMed

    Farooqi, Kanwal M; Lengua, Carlos Gonzalez; Weinberg, Alan D; Nielsen, James C; Sanz, Javier

    2016-08-01

    The method of cardiac magnetic resonance (CMR) three-dimensional (3D) image acquisition and post-processing which should be used to create optimal virtual models for 3D printing has not been studied systematically. Patients (n = 19) who had undergone CMR including both 3D balanced steady-state free precession (bSSFP) imaging and contrast-enhanced magnetic resonance angiography (MRA) were retrospectively identified. Post-processing for the creation of virtual 3D models involved using both myocardial (MS) and blood pool (BP) segmentation, resulting in four groups: Group 1-bSSFP/MS, Group 2-bSSFP/BP, Group 3-MRA/MS and Group 4-MRA/BP. The models created were assessed by two raters for overall quality (1-poor; 2-good; 3-excellent) and ability to identify predefined vessels (1-5: superior vena cava, inferior vena cava, main pulmonary artery, ascending aorta and at least one pulmonary vein). A total of 76 virtual models were created from 19 patient CMR datasets. The mean overall quality scores for Raters 1/2 were 1.63 ± 0.50/1.26 ± 0.45 for Group 1, 2.12 ± 0.50/2.26 ± 0.73 for Group 2, 1.74 ± 0.56/1.53 ± 0.61 for Group 3 and 2.26 ± 0.65/2.68 ± 0.48 for Group 4. The numbers of identified vessels for Raters 1/2 were 4.11 ± 1.32/4.05 ± 1.31 for Group 1, 4.90 ± 0.46/4.95 ± 0.23 for Group 2, 4.32 ± 1.00/4.47 ± 0.84 for Group 3 and 4.74 ± 0.56/4.63 ± 0.49 for Group 4. Models created using BP segmentation (Groups 2 and 4) received significantly higher ratings than those created using MS for both overall quality and number of vessels visualized (p < 0.05), regardless of the acquisition technique. There were no significant differences between Groups 1 and 3. The ratings for Raters 1 and 2 had good correlation for overall quality (ICC = 0.63) and excellent correlation for the total number of vessels visualized (ICC = 0.77). The intra-rater reliability was good for Rater A (ICC = 0.65). Three models were successfully printed

  10. Accurate segmentation of partially overlapping cervical cells based on dynamic sparse contour searching and GVF snake model.

    PubMed

    Guan, Tao; Zhou, Dongxiang; Liu, Yunhui

    2015-07-01

    Overlapping cells segmentation is one of the challenging topics in medical image processing. In this paper, we propose to approximately represent the cell contour as a set of sparse contour points, which can be further partitioned into two parts: the strong contour points and the weak contour points. We consider the cell contour extraction as a contour points locating problem and propose an effective and robust framework for segmentation of partially overlapping cells in cervical smear images. First, the cell nucleus and the background are extracted by a morphological filtering-based K-means clustering algorithm. Second, a gradient decomposition-based edge enhancement method is developed for enhancing the true edges belonging to the center cell. Then, a dynamic sparse contour searching algorithm is proposed to gradually locate the weak contour points in the cell overlapping regions based on the strong contour points. This algorithm involves the least squares estimation and a dynamic searching principle, and is thus effective to cope with the cell overlapping problem. Using the located contour points, the Gradient Vector Flow Snake model is finally employed to extract the accurate cell contour. Experiments have been performed on two cervical smear image datasets containing both single cells and partially overlapping cells. The high accuracy of the cell contour extraction result validates the effectiveness of the proposed method.

  11. 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.

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

    PubMed

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

    2015-07-14

    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.

  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. Graphite/Larc-160 technology demonstration segment test results

    NASA Technical Reports Server (NTRS)

    Morita, W. H.; Graves, S. R.

    1983-01-01

    A structural test program was conducted on a Celion/LARC-160 graphite/polyimide technology demonstration segment (TDS) to verify the technology. The 137 x 152 cm (54 x 60 in.) TDS simulates a full-scale section of the orbiter composite body flap design incorporating three ribs and extending from the forward cove back to the rear spar. The TDS was successfully subjected to mechanical loads and thermal environments (-170 to 316 C) simulating 100 shuttle orbiter missions. Successful completion of the test program verified the design, analysis, and fabrication methodology for bonded Gr/PI honeycomb sandwich structure and demonstration that Gr/PI composite technology readiness is established.

  15. Segments.

    ERIC Educational Resources Information Center

    Zemsky, Robert; Shaman, Susan; Shapiro, Daniel B.

    2001-01-01

    Presents a market taxonomy for higher education, including what it reveals about the structure of the market, the model's technical attributes, and its capacity to explain pricing behavior. Details the identification of the principle seams separating one market segment from another and how student aspirations help to organize the market, making…

  16. Multi-object active shape model construction for abdomen segmentation: preliminary results.

    PubMed

    Gollmer, Sebastian T; Simon, Martin; Bischof, Arpad; Barkhausen, Jorg; Buzug, Thorsten M

    2012-01-01

    The automatic segmentation of abdominal organs is a pre-requisite for many medical applications. Successful methods typically rely on prior knowledge about the to be segmented anatomy as it is for instance provided by means of active shape models (ASMs). Contrary to most previous ASM based methods, this work does not focus on individual organs. Instead, a more holistic approach that aims at exploiting inter-organ relationships to eventually segment a complex of organs is proposed. Accordingly, a flexible framework for automatic construction of multi-object ASMs is introduced, employed for coupled shape modeling, and used for co-segmentation of liver and spleen based on a new coupled shape/separate pose approach. Our first results indicate feasible segmentation accuracies, whereas pose decoupling leads to substantially better segmentation results and performs in average also slightly better than the standard single-object ASM approach.

  17. Automated detection of discourse segment and experimental types from the text of cancer pathway results sections

    PubMed Central

    Burns, Gully A.P.C.; Dasigi, Pradeep; de Waard, Anita; Hovy, Eduard H.

    2016-01-01

    Automated machine-reading biocuration systems typically use sentence-by-sentence information extraction to construct meaning representations for use by curators. This does not directly reflect the typical discourse structure used by scientists to construct an argument from the experimental data available within a article, and is therefore less likely to correspond to representations typically used in biomedical informatics systems (let alone to the mental models that scientists have). In this study, we develop Natural Language Processing methods to locate, extract, and classify the individual passages of text from articles’ Results sections that refer to experimental data. In our domain of interest (molecular biology studies of cancer signal transduction pathways), individual articles may contain as many as 30 small-scale individual experiments describing a variety of findings, upon which authors base their overall research conclusions. Our system automatically classifies discourse segments in these texts into seven categories (fact, hypothesis, problem, goal, method, result, implication) with an F-score of 0.68. These segments describe the essential building blocks of scientific discourse to (i) provide context for each experiment, (ii) report experimental details and (iii) explain the data’s meaning in context. We evaluate our system on text passages from articles that were curated in molecular biology databases (the Pathway Logic Datum repository, the Molecular Interaction MINT and INTACT databases) linking individual experiments in articles to the type of assay used (coprecipitation, phosphorylation, translocation etc.). We use supervised machine learning techniques on text passages containing unambiguous references to experiments to obtain baseline F1 scores of 0.59 for MINT, 0.71 for INTACT and 0.63 for Pathway Logic. Although preliminary, these results support the notion that targeting information extraction methods to experimental results could provide

  18. Visualizing MCNP Tally Segment Geometry and Coupling Results with ABAQUS

    SciTech Connect

    J. R. Parry; J. A. Galbraith

    2007-11-01

    The Advanced Graphite Creep test, AGC-1, is planned for irradiation in the Advanced Test Reactor (ATR) in support of the Next Generation Nuclear Plant program. The experiment requires very detailed neutronics and thermal hydraulics analyses to show compliance with programmatic and ATR safety requirements. The MCNP model used for the neutronics analysis required hundreds of tally regions to provide the desired detail. A method for visualizing the hundreds of tally region geometries and the tally region results in 3 dimensions has been created to support the AGC-1 irradiation. Additionally, a method was created which would allow ABAQUS to access the results directly for the thermal analysis of the AGC-1 experiment.

  19. Viral genome segmentation can result from a trade-off between genetic content and particle stability.

    PubMed

    Ojosnegros, Samuel; García-Arriaza, Juan; Escarmís, Cristina; Manrubia, Susanna C; Perales, Celia; Arias, Armando; Mateu, Mauricio García; Domingo, Esteban

    2011-03-01

    The evolutionary benefit of viral genome segmentation is a classical, yet unsolved question in evolutionary biology and RNA genetics. Theoretical studies anticipated that replication of shorter RNA segments could provide a replicative advantage over standard size genomes. However, this question has remained elusive to experimentalists because of the lack of a proper viral model system. Here we present a study with a stable segmented bipartite RNA virus and its ancestor non-segmented counterpart, in an identical genomic nucleotide sequence context. Results of RNA replication, protein expression, competition experiments, and inactivation of infectious particles point to a non-replicative trait, the particle stability, as the main driver of fitness gain of segmented genomes. Accordingly, measurements of the volume occupation of the genome inside viral capsids indicate that packaging shorter genomes involves a relaxation of the packaging density that is energetically favourable. The empirical observations are used to design a computational model that predicts the existence of a critical multiplicity of infection for domination of segmented over standard types. Our experiments suggest that viral segmented genomes may have arisen as a molecular solution for the trade-off between genome length and particle stability. Genome segmentation allows maximizing the genetic content without the detrimental effect in stability derived from incresing genome length.

  20. Viral Genome Segmentation Can Result from a Trade-Off between Genetic Content and Particle Stability

    PubMed Central

    Ojosnegros, Samuel; García-Arriaza, Juan; Escarmís, Cristina; Manrubia, Susanna C.; Perales, Celia; Arias, Armando; Mateu, Mauricio García; Domingo, Esteban

    2011-01-01

    The evolutionary benefit of viral genome segmentation is a classical, yet unsolved question in evolutionary biology and RNA genetics. Theoretical studies anticipated that replication of shorter RNA segments could provide a replicative advantage over standard size genomes. However, this question has remained elusive to experimentalists because of the lack of a proper viral model system. Here we present a study with a stable segmented bipartite RNA virus and its ancestor non-segmented counterpart, in an identical genomic nucleotide sequence context. Results of RNA replication, protein expression, competition experiments, and inactivation of infectious particles point to a non-replicative trait, the particle stability, as the main driver of fitness gain of segmented genomes. Accordingly, measurements of the volume occupation of the genome inside viral capsids indicate that packaging shorter genomes involves a relaxation of the packaging density that is energetically favourable. The empirical observations are used to design a computational model that predicts the existence of a critical multiplicity of infection for domination of segmented over standard types. Our experiments suggest that viral segmented genomes may have arisen as a molecular solution for the trade-off between genome length and particle stability. Genome segmentation allows maximizing the genetic content without the detrimental effect in stability derived from incresing genome length. PMID:21437265

  1. An Accurate Heading Solution using MEMS-based Gyroscope and Magnetometer Integrated System (Preliminary Results)

    NASA Astrophysics Data System (ADS)

    El-Diasty, M.

    2014-11-01

    An accurate heading solution is required for many applications and it can be achieved by high grade (high cost) gyroscopes (gyros) which may not be suitable for such applications. Micro-Electro Mechanical Systems-based (MEMS) is an emerging technology, which has the potential of providing heading solution using a low cost MEMS-based gyro. However, MEMS-gyro-based heading solution drifts significantly over time. The heading solution can also be estimated using MEMS-based magnetometer by measuring the horizontal components of the Earth magnetic field. The MEMS-magnetometer-based heading solution does not drift over time, but are contaminated by high level of noise and may be disturbed by the presence of magnetic field sources such as metal objects. This paper proposed an accurate heading estimation procedure based on the integration of MEMS-based gyro and magnetometer measurements that correct gyro and magnetometer measurements where gyro angular rates of changes are estimated using magnetometer measurements and then integrated with the measured gyro angular rates of changes with a robust filter to estimate the heading. The proposed integration solution is implemented using two data sets; one was conducted in static mode without magnetic disturbances and the second was conducted in kinematic mode with magnetic disturbances. The results showed that the proposed integrated heading solution provides accurate, smoothed and undisturbed solution when compared with magnetometerbased and gyro-based heading solutions.

  2. Optimization of sample preparation for accurate results in quantitative NMR spectroscopy

    NASA Astrophysics Data System (ADS)

    Yamazaki, Taichi; Nakamura, Satoe; Saito, Takeshi

    2017-04-01

    Quantitative nuclear magnetic resonance (qNMR) spectroscopy has received high marks as an excellent measurement tool that does not require the same reference standard as the analyte. Measurement parameters have been discussed in detail and high-resolution balances have been used for sample preparation. However, the high-resolution balances, such as an ultra-microbalance, are not general-purpose analytical tools and many analysts may find those balances difficult to use, thereby hindering accurate sample preparation for qNMR measurement. In this study, we examined the relationship between the resolution of the balance and the amount of sample weighed during sample preparation. We were able to confirm the accuracy of the assay results for samples weighed on a high-resolution balance, such as the ultra-microbalance. Furthermore, when an appropriate tare and amount of sample was weighed on a given balance, accurate assay results were obtained with another high-resolution balance. Although this is a fundamental result, it offers important evidence that would enhance the versatility of the qNMR method.

  3. 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

  4. Intraabdominal hemorrhage as a result of segmental mediolytic arteritis of an omental artery: case report.

    PubMed

    Heritz, D M; Butany, J; Johnston, K W; Sniderman, K W

    1990-11-01

    This article describes the fifth reported case of segmental mediolytic arteritis and the second in a survivor. The patient had intraabdominal bleeding as a result of a ruptured omental artery. The pathologic and arteriographic findings are described. The pathology is characterized by segmental disruption of the medial smooth muscle cells and the initiation of mediolysis. Mediolysis is associated with marked segmental thinning of the vessel wall, often with only the adventitia intact. Fibrin is deposited at the adventitial and medial surfaces, and hemorrhage into the media may occur. As in this reported case, lysis of the adventitia leads to sudden, often catastrophic intraabdominal hemorrhage. Little associated adventitial inflammation occurred. Segmental mediolytic arteritis seems to involve the intra-abdominal muscular arteries in elderly patients with nonspecific abdominal symptoms. An angiogram showed patchy areas of narrowing involving ileal, gastroduodenal, and renal arteries that correlated with the pathologic findings observed in the excised omental arteries.

  5. 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

  6. Bronchopulmonary segments approximation using anatomical atlas

    NASA Astrophysics Data System (ADS)

    Busayarat, Sata; Zrimec, Tatjana

    2007-03-01

    Bronchopulmonary segments are valuable as they give more accurate localization than lung lobes. Traditionally, determining the segments requires segmentation and identification of segmental bronchi, which, in turn, require volumetric imaging data. In this paper, we present a method for approximating the bronchopulmonary segments for sparse data by effectively using an anatomical atlas. The atlas is constructed from a volumetric data and contains accurate information about bronchopulmonary segments. A new ray-tracing based image registration is used for transferring the information from the atlas to a query image. Results show that the method is able to approximate the segments on sparse HRCT data with slice gap up to 25 millimeters.

  7. Segmenting Business Students Using Cluster Analysis Applied to Student Satisfaction Survey Results

    ERIC Educational Resources Information Center

    Gibson, Allen

    2009-01-01

    This paper demonstrates a new application of cluster analysis to segment business school students according to their degree of satisfaction with various aspects of the academic program. The resulting clusters provide additional insight into drivers of student satisfaction that are not evident from analysis of the responses of the student body as a…

  8. Random walks based multi-image segmentation: Quasiconvexity results and GPU-based solutions

    PubMed Central

    Collins, Maxwell D.; Xu, Jia; Grady, Leo; Singh, Vikas

    2012-01-01

    We recast the Cosegmentation problem using Random Walker (RW) segmentation as the core segmentation algorithm, rather than the traditional MRF approach adopted in the literature so far. Our formulation is similar to previous approaches in the sense that it also permits Cosegmentation constraints (which impose consistency between the extracted objects from ≥ 2 images) using a nonparametric model. However, several previous nonparametric cosegmentation methods have the serious limitation that they require adding one auxiliary node (or variable) for every pair of pixels that are similar (which effectively limits such methods to describing only those objects that have high entropy appearance models). In contrast, our proposed model completely eliminates this restrictive dependence –the resulting improvements are quite significant. Our model further allows an optimization scheme exploiting quasiconvexity for model-based segmentation with no dependence on the scale of the segmented foreground. Finally, we show that the optimization can be expressed in terms of linear algebra operations on sparse matrices which are easily mapped to GPU architecture. We provide a highly specialized CUDA library for Cosegmentation exploiting this special structure, and report experimental results showing these advantages. PMID:25278742

  9. Random walks based multi-image segmentation: Quasiconvexity results and GPU-based solutions.

    PubMed

    Collins, Maxwell D; Xu, Jia; Grady, Leo; Singh, Vikas

    2012-01-01

    We recast the Cosegmentation problem using Random Walker (RW) segmentation as the core segmentation algorithm, rather than the traditional MRF approach adopted in the literature so far. Our formulation is similar to previous approaches in the sense that it also permits Cosegmentation constraints (which impose consistency between the extracted objects from ≥ 2 images) using a nonparametric model. However, several previous nonparametric cosegmentation methods have the serious limitation that they require adding one auxiliary node (or variable) for every pair of pixels that are similar (which effectively limits such methods to describing only those objects that have high entropy appearance models). In contrast, our proposed model completely eliminates this restrictive dependence -the resulting improvements are quite significant. Our model further allows an optimization scheme exploiting quasiconvexity for model-based segmentation with no dependence on the scale of the segmented foreground. Finally, we show that the optimization can be expressed in terms of linear algebra operations on sparse matrices which are easily mapped to GPU architecture. We provide a highly specialized CUDA library for Cosegmentation exploiting this special structure, and report experimental results showing these advantages.

  10. Failure of the human lumbar motion-segments resulting from anterior shear fatigue loading

    PubMed Central

    SKRZYPIEC, Daniel M.; NAGEL, Katrin; SELLENSCHLOH, Kay; KLEIN, Anke; PÜSCHEL, Klaus; MORLOCK, Michael M.; HUBER, Gerd

    2016-01-01

    An in-vitro experiment was designed to investigate the mode of failure following shear fatigue loading of lumbar motion-segments. Human male lumbar motion-segments (age 32–42 years, n=6) were immersed in Ringer solution at 37°C and repeatedly loaded, using a modified materials testing machine. Fatigue loading consisted of a sinusoidal shear load from 0 N to 1,500 N (750 N±750 N) applied to the upper vertebra of the motion-segment, at a frequency of 5 Hz. During fatigue experiments, several failure events were observed in the dynamic creep curves. Post-test x-ray, CT and dissection revealed that all specimens had delamination of the intervertebral disc. Anterior shear fatigue predominantly resulted in fracture of the apophyseal processes of the upper vertebrae (n=4). Exposure to the anterior shear fatigue loading caused motion-segment instability and resulted in vertebral slip corresponding to grade I and ‘mild’ grade II spondylolisthesis, as observed clinically. PMID:26829975

  11. Preliminary results of automated removal of degenerative joint disease in bone scan lesion segmentation

    NASA Astrophysics Data System (ADS)

    Chu, Gregory H.; Lo, Pechin; Kim, Hyun J.; Auerbach, Martin; Goldin, Jonathan; Henkel, Keith; Banola, Ashley; Morris, Darren; Coy, Heidi; Brown, Matthew S.

    2013-03-01

    Whole-body bone scintigraphy (or bone scan) is a highly sensitive method for visualizing bone metastases and is the accepted standard imaging modality for detection of metastases and assessment of treatment outcomes. The development of a quantitative biomarker using computer-aided detection on bone scans for treatment response assessment may have a significant impact on the evaluation of novel oncologic drugs directed at bone metastases. One of the challenges to lesion segmentation on bone scans is the non-specificity of the radiotracer, manifesting as high activity related to non-malignant processes like degenerative joint disease, sinuses, kidneys, thyroid and bladder. In this paper, we developed an automated bone scan lesion segmentation method that implements intensity normalization, a two-threshold model, and automated detection and removal of areas consistent with non-malignant processes from the segmentation. The two-threshold model serves to account for outlier bone scans with elevated and diffuse intensity distributions. Parameters to remove degenerative joint disease were trained using a multi-start Nelder-Mead simplex optimization scheme. The segmentation reference standard was constructed manually by a panel of physicians. We compared the performance of the proposed method against a previously published method. The results of a two-fold cross validation show that the overlap ratio improved in 67.0% of scans, with an average improvement of 5.1% points.

  12. An accurate multimodal 3-D vessel segmentation method based on brightness variations on OCT layers and curvelet domain fundus image analysis.

    PubMed

    Kafieh, Raheleh; Rabbani, Hossein; Hajizadeh, Fedra; Ommani, Mohammadreza

    2013-10-01

    This paper proposes a multimodal approach for vessel segmentation of macular optical coherence tomography (OCT) slices along with the fundus image. The method is comprised of two separate stages; the first step is 2-D segmentation of blood vessels in curvelet domain, enhanced by taking advantage of vessel information in crossing OCT slices (named feedback procedure), and improved by suppressing the false positives around the optic nerve head. The proposed method for vessel localization of OCT slices is also enhanced utilizing the fact that retinal nerve fiber layer becomes thicker in the presence of the blood vessels. The second stage of this method is axial localization of the vessels in OCT slices and 3-D reconstruction of the blood vessels. Twenty-four macular spectral 3-D OCT scans of 16 normal subjects were acquired using a Heidelberg HRA OCT scanner. Each dataset consisted of a scanning laser ophthalmoscopy (SLO) image and limited number of OCT scans with size of 496 × 512 (namely, for a data with 19 selected OCT slices, the whole data size was 496 × 512 × 19). The method is developed with least complicated algorithms and the results show considerable improvement in accuracy of vessel segmentation over similar methods to produce a local accuracy of 0.9632 in area of SLO, covered with OCT slices, and the overall accuracy of 0.9467 in the whole SLO image. The results are also demonstrative of a direct relation between the overall accuracy and percentage of SLO coverage by OCT slices.

  13. Ensemble segmentation using efficient integer linear programming.

    PubMed

    Alush, Amir; Goldberger, Jacob

    2012-10-01

    We present a method for combining several segmentations of an image into a single one that in some sense is the average segmentation in order to achieve a more reliable and accurate segmentation result. The goal is to find a point in the "space of segmentations" which is close to all the individual segmentations. We present an algorithm for segmentation averaging. The image is first oversegmented into superpixels. Next, each segmentation is projected onto the superpixel map. An instance of the EM algorithm combined with integer linear programming is applied on the set of binary merging decisions of neighboring superpixels to obtain the average segmentation. Apart from segmentation averaging, the algorithm also reports the reliability of each segmentation. The performance of the proposed algorithm is demonstrated on manually annotated images from the Berkeley segmentation data set and on the results of automatic segmentation algorithms.

  14. Electrocardiograhic findings resulting in inappropriate cardiac catheterization laboratory activation for ST-segment elevation myocardial infarction

    PubMed Central

    Shamim, Shariq; McCrary, Justin; Wayne, Lori; Gratton, Matthew

    2014-01-01

    Background Prompt reperfusion has been shown to improve outcomes in patients with acute ST-segment elevation myocardial infarction (STEMI) with a goal of culprit vessel patency in <90 minutes. This requires a coordinated approach between the emergency medical services (EMS), emergency department (ED) and interventional cardiology. The urgency of this process can contribute to inappropriate cardiac catheterization laboratory (CCL) activations. Objectives One of the major determinants of inappropriate activations has been misinterpretation of the electrocardiogram (ECG) in the patient with acute chest pain. Methods We report the ECG findings for all CCL activations over an 18-month period after the inception of a STEMI program at our institution. Results There were a total of 139 activations with 77 having a STEMI diagnosis confirmed and 62 activations where there was no STEMI. The inappropriate activations resulted from a combination of atypical symptoms and misinterpretation of the ECG (45% due to anterior ST-segment elevation) on patient presentation. The electrocardiographic abnormalities were particularly problematic in African-Americans with left ventricular hypertrophy. Conclusions In this single-center, prospective observational study, nearly half of the inappropriate STEMI activations were due to the misinterpretation of anterior ST-segment elevation and this finding was commonly seen in African-Americans with left ventricular hypertrophy. PMID:25009790

  15. A short time existence/uniqueness result for a nonlocal topology-preserving segmentation model

    NASA Astrophysics Data System (ADS)

    Forcadel, Nicolas; Le Guyader, Carole

    Motivated by a prior applied work of Vese and the second author dedicated to segmentation under topological constraints, we derive a slightly modified model phrased as a functional minimization problem, and propose to study it from a theoretical viewpoint. The mathematical model leads to a second order nonlinear PDE with a singularity at Du=0 and containing a nonlocal term. A suitable setting is thus the one of the viscosity solution theory and, in this framework, we establish a short time existence/uniqueness result as well as a Lipschitz regularity result for the solution.

  16. Geomorphic Segmentation, Hydraulic Geometry, and Hydraulic Microhabitats of the Niobrara River, Nebraska - Methods and Initial Results

    USGS Publications Warehouse

    Alexander, Jason S.; Zelt, Ronald B.; Schaepe, Nathaniel J.

    2009-01-01

    The Niobrara River of Nebraska is a geologically, ecologically, and economically significant resource. The State of Nebraska has recognized the need to better manage the surface- and ground-water resources of the Niobrara River so they are sustainable in the long term. In cooperation with the Nebraska Game and Parks Commission, the U.S. Geological Survey is investigating the hydrogeomorphic settings and hydraulic geometry of the Niobrara River to assist in characterizing the types of broad-scale physical habitat attributes that may be of importance to the ecological resources of the river system. This report includes an inventory of surface-water and ground-water hydrology data, surface water-quality data, a longitudinal geomorphic segmentation and characterization of the main channel and its valley, and hydraulic geometry relations for the 330-mile section of the Niobrara River from Dunlap Diversion Dam in western Nebraska to the Missouri River confluence. Hydraulic microhabitats also were analyzed using available data from discharge measurements to demonstrate the potential application of these data and analysis methods. The main channel of the Niobrara was partitioned into three distinct fluvial geomorphic provinces: an upper province characterized by open valleys and a sinuous, equiwidth channel; a central province characterized by mixed valley and channel settings, including several entrenched canyon reaches; and a lower province where the valley is wide, yet restricted, but the river also is wide and persistently braided. Within the three fluvial geomorphic provinces, 36 geomorphic segments were identified using a customized, process-orientated classification scheme, which described the basic physical characteristics of the Niobrara River and its valley. Analysis of the longitudinal slope characteristics indicated that the Niobrara River longitudinal profile may be largely bedrock-controlled, with slope inflections co-located at changes in bedrock type at

  17. Medical image segmentation by MDP model

    NASA Astrophysics Data System (ADS)

    Lu, Yisu; Chen, Wufan

    2011-11-01

    MDP (Dirichlet Process Mixtures) model is applied to segment medical images in this paper. Segmentation can been automatically done without initializing segmentation class numbers. The MDP model segmentation algorithm is used to segment natural images and MR (Magnetic Resonance) images in the paper. To demonstrate the accuracy of the MDP model segmentation algorithm, many compared experiments, such as EM (Expectation Maximization) image segmentation algorithm, K-means image segmentation algorithm and MRF (Markov Field) image segmentation algorithm, have been done to segment medical MR images. All the methods are also analyzed quantitatively by using DSC (Dice Similarity Coefficients). The experiments results show that DSC of MDP model segmentation algorithm of all slices exceed 90%, which show that the proposed method is robust and accurate.

  18. The EM/MPM algorithm for segmentation of textured images: analysis and further experimental results.

    PubMed

    Comer, M L; Delp, E J

    2000-01-01

    In this paper we present new results relative to the "expectation-maximization/maximization of the posterior marginals" (EM/MPM) algorithm for simultaneous parameter estimation and segmentation of textured images. The EM/MPM algorithm uses a Markov random field model for the pixel class labels and alternately approximates the MPM estimate of the pixel class labels and estimates parameters of the observed image model. The goal of the EM/MPM algorithm is to minimize the expected value of the number of misclassified pixels. We present new theoretical results in this paper which show that the algorithm can be expected to achieve this goal, to the extent that the EM estimates of the model parameters are close to the true values of the model parameters. We also present new experimental results demonstrating the performance of the EM/MPM algorithm.

  19. Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression. Preliminary results.

    PubMed

    Ruiz-Espana, Silvia; Domingo, Juan; Diaz-Parra, Antonio; Dura, Esther; D'Ocon-Alcaniz, Victor; Arana, Estanislao; Moratal, David

    2015-01-01

    Spine is a structure commonly involved in several prevalent diseases. In clinical diagnosis, therapy, and surgical intervention, the identification and segmentation of the vertebral bodies are crucial steps. However, automatic and detailed segmentation of vertebrae is a challenging task, especially due to the proximity of the vertebrae to the corresponding ribs and other structures such as blood vessels. In this study, to overcome these problems, a probabilistic atlas of the spine, including cervical, thoracic and lumbar vertebrae has been built to introduce anatomical knowledge in the segmentation process, aiming to deal with overlapping gray levels and the proximity to other structures. From a set of 3D images manually segmented by a physician (training data), a 3D volume indicating the probability of each voxel of belonging to the spine has been developed, being necessary the generation of a probability map and its deformation to adapt to each patient. To validate the improvement of the segmentation using the atlas developed in the testing data, we computed the Hausdorff distance between the manually-segmented ground truth and an automatic segmentation and also between the ground truth and the automatic segmentation refined with the atlas. The results are promising, obtaining a higher improvement especially in the thoracic region, where the ribs can be found and appropriately eliminated.

  20. [Remote results of angioplasty using drug-coated balloons in lesions of the femoropopliteal segment].

    PubMed

    Zatevakhin, I I; Shipovskiĭ, V N; Tursunov, S B; Bagdat'ev, V E; Dzhurakulov, Sh R

    2014-01-01

    The authors analysed the results of balloon angioplasty using paclitaxel-coated balloons (IN.PACT Admiral, Medtronic Inc., USA) in patients with occlusive and stenotic lesions of arteries of the femoropopliteal segment. The Study Group was composed of 30 patients subjected to angioplasty with drug-coated balloons and the Control Group consisted of 32 patients undergoing balloon angioplasty with uncoated balloons. Primary patency of the angioplasty zone within the terms up to 30 months after the intervention using drug-coated balloons amounted to 70% and in the Control Group to 37.5%, with the limb-salvage rate equalling 96.7% and 87.5%, respectively. It was shown that angioplasty using drug-coated balloons in treatment of occlusive and stenotic lesions of femoropopliteal arteries considerably improves the results of treatment at the expense of preventing restenosis of the angioplasty zone both in the short- and long-term periods of follow up.

  1. Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results

    PubMed Central

    Lyden, Hannah; Gimbel, Sarah I.; Del Piero, Larissa; Tsai, A. Bryna; Sachs, Matthew E.; Kaplan, Jonas T.; Margolin, Gayla; Saxbe, Darby

    2016-01-01

    Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation approaches. In the current study, 23 adolescents participated in two waves of a longitudinal study. Family aggression was measured when the youths were 12 years old, and structural scans were acquired an average of 4 years later. Bilateral amygdalae and hippocampi were segmented using three different methods (manual tracing, FSL, and NeuroQuant). The segmentation estimates were compared, and linear regressions were run to assess the relationship between early family aggression exposure and all three volume segmentation estimates. Manual tracing results showed a positive relationship between family aggression and right amygdala volume, whereas FSL segmentation showed negative relationships between family aggression and both the left and right hippocampi. However, results indicate poor overlap between methods, and different associations were found between early family aggression exposure and brain volume depending on the segmentation method used. PMID:27656121

  2. SALT segmented primary mirror: laboratory test results for FOGALE inductive edge sensors

    NASA Astrophysics Data System (ADS)

    Menzies, John; Gajjar, Hitesh; Buous, Sébastien; Buckley, David; Gillingham, Peter

    2010-07-01

    At the Southern African Large Telescope (SALT), in collaboration with FOGALE Nanotech, we have been testing the recently-developed new generation inductive edge sensors. The Fogale inductive sensor is one technology being evaluated as a possible replacement for the now defunct capacitance-based edge sensing system. We present the results of exhaustive environmental testing of two variants of the inductive sensor. In addition to the environmental testing including RH and temperature cycles, the sensor was tested for sensitivity to dust and metals. We also consider long-term sensor stability, as well as that of the electronics and of the glue used to bond the sensor to its supporting structure. A prototype design for an adjustable mount is presented which will allow for in-plane gap and shear variations present in the primary mirror configuration without adversely disturbing the figure of the individual mirror segments or the measurement accuracy.

  3. Pancreas and cyst segmentation

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  4. Recent Results on the Accurate Measurements of the Dielectric Constant of Seawater at 1.413GHZ

    NASA Technical Reports Server (NTRS)

    Lang, R.H.; Tarkocin, Y.; Utku, C.; Le Vine, D.M.

    2008-01-01

    Measurements of the complex. dielectric constant of seawater at 30.00 psu, 35.00 psu and 38.27 psu over the temperature range from 5 C to 3 5 at 1.413 GHz are given and compared with the Klein-Swift results. A resonant cavity technique is used. The calibration constant used in the cavity perturbation formulas is determined experimentally using methanol and ethanediol (ethylene glycol) as reference liquids. Analysis of the data shows that the measurements are accurate to better than 1.0% in almost all cases studied.

  5. Geology of a dying backarc spreading segment: results of high-density samplings of Godzilla Megamullion

    NASA Astrophysics Data System (ADS)

    Ohara, Y.; Snow, J. E.; Michibayashi, K.; Dick, H. J.; Harigane, Y.; Tani, K.; Yamashita, H.; Ishizuka, O.; Loocke, M. P.; Ishii, T.; Okino, K.

    2011-12-01

    of the terminal phase of a dying backarc spreading segment using DSV Shinkai 6500 and deep-tow camera. By completing this cruise, we will have more than 40 sampling locations from Godzilla Megamullion. Here we report the results of this cruise, synthesizing our current understanding of the processes that were responsible for the formation of the world's largest OCC.

  6. Adsorption of lactate dehydrogenase enzyme on carbon nanotubes: how to get accurate results for the cytotoxicity of these nanomaterials.

    PubMed

    Forest, Valérie; Figarol, Agathe; Boudard, Delphine; Cottier, Michèle; Grosseau, Philippe; Pourchez, Jérémie

    2015-03-31

    Carbon nanotube (CNT) cytotoxicity is frequently investigated using in vitro classical toxicology assays. However, these cellular tests, usually based on the use of colorimetric or fluorimetric dyes, were designed for chemicals and may not be suitable for nanosized materials. Indeed, because of their unique physicochemical properties CNT can interfere with the assays and bias the results. To get accurate data and draw reliable conclusions, these artifacts should be carefully taken into account. The aim of this study was to evaluate qualitatively and quantitatively the interferences occurring between CNT and the commonly used lactate dehydrogenase (LDH) assay. Experiments under cell-free conditions were performed, and it was clearly demonstrated that artifacts occurred. They were due to the intrinsic absorbance of CNT on one hand and the adsorption of LDH at the CNT surface on the other hand. The adsorption of LDH on CNT was modeled and was found to fit the Langmuir model. The K(ads) and n(eq) constants were defined, allowing the correction of results obtained from cellular experiments to get more accurate data and lead to proper conclusions on the cytotoxicity of CNT.

  7. Prototyping results for a wide-field fiber positioner for the Giant Segmented Mirror Telescope

    NASA Astrophysics Data System (ADS)

    Moore, Anna M.; McGrath, Andrew J.

    2004-07-01

    Given the physical size of the GSMT prime focus field is approximately equivalent to that of the Subaru telescope it is possible to directly apply current technology developed for the Fiber Multi-Object Spectrograph instrument (FMOS, to be commissioned in 2005) and substantially reduce the risk associated with developing a new solution for wide-field multi-object spectroscopy on an ELT. The Anglo-Australian Observatory has recently completed a design study for an ~1000 fiber, Echidna-style positioner for the prime focus of the Giant Segmented Mirror Telescope (GSMT). The positioner forms part of the wide-field Multi-Object Multi-Fiber Optical Spectrograph (MOMFOS), an ELT prime focus instrument offering a minimum of 800 fibers patrolling the corrected 20 arcmin field. The design study identified 2 components of an equivalent MOMFOS positioner design that required prototyping. Firstly, a higher spine packing density is required to satisfy the proposed scientific program. Secondly, the fiber position measurement system adopted for FMOS cannot be simply scaled and applied to MOMFOS given space constraints in the top end unit. As such a new and, if possible, simpler system was required. Prototyping results for both components are presented.

  8. New Insights on the Crustal Structure beneath the Western Segment of NAF: Preliminary Results from a Dense Seismic Array

    NASA Astrophysics Data System (ADS)

    Turkelli, Niyazi; Poyraz, Selda A.; Kahraman, Metin; Ugur Teoman, M.; Rost, Sebastian; Houseman, Greg A.; Thompson, David; Cornwell, David

    2013-04-01

    North Anatolian Fault (NAF) is one of the major strike slip fault systems on earth comparable to San Andreas Fault some ways. Devastating earthquakes have occurred along this system causing major damage and casualties. In order to comprehensively investigate the shallow and deep crustal structure beneath the western segment of North Anatolian Fault (NAF), a temporary dense seismic network consisting of 73 broadband sensors was deployed in early May 2012 with support from The Natural Environment Research Council (NERC) and partial support from Bogazci University Research Fund. This joint project involves researchers from University of Leeds, UK and Bogazici University, Kandilli Observatory and Earthquake Research Institute (KOERI). In addition to the 63 sensors provided by SEIS-UK instrument pool and three permanent KOERI sites in the region, another seven stations of KOERI-Department of Geophysics were installed surrounding the rectangular grid with the aim of further enhancing the detection capability of this dense seismic array (map). Six months of seismic data have been collected and initial analysis underway. This research focuses on upper crustal studies such as earthquake locations (especially micro-seismic activity), moment tensor inversions and ambient noise correlations. Accurate earthquake locations will eventually lead to high resolution seismic images of NAF including both the northern and the southern branches in the upper crust. In order to put additional constraints on the active tectonics of the western part of NAF, we determined fault plane solutions using Regional Moment Tensor Inversion (RMT) and P wave first motion method. For the analysis of high quality fault plane solutions, data from KOERI and the DANA project were merged. Furthermore, detailed Moho topography will be revealed via receiver function method. Iterative time domain deconvolution was used to obtain receiver functions and H-K stacking was applied to calculate crustal thickness

  9. Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion.

    PubMed

    Collins, D Louis; Pruessner, Jens C

    2010-10-01

    We describe progress towards fully automatic segmentation of the hippocampus (HC) and amygdala (AG) in human subjects from MRI data. Three methods are described and tested with a set of MRIs from 80 young normal controls, using manual labeling of the HC and AG as a gold standard. The methods include: 1) our ANIMAL atlas-based method that uses non-linear registration to a pre-labeled non-linear average template (ICBM152). HC and AG labels, defined on the template are mapped through the inverse transformation to segment these structures on the subject's MRI. 2) We select the most similar MRI from the set of 80 labeled datasets to use as a template in the standard ANIMAL segmentation scheme. 3) We use label fusion techniques to combine segmentations from the 'n' most similar templates. The label fusion technique yields an optimal median Dice Kappa of 0.886 and similarity of 0.795 for HC, and 0.826 and 0.703 respectively for AG.

  10. Modal characterization of the ASCIE segmented optics testbed: New algorithms and experimental results

    NASA Technical Reports Server (NTRS)

    Carrier, Alain C.; Aubrun, Jean-Noel

    1993-01-01

    New frequency response measurement procedures, on-line modal tuning techniques, and off-line modal identification algorithms are developed and applied to the modal identification of the Advanced Structures/Controls Integrated Experiment (ASCIE), a generic segmented optics telescope test-bed representative of future complex space structures. The frequency response measurement procedure uses all the actuators simultaneously to excite the structure and all the sensors to measure the structural response so that all the transfer functions are measured simultaneously. Structural responses to sinusoidal excitations are measured and analyzed to calculate spectral responses. The spectral responses in turn are analyzed as the spectral data become available and, which is new, the results are used to maintain high quality measurements. Data acquisition, processing, and checking procedures are fully automated. As the acquisition of the frequency response progresses, an on-line algorithm keeps track of the actuator force distribution that maximizes the structural response to automatically tune to a structural mode when approaching a resonant frequency. This tuning is insensitive to delays, ill-conditioning, and nonproportional damping. Experimental results show that is useful for modal surveys even in high modal density regions. For thorough modeling, a constructive procedure is proposed to identify the dynamics of a complex system from its frequency response with the minimization of a least-squares cost function as a desirable objective. This procedure relies on off-line modal separation algorithms to extract modal information and on least-squares parameter subset optimization to combine the modal results and globally fit the modal parameters to the measured data. The modal separation algorithms resolved modal density of 5 modes/Hz in the ASCIE experiment. They promise to be useful in many challenging applications.

  11. Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation.

    PubMed

    Chiu, Stephanie J; Li, Xiao T; Nicholas, Peter; Toth, Cynthia A; Izatt, Joseph A; Farsiu, Sina

    2010-08-30

    Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader.

  12. Accurate quantitation of Ki67-positive proliferating hepatocytes in rabbit liver by a multicolor immunohistochemical (IHC) approach analyzed with automated tissue and cell segmentation software.

    PubMed

    van der Loos, Chris M; de Boer, Onno J; Mackaaij, Claire; Hoekstra, Lisette T; van Gulik, Thomas M; Verheij, Joanne

    2013-01-01

    Determination of hepatocyte proliferation activity is hampered by the presence of Ki67-positive non-parenchymal cells. We validated a multicolor immunohistochemical (IHC) approach using multispectral tissue and cell segmentation software. Portal vein branches to the cranial liver lobes of 10 rabbits were embolized, leading to atrophy of the cranial lobes and hyperplasia of the caudal lobes. Slides from cranial and caudal lobes (n=20) were double-stained (CK8+18 and Ki67) and triple-stained (CK8+18, Ki67, and CD31). The Ki67 proliferation index was calculated using automated tissue and cell segmentation software and compared with manual counting by two independent observers. A substantial variation was seen in the number of Ki67-positive hepatocytes in the different specimens in both double and triple staining (range, 0-50). Correlation coefficients between manual counting and the digital analysis were 0.76 for observer 1 (p<0.001) and 0.78 for observer 2 (p<0.001) with double staining and R(2) = 0.91 for observer 1 and R(2) = 0.89 for observer 2, p<0.001 with triple staining. In conclusion, in rabbit, the hepatocellular proliferation index can be reliably determined using automated tissue and cell segmentation software in combination with IHC multiple staining. Our findings may be useful in clinical practice when Ki67 proliferation index yields prognostic significance.

  13. Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries

    PubMed Central

    Davuluri, Pavani; Wu, Jie; Tang, Yang; Cockrell, Charles H.; Ward, Kevin R.; Najarian, Kayvan; Hargraves, Rosalyn H.

    2012-01-01

    Automated hemorrhage detection and segmentation in traumatic pelvic injuries is vital for fast and accurate treatment decision making. Hemorrhage is the main cause of deaths in patients within first 24 hours after the injury. It is very time consuming for physicians to analyze all Computed Tomography (CT) images manually. As time is crucial in emergence medicine, analyzing medical images manually delays the decision-making process. Automated hemorrhage detection and segmentation can significantly help physicians to analyze these images and make fast and accurate decisions. Hemorrhage segmentation is a crucial step in the accurate diagnosis and treatment decision-making process. This paper presents a novel rule-based hemorrhage segmentation technique that utilizes pelvic anatomical information to segment hemorrhage accurately. An evaluation measure is used to quantify the accuracy of hemorrhage segmentation. The results show that the proposed method is able to segment hemorrhage very well, and the results are promising. PMID:22919433

  14. Feasibility analysis and residual evaluation of automated planar segmentation results of large-scale Martian surface structures

    NASA Astrophysics Data System (ADS)

    Székely, B.; Dorninger, P.; Koma, Zs.; Jansa, J.; Kovács, G.; Nothegger, C.

    2012-04-01

    As increasingly larger coverage of DTMs is available for the Martian surface, not only the number of studies on individual specific Martian features increase, but the need for large-scale geomorphometric evaluation is amplified as well. The computer power and the increasingly sophisticated methods are about to allow such extensive studies. Our DTM segmentation method that has been tailored and tested recently for various geoscientific applications, now allows to process large DTMs created within the framework of ESA Mars Express HRSC project. The implementation uses computation parallelization, kd-tree approach for storage and several sophisticated techniques in seeking for seed points to improve performance. Test runs on high-capacity multi-core computers demonstrate that now processing of complete DTMs of an orbit is feasible. The possibility to process large areas also implies that the segmentation results in high number of planar facets, typically several thousand features. Furthermore, the segmentation is often sensitive to the initial parameters (number of points to calculate local normal vectors, point-to-plane distance, angular tolerance, etc.) and also the use of splitting segments parameter has typically a stronger influence on the corresponding segmentation pattern. This complexity may complicate the evaluation of the results. In order to recognize the general behaviour a number of test runs have been carried out. The resulting sets of planar facets were then evaluated whether the segmentation fulfilled the original purpose (e.g., in the case of the modeling of an impact crater, its typical features should be modeled. In case of unsatisfying coverage or residual values those models have been sorted out. Model results considered to be satisfying are then analysed from the point of view of the residual values (the pointwise difference of measured height and modeled height). The distributions of the residuals are sometimes asymmetric, but the results are

  15. A robust and accurate approach to automatic blood vessel detection and segmentation from angiography x-ray images using multistage random forests

    NASA Astrophysics Data System (ADS)

    Gupta, Vipin; Kale, Amit; Sundar, Hari

    2012-03-01

    In this paper we propose a novel approach based on multi-stage random forests to address problems faced by traditional vessel segmentation algorithms on account of image artifacts such as stitches organ shadows etc.. Our approach consists of collecting a very large number of training data consisting of positive and negative examples of valid seed points. The method makes use of a 14x14 window around a putative seed point. For this window three types of feature vectors are computed viz. vesselness, eigenvalue and a novel effective margin feature. A random forest RF is trained for each of the feature vectors. At run time the three RFs are applied in succession to a putative seed point generated by a naiive vessel detection algorithm based on vesselness. Our approach will prune this set of putative seed points to correctly identify true seed points thereby avoiding false positives. We demonstrate the effectiveness of our algorithm on a large dataset of angio images.

  16. Can Community Health Workers Report Accurately on Births and Deaths? Results of Field Assessments in Ethiopia, Malawi and Mali

    PubMed Central

    Silva, Romesh; Amouzou, Agbessi; Munos, Melinda; Marsh, Andrew; Hazel, Elizabeth; Victora, Cesar; Black, Robert; Bryce, Jennifer

    2016-01-01

    Introduction Most low-income countries lack complete and accurate vital registration systems. As a result, measures of under-five mortality rates rely mostly on household surveys. In collaboration with partners in Ethiopia, Ghana, Malawi, and Mali, we assessed the completeness and accuracy of reporting of births and deaths by community-based health workers, and the accuracy of annualized under-five mortality rate estimates derived from these data. Here we report on results from Ethiopia, Malawi and Mali. Method In all three countries, community health workers (CHWs) were trained, equipped and supported to report pregnancies, births and deaths within defined geographic areas over a period of at least fifteen months. In-country institutions collected these data every month. At each study site, we administered a full birth history (FBH) or full pregnancy history (FPH), to women of reproductive age via a census of households in Mali and via household surveys in Ethiopia and Malawi. Using these FBHs/FPHs as a validation data source, we assessed the completeness of the counts of births and deaths and the accuracy of under-five, infant, and neonatal mortality rates from the community-based method against the retrospective FBH/FPH for rolling twelve-month periods. For each method we calculated total cost, average annual cost per 1,000 population, and average cost per vital event reported. Results On average, CHWs submitted monthly vital event reports for over 95 percent of catchment areas in Ethiopia and Malawi, and for 100 percent of catchment areas in Mali. The completeness of vital events reporting by CHWs varied: we estimated that 30%-90% of annualized expected births (i.e. the number of births estimated using a FPH) were documented by CHWs and 22%-91% of annualized expected under-five deaths were documented by CHWs. Resulting annualized under-five mortality rates based on the CHW vital events reporting were, on average, under-estimated by 28% in Ethiopia, 32% in

  17. Computer Aided Segmentation Analysis: New Software for College Admissions Marketing.

    ERIC Educational Resources Information Center

    Lay, Robert S.; Maguire, John J.

    1983-01-01

    Compares segmentation solutions obtained using a binary segmentation algorithm (THAID) and a new chi-square-based procedure (CHAID) that segments the prospective pool of college applicants using application and matriculation as criteria. Results showed a higher number of estimated qualified inquiries and more accurate estimates with CHAID. (JAC)

  18. Paleoseismology and Tectonic Geomorphology: Results From the Parkfield, CA Segment of the San Andreas Fault

    NASA Astrophysics Data System (ADS)

    Toké, N. A.; Arrowsmith, J. R.; Crosby, C. J.; Young, J. J.

    2004-12-01

    The Parkfield segment of the San Andreas Fault (SAF) is the transition zone between the creep-dominated segment of the SAF to the northwest and the locked segment to the southeast. Geodetic studies indicate ~10 mm/year of the SAF 35 mm/year slip-rate is accommodated by creep at shallow depths of the SAF near Carr Hill, suggesting the remaining slip may occur in moderate to large earthquakes. Foreshock intensities of the 1857 M7.8 Fort Tejon earthquake were similar to 20th century Parkfield events, suggesting that the 1857 event may have nucleated in the Parkfield area. Paleoseismic investigations near Parkfield have achieved limited success in exposing useful fault zone stratigraphy. Our goals were to investigate the style and timing of late Pleistocene and Holocene faulting along this segment of the SAF. Specifically, did the 1857 or similar large prehistoric earthquakes rupture through the Parkfield segment? In addition, is it possible to distinguish deformation caused by these large magnitude earthquakes from deformation associated with the moderate 1966-style earthquakes and aseismic creep? Geomorphic mapping from Middle Mountain to Carr Hill revealed numerous tectonic landforms that define the fault trace and indicated possible excavation sites, including the sites used for this study. Two fault-perpendicular excavations were cut in a late Pleistocene fluvial terrace of Little Cholame Creek just north-northeast of Carr Hill. A thirty-meter excavation across an elongate sag pond, containing an apparently right-laterally offset fluvial channel and several small springs, revealed four fault zones. The outer fault zones were parallel with the regional trend of the SAF (313°), while the inner fault zones trended obliquely at ~350°. The three easternmost fault zones show apparent dip-slip deformation including truncation and down warping of sag and terrace deposits. The westernmost fault zone was characterized by upward splaying sub-vertical clay shear bands

  19. Main results and experience obtained on Mir space station and experiment program for Russian segment of ISS.

    PubMed

    Utkin, V F; Lukjashchenko, V I; Borisov, V V; Suvorov, V V; Tsymbalyuk, M M

    2003-07-01

    This article presents main scientific and practical results obtained in course of scientific and applied research and experiments on Mir space station. Based on Mir experience, processes of research program formation for the Russian Segment of the ISS are briefly described. The major trends of activities planned in the frames of these programs as well as preliminary results of increment research programs implementation in the ISS' first missions are also presented.

  20. In situ kinetic modelling of intestinal efflux in rats: functional characterization of segmental differences and correlation with in vitro results.

    PubMed

    González-Alvarez, Isabel; Fernández-Teruel, Carlos; Casabó-Alós, Vicente G; Garrigues, Teresa M; Polli, James E; Ruiz-García, Ana; Bermejo, Marival

    2007-07-01

    The objective was to devise and apply a novel modelling approach to combine segmental in situ rat perfusion data and in vitro cell culture data, in order to elucidate the contribution of efflux in drug absorption kinetics. The fluoroquinolone CNV97100 was used as a model P-gp substrate. In situ intestinal perfusion was performed in rat duodenum, jejunum, ileum and colon to measure the influence of P-gp expression on efflux. Inhibition studies of CNV97100 were performed in the presence of verapamil, quinidine, cyclosporin A and p-aminohippuric acid. Absorption/efflux parameters were modelled simultaneously, using data from both in situ studies as well as in vitro studies. The maximal efflux velocity was modelled as a baseline value, corrected for each segment based on the expression level. CNV97100 passive diffusional permeability (P(diff)) and its affinity for the efflux carrier (K(m)) were assumed to be the same in all segments. The results indicate the new approach to combine in situ data and in vitro data succeed in yielding a unified, quantitative model for absorption/efflux. The model incorporated a quantitative relationship between P-gp expression level and the efflux functionality, both across in situ and in vitro systems, as well across different intestinal segments in the in situ studies. Permeability values decreased from duodenum to ileum in accordance with the increasing P-gp expression levels in rat intestine. The developed model reflects a strong correlation between in vitro and in situ results, including intrinsic differences in surface area. The successful application of a model approach to combine absorption data from two different experimental systems holds promise for future efforts to predict absorption results from one system to a second system.

  1. Autologous bone grafting on steroids: preliminary clinical results. A novel treatment for nonunions and segmental bone defects.

    PubMed

    Miller, Micah A; Ivkovic, Alan; Porter, Ryan; Harris, Mitchel B; Estok, Daniel M; Smith, R Malcolm; Evans, Christopher H; Vrahas, Mark S

    2011-04-01

    Clinical management of delayed healing or nonunion of long bone fractures and segmental bone defects poses a substantial orthopaedic challenge. Surgical advances and bone tissue engineering are providing new avenues to stimulate bone growth in cases of bone loss and nonunion. The reamer-irrigator-aspirator (RIA) device allows surgeons to aspirate the medullary contents of long bones and use the progenitor-rich "flow-through" fraction in autologous bone grafting. Dexamethasone (DEX) is a synthetic steroid that has been shown to induce osteoblastic differentiation. A series of 13 patients treated with RIA bone grafting enhanced with DEX for nonunion or segmental defect was examined retrospectively to assess the quality of bony union and clinical outcomes. Despite the initial poor prognoses, promising results were achieved using this technique; and given the complexity of these cases the observed success is of great value and warrants controlled study into both standardisation of the procedure and concentration of the grafting material.

  2. Vibration damping for the Segmented Mirror Telescope

    NASA Astrophysics Data System (ADS)

    Maly, Joseph R.; Yingling, Adam J.; Griffin, Steven F.; Agrawal, Brij N.; Cobb, Richard G.; Chambers, Trevor S.

    2012-09-01

    The Segmented Mirror Telescope (SMT) at the Naval Postgraduate School (NPS) in Monterey is a next-generation deployable telescope, featuring a 3-meter 6-segment primary mirror and advanced wavefront sensing and correction capabilities. In its stowed configuration, the SMT primary mirror segments collapse into a small volume; once on location, these segments open to the full 3-meter diameter. The segments must be very accurately aligned after deployment and the segment surfaces are actively controlled using numerous small, embedded actuators. The SMT employs a passive damping system to complement the actuators and mitigate the effects of low-frequency (<40 Hz) vibration modes of the primary mirror segments. Each of the six segments has three or more modes in this bandwidth, and resonant vibration excited by acoustics or small disturbances on the structure can result in phase mismatches between adjacent segments thereby degrading image quality. The damping system consists of two tuned mass dampers (TMDs) for each of the mirror segments. An adjustable TMD with passive magnetic damping was selected to minimize sensitivity to changes in temperature; both frequency and damping characteristics can be tuned for optimal vibration mitigation. Modal testing was performed with a laser vibrometry system to characterize the SMT segments with and without the TMDs. Objectives of this test were to determine operating deflection shapes of the mirror and to quantify segment edge displacements; relative alignment of λ/4 or better was desired. The TMDs attenuated the vibration amplitudes by 80% and reduced adjacent segment phase mismatches to acceptable levels.

  3. Software pipeline for midsagittal corpus callosum thickness profile processing : automated segmentation, manual editor, thickness profile generator, group-wise statistical comparison and results display.

    PubMed

    Adamson, Chris; Beare, Richard; Walterfang, Mark; Seal, Marc

    2014-10-01

    This paper presents a fully automated pipeline for thickness profile evaluation and analysis of the human corpus callosum (CC) in 3D structural T 1-weighted magnetic resonance images. The pipeline performs the following sequence of steps: midsagittal plane extraction, CC segmentation algorithm, quality control tool, thickness profile generation, statistical analysis and results figure generator. The CC segmentation algorithm is a novel technique that is based on a template-based initialisation with refinement using mathematical morphology operations. The algorithm is demonstrated to have high segmentation accuracy when compared to manual segmentations on two large, publicly available datasets. Additionally, the resultant thickness profiles generated from the automated segmentations are shown to be highly correlated to those generated from the ground truth segmentations. The manual editing tool provides a user-friendly environment for correction of errors and quality control. Statistical analysis and a novel figure generator are provided to facilitate group-wise morphological analysis of the CC.

  4. ST-segment Elevation Myocardial Infarction Resulting from Stent Thrombosis in Contemporary Real-World Practice.

    PubMed

    Kanei, Yumiko; Nallu, Kishore; Makker, Parth; Behuria, Supreeti; Fox, John

    2017-03-01

    Stent thrombosis (ST) is a rare but devastating complication after percutaneous coronary intervention. Newer generation drug-eluting stents (DES) and newer antiplatelet therapies have been shown to decrease the incidence of ST, but we continue to observe ST-segment elevation myocardial infarction (STEMI) due to ST in contemporary practice. A retrospective analysis of 527 patients who presented with STEMI was performed; 57 patients (11%) with angiographically confirmed ST were compared with the patients with STEMI due to de novo lesion. The type of previous stent, the timing of ST, and the use of antiplatelet therapy were reviewed in patients with ST. Patients with ST had higher prevalence of comorbid conditions, such as hypertension, diabetes mellitus, and coronary artery disease, and had lower left ventricular ejection fraction (37 ± 5 vs. 44 ± 16%, p = 0.0011). There was no difference in in-hospital mortality (2 vs. 4%, p = 0.7082). ST was seen most commonly as "very late" (56%), and with previous second-generation DES (40%). Eighty-two percent of patients among early ST, compared with 22% of patients with very late ST were on dual antiplatelet therapy (DAPT). In 12% of patients, ST happened after DAPT was stopped by physician for procedures. ST is seen in a variety of clinical settings with the most common presentation being very late ST and in second-generation DES, which most likely represent the growing population with previous second-generation stents.

  5. Duplicate portion sampling combined with spectrophotometric analysis affords the most accurate results when assessing daily dietary phosphorus intake.

    PubMed

    Navarro-Alarcon, Miguel; Zambrano, Esmeralda; Moreno-Montoro, Miriam; Agil, Ahmad; Olalla, Manuel

    2012-08-01

    The assessment of daily dietary phosphorus (P) intake is a major concern in human nutrition because of its relationship with Ca and Mg metabolism and osteoporosis. Within this context, we hypothesized that several of the methods available for the assessment of daily dietary intake of P are equally accurate and reliable, although few studies have been conducted to confirm this. The aim of this study then was to evaluate daily dietary P intake, which we did by 3 methods: duplicate portion sampling of 108 hospital meals, combined either with spectrophotometric analysis or the use of food composition tables, and 24-hour dietary recall for 3 consecutive days plus the use of food composition tables. The mean P daily dietary intakes found were 1106 ± 221, 1480 ± 221, and 1515 ± 223 mg/d, respectively. Daily dietary intake of P determined by spectrophotometric analysis was significantly lower (P < .001) and closer to dietary reference intakes for adolescents aged from 14 to 18 years (88.5%) and adult subjects (158.1%) compared with the other 2 methods. Duplicate portion sampling with P analysis takes into account the influence of technological and cooking processes on the P content of foods and meals and therefore afforded the most accurate and reliable P daily dietary intakes. The use of referred food composition tables overestimated daily dietary P intake. No adverse effects in relation to P nutrition (deficiencies or toxic effects) were encountered.

  6. Interaction of a mantle plume and a segmented mid-ocean ridge: Results from numerical modeling

    NASA Astrophysics Data System (ADS)

    Georgen, Jennifer E.

    2014-04-01

    Previous investigations have proposed that changes in lithospheric thickness across a transform fault, due to the juxtaposition of seafloor of different ages, can impede lateral dispersion of an on-ridge mantle plume. The application of this “transform damming” mechanism has been considered for several plume-ridge systems, including the Reunion hotspot and the Central Indian Ridge, the Amsterdam-St. Paul hotspot and the Southeast Indian Ridge, the Cobb hotspot and the Juan de Fuca Ridge, the Iceland hotspot and the Kolbeinsey Ridge, the Afar plume and the ridges of the Gulf of Aden, and the Marion/Crozet hotspot and the Southwest Indian Ridge. This study explores the geodynamics of the transform damming mechanism using a three-dimensional finite element numerical model. The model solves the coupled steady-state equations for conservation of mass, momentum, and energy, including thermal buoyancy and viscosity that is dependent on pressure and temperature. The plume is introduced as a circular thermal anomaly on the bottom boundary of the numerical domain. The center of the plume conduit is located directly beneath a spreading segment, at a distance of 200 km (measured in the along-axis direction) from a transform offset with length 100 km. Half-spreading rate is 0.5 cm/yr. In a series of numerical experiments, the buoyancy flux of the modeled plume is progressively increased to investigate the effects on the temperature and velocity structure of the upper mantle in the vicinity of the transform. Unlike earlier studies, which suggest that a transform always acts to decrease the along-axis extent of plume signature, these models imply that the effect of a transform on plume dispersion may be complex. Under certain ranges of plume flux modeled in this study, the region of the upper mantle undergoing along-axis flow directed away from the plume could be enhanced by the three-dimensional velocity and temperature structure associated with ridge

  7. Image Segmentation Using Hierarchical Merge Tree.

    PubMed

    Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga

    2016-07-18

    This paper investigates one of the most fundamental computer vision problems: image segmentation. We propose a supervised hierarchical approach to object-independent image segmentation. Starting with over-segmenting superpixels, we use a tree structure to represent the hierarchy of region merging, by which we reduce the problem of segmenting image regions to finding a set of label assignment to tree nodes. We formulate the tree structure as a constrained conditional model to associate region merging with likelihoods predicted using an ensemble boundary classifier. Final segmentations can then be inferred by finding globally optimal solutions to the model efficiently. We also present an iterative training and testing algorithm that generates various tree structures and combines them to emphasize accurate boundaries by segmentation accumulation. Experiment results and comparisons with other recent methods on six public data sets demonstrate that our approach achieves state-of-the-art region accuracy and is competitive in image segmentation without semantic priors.

  8. A novel method for the automatic segmentation of activity data from a wrist worn device: Preliminary results.

    PubMed

    Amor, James D; Ahanathapillai, Vijayalakshmi; James, Christopher J

    2014-01-01

    Activity monitoring is used in a number of fields in order to assess the physical activity of the user. Applications include health and well-being, rehabilitation and enhancing independent living. Data are often gathered from multiple accelerometers and analysis focuses on multi-parametric classification. For longer term monitoring this is unsuitable and it is desirable to develop a method for the precise analysis of activity data with respect to time. This paper presents the initial results of a novel approach to this problem which is capable of segmenting activity data collected from a single accelerometer recording naturalized activity.

  9. DSS1/DSS2 astrometry for 1101 First Byurakan Survey blue stellar objects: Accurate positions and other results

    NASA Astrophysics Data System (ADS)

    Mickaelian, A. M.

    2004-10-01

    Accurate measurements of the positions of 1101 First Byurakan Survey (FBS) blue stellar objects (the Second part of the FBS) have been carried out on the DSS1 and DSS2 (red and blue images). To establish the accuracy of the DSS1 and DSS2, measurements have been made for 153 AGN for which absolute VLBI coordinates have been published. The rms errors are: 0.45 arcsec for DSS1, 0.33 arcsec for DSS2 red, and 0.59 arcsec for DSS2 blue in each coordinate, the corresponding total positional errors being 0.64 arcsec, 0.46 arcsec, and 0.83 arcsec, respectively. The highest accuracy (0.42 arcsec) is obtained by weighted averaging of the DSS1 and DSS2 red positions. It is shown that by using all three DSS images accidental errors can be significantly reduced. The comparison of DSS2 and DSS1 images made it possible to reveal positional differences and proper motions for 78 objects (for 62 of these for the first time), including new high-probability candidate white dwarfs, and to find objects showing strong variability, i.e. high-probability candidate cataclysmic variables. Table 1 is only available in electronic form at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/426/367

  10. A novel, integrated PET-guided MRS technique resulting in more accurate initial diagnosis of high-grade glioma.

    PubMed

    Kim, Ellen S; Satter, Martin; Reed, Marilyn; Fadell, Ronald; Kardan, Arash

    2016-06-01

    Glioblastoma multiforme (GBM) is the most common and lethal malignant glioma in adults. Currently, the modality of choice for diagnosing brain tumor is high-resolution magnetic resonance imaging (MRI) with contrast, which provides anatomic detail and localization. Studies have demonstrated, however, that MRI may have limited utility in delineating the full tumor extent precisely. Studies suggest that MR spectroscopy (MRS) can also be used to distinguish high-grade from low-grade gliomas. However, due to operator dependent variables and the heterogeneous nature of gliomas, the potential for error in diagnostic accuracy with MRS is a concern. Positron emission tomography (PET) imaging with (11)C-methionine (MET) and (18)F-fluorodeoxyglucose (FDG) has been shown to add additional information with respect to tumor grade, extent, and prognosis based on the premise of biochemical changes preceding anatomic changes. Combined PET/MRS is a technique that integrates information from PET in guiding the location for the most accurate metabolic characterization of a lesion via MRS. We describe a case of glioblastoma multiforme in which MRS was initially non-diagnostic for malignancy, but when MRS was repeated with PET guidance, demonstrated elevated choline/N-acetylaspartate (Cho/NAA) ratio in the right parietal mass consistent with a high-grade malignancy. Stereotactic biopsy, followed by PET image-guided resection, confirmed the diagnosis of grade IV GBM. To our knowledge, this is the first reported case of an integrated PET/MRS technique for the voxel placement of MRS. Our findings suggest that integrated PET/MRS may potentially improve diagnostic accuracy in high-grade gliomas.

  11. EVENT SEGMENTATION

    PubMed Central

    Zacks, Jeffrey M.; Swallow, Khena M.

    2012-01-01

    One way to understand something is to break it up into parts. New research indicates that segmenting ongoing activity into meaningful events is a core component of ongoing perception, with consequences for memory and learning. Behavioral and neuroimaging data suggest that event segmentation is automatic and that people spontaneously segment activity into hierarchically organized parts and sub-parts. This segmentation depends on the bottom-up processing of sensory features such as movement, and on the top-down processing of conceptual features such as actors’ goals. How people segment activity affects what they remember later; as a result, those who identify appropriate event boundaries during perception tend to remember more and learn more proficiently. PMID:22468032

  12. The role of variations in growth rate and sample collection on interpreting results of segmental analyses of hair.

    PubMed

    LeBeau, Marc A; Montgomery, Madeline A; Brewer, Jason D

    2011-07-15

    Segmental analysis of hair for drugs, metabolites, and poisons has been widely reported in the scientific literature over the past two decades. Two fundamental assumptions in interpreting results of such analyses are (1) an average linear growth rate of head hair of 1cm/month and (2) that sample collections occur with the hair being cut directly next to the scalp. The purpose of this study was to evaluate the variability associated with growth rate of human head hair, as well as the ability to uniformly collect hair next to the scalp. The results were used to determine how these factors affect the interpretation of results generated in segmental analysis of hair. A thorough literature review was conducted to assess the range of linear growth of human head hair from the vertex posterior and occipital regions. The results were compiled to establish the average (1.06cm/month), as well as the range of possible growth rates of head hair. The range was remarkable and suggests that conclusions based on the 1-cm/month growth rate could be significantly skewed. A separate study was undertaken to evaluate collection of hair next to the scalp. Fourteen individuals were provided oral instructions, as well as a written standard collection procedure for head hair. The experience levels among the collectors varied from novice to expert. Each individual collected hair from dolls with short- and long-hair. Immediately following each collection, the sampling area was evaluated to determine how close to the scalp the cuts were made, as well as the variability in the lengths of hair remaining at the sampled area. From our collection study, we determined that 0.8±0.1cm of hair was left on the scalp after cutting. When taking into account the amount of hair left on the scalp after collecting, the use of a growth rate of 1.06cm/month, and the assumption that it takes two weeks for newly formed hair in the follicle to reach the scalp, we find that the first 1-cm segment of hair typically

  13. MID-INFRARED SIZE SURVEY OF YOUNG STELLAR OBJECTS: DESCRIPTION OF KECK SEGMENT-TILTING EXPERIMENT AND BASIC RESULTS

    SciTech Connect

    Monnier, J. D.; Tannirkulam, A.; Tuthill, P. G.; Ireland, M.; Cohen, R.; Perrin, M. D.

    2009-07-20

    The mid-infrared properties of pre-planetary disks are sensitive to the temperature and flaring profiles of disks for the regions where planet formation is expected to occur. In order to constrain theories of planet formation, we have carried out a mid-infrared ({lambda} = 10.7 {mu}m) size survey of young stellar objects using the segmented Keck telescope in a novel configuration. We introduced a customized pattern of tilts to individual mirror segments to allow efficient sparse-aperture interferometry, allowing full aperture synthesis imaging with higher calibration precision than traditional imaging. In contrast to previous surveys on smaller telescopes and with poorer calibration precision, we find that most objects in our sample are partially resolved. Here, we present the main observational results of our survey of five embedded massive protostars, 25 Herbig Ae/Be stars, 3 T Tauri stars, 1 FU Ori system, and five emission-line objects of uncertain classification. The observed mid-infrared sizes do not obey the size-luminosity relation found at near-infrared wavelengths and a companion paper will provide further modeling analysis of this sample. In addition, we report imaging results for a few of the most resolved objects, including complex emission around embedded massive protostars, the photoevaporating circumbinary disk around MWC 361A, and the subarcsecond binaries T Tau, FU Ori, and MWC 1080.

  14. An interactive medical image segmentation framework using iterative refinement.

    PubMed

    Kalshetti, Pratik; Bundele, Manas; Rahangdale, Parag; Jangra, Dinesh; Chattopadhyay, Chiranjoy; Harit, Gaurav; Elhence, Abhay

    2017-02-13

    Segmentation is often performed on medical images for identifying diseases in clinical evaluation. Hence it has become one of the major research areas. Conventional image segmentation techniques are unable to provide satisfactory segmentation results for medical images as they contain irregularities. They need to be pre-processed before segmentation. In order to obtain the most suitable method for medical image segmentation, we propose MIST (Medical Image Segmentation Tool), a two stage algorithm. The first stage automatically generates a binary marker image of the region of interest using mathematical morphology. This marker serves as the mask image for the second stage which uses GrabCut to yield an efficient segmented result. The obtained result can be further refined by user interaction, which can be done using the proposed Graphical User Interface (GUI). Experimental results show that the proposed method is accurate and provides satisfactory segmentation results with minimum user interaction on medical as well as natural images.

  15. The MSAT-X MARECS B2 satellite experiment - Ground segment results

    NASA Technical Reports Server (NTRS)

    Jedrey, Thomas C.; Lay, Norman E.; Dessouky, Khaled I.; Cheetham, Craig M.; Parkyn, James F.

    1989-01-01

    The results of a recently completed satellite experiment employing the JPL MSAT-X developed land-mobile satellite communication terminal are described. In this experiment, a full duplex 4800-b/s digital data and voice communication link was established through the INMARSAT Marecs B2 satellite between Atlantic City, New Jersey, and Southbury, Connecticut. A series of experiments was performed to characterize the terminal performance over this link. The basic experimental setup and the preliminary results of the speech and data experiments are presented. The satellite environment proved to be near to what was expected, and as a result the experimental results were very close to theory/simulation/laboratory experiments. It was found that the ground-to-ground communication links were more benign links than the ground-to-air and air-to-ground links, and this is reflected in the improved margins for the ground-to-ground links (approximately 5 dB versus 3.2 dB for the aeronautical links).

  16. Interstitial deletion 5p accompanied by dicentric ring formation of the deleted segment resulting in trisomy 5p13-cen

    SciTech Connect

    Schuffenhauer, S.; Daumer-Haas, C.; Murken, J.

    1996-10-02

    Karyotypes with an interstitial deletion and a marker chromosome formed from the deleted segment are rare. We identified such a rearrangement in a newborn infant, who presented with macrocephaly, asymmetric square skull, minor facial anomalies, omphalocele, inguinal hernias, hypospadias, and club feet. The karyotype 46,XY,del(5)(pter{r_arrow}p13::cen{r_arrow}qter)/47,XY,+dicr(5)(:p13{r_arrow}cen::p13{r_arrow}cen),del(5)(pter{r_arrow}p13::cen{r_arrow}qter) was identified by banding studies and FISH analysis in the peripheral lymphocytes. One breakpoint on the del(5) maps distal to GDNF, and FISH analysis using an {alpha}-satellite probe suggests that the proximal breakpoint maps within the centromere. The dicentric r(5) consists of two copies of the segment deleted in the del(5), resulting in trisomy of proximal 5p (5p13-cen). The phenotype of the propositus is compared with other trisomy 5p cases and possible mechanisms for the generation of this unique chromosomal rearrangement are discussed. 27 refs., 3 figs.

  17. Report: EPA Needs Accurate Data on Results of Pollution Prevention Grants to Maintain Program Integrity and Measure Effectiveness of Grants

    EPA Pesticide Factsheets

    Report #15-P-0276, September 4, 2015. Inaccurate reporting of results misrepresents the impacts of pollution prevention activities provided to the public, and misinforms EPA management on the effectiveness of its investment in the program.

  18. Seismicity and Crustal Anisotropy Beneath the Western Segment of the North Anatolian Fault: Results from a Dense Seismic Array

    NASA Astrophysics Data System (ADS)

    Turkelli, N.; Teoman, U.; Altuncu Poyraz, S.; Cambaz, D.; Mutlu, A. K.; Kahraman, M.; Houseman, G. A.; Rost, S.; Thompson, D. A.; Cornwell, D. G.; Utkucu, M.; Gülen, L.

    2013-12-01

    The North Anatolian Fault (NAF) is one of the major strike slip fault systems on Earth comparable to San Andreas Fault in some ways. Devastating earthquakes have occurred along this system causing major damage and casualties. In order to comprehensively investigate the shallow and deep crustal structure beneath the western segment of NAF, a temporary dense seismic network for North Anatolia (DANA) consisting of 73 broadband sensors was deployed in early May 2012 surrounding a rectangular grid of by 70 km and a nominal station spacing of 7 km with the aim of further enhancing the detection capability of this dense seismic array. This joint project involves researchers from University of Leeds, UK, Bogazici University Kandilli Observatory and Earthquake Research Institute (KOERI), and University of Sakarya and primarily focuses on upper crustal studies such as earthquake locations (especially micro-seismic activity), receiver functions, moment tensor inversions, shear wave splitting, and ambient noise correlations. To begin with, we obtained the hypocenter locations of local earthquakes that occured within the DANA network. The dense 2-D grid geometry considerably enhanced the earthquake detection capability which allowed us to precisely locate events with local magnitudes (Ml) less than 1.0. Accurate earthquake locations will eventually lead to high resolution images of the upper crustal structure beneath the northern and southern branches of NAF in Sakarya region. In order to put additional constraints on the active tectonics of the western part of NAF, we also determined fault plane solutions using Regional Moment Tensor Inversion (RMT) and P wave first motion methods. For the analysis of high quality fault plane solutions, data from KOERI and the DANA project were merged. Furthermore, with the aim of providing insights on crustal anisotropy, shear wave splitting parameters such as lag time and fast polarization direction were obtained for local events recorded

  19. Strehl ratio and modulation transfer function for segmented mirror telescopes as functions of segment phase error.

    PubMed

    Chanan, G; Troy, M

    1999-11-01

    We derive the Strehl ratio for a segmented mirror telescope as a function of the rms segment phase error and the observing wavelength, with and without the effects of the atmosphere. A simple analytical expression is given for the atmosphere-free case. Although our specific results are in the context of the Keck telescope, they are presented in a way that should be readily adaptable to other segmented geometries. We also derive the corresponding modulation transfer functions. These results are useful in determining how accurately a segmented mirror telescope needs to be phased for a variety of observing applications.

  20. Repairing bad co-segmentation using its quality evaluation and segment propagation.

    PubMed

    Li, Hongliang; Meng, Fanman; Luo, Bing; Zhu, Shuyuan

    2014-08-01

    In this paper, we improve co-segmentation performance by repairing bad segments based on their quality evaluation and segment propagation. Starting from co-segmentation results of the existing co-segmentation method, we first perform co-segmentation quality evaluation to score each segment. Good segments can be filter out based on the scores. Then, a propagation method is designed to transfer good segments to the rest bad ones so as to repair the bad segmentation. In our method, the quality evaluation is implemented by the measurements of foreground consistency and segment completeness. Two propagation methods such as global propagation and local region propagation are then defined to achieve the more accurate propagation. We verify the proposed method using four state-of-the-arts co-segmentation methods and two public datasets such as ICoseg dataset and MSRC dataset. The experimental results demonstrate the effectiveness of the proposed quality evaluation method. Furthermore, the proposed method can significantly improve the performance of existing methods with larger intersection-over-union score values.

  1. Infectious titres of sheep scrapie and bovine spongiform encephalopathy agents cannot be accurately predicted from quantitative laboratory test results.

    PubMed

    González, Lorenzo; Thorne, Leigh; Jeffrey, Martin; Martin, Stuart; Spiropoulos, John; Beck, Katy E; Lockey, Richard W; Vickery, Christopher M; Holder, Thomas; Terry, Linda

    2012-11-01

    It is widely accepted that abnormal forms of the prion protein (PrP) are the best surrogate marker for the infectious agent of prion diseases and, in practice, the detection of such disease-associated (PrP(d)) and/or protease-resistant (PrP(res)) forms of PrP is the cornerstone of diagnosis and surveillance of the transmissible spongiform encephalopathies (TSEs). Nevertheless, some studies question the consistent association between infectivity and abnormal PrP detection. To address this discrepancy, 11 brain samples of sheep affected with natural scrapie or experimental bovine spongiform encephalopathy were selected on the basis of the magnitude and predominant types of PrP(d) accumulation, as shown by immunohistochemical (IHC) examination; contra-lateral hemi-brain samples were inoculated at three different dilutions into transgenic mice overexpressing ovine PrP and were also subjected to quantitative analysis by three biochemical tests (BCTs). Six samples gave 'low' infectious titres (10⁶·⁵ to 10⁶·⁷ LD₅₀ g⁻¹) and five gave 'high titres' (10⁸·¹ to ≥ 10⁸·⁷ LD₅₀ g⁻¹) and, with the exception of the Western blot analysis, those two groups tended to correspond with samples with lower PrP(d)/PrP(res) results by IHC/BCTs. However, no statistical association could be confirmed due to high individual sample variability. It is concluded that although detection of abnormal forms of PrP by laboratory methods remains useful to confirm TSE infection, infectivity titres cannot be predicted from quantitative test results, at least for the TSE sources and host PRNP genotypes used in this study. Furthermore, the near inverse correlation between infectious titres and Western blot results (high protease pre-treatment) argues for a dissociation between infectivity and PrP(res).

  2. Towards more accurate isoscapes encouraging results from wine, water and marijuana data/model and model/model comparisons.

    NASA Astrophysics Data System (ADS)

    West, J. B.; Ehleringer, J. R.; Cerling, T.

    2006-12-01

    Understanding how the biosphere responds to change it at the heart of biogeochemistry, ecology, and other Earth sciences. The dramatic increase in human population and technological capacity over the past 200 years or so has resulted in numerous, simultaneous changes to biosphere structure and function. This, then, has lead to increased urgency in the scientific community to try to understand how systems have already responded to these changes, and how they might do so in the future. Since all biospheric processes exhibit some patchiness or patterns over space, as well as time, we believe that understanding the dynamic interactions between natural systems and human technological manipulations can be improved if these systems are studied in an explicitly spatial context. We present here results of some of our efforts to model the spatial variation in the stable isotope ratios (δ2H and δ18O) of plants over large spatial extents, and how these spatial model predictions compare to spatially explicit data. Stable isotopes trace and record ecological processes and as such, if modeled correctly over Earth's surface allow us insights into changes in biosphere states and processes across spatial scales. The data-model comparisons show good agreement, in spite of the remaining uncertainties (e.g., plant source water isotopic composition). For example, inter-annual changes in climate are recorded in wine stable isotope ratios. Also, a much simpler model of leaf water enrichment driven with spatially continuous global rasters of precipitation and climate normals largely agrees with complex GCM modeling that includes leaf water δ18O. Our results suggest that modeling plant stable isotope ratios across large spatial extents may be done with reasonable accuracy, including over time. These spatial maps, or isoscapes, can now be utilized to help understand spatially distributed data, as well as to help guide future studies designed to understand ecological change across

  3. Milling Stability Analysis Based on Chebyshev Segmentation

    NASA Astrophysics Data System (ADS)

    HUANG, Jianwei; LI, He; HAN, Ping; Wen, Bangchun

    2016-09-01

    Chebyshev segmentation method was used to discretize the time period contained in delay differential equation, then the Newton second-order difference quotient method was used to calculate the cutter motion vector at each time endpoint, and the Floquet theory was used to determine the stability of the milling system after getting the transfer matrix of milling system. Using the above methods, a two degree of freedom milling system stability issues were investigated, and system stability lobe diagrams were got. The results showed that the proposed methods have the following advantages. Firstly, with the same calculation accuracy, the points needed to represent the time period are less by the Chebyshev Segmentation than those of the average segmentation, and the computational efficiency of the Chebyshev Segmentation is higher. Secondly, if the time period is divided into the same parts, the stability lobe diagrams got by Chebyshev segmentation method are more accurate than those of the average segmentation.

  4. The route to MBxNyCz molecular wheels: II. Results using accurate functionals and basis sets

    NASA Astrophysics Data System (ADS)

    Güthler, A.; Mukhopadhyay, S.; Pandey, R.; Boustani, I.

    2014-04-01

    Applying ab initio quantum chemical methods, molecular wheels composed of metal and light atoms were investigated. High quality basis sets 6-31G*, TZPV, and cc-pVTZ as well as exchange and non-local correlation functionals B3LYP, BP86 and B3P86 were used. The ground-state energy and structures of cyclic planar and pyramidal clusters TiBn (for n = 3-10) were computed. In addition, the relative stability and electronic structures of molecular wheels TiBxNyCz (for x, y, z = 0-10) and MBnC10-n (for n = 2 to 5 and M = Sc to Zn) were determined. This paper sustains a follow-up study to the previous one of Boustani and Pandey [Solid State Sci. 14 (2012) 1591], in which the calculations were carried out at the HF-SCF/STO3G/6-31G level of theory to determine the initial stability and properties. The results show that there is a competition between the 2D planar and the 3D pyramidal TiBn clusters (for n = 3-8). Different isomers of TiB10 clusters were also studied and a structural transition of 3D-isomer into 2D-wheel is presented. Substitution boron in TiB10 by carbon or/and nitrogen atoms enhances the stability and leads toward the most stable wheel TiB3C7. Furthermore, the computations show that Sc, Ti and V at the center of the molecular wheels are energetically favored over other transition metal atoms of the first row.

  5. Nucleic Acids Research Group (NRG): The Importance of DNA Extraction in Metagenomics: The Gatekeeper to Accurate Results!

    PubMed Central

    Carmical, R.; Nadella, V.; Herbert, Z.; Beckloff, N.; Chittur, S.; Rosato, C.; Perera, A.; Auer, H.; Robinson, M.; Tighe, S.; Holbrook, Jennifer

    2013-01-01

    It is well recognized that the field of metagenomics is becoming a critical tool for studying previously unobtainable population dynamics at both an identification of species level and a functional or transcriptional level. Because the power to resolve microbial information is so important for identifying the components in an mixed sample, metagenomics can be used to study nearly any possible environment or system including clinical, environmental, and industrial, to name a few. Clinically, it may be used to determine sub-populations colonizing regions of the body or determining a rare infection to assist in treatment strategies. Environmentally it may be used to identify microbial populations within a soil, water or air sample, or within a bioreactor to characterize a population- based functional process. The possibilities are endless. However, the accuracy of a metagenomics dataset relies on three important “gatekeepers” including 1) The ability to effectively extract all DNA or RNA from every cell within a sample, 2) The reliability of the methods used for deep or high-throughput sequencing, and 3) The software used to analyze the data. Since DNA extraction is the first step in the technical process of metagenomics, the Nucleic Acid Research Group (NARG) conducted a study to evaluate extraction methods using a synthetic microbial sample. The synthetic microbial sample was prepared from 10 known bacteria at specific concentrations and ranging in diversity. Samples were extracted in duplicate using various popular kit based methods as well as several homebrew protocols then analyzed by NextGen sequencing on an Illumina HiSeq. Results of the study include determining the percent recovery of those organisms by comparing to the known quantity in the original synthetic mix.

  6. Automated Tumor Volumetry Using Computer-Aided Image Segmentation

    PubMed Central

    Bilello, Michel; Sadaghiani, Mohammed Salehi; Akbari, Hamed; Atthiah, Mark A.; Ali, Zarina S.; Da, Xiao; Zhan, Yiqang; O'Rourke, Donald; Grady, Sean M.; Davatzikos, Christos

    2015-01-01

    Rationale and Objectives Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. Materials and Methods A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. Results Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0–5 rating scale where 5 indicated perfect segmentation. Conclusions The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation. PMID:25770633

  7. Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation

    PubMed Central

    Chiu, Stephanie J.; Li, Xiao T.; Nicholas, Peter; Toth, Cynthia A.; Izatt, Joseph A.; Farsiu, Sina

    2010-01-01

    Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader. PMID:20940837

  8. Results.

    ERIC Educational Resources Information Center

    Zemsky, Robert; Shaman, Susan; Shapiro, Daniel B.

    2001-01-01

    Describes the Collegiate Results Instrument (CRI), which measures a range of collegiate outcomes for alumni 6 years after graduation. The CRI was designed to target alumni from institutions across market segments and assess their values, abilities, work skills, occupations, and pursuit of lifelong learning. (EV)

  9. Moving-object segmentation using a foreground history map.

    PubMed

    Kwak, Sooyeong; Bae, Guntae; Byun, Hyeran

    2010-02-01

    This paper describes a real-time foreground segmentation method in monocular video sequences for video teleconferencing. Background subtraction is widely used in foreground segmentation for static cameras. However, the results are usually not accurate enough for background substitution tasks. In this paper, we propose a novel strategy for fast and accurate foreground segmentation. The strategy consists of two steps: initial foreground segmentation and fine foreground segmentation. The key to our algorithm consists of two steps. In the first step, a moving object is roughly segmented using the background subtraction method. In order to update the initial foreground segmentation results in the second step, a region-based segmentation method and a foreground history map (FHM)-based segmentation representing the combination of temporal and spatial information were developed. The segmentation accuracy of the proposed algorithm was evaluated with respect to the ground truth, which was the manually cropped foreground. The experimental results showed that the proposed algorithm improved the accuracy of segmentation with respect to Horprasert's well-known algorithm.

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

    PubMed

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

    2015-12-01

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

  11. Cost-effectiveness of a European ST-segment elevation myocardial infarction network: results from the Catalan Codi Infart network

    PubMed Central

    Bosch, Julia; Martín-Yuste, Victoria; Rosas, Alba; Faixedas, Maria Teresa; Gómez-Hospital, Joan Antoni; Figueras, Jaume; Curós, Antoni; Cequier, Angel; Goicolea, Javier; Fernández-Ortiz, Antonio; Macaya, Carlos; Tresserras, Ricard; Pellisé, Laura; Sabaté, Manel

    2015-01-01

    Objectives To evaluate the cost-effectiveness of the ST-segment elevation myocardial infarction (STEMI) network of Catalonia (Codi Infart). Design Cost-utility analysis. Setting The analysis was from the Catalonian Autonomous Community in Spain, with a population of about 7.5 million people. Participants Patients with STEMI treated within the autonomous community of Catalonia (Spain) included in the IAM CAT II-IV and Codi Infart registries. Outcome measures Costs included hospitalisation, procedures and additional personnel and were obtained according to the reperfusion strategy. Clinical outcomes were defined as 30-day avoided mortality and quality-adjusted life-years (QALYs), before (N=356) and after network implementation (N=2140). Results A substitution effect and a technology effect were observed; aggregate costs increased by 2.6%. The substitution effect resulted from increased use of primary coronary angioplasty, a relatively expensive procedure and a decrease in fibrinolysis. Primary coronary angioplasty increased from 31% to 89% with the network, and fibrinolysis decreased from 37% to 3%. Rescue coronary angioplasty declined from 11% to 4%, and no reperfusion from 21% to 4%. The technological effect was related to improvements in the percutaneous coronary intervention procedure that increased efficiency, reducing the average length of the hospital stay. Mean costs per patient decreased from €8306 to €7874 for patients with primary coronary angioplasty. Clinical outcomes in patients treated with primary coronary angioplasty did not change significantly, although 30-day mortality decreased from 7.5% to 5.6%. The incremental cost-effectiveness ratio resulted in an extra cost of €4355 per life saved (30-day mortality) and €495 per QALY. Below a cost threshold of €30 000, results were sensitive to variations in costs and outcomes. Conclusions The Catalan STEMI network (Codi Infart) is cost-efficient. Further studies are needed in geopolitical

  12. Accurate detection of coronary artery disease by integrated analysis of the ST-segment depression/heart rate patterns during the exercise and recovery phases of the exercise electrocardiography test.

    PubMed

    Lehtinen, R; Sievänen, H; Viik, J; Turjanmaa, V; Niemelä, K; Malmivuo, J

    1996-11-01

    In this comparative cross-sectional study, we evaluated whether a novel computerized diagnostic variable, ST-segment depression/heart rate ST/HR analysis during both the exercise and postexercise recovery phases of the exercise electrocardiography (ECG) test, can detect coronary artery disease more accurately than methods using either exercise or recovery phase alone. The study population comprised 347 clinical patients referred for a routine bicycle exercise ECG test at Tampere University Hospital, Finland. Of these, 127 had angiographically proven coronary artery disease, whereas 13 had no coronary artery disease according to angiography, 18 had no perfusion defect according to technetium-99m sestamibi single-photon emission computed tomography, and 189 were clinically normal with respect to cardiac diseases. For each patient, the maximum values of the ST/HR hysteresis, ST/HR index, end-exercise ST depression, and recovery ST depression were determined from the Mason-Likar modification of the standard 12-lead exercise electrocardiogram [aVL, aVR, and V1 excluded]. The diagnostic performance of these continuous diagnostic variables was compared by means of receiver-operating characteristic analysis. The area under the receiver-operating characteristic curve of the ST/HR hysteresis was 89%, which was significantly larger than that of the end-exercise ST depression (76%, p < or = 0.0001), recovery ST depression (84%, p = 0.0063), or ST/HR index (83%, p = 0.0023), indicating superior diagnostic performance of the ST/HR hysteresis independent of the partition value selection. In conclusion, computerized analysis of the HR-adjusted ST depression pattern during the exercise phase, integrated with the HR-adjusted ST depression pattern during the recovery phase after exercise, can significantly improve the diagnostic performance and clinical utility of the exercise ECG test for the detection of coronary artery disease.

  13. Unsupervised Segmentation Of Texture Images

    NASA Astrophysics Data System (ADS)

    Michel, Xavier; Leonardi, Riccardo; Gersho, Allen

    1988-10-01

    technique has been tested on several multi-texture images yielding accu-rate segmentation results comparable or superior to the performance obtained by human visual segmentation.

  14. An entropy-based objective evaluation method for image segmentation

    NASA Astrophysics Data System (ADS)

    Zhang, Hui; Fritts, Jason E.; Goldman, Sally A.

    2003-12-01

    Accurate image segmentation is important for many image, video and computer vision applications. Over the last few decades, many image segmentation methods have been proposed. However, the results of these segmentation methods are usually evaluated only visually, qualitatively, or indirectly by the effectiveness of the segmentation on the subsequent processing steps. Such methods are either subjective or tied to particular applications. They do not judge the performance of a segmentation method objectively, and cannot be used as a means to compare the performance of different segmentation techniques. A few quantitative evaluation methods have been proposed, but these early methods have been based entirely on empirical analysis and have no theoretical grounding. In this paper, we propose a novel objective segmentation evaluation method based on information theory. The new method uses entropy as the basis for measuring the uniformity of pixel characteristics (luminance is used in this paper) within a segmentation region. The evaluation method provides a relative quality score that can be used to compare different segmentations of the same image. This method can be used to compare both various parameterizations of one particular segmentation method as well as fundamentally different segmentation techniques. The results from this preliminary study indicate that the proposed evaluation method is superior to the prior quantitative segmentation evaluation techniques, and identify areas for future research in objective segmentation evaluation.

  15. Preliminary Results on Earthquake Recurrence Intervals, Rupture Segmentation, and Potential Earthquake Moment Magnitudes along the Tahoe-Sierra Frontal Fault Zone, Lake Tahoe, California

    NASA Astrophysics Data System (ADS)

    Howle, J.; Bawden, G. W.; Schweickert, R. A.; Hunter, L. E.; Rose, R.

    2012-12-01

    Utilizing high-resolution bare-earth LiDAR topography, field observations, and earlier results of Howle et al. (2012), we estimate latest Pleistocene/Holocene earthquake-recurrence intervals, propose scenarios for earthquake-rupture segmentation, and estimate potential earthquake moment magnitudes for the Tahoe-Sierra frontal fault zone (TSFFZ), west of Lake Tahoe, California. We have developed a new technique to estimate the vertical separation for the most recent and the previous ground-rupturing earthquakes at five sites along the Echo Peak and Mt. Tallac segments of the TSFFZ. At these sites are fault scarps with two bevels separated by an inflection point (compound fault scarps), indicating that the cumulative vertical separation (VS) across the scarp resulted from two events. This technique, modified from the modeling methods of Howle et al. (2012), uses the far-field plunge of the best-fit footwall vector and the fault-scarp morphology from high-resolution LiDAR profiles to estimate the per-event VS. From this data, we conclude that the adjacent and overlapping Echo Peak and Mt. Tallac segments have ruptured coseismically twice during the Holocene. The right-stepping, en echelon range-front segments of the TSFFZ show progressively greater VS rates and shorter earthquake-recurrence intervals from southeast to northwest. Our preliminary estimates suggest latest Pleistocene/ Holocene earthquake-recurrence intervals of 4.8±0.9x103 years for a coseismic rupture of the Echo Peak and Mt. Tallac segments, located at the southeastern end of the TSFFZ. For the Rubicon Peak segment, northwest of the Echo Peak and Mt. Tallac segments, our preliminary estimate of the maximum earthquake-recurrence interval is 2.8±1.0x103 years, based on data from two sites. The correspondence between high VS rates and short recurrence intervals suggests that earthquake sequences along the TSFFZ may initiate in the northwest part of the zone and then occur to the southeast with a lower

  16. Simultaneous Segmentation and Statistical Label Fusion.

    PubMed

    Asman, Andrew J; Landmana, Bennett A

    2012-02-23

    Labeling or segmentation of structures of interest in medical imaging plays an essential role in both clinical and scientific understanding. Two of the common techniques to obtain these labels are through either fully automated segmentation or through multi-atlas based segmentation and label fusion. Fully automated techniques often result in highly accurate segmentations but lack the robustness to be viable in many cases. On the other hand, label fusion techniques are often extremely robust, but lack the accuracy of automated algorithms for specific classes of problems. Herein, we propose to perform simultaneous automated segmentation and statistical label fusion through the reformulation of a generative model to include a linkage structure that explicitly estimates the complex global relationships between labels and intensities. These relationships are inferred from the atlas labels and intensities and applied to the target using a non-parametric approach. The novelty of this approach lies in the combination of previously exclusive techniques and attempts to combine the accuracy benefits of automated segmentation with the robustness of a multi-atlas based approach. The accuracy benefits of this simultaneous approach are assessed using a multi-label multi- atlas whole-brain segmentation experiment and the segmentation of the highly variable thyroid on computed tomography images. The results demonstrate that this technique has major benefits for certain types of problems and has the potential to provide a paradigm shift in which the lines between statistical label fusion and automated segmentation are dramatically blurred.

  17. Interactive medical image segmentation using snake and multiscale curve editing.

    PubMed

    Zhou, Wu; Xie, Yaoqin

    2013-01-01

    Image segmentation is typically applied to locate objects and boundaries, and it is an essential process that supports medical diagnosis, surgical planning, and treatments in medical applications. Generally, this process is done by clinicians manually, which may be accurate but tedious and very time consuming. To facilitate the process, numerous interactive segmentation methods have been proposed that allow the user to intervene in the process of segmentation by incorporating prior knowledge, validating results and correcting errors. The accurate segmentation results can potentially be obtained by such user-interactive process. In this work, we propose a novel framework of interactive medical image segmentation for clinical applications, which combines digital curves and the active contour model to obtain promising results. It allows clinicians to quickly revise or improve contours by simple mouse actions. Meanwhile, the snake model becomes feasible and practical in clinical applications. Experimental results demonstrate the effectiveness of the proposed method for medical images in clinical applications.

  18. A Segmentation Framework of Pulmonary Nodules in Lung CT Images.

    PubMed

    Mukhopadhyay, Sudipta

    2016-02-01

    Accurate segmentation of pulmonary nodules is a prerequisite for acceptable performance of computer-aided detection (CAD) system designed for diagnosis of lung cancer from lung CT images. Accurate segmentation helps to improve the quality of machine level features which could improve the performance of the CAD system. The well-circumscribed solid nodules can be segmented using thresholding, but segmentation becomes difficult for part-solid, non-solid, and solid nodules attached with pleura or vessels. We proposed a segmentation framework for all types of pulmonary nodules based on internal texture (solid/part-solid and non-solid) and external attachment (juxta-pleural and juxta-vascular). In the proposed framework, first pulmonary nodules are categorized into solid/part-solid and non-solid category by analyzing intensity distribution in the core of the nodule. Two separate segmentation methods are developed for solid/part-solid and non-solid nodules, respectively. After determining the category of nodule, the particular algorithm is set to remove attached pleural surface and vessels from the nodule body. The result of segmentation is evaluated in terms of four contour-based metrics and six region-based metrics for 891 pulmonary nodules from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database. The experimental result shows that the proposed segmentation framework is reliable for segmentation of various types of pulmonary nodules with improved accuracy compared to existing segmentation methods.

  19. Multiquadric Spline-Based Interactive Segmentation of Vascular Networks

    PubMed Central

    Meena, Sachin; Surya Prasath, V. B.; Kassim, Yasmin M.; Maude, Richard J.; Glinskii, Olga V.; Glinsky, Vladislav V.; Huxley, Virginia H.; Palaniappan, Kannappan

    2016-01-01

    Commonly used drawing tools for interactive image segmentation and labeling include active contours or boundaries, scribbles, rectangles and other shapes. Thin vessel shapes in images of vascular networks are difficult to segment using automatic or interactive methods. This paper introduces the novel use of a sparse set of user-defined seed points (supervised labels) for precisely, quickly and robustly segmenting complex biomedical images. A multiquadric spline-based binary classifier is proposed as a unique approach for interactive segmentation using as features color values and the location of seed points. Epifluorescence imagery of the dura mater microvasculature are difficult to segment for quantitative applications due to challenging tissue preparation, imaging conditions, and thin, faint structures. Experimental results based on twenty epifluorescence images is used to illustrate the benefits of using a set of seed points to obtain fast and accurate interactive segmentation compared to four interactive and automatic segmentation approaches. PMID:28227856

  20. Multiquadric Spline-Based Interactive Segmentation of Vascular Networks.

    PubMed

    Meena, Sachin; Surya Prasath, V B; Kassim, Yasmin M; Maude, Richard J; Glinskii, Olga V; Glinsky, Vladislav V; Huxley, Virginia H; Palaniappan, Kannappan

    2016-08-01

    Commonly used drawing tools for interactive image segmentation and labeling include active contours or boundaries, scribbles, rectangles and other shapes. Thin vessel shapes in images of vascular networks are difficult to segment using automatic or interactive methods. This paper introduces the novel use of a sparse set of user-defined seed points (supervised labels) for precisely, quickly and robustly segmenting complex biomedical images. A multiquadric spline-based binary classifier is proposed as a unique approach for interactive segmentation using as features color values and the location of seed points. Epifluorescence imagery of the dura mater microvasculature are difficult to segment for quantitative applications due to challenging tissue preparation, imaging conditions, and thin, faint structures. Experimental results based on twenty epifluorescence images is used to illustrate the benefits of using a set of seed points to obtain fast and accurate interactive segmentation compared to four interactive and automatic segmentation approaches.

  1. Feature-driven model-based segmentation

    NASA Astrophysics Data System (ADS)

    Qazi, Arish A.; Kim, John; Jaffray, David A.; Pekar, Vladimir

    2011-03-01

    The accurate delineation of anatomical structures is required in many medical image analysis applications. One example is radiation therapy planning (RTP), where traditional manual delineation is tedious, labor intensive, and can require hours of clinician's valuable time. Majority of automated segmentation methods in RTP belong to either model-based or atlas-based approaches. One substantial limitation of model-based segmentation is that its accuracy may be restricted by the uncertainties in image content, specifically when segmenting low-contrast anatomical structures, e.g. soft tissue organs in computed tomography images. In this paper, we introduce a non-parametric feature enhancement filter which replaces raw intensity image data by a high level probabilistic map which guides the deformable model to reliably segment low-contrast regions. The method is evaluated by segmenting the submandibular and parotid glands in the head and neck region and comparing the results to manual segmentations in terms of the volume overlap. Quantitative results show that we are in overall good agreement with expert segmentations, achieving volume overlap of up to 80%. Qualitatively, we demonstrate that we are able to segment low-contrast regions, which otherwise are difficult to delineate with deformable models relying on distinct object boundaries from the original image data.

  2. Spinal cord grey matter segmentation challenge.

    PubMed

    Prados, Ferran; Ashburner, John; Blaiotta, Claudia; Brosch, Tom; Carballido-Gamio, Julio; Cardoso, Manuel Jorge; Conrad, Benjamin N; Datta, Esha; Dávid, Gergely; Leener, Benjamin De; Dupont, Sara M; Freund, Patrick; Wheeler-Kingshott, Claudia A M Gandini; Grussu, Francesco; Henry, Roland; Landman, Bennett A; Ljungberg, Emil; Lyttle, Bailey; Ourselin, Sebastien; Papinutto, Nico; Saporito, Salvatore; Schlaeger, Regina; Smith, Seth A; Summers, Paul; Tam, Roger; Yiannakas, Marios C; Zhu, Alyssa; Cohen-Adad, Julien

    2017-03-07

    An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication.

  3. Automated area segmentation for ocean bottom surveys

    NASA Astrophysics Data System (ADS)

    Hyland, John C.; Smith, Cheryl M.

    2015-05-01

    In practice, environmental information about an ocean bottom area to be searched using SONAR is often known a priori to some coarse level of resolution. The SONAR search sensor then typically has a different performance characterization function for each environmental classification. Large ocean bottom surveys using search SONAR can pose some difficulties when the environmental conditions vary significantly over the search area because search planning tools cannot adequately segment the area into sub-regions of homogeneous search sensor performance. Such segmentation is critically important to unmanned search vehicles; homogenous bottom segmentation will result in more accurate predictions of search performance and area coverage rate. The Naval Surface Warfare Center, Panama City Division (NSWC PCD) has developed an automated area segmentation algorithm that subdivides the mission area under the constraint that the variation of the search sensor's performance within each sub-mission area cannot exceed a specified threshold, thereby creating sub-regions of homogeneous sensor performance. The algorithm also calculates a new, composite sensor performance function for each sub-mission area. The technique accounts for practical constraints such as enforcing a minimum sub-mission area size and requiring sub-mission areas to be rectangular. Segmentation occurs both across the rows and down the columns of the mission area. Ideally, mission planning should consider both segmentation directions and choose the one with the more favorable result. The Automated Area Segmentation Algorithm was tested using two a priori bottom segmentations: rectangular and triangular; and two search sensor configurations: a set of three bi-modal curves and a set of three uni-modal curves. For each of these four scenarios, the Automated Area Segmentation Algorithm automatically partitioned the mission area across rows and down columns to create regions with homogeneous sensor performance. The

  4. Segmentation and segment connection of obstructed colon

    NASA Astrophysics Data System (ADS)

    Medved, Mario; Truyen, Roel; Likar, Bostjan; Pernus, Franjo

    2004-05-01

    Segmentation of colon CT images is the main factor that inhibits automation of virtual colonoscopy. There are two main reasons that make efficient colon segmentation difficult. First, besides the colon, the small bowel, lungs, and stomach are also gas-filled organs in the abdomen. Second, peristalsis or residual feces often obstruct the colon, so that it consists of multiple gas-filled segments. In virtual colonoscopy, it is very useful to automatically connect the centerlines of these segments into a single colon centerline. Unfortunately, in some cases this is a difficult task. In this study a novel method for automated colon segmentation and connection of colon segments' centerlines is proposed. The method successfully combines features of segments, such as centerline and thickness, with information on main colon segments. The results on twenty colon cases show that the method performs well in cases of small obstructions of the colon. Larger obstructions are mostly also resolved properly, especially if they do not appear in the sigmoid part of the colon. Obstructions in the sigmoid part of the colon sometimes cause improper classification of the small bowel segments. If a segment is too small, it is classified as the small bowel segment. However, such misclassifications have little impact on colon analysis.

  5. Feature extraction for MRI segmentation.

    PubMed

    Velthuizen, R P; Hall, L O; Clarke, L P

    1999-04-01

    Magnetic resonance images (MRIs) of the brain are segmented to measure the efficacy of treatment strategies for brain tumors. To date, no reproducible technique for measuring tumor size is available to the clinician, which hampers progress of the search for good treatment protocols. Many segmentation techniques have been proposed, but the representation (features) of the MRI data has received little attention. A genetic algorithm (GA) search was used to discover a feature set from multi-spectral MRI data. Segmentations were performed using the fuzzy c-means (FCM) clustering technique. Seventeen MRI data sets from five patients were evaluated. The GA feature set produces a more accurate segmentation. The GA fitness function that achieves the best results is the Wilks's lambda statistic when applied to FCM clusters. Compared to linear discriminant analysis, which requires class labels, the same or better accuracy is obtained by the features constructed from a GA search without class labels, allowing fully operator independent segmentation. The GA approach therefore provides a better starting point for the measurement of the response of a brain tumor to treatment.

  6. Automatic segmentation of psoriasis lesions

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

  7. Tissue probability map constrained CLASSIC for increased accuracy and robustness in serial image segmentation

    NASA Astrophysics Data System (ADS)

    Xue, Zhong; Shen, Dinggang; Wong, Stephen T. C.

    2009-02-01

    Traditional fuzzy clustering algorithms have been successfully applied in MR image segmentation for quantitative morphological analysis. However, the clustering results might be biased due to the variability of tissue intensities and anatomical structures. 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 serialMR brain image segmentation for longitudinal study of human brains. The tissue probability maps consist of segmentation priors obtained from a population and reflect the probability of different tissue types. More accurate image segmentation can be achieved by using these segmentation priors in the clustering algorithm. Experimental results of both simulated longitudinal MR brain data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) data using the new serial image segmentation algorithm in the framework of CLASSIC show more accurate and robust longitudinal measures.

  8. A generative model for image segmentation based on label fusion.

    PubMed

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

    2010-10-01

    We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interpreted as special cases of our framework. We conduct two sets of experiments to validate the proposed methods. In the first set of experiments, we use 39 brain MRI scans-with manually segmented white matter, cerebral cortex, ventricles and subcortical structures-to compare different label fusion algorithms and the widely-used FreeSurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 282 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal volume changes in a study of aging and Alzheimer's Disease.

  9. Robust pulmonary lobe segmentation against incomplete fissures

    NASA Astrophysics Data System (ADS)

    Gu, Suicheng; Zheng, Qingfeng; Siegfried, Jill; Pu, Jiantao

    2012-03-01

    As important anatomical landmarks of the human lung, accurate lobe segmentation may be useful for characterizing specific lung diseases (e.g., inflammatory, granulomatous, and neoplastic diseases). A number of investigations showed that pulmonary fissures were often incomplete in image depiction, thereby leading to the computerized identification of individual lobes a challenging task. Our purpose is to develop a fully automated algorithm for accurate identification of individual lobes regardless of the integrity of pulmonary fissures. The underlying idea of the developed lobe segmentation scheme is to use piecewise planes to approximate the detected fissures. After a rotation and a global smoothing, a number of small planes were fitted using local fissures points. The local surfaces are finally combined for lobe segmentation using a quadratic B-spline weighting strategy to assure that the segmentation is smooth. The performance of the developed scheme was assessed by comparing with a manually created reference standard on a dataset of 30 lung CT examinations. These examinations covered a number of lung diseases and were selected from a large chronic obstructive pulmonary disease (COPD) dataset. The results indicate that our scheme of lobe segmentation is efficient and accurate against incomplete fissures.

  10. Femoropopliteal bypass vs percutaneous transluminal angioplasty and stenting in treatment of peripheral artery diseases of infrainquinal segment - short-term results.

    PubMed

    Cvetanovski, M V; Jovev, S; Cvetanovska, M; Blazevski, B; Colanceski, R; Andreevska, T; Gramatnikovski, N; Kartalov, A

    2009-07-01

    (Full text is available at http://www.manu.edu.mk/prilozi). Critical limb ischaemia is a result of occlusive arterial disease in the infrainquinal segment and is a major indication for arterial revascularization, which implies a femoropopliteal bypass procedure or an interventional procedure - stent graf notting of the occluded segment. Although indications for both techniques are clearly defined, there are still controversies. Thus, the aim of this study was to determine short-term results in patients treated with these two treatment modalities. In the period between 2002 and 2008 a total of 70 patients with occlusive arte notrial diseases of the low extremity were analysed. In 50 out of 70 patients a femo notro notpopliteal bypass was made. Of these, in 30 (60%) patients PTFE material was used and in 20 (40%) patients an autologous saphenous vein graft was used. The other group comprised 20 patients who underwent stenting. In patients treated with surgical revas notcularization, the major indication for surgery was occlusive arterial disease in: stage II - in 10 patients (20%), stage III - in 5 patients (10%), stage IV - in 25 patients (50%) and the remaining 10 patients (20%) had subacute ischaemia. Arteriography showed three crural patent tributaries in 18% of the patients, two patent crural tributaries in 40% of the patients and one crural patent tributary in 32% of the patients. There were no significant differences concerning indications and arteriographic findings between the two subgroups. The follow-up period lasted for 6 months and the patency rate was 85% (17) for venous bypass, 11 (64.6%) - short-segment lesions (< 4 cm) and 6 (35.3%) lon notger segment lesions (> 4 cm) versus 76.5% (23) for PTFE graft (p < 0.05), of which 13 (56.5%) were short-segment (<4 cm) and 10 (43.5%) longer segment lesions (> 4 cm). The following results were obtained for the second group of patients: initially successful stents in 85%; failure in 15% or 2 patients; technical

  11. Accurate upper body rehabilitation system using kinect.

    PubMed

    Sinha, Sanjana; Bhowmick, Brojeshwar; Chakravarty, Kingshuk; Sinha, Aniruddha; Das, Abhijit

    2016-08-01

    The growing importance of Kinect as a tool for clinical assessment and rehabilitation is due to its portability, low cost and markerless system for human motion capture. However, the accuracy of Kinect in measuring three-dimensional body joint center locations often fails to meet clinical standards of accuracy when compared to marker-based motion capture systems such as Vicon. The length of the body segment connecting any two joints, measured as the distance between three-dimensional Kinect skeleton joint coordinates, has been observed to vary with time. The orientation of the line connecting adjoining Kinect skeletal coordinates has also been seen to differ from the actual orientation of the physical body segment. Hence we have proposed an optimization method that utilizes Kinect Depth and RGB information to search for the joint center location that satisfies constraints on body segment length and as well as orientation. An experimental study have been carried out on ten healthy participants performing upper body range of motion exercises. The results report 72% reduction in body segment length variance and 2° improvement in Range of Motion (ROM) angle hence enabling to more accurate measurements for upper limb exercises.

  12. Molecular definition of deletions of different segments of distal 5p that result in distinct phenotypic features

    SciTech Connect

    Church, D.M.; Bengtsson, U.; Wasmuth, J.J.; Niebuhr, E.

    1995-05-01

    Cri du chat syndrome (CDC) is a segmental aneusomy associated with deletions of chromosome 5p15. In an effort to define regions that produce the phenotypes associated with CDC, we have analyzed deletions from 17 patients. The majority of these patients had atypical CDC features or were asymptomatic. Using these patients, we have mapped several phenotypes associated with deletions of 5p, including speech delay, catlike cry, newborn facial dysmorphism, and adult facial dysmorphism. This phenotypic map should provide a framework with which to begin identification of genes associated with various phenotypic features associated with deletions of distal 5p. We have also analyzed the parental origin of the de novo deletions, to determine if genomic imprinting could be occurring in this region. In addition, we have isolated cosmids that could be useful for both prenatal and postnatal assessments of del5(p) individuals. 25 refs., 4 figs., 3 tabs.

  13. Segmentation of the foveal microvasculature using deep learning networks

    NASA Astrophysics Data System (ADS)

    Prentašić, Pavle; Heisler, Morgan; Mammo, Zaid; Lee, Sieun; Merkur, Andrew; Navajas, Eduardo; Beg, Mirza Faisal; Šarunić, Marinko; Lončarić, Sven

    2016-07-01

    Accurate segmentation of the retinal microvasculature is a critical step in the quantitative analysis of the retinal circulation, which can be an important marker in evaluating the severity of retinal diseases. As manual segmentation remains the gold standard for segmentation of optical coherence tomography angiography (OCT-A) images, we present a method for automating the segmentation of OCT-A images using deep neural networks (DNNs). Eighty OCT-A images of the foveal region in 12 eyes from 6 healthy volunteers were acquired using a prototype OCT-A system and subsequently manually segmented. The automated segmentation of the blood vessels in the OCT-A images was then performed by classifying each pixel into vessel or nonvessel class using deep convolutional neural networks. When the automated results were compared against the manual segmentation results, a maximum mean accuracy of 0.83 was obtained. When the automated results were compared with inter and intrarater accuracies, the automated results were shown to be comparable to the human raters suggesting that segmentation using DNNs is comparable to a second manual rater. As manually segmenting the retinal microvasculature is a tedious task, having a reliable automated output such as automated segmentation by DNNs, is an important step in creating an automated output.

  14. A New MRI-Based Pediatric Subcortical Segmentation Technique (PSST).

    PubMed

    Loh, Wai Yen; Connelly, Alan; Cheong, Jeanie L Y; Spittle, Alicia J; Chen, Jian; Adamson, Christopher; Ahmadzai, Zohra M; Fam, Lillian Gabra; Rees, Sandra; Lee, Katherine J; Doyle, Lex W; Anderson, Peter J; Thompson, Deanne K

    2016-01-01

    Volumetric and morphometric neuroimaging studies of the basal ganglia and thalamus in pediatric populations have utilized existing automated segmentation tools including FIRST (Functional Magnetic Resonance Imaging of the Brain's Integrated Registration and Segmentation Tool) and FreeSurfer. These segmentation packages, however, are mostly based on adult training data. Given that there are marked differences between the pediatric and adult brain, it is likely an age-specific segmentation technique will produce more accurate segmentation results. In this study, we describe a new automated segmentation technique for analysis of 7-year-old basal ganglia and thalamus, called Pediatric Subcortical Segmentation Technique (PSST). PSST consists of a probabilistic 7-year-old subcortical gray matter atlas (accumbens, caudate, pallidum, putamen and thalamus) combined with a customized segmentation pipeline using existing tools: ANTs (Advanced Normalization Tools) and SPM (Statistical Parametric Mapping). The segmentation accuracy of PSST in 7-year-old data was compared against FIRST and FreeSurfer, relative to manual segmentation as the ground truth, utilizing spatial overlap (Dice's coefficient), volume correlation (intraclass correlation coefficient, ICC) and limits of agreement (Bland-Altman plots). PSST achieved spatial overlap scores ≥90% and ICC scores ≥0.77 when compared with manual segmentation, for all structures except the accumbens. Compared with FIRST and FreeSurfer, PSST showed higher spatial overlap (p FDR  < 0.05) and ICC scores, with less volumetric bias according to Bland-Altman plots. PSST is a customized segmentation pipeline with an age-specific atlas that accurately segments typical and atypical basal ganglia and thalami at age 7 years, and has the potential to be applied to other pediatric datasets.

  15. Stereotactic hypofractionated accurate radiotherapy of the prostate (SHARP), 33.5 Gy in five fractions for localized disease: First clinical trial results

    SciTech Connect

    Madsen, Berit L. . E-mail: ronblm@vmmc.org; Hsi, R. Alex; Pham, Huong T.; Fowler, Jack F.; Esagui, Laura C.; Corman, John

    2007-03-15

    Purpose: To evaluate the feasibility and toxicity of stereotactic hypofractionated accurate radiotherapy (SHARP) for localized prostate cancer. Methods and Materials: A Phase I/II trial of SHARP performed for localized prostate cancer using 33.5 Gy in 5 fractions, calculated to be biologically equivalent to 78 Gy in 2 Gy fractions ({alpha}/{beta} ratio of 1.5 Gy). Noncoplanar conformal fields and daily stereotactic localization of implanted fiducials were used for treatment. Genitourinary (GU) and gastrointestinal (GI) toxicity were evaluated by American Urologic Association (AUA) score and Common Toxicity Criteria (CTC). Prostate-specific antigen (PSA) values and self-reported sexual function were recorded at specified follow-up intervals. Results: The study includes 40 patients. The median follow-up is 41 months (range, 21-60 months). Acute toxicity Grade 1-2 was 48.5% (GU) and 39% (GI); 1 acute Grade 3 GU toxicity. Late Grade 1-2 toxicity was 45% (GU) and 37% (GI). No late Grade 3 or higher toxicity was reported. Twenty-six patients reported potency before therapy; 6 (23%) have developed impotence. Median time to PSA nadir was 18 months with the majority of nadirs less than 1.0 ng/mL. The actuarial 48-month biochemical freedom from relapse is 70% for the American Society for Therapeutic Radiology and Oncology definition and 90% by the alternative nadir + 2 ng/mL failure definition. Conclusions: SHARP for localized prostate cancer is feasible with minimal acute or late toxicity. Dose escalation should be possible.

  16. 4D MR phase and magnitude segmentations with GPU parallel computing.

    PubMed

    Bergen, Robert V; Lin, Hung-Yu; Alexander, Murray E; Bidinosti, Christopher P

    2015-01-01

    The increasing size and number of data sets of large four dimensional (three spatial, one temporal) magnetic resonance (MR) cardiac images necessitates efficient segmentation algorithms. Analysis of phase-contrast MR images yields cardiac flow information which can be manipulated to produce accurate segmentations of the aorta. Phase contrast segmentation algorithms are proposed that use simple mean-based calculations and least mean squared curve fitting techniques. The initial segmentations are generated on a multi-threaded central processing unit (CPU) in 10 seconds or less, though the computational simplicity of the algorithms results in a loss of accuracy. A more complex graphics processing unit (GPU)-based algorithm fits flow data to Gaussian waveforms, and produces an initial segmentation in 0.5 seconds. Level sets are then applied to a magnitude image, where the initial conditions are given by the previous CPU and GPU algorithms. A comparison of results shows that the GPU algorithm appears to produce the most accurate segmentation.

  17. 3D Brain Segmentation Using Dual-Front Active Contours with Optional User Interaction

    PubMed Central

    Yezzi, Anthony; Cohen, Laurent D.

    2006-01-01

    Important attributes of 3D brain cortex segmentation algorithms include robustness, accuracy, computational efficiency, and facilitation of user interaction, yet few algorithms incorporate all of these traits. Manual segmentation is highly accurate but tedious and laborious. Most automatic techniques, while less demanding on the user, are much less accurate. It would be useful to employ a fast automatic segmentation procedure to do most of the work but still allow an expert user to interactively guide the segmentation to ensure an accurate final result. We propose a novel 3D brain cortex segmentation procedure utilizing dual-front active contours which minimize image-based energies in a manner that yields flexibly global minimizers based on active regions. Region-based information and boundary-based information may be combined flexibly in the evolution potentials for accurate segmentation results. The resulting scheme is not only more robust but much faster and allows the user to guide the final segmentation through simple mouse clicks which add extra seed points. Due to the flexibly global nature of the dual-front evolution model, single mouse clicks yield corrections to the segmentation that extend far beyond their initial locations, thus minimizing the user effort. Results on 15 simulated and 20 real 3D brain images demonstrate the robustness, accuracy, and speed of our scheme compared with other methods. PMID:23165037

  18. Freehand 3D ultrasound breast tumor segmentation

    NASA Astrophysics Data System (ADS)

    Liu, Qi; Ge, Yinan; Ou, Yue; Cao, Biao

    2007-12-01

    It is very important for physicians to accurately determine breast tumor location, size and shape in ultrasound image. The precision of breast tumor volume quantification relies on the accurate segmentation of the images. Given the known location and orientation of the ultrasound probe, We propose using freehand three dimensional (3D) ultrasound to acquire original images of the breast tumor and the surrounding tissues in real-time, after preprocessing with anisotropic diffusion filtering, the segmentation operation is performed slice by slice based on the level set method in the image stack. For the segmentation on each slice, the user can adjust the parameters to fit the requirement in the specified image in order to get the satisfied result. By the quantification procedure, the user can know the tumor size varying in different images in the stack. Surface rendering and interpolation are used to reconstruct the 3D breast tumor image. And the breast volume is constructed by the segmented contours in the stack of images. After the segmentation, the volume of the breast tumor in the 3D image data can be obtained.

  19. Segmentation of histological structures for fractal analysis

    NASA Astrophysics Data System (ADS)

    Dixon, Vanessa; Kouznetsov, Alexei; Tambasco, Mauro

    2009-02-01

    Pathologists examine histology sections to make diagnostic and prognostic assessments regarding cancer based on deviations in cellular and/or glandular structures. However, these assessments are subjective and exhibit some degree of observer variability. Recent studies have shown that fractal dimension (a quantitative measure of structural complexity) has proven useful for characterizing structural deviations and exhibits great potential for automated cancer diagnosis and prognosis. Computing fractal dimension relies on accurate image segmentation to capture the architectural complexity of the histology specimen. For this purpose, previous studies have used techniques such as intensity histogram analysis and edge detection algorithms. However, care must be taken when segmenting pathologically relevant structures since improper edge detection can result in an inaccurate estimation of fractal dimension. In this study, we established a reliable method for segmenting edges from grayscale images. We used a Koch snowflake, an object of known fractal dimension, to investigate the accuracy of various edge detection algorithms and selected the most appropriate algorithm to extract the outline structures. Next, we created validation objects ranging in fractal dimension from 1.3 to 1.9 imitating the size, structural complexity, and spatial pixel intensity distribution of stained histology section images. We applied increasing intensity thresholds to the validation objects to extract the outline structures and observe the effects on the corresponding segmentation and fractal dimension. The intensity threshold yielding the maximum fractal dimension provided the most accurate fractal dimension and segmentation, indicating that this quantitative method could be used in an automated classification system for histology specimens.

  20. Accurate measurements of vadose zone fluxes using automated equilibrium tension plate lysimeters: A synopsis of results from the Spydia research facility, New Zealand.

    NASA Astrophysics Data System (ADS)

    Wöhling, Thomas; Barkle, Greg; Stenger, Roland; Moorhead, Brian; Wall, Aaron; Clague, Juliet

    2014-05-01

    Automated equilibrium tension plate lysimeters (AETLs) are arguably the most accurate method to measure unsaturated water and contaminant fluxes below the root zone at the scale of up to 1 m². The AETL technique utilizes a porous sintered stainless-steel plate to provide a comparatively large sampling area with a continuously controlled vacuum that is in "equilibrium" with the surrounding vadose zone matric pressure to ensure measured fluxes represent those under undisturbed conditions. This novel lysimeter technique was used at an intensive research site for investigations of contaminant pathways from the land surface to the groundwater on a sheep and beef farm under pastoral land use in the Tutaeuaua subcatchment, New Zealand. The Spydia research facility was constructed in 2005 and was fully operational between 2006 and 2011. Extending from a central access caisson, 15 separately controlled AETLs with 0.2 m² surface area were installed at five depths between 0.4 m and 5.1 m into the undisturbed volcanic vadose zone materials. The unique setup of the facility ensured minimum interference of the experimental equipment and external factors with the measurements. Over the period of more than five years, a comprehensive data set was collected at each of the 15 AETL locations which comprises of time series of soil water flux, pressure head, volumetric water contents, and soil temperature. The soil water was regularly analysed for EC, pH, dissolved carbon, various nitrogen compounds (including nitrate, ammonia, and organic N), phosphorus, bromide, chloride, sulphate, silica, and a range of other major ions, as well as for various metals. Climate data was measured directly at the site (rainfall) and a climate station at 500m distance. The shallow groundwater was sampled at three different depths directly from the Spydia caisson and at various observation wells surrounding the facility. Two tracer experiments were conducted at the site in 2009 and 2010. In the 2009

  1. Kidney segmentation in CT sequences using SKFCM and improved GrowCut algorithm

    PubMed Central

    2015-01-01

    Background Organ segmentation is an important step in computer-aided diagnosis and pathology detection. Accurate kidney segmentation in abdominal computed tomography (CT) sequences is an essential and crucial task for surgical planning and navigation in kidney tumor ablation. However, kidney segmentation in CT is a substantially challenging work because the intensity values of kidney parenchyma are similar to those of adjacent structures. Results In this paper, a coarse-to-fine method was applied to segment kidney from CT images, which consists two stages including rough segmentation and refined segmentation. The rough segmentation is based on a kernel fuzzy C-means algorithm with spatial information (SKFCM) algorithm and the refined segmentation is implemented with improved GrowCut (IGC) algorithm. The SKFCM algorithm introduces a kernel function and spatial constraint into fuzzy c-means clustering (FCM) algorithm. The IGC algorithm makes good use of the continuity of CT sequences in space which can automatically generate the seed labels and improve the efficiency of segmentation. The experimental results performed on the whole dataset of abdominal CT images have shown that the proposed method is accurate and efficient. The method provides a sensitivity of 95.46% with specificity of 99.82% and performs better than other related methods. Conclusions Our method achieves high accuracy in kidney segmentation and considerably reduces the time and labor required for contour delineation. In addition, the method can be expanded to 3D segmentation directly without modification. PMID:26356850

  2. Object density-based image segmentation and its applications in biomedical image analysis.

    PubMed

    Yu, Jinhua; Tan, Jinglu

    2009-12-01

    In many applications of medical image analysis, the density of an object is the most important feature for isolating an area of interest (image segmentation). In this research, an object density-based image segmentation methodology is developed, which incorporates intensity-based, edge-based and texture-based segmentation techniques. The proposed method consists of three main stages: preprocessing, object segmentation and final segmentation. Image enhancement, noise reduction and layer-of-interest extraction are several subtasks of preprocessing. Object segmentation utilizes a marker-controlled watershed technique to identify each object of interest (OI) from the background. A marker estimation method is proposed to minimize over-segmentation resulting from the watershed algorithm. Object segmentation provides an accurate density estimation of OI which is used to guide the subsequent segmentation steps. The final stage converts the distribution of OI into textural energy by using fractal dimension analysis. An energy-driven active contour procedure is designed to delineate the area with desired object density. Experimental results show that the proposed method is 98% accurate in segmenting synthetic images. Segmentation of microscopic images and ultrasound images shows the potential utility of the proposed method in different applications of medical image processing.

  3. Graph cut and image intensity-based splitting improves nuclei segmentation in high-content screening

    NASA Astrophysics Data System (ADS)

    Farhan, Muhammad; Ruusuvuori, Pekka; Emmenlauer, Mario; Rämö, Pauli; Yli-Harja, Olli; Dehio, Christoph

    2013-02-01

    Quantification of phenotypes in high-content screening experiments depends on the accuracy of single cell analysis. In such analysis workflows, cell nuclei segmentation is typically the first step and is followed by cell body segmentation, feature extraction, and subsequent data analysis workflows. Therefore, it is of utmost importance that the first steps of high-content analysis are done accurately in order to guarantee correctness of the final analysis results. In this paper, we present a novel cell nuclei image segmentation framework which exploits robustness of graph cut to obtain initial segmentation for image intensity-based clump splitting method to deliver the accurate overall segmentation. By using quantitative benchmarks and qualitative comparison with real images from high-content screening experiments with complicated multinucleate cells, we show that our method outperforms other state-of-the-art nuclei segmentation methods. Moreover, we provide a modular and easy-to-use implementation of the method for a widely used platform.

  4. Conditional deletion of AP-2β in mouse cranial neural crest results in anterior segment dysgenesis and early-onset glaucoma

    PubMed Central

    Martino, Vanessa B.; Sabljic, Thomas; Deschamps, Paula; Green, Rebecca M.; Akula, Monica; Peacock, Erica; Ball, Alexander

    2016-01-01

    ABSTRACT Anterior segment dysgenesis (ASD) encompasses a group of developmental disorders in which a closed angle phenotype in the anterior chamber of the eye can occur and 50% of patients develop glaucoma. Many ASDs are thought to involve an inappropriate patterning and migration of the periocular mesenchyme (POM), which is derived from cranial neural crest cells (NCCs) and mesoderm. Although, the mechanism of this disruption is not well understood, a number of transcriptional regulatory molecules have previously been implicated in ASDs. Here, we investigate the function of the transcription factor AP-2β, encoded by Tfap2b, which is expressed in NCCs and their derivatives. Wnt1-Cre-mediated conditional deletion of Tfap2b in NCCs resulted in post-natal ocular defects typified by opacity. Histological data revealed that the conditional AP-2β NCC knockout (KO) mutants exhibited dysgenesis of multiple structures in the anterior segment of the eye including defects in the corneal endothelium, corneal stroma, ciliary body and disruption in the iridocorneal angle with adherence of the iris to the cornea. We further show that this phenotype leads to a significant increase in intraocular pressure and a subsequent loss of retinal ganglion cells and optic nerve degeneration, features indicative of glaucoma. Overall, our findings demonstrate that AP-2β is required in the POM for normal development of the anterior segment of the eye and that the AP-2β NCC KO mice might serve as a new and exciting model of ASD and glaucoma that is fully penetrant and with early post-natal onset. PMID:27483349

  5. Influence of preoperative nucleus pulposus status and radiculopathy on outcomes in mono-segmental lumbar total disc replacement: results from a nationwide registry

    PubMed Central

    2011-01-01

    Background Currently, herniated nucleus pulposus (HNP) with radiculopathy and other preconditions are regarded as relative or absolute contraindications for lumbar total disc replacement (TDR). In Switzerland it is left to the surgeon's discretion when to operate. The present study is based on the dataset of SWISSspine, a governmentally mandated health technology assessment registry. We hypothesized that preoperative nucleus pulposus status and presence or absence of radiculopathy has an influence on clinical outcomes in patients treated with mono-segmental lumbar TDR. Methods Between March 2005 and April 2009, 416 patients underwent mono-segmental lumbar TDR, which was documented in a prospective observational multicenter mode. The data collection consisted of perioperative and follow-up data (physician based) and clinical outcomes (NASS, EQ-5D). Patients were divided into four groups according to their preoperative status: 1) group degenerative disc disease ("DDD"): 160 patients without HNP and no radiculopathy, classic precondition for TDR; 2) group "HNP-No radiculopathy": 68 patients with HNP but without radiculopathy; 3) group "Stenosis": 73 patients without HNP but with radiculopathy, and 4) group "HNP-Radiculopathy": 132 patients with HNP and radiculopathy. The groups were compared regarding preoperative patient characteristics and pre- and postoperative VAS and EQ-5D scores using general linear modeling. Results Demographics in all four groups were comparable. Regarding the improvement of quality of life (EQ-5D) there were no differences across the four groups. For the two main groups DDD and HNP-Radiculopathy no differences were found in the adjusted postoperative back- and leg pain alleviation levels, in the stenosis group back- and leg pain relief were lower. Conclusions Despite higher preoperative leg pain levels, outcomes in lumbar TDR patients with HNP and radiculopathy were similar to outcomes in patients with the classic indication; this because

  6. 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

  7. 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

  8. 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

  9. Paleoseismology of the Nephi Segment of the Wasatch Fault Zone, Juab County, Utah - Preliminary Results From Two Large Exploratory Trenches at Willow Creek

    USGS Publications Warehouse

    Machette, Michael N.; Crone, Anthony J.; Personius, Stephen F.; Mahan, Shannon; Dart, Richard L.; Lidke, David J.; Olig, Susan S.

    2007-01-01

    In 2004, we identified a small parcel of U.S. Forest Service land at the mouth of Willow Creek (about 5 km west of Mona, Utah) that was suitable for trenching. At the Willow Creek site, which is near the middle of the southern strand of the Nephi segment, the WFZ has vertically displaced alluvial-fan deposits >6-7 m, forming large, steep, multiple-event scarps. In May 2005, we dug two 4- to 5-m-deep backhoe trenches at the Willow Creek site, identified three colluvial wedges in each trench, and collected samples of charcoal and A-horizon organic material for AMS (acceleration mass spectrometry) radiocarbon dating, and sampled fine-grained eolian and colluvial sediment for luminescence dating. The trenches yielded a stratigraphic assemblage composed of moderately coarse-grained fluvial and debris-flow deposits and discrete colluvial wedges associated with three faulting events (P1, P2, and P3). About one-half of the net vertical displacement is accommodated by monoclinal tilting of fan deposits on the hanging-wall block, possibly related to massive ductile landslide deposits that are present beneath the Willow Creek fan. The timing of the three surface-faulting events is bracketed by radiocarbon dates and results in a much different fault chronology and higher slip rates than previously considered for this segment of the Wasatch fault zone.

  10. Loss of Msx2 Function Down-Regulates the FoxE3 Expression and Results in Anterior Segment Dysgenesis Resembling Peters Anomaly

    PubMed Central

    Zhao, Jiangyue; Kawai, Kirio; Wang, Hongyan; Wu, Di; Wang, Mingwu; Yue, Zhicao; Zhang, Jinsong; Liu, Yi-Hsin

    2012-01-01

    Complex molecular interactions dictate the developmental steps that lead to a mature and functional cornea and lens. Peters anomaly is one subtype of anterior segment dysgenesis especially due to abnormal development of the cornea and lens. MSX2 was recently implicated as a potential gene that is critical for anterior segment development. However, the role of MSX2 within the complex mechanisms of eye development remains elusive. Our present study observed the morphologic changes in conventional Msx2 knockout (KO) mice and found phenotypes consistent with Peters anomaly and microphthalmia seen in humans. The role of Msx2 in cornea and lens development was further investigated using IHC, in situ hybridization, and quantification of proliferative and apoptotic lens cells. Loss of Msx2 down-regulated FoxE3 expression and up-regulated Prox1 and crystallin expression in the lens. The FoxE3 and Prox1 malfunction and precocious Prox1 and crystallin expression contribute to a disturbed lens cell cycle in lens vesicles and eventually to cornea-lentoid adhesions and microphthalmia in Msx2 KO mice. The observed changes in the expression of FoxE3 suggest that Msx2 is an important contributor in controlling transcription of target genes critical for early eye development. These results provide the first direct genetic evidence of the involvement of MSX2 in Peters anomaly and the distinct function of MSX2 in regulating the growth and development of lens vesicles. PMID:22503753

  11. Interactive Tooth Separation from Dental Model Using Segmentation Field

    PubMed Central

    2016-01-01

    Tooth segmentation on dental model is an essential step of computer-aided-design systems for orthodontic virtual treatment planning. However, fast and accurate identifying cutting boundary to separate teeth from dental model still remains a challenge, due to various geometrical shapes of teeth, complex tooth arrangements, different dental model qualities, and varying degrees of crowding problems. Most segmentation approaches presented before are not able to achieve a balance between fine segmentation results and simple operating procedures with less time consumption. In this article, we present a novel, effective and efficient framework that achieves tooth segmentation based on a segmentation field, which is solved by a linear system defined by a discrete Laplace-Beltrami operator with Dirichlet boundary conditions. A set of contour lines are sampled from the smooth scalar field, and candidate cutting boundaries can be detected from concave regions with large variations of field data. The sensitivity to concave seams of the segmentation field facilitates effective tooth partition, as well as avoids obtaining appropriate curvature threshold value, which is unreliable in some case. Our tooth segmentation algorithm is robust to dental models with low quality, as well as is effective to dental models with different levels of crowding problems. The experiments, including segmentation tests of varying dental models with different complexity, experiments on dental meshes with different modeling resolutions and surface noises and comparison between our method and the morphologic skeleton segmentation method are conducted, thus demonstrating the effectiveness of our method. PMID:27532266

  12. Pupil segmentation using active contour with shape prior

    NASA Astrophysics Data System (ADS)

    Ukpai, Charles O.; Dlay, Satnam S.; Woo, Wai L.

    2015-03-01

    Iris segmentation is the process of defining the valid part of the eye image used for further processing (feature extraction, matching and decision making). Segmentation of the iris mostly starts with pupil boundary segmentation. Most pupil segmentation techniques are based on the assumption that the pupil is circular shape. In this paper, we propose a new pupil segmentation technique which combines shape, location and spatial information for accurate and efficient segmentation of the pupil. Initially, the pupil's position and radius is estimated using a statistical approach and circular Hough transform. In order to segment the irregular boundary of the pupil, an active contour model is initialized close to the estimated boundary using information from the first step and segmentation is achieved using energy minimization based active contour. Pre-processing and post-processing were carried out to remove noise and occlusions respectively. Experimental results on CASIA V1.0 and 4.0 shows that the proposed method is highly effective at segmenting irregular boundaries of the pupil.

  13. CERES: A new cerebellum lobule segmentation method.

    PubMed

    Romero, Jose E; Coupé, Pierrick; Giraud, Rémi; Ta, Vinh-Thong; Fonov, Vladimir; Park, Min Tae M; Chakravarty, M Mallar; Voineskos, Aristotle N; Manjón, Jose V

    2017-02-15

    The human cerebellum is involved in language, motor tasks and cognitive processes such as attention or emotional processing. Therefore, an automatic and accurate segmentation method is highly desirable to measure and understand the cerebellum role in normal and pathological brain development. In this work, we propose a patch-based multi-atlas segmentation tool called CERES (CEREbellum Segmentation) that is able to automatically parcellate the cerebellum lobules. The proposed method works with standard resolution magnetic resonance T1-weighted images and uses the Optimized PatchMatch algorithm to speed up the patch matching process. The proposed method was compared with related recent state-of-the-art methods showing competitive results in both accuracy (average DICE of 0.7729) and execution time (around 5 minutes).

  14. 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.

  15. 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.

  16. 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

  17. Prostate segmentation by sparse representation based classification

    PubMed Central

    Gao, Yaozong; Liao, Shu; Shen, Dinggang

    2012-01-01

    Purpose: The segmentation of prostate in CT images is of essential importance to external beam radiotherapy, which is one of the major treatments for prostate cancer nowadays. During the radiotherapy, the prostate is radiated by high-energy x rays from different directions. In order to maximize the dose to the cancer and minimize the dose to the surrounding healthy tissues (e.g., bladder and rectum), the prostate in the new treatment image needs to be accurately localized. Therefore, the effectiveness and efficiency of external beam radiotherapy highly depend on the accurate localization of the prostate. However, due to the low contrast of the prostate with its surrounding tissues (e.g., bladder), the unpredicted prostate motion, and the large appearance variations across different treatment days, it is challenging to segment the prostate in CT images. In this paper, the authors present a novel classification based segmentation method to address these problems. Methods: To segment the prostate, the proposed method first uses sparse representation based classification (SRC) to enhance the prostate in CT images by pixel-wise classification, in order to overcome the limitation of poor contrast of the prostate images. Then, based on the classification results, previous segmented prostates of the same patient are used as patient-specific atlases to align onto the current treatment image and the majority voting strategy is finally adopted to segment the prostate. In order to address the limitations of the traditional SRC in pixel-wise classification, especially for the purpose of segmentation, the authors extend SRC from the following four aspects: (1) A discriminant subdictionary learning method is proposed to learn a discriminant and compact representation of training samples for each class so that the discriminant power of SRC can be increased and also SRC can be applied to the large-scale pixel-wise classification. (2) The L1 regularized sparse coding is replaced by

  18. Accurate Finite Difference Algorithms

    NASA Technical Reports Server (NTRS)

    Goodrich, John W.

    1996-01-01

    Two families of finite difference algorithms for computational aeroacoustics are presented and compared. All of the algorithms are single step explicit methods, they have the same order of accuracy in both space and time, with examples up to eleventh order, and they have multidimensional extensions. One of the algorithm families has spectral like high resolution. Propagation with high order and high resolution algorithms can produce accurate results after O(10(exp 6)) periods of propagation with eight grid points per wavelength.

  19. Consistent cortical reconstruction and multi-atlas brain segmentation.

    PubMed

    Huo, Yuankai; Plassard, Andrew J; Carass, Aaron; Resnick, Susan M; Pham, Dzung L; Prince, Jerry L; Landman, Bennett A

    2016-09-01

    Whole brain segmentation and cortical surface reconstruction are two essential techniques for investigating the human brain. Spatial inconsistences, which can hinder further integrated analyses of brain structure, can result due to these two tasks typically being conducted independently of each other. FreeSurfer obtains self-consistent whole brain segmentations and cortical surfaces. It starts with subcortical segmentation, then carries out cortical surface reconstruction, and ends with cortical segmentation and labeling. However, this "segmentation to surface to parcellation" strategy has shown limitations in various cohorts such as older populations with large ventricles. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. A modification called MaCRUISE(+) is designed to perform well when white matter lesions are present. Comparing to the benchmarks CRUISE and FreeSurfer, the surface accuracy of MaCRUISE and MaCRUISE(+) is validated using two independent datasets with expertly placed cortical landmarks. A third independent dataset with expertly delineated volumetric labels is employed to compare segmentation performance. Finally, 200MR volumetric images from an older adult sample are used to assess the robustness of MaCRUISE and FreeSurfer. The advantages of MaCRUISE are: (1) MaCRUISE constructs self-consistent voxelwise segmentations and cortical surfaces, while MaCRUISE(+) is robust to white matter pathology. (2) MaCRUISE achieves more accurate whole brain segmentations than independently conducting the multi-atlas segmentation. (3) MaCRUISE is comparable in accuracy to FreeSurfer (when FreeSurfer does not exhibit global failures) while achieving greater robustness across an older adult population. MaCRUISE has been made freely

  20. a Segment-Based Approach for DTM Derivation of Airborne LIDAR Data

    NASA Astrophysics Data System (ADS)

    Tang, Dejin; Zhou, Xiaoming; Jiang, Jie; Li, Caiping

    2016-06-01

    With the characteristics of LIDAR system, raw point clouds represent both terrain and non-terrain surface. In order to generate DTM, the paper introduces one improved filtering method based on the segment-based algorithms. The method generates segments by clustering points based on surface fitting and uses topological and geometric properties for classification. In the process, three major steps are involved. First, the whole datasets is split into several small overlapping tiles. For each tile, by removing wall and vegetation points, accurate segments are found. The segments from all tiles are assigned unique segment number. In the following step, topological descriptions for the segment distribution pattern and height jump between adjacent segments are identified in each tile. Based on the topology and geometry, segment-based filtering algorithm is performed for classification in each tile. Then, based on the spatial location of the segment in one tile, two confidence levels are assigned to the classified segments. The segments with low confidence level are because of losing geometric or topological information in one tile. Thus, a combination algorithm is generated to detect corresponding parts of incomplete segment from multiple tiles. Then another classification algorithm is performed for these segments. The result of these segments will have high confidence level. After that, all the segments in one tile have high confidence level of classification result. The final DTM will add all the terrain segments and avoid duplicate points. At the last of the paper, the experiment show the filtering result and be compared with the other classical filtering methods, the analysis proves the method has advantage in the precision of DTM. But because of the complicated algorithms, the processing speed is little slower, that is the future improvement which should been researched.

  1. An interactive method based on the live wire for segmentation of the breast in mammography images.

    PubMed

    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.

  2. Segmentation in Tardigrada and diversification of segmental patterns in Panarthropoda.

    PubMed

    Smith, Frank W; Goldstein, Bob

    2016-10-31

    The origin and diversification of segmented metazoan body plans has fascinated biologists for over a century. The superphylum Panarthropoda includes three phyla of segmented animals-Euarthropoda, Onychophora, and Tardigrada. This superphylum includes representatives with relatively simple and representatives with relatively complex segmented body plans. At one extreme of this continuum, euarthropods exhibit an incredible diversity of serially homologous segments. Furthermore, distinct tagmosis patterns are exhibited by different classes of euarthropods. At the other extreme, all tardigrades share a simple segmented body plan that consists of a head and four leg-bearing segments. The modular body plans of panarthropods make them a tractable model for understanding diversification of animal body plans more generally. Here we review results of recent morphological and developmental studies of tardigrade segmentation. These results complement investigations of segmentation processes in other panarthropods and paleontological studies to illuminate the earliest steps in the evolution of panarthropod body plans.

  3. 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

  4. Comprehensive brain MRI segmentation in high risk preterm newborns.

    PubMed

    Yu, Xintian; Zhang, Yanjie; Lasky, Robert E; Datta, Sushmita; Parikh, Nehal A; Narayana, Ponnada A

    2010-11-08

    Most extremely preterm newborns exhibit cerebral atrophy/growth disturbances and white matter signal abnormalities on MRI at term-equivalent age. MRI brain volumes could serve as biomarkers for evaluating the effects of neonatal intensive care and predicting neurodevelopmental outcomes. This requires detailed, accurate, and reliable brain MRI segmentation methods. We describe our efforts to develop such methods in high risk newborns using a combination of manual and automated segmentation tools. After intensive efforts to accurately define structural boundaries, two trained raters independently performed manual segmentation of nine subcortical structures using axial T2-weighted MRI scans from 20 randomly selected extremely preterm infants. All scans were re-segmented by both raters to assess reliability. High intra-rater reliability was achieved, as assessed by repeatability and intra-class correlation coefficients (ICC range: 0.97 to 0.99) for all manually segmented regions. Inter-rater reliability was slightly lower (ICC range: 0.93 to 0.99). A semi-automated segmentation approach was developed that combined the parametric strengths of the Hidden Markov Random Field Expectation Maximization algorithm with non-parametric Parzen window classifier resulting in accurate white matter, gray matter, and CSF segmentation. Final manual correction of misclassification errors improved accuracy (similarity index range: 0.87 to 0.89) and facilitated objective quantification of white matter signal abnormalities. The semi-automated and manual methods were seamlessly integrated to generate full brain segmentation within two hours. This comprehensive approach can facilitate the evaluation of large cohorts to rigorously evaluate the utility of regional brain volumes as biomarkers of neonatal care and surrogate endpoints for neurodevelopmental outcomes.

  5. Automated segmentation and dose-volume analysis with DICOMautomaton

    NASA Astrophysics Data System (ADS)

    Clark, H.; Thomas, S.; Moiseenko, V.; Lee, R.; Gill, B.; Duzenli, C.; Wu, J.

    2014-03-01

    Purpose: Exploration of historical data for regional organ dose sensitivity is limited by the effort needed to (sub-)segment large numbers of contours. A system has been developed which can rapidly perform autonomous contour sub-segmentation and generic dose-volume computations, substantially reducing the effort required for exploratory analyses. Methods: A contour-centric approach is taken which enables lossless, reversible segmentation and dramatically reduces computation time compared with voxel-centric approaches. Segmentation can be specified on a per-contour, per-organ, or per-patient basis, and can be performed along either an embedded plane or in terms of the contour's bounds (e.g., split organ into fractional-volume/dose pieces along any 3D unit vector). More complex segmentation techniques are available. Anonymized data from 60 head-and-neck cancer patients were used to compare dose-volume computations with Varian's EclipseTM (Varian Medical Systems, Inc.). Results: Mean doses and Dose-volume-histograms computed agree strongly with Varian's EclipseTM. Contours which have been segmented can be injected back into patient data permanently and in a Digital Imaging and Communication in Medicine (DICOM)-conforming manner. Lossless segmentation persists across such injection, and remains fully reversible. Conclusions: DICOMautomaton allows researchers to rapidly, accurately, and autonomously segment large amounts of data into intricate structures suitable for analyses of regional organ dose sensitivity.

  6. Edge Segment-Based Automatic Video Surveillance

    NASA Astrophysics Data System (ADS)

    Hossain, M. Julius; Dewan, M. Ali Akber; Chae, Oksam

    2007-12-01

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

  7. A Hybrid Approach for Improving Image Segmentation: Application to Phenotyping of Wheat Leaves

    PubMed Central

    Chopin, Joshua; Laga, Hamid; Miklavcic, Stanley J.

    2016-01-01

    In this article we propose a novel tool that takes an initial segmented image and returns a more accurate segmentation that accurately captures sharp features such as leaf tips, twists and axils. Our algorithm utilizes basic a-priori information about the shape of plant leaves and local image orientations to fit active contour models to important plant features that have been missed during the initial segmentation. We compare the performance of our approach with three state-of-the-art segmentation techniques, using three error metrics. The results show that leaf tips are detected with roughly one half of the original error, segmentation accuracy is almost always improved and more than half of the leaf breakages are corrected. PMID:27992594

  8. Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases.

    PubMed

    Pulagam, Ammi Reddy; Kande, Giri Babu; Ede, Venkata Krishna Rao; Inampudi, Ramesh Babu

    2016-08-01

    Performing accurate and fully automated lung segmentation of high-resolution computed tomography (HRCT) images affected by dense abnormalities is a challenging problem. This paper presents a novel algorithm for automated segmentation of lungs based on modified convex hull algorithm and mathematical morphology techniques. Sixty randomly selected lung HRCT scans with different abnormalities are used to test the proposed algorithm, and experimental results show that the proposed approach can accurately segment the lungs even in the presence of disease patterns, with some limitations in the apices and bases of lungs. The algorithm demonstrates a high segmentation accuracy (dice similarity coefficient = 98.62 and shape differentiation metrics dmean = 1.39 mm, and drms = 2.76 mm). Therefore, the developed automated lung segmentation algorithm is a good candidate for the first stage of a computer-aided diagnosis system for diffuse lung diseases.

  9. The allele distribution in next-generation sequencing data sets is accurately described as the result of a stochastic branching process.

    PubMed

    Heinrich, Verena; Stange, Jens; Dickhaus, Thorsten; Imkeller, Peter; Krüger, Ulrike; Bauer, Sebastian; Mundlos, Stefan; Robinson, Peter N; Hecht, Jochen; Krawitz, Peter M

    2012-03-01

    With the availability of next-generation sequencing (NGS) technology, it is expected that sequence variants may be called on a genomic scale. Here, we demonstrate that a deeper understanding of the distribution of the variant call frequencies at heterozygous loci in NGS data sets is a prerequisite for sensitive variant detection. We model the crucial steps in an NGS protocol as a stochastic branching process and derive a mathematical framework for the expected distribution of alleles at heterozygous loci before measurement that is sequencing. We confirm our theoretical results by analyzing technical replicates of human exome data and demonstrate that the variance of allele frequencies at heterozygous loci is higher than expected by a simple binomial distribution. Due to this high variance, mutation callers relying on binomial distributed priors are less sensitive for heterozygous variants that deviate strongly from the expected mean frequency. Our results also indicate that error rates can be reduced to a greater degree by technical replicates than by increasing sequencing depth.

  10. Segmenting the Adult Education Market.

    ERIC Educational Resources Information Center

    Aurand, Tim

    1994-01-01

    Describes market segmentation and how the principles of segmentation can be applied to the adult education market. Indicates that applying segmentation techniques to adult education programs results in programs that are educationally and financially satisfying and serve an appropriate population. (JOW)

  11. 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.

  12. ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented.

    PubMed

    Saraswathi, Saras; Sundaram, Suresh; Sundararajan, Narasimhan; Zimmermann, Michael; Nilsen-Hamilton, Marit

    2011-01-01

    A combination of Integer-Coded Genetic Algorithm (ICGA) and Particle Swarm Optimization (PSO), coupled with the neural-network-based Extreme Learning Machine (ELM), is used for gene selection and cancer classification. ICGA is used with PSO-ELM to select an optimal set of genes, which is then used to build a classifier to develop an algorithm (ICGA_PSO_ELM) that can handle sparse data and sample imbalance. We evaluate the performance of ICGA-PSO-ELM and compare our results with existing methods in the literature. An investigation into the functions of the selected genes, using a systems biology approach, revealed that many of the identified genes are involved in cell signaling and proliferation. An analysis of these gene sets shows a larger representation of genes that encode secreted proteins than found in randomly selected gene sets. Secreted proteins constitute a major means by which cells interact with their surroundings. Mounting biological evidence has identified the tumor microenvironment as a critical factor that determines tumor survival and growth. Thus, the genes identified by this study that encode secreted proteins might provide important insights to the nature of the critical biological features in the microenvironment of each tumor type that allow these cells to thrive and proliferate.

  13. An improved level set method for vertebra CT image segmentation

    PubMed Central

    2013-01-01

    Background Clinical diagnosis and therapy for the lumbar disc herniation requires accurate vertebra segmentation. The complex anatomical structure and the degenerative deformations of the vertebrae makes its segmentation challenging. Methods An improved level set method, namely edge- and region-based level set method (ERBLS), is proposed for vertebra CT images segmentation. By considering the gradient information and local region characteristics of images, the proposed model can efficiently segment images with intensity inhomogeneity and blurry or discontinuous boundaries. To reduce the dependency on manual initialization in many active contour models and for an automatic segmentation, a simple initialization method for the level set function is built, which utilizes the Otsu threshold. In addition, the need of the costly re-initialization procedure is completely eliminated. Results Experimental results on both synthetic and real images demonstrated that the proposed ERBLS model is very robust and efficient. Compared with the well-known local binary fitting (LBF) model, our method is much more computationally efficient and much less sensitive to the initial contour. The proposed method has also applied to 56 patient data sets and produced very promising results. Conclusions An improved level set method suitable for vertebra CT images segmentation is proposed. It has the flexibility of segmenting the vertebra CT images with blurry or discontinuous edges, internal inhomogeneity and no need of re-initialization. PMID:23714300

  14. Proximal femur segmentation in conventional pelvic x ray

    SciTech Connect

    Pilgram, Roland; Walch, Claudia; Kuhn, Volker; Schubert, Rainer; Staudinger, Roland

    2008-06-15

    A solid and accurate proximal femur segmentation technique using the popular active shape model (ASM) is proposed. For generating an optimal shape prior, the minimum description length, based on 200 supervised manual segmented proximal femur shapes, is used. The segmentation is based on a coarse to fine scaling technique including a profile scale space method. The segmentation results are compared using an optimal defined initial pose and a pose based on a registration technique. Using ideal template initialization, 95% of the shapes have been recovered exactly (average point-to-point error {approx}13 pixels, average point-to-boundary error {approx}7 pixels). Using a template-based initialization based on a registration technique, a successful segmentation rate of {approx}89% is achieved, with an average point-to-point error {approx}12 pixels, and an average point-to-boundary error {approx}8 pixels. With an adequate template initialization and an improved ASM, this method seems to provide an accurate tool for segmentation of the proximal femur shapes on conventional hip overview x-ray images.

  15. Robust model for segmenting images with/without intensity inhomogeneities.

    PubMed

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

    2013-08-01

    Intensity inhomogeneities and different types/levels of image noise are the two major obstacles to accurate image segmentation by region-based level set models. To provide a more general solution to these challenges, we propose a novel segmentation model that considers global and local image statistics to eliminate the influence of image noise and to compensate for intensity inhomogeneities. In our model, the global energy derived from a Gaussian model estimates the intensity distribution of the target object and background; the local energy derived from the mutual influences of neighboring pixels can eliminate the impact of image noise and intensity inhomogeneities. The robustness of our method is validated on segmenting synthetic images with/without intensity inhomogeneities, and with different types/levels of noise, including Gaussian noise, speckle noise, and salt and pepper noise, as well as images from different medical imaging modalities. Quantitative experimental comparisons demonstrate that our method is more robust and more accurate in segmenting the images with intensity inhomogeneities than the local binary fitting technique and its more recent systematic model. Our technique also outperformed the region-based Chan–Vese model when dealing with images without intensity inhomogeneities and produce better segmentation results than the graph-based algorithms including graph-cuts and random walker when segmenting noisy images.

  16. Task-based evaluation of segmentation algorithms for diffusion-weighted MRI without using a gold standard.

    PubMed

    Jha, Abhinav K; Kupinski, Matthew A; Rodríguez, Jeffrey J; Stephen, Renu M; Stopeck, Alison T

    2012-07-07

    In many studies, the estimation of the apparent diffusion coefficient (ADC) of lesions in visceral organs in diffusion-weighted (DW) magnetic resonance images requires an accurate lesion-segmentation algorithm. To evaluate these lesion-segmentation algorithms, region-overlap measures are used currently. However, the end task from the DW images is accurate ADC estimation, and the region-overlap measures do not evaluate the segmentation algorithms on this task. Moreover, these measures rely on the existence of gold-standard segmentation of the lesion, which is typically unavailable. In this paper, we study the problem of task-based evaluation of segmentation algorithms in DW imaging in the absence of a gold standard. We first show that using manual segmentations instead of gold-standard segmentations for this task-based evaluation is unreliable. We then propose a method to compare the segmentation algorithms that does not require gold-standard or manual segmentation results. The no-gold-standard method estimates the bias and the variance of the error between the true ADC values and the ADC values estimated using the automated segmentation algorithm. The method can be used to rank the segmentation algorithms on the basis of both the ensemble mean square error and precision. We also propose consistency checks for this evaluation technique.

  17. Keypoint Transfer Segmentation

    PubMed Central

    Toews, M.; Langs, G.; Wells, W.; Golland, P.

    2015-01-01

    We present an image segmentation method that transfers label maps of entire organs from the training images to the novel image to be segmented. The transfer is based on sparse correspondences between keypoints that represent automatically identified distinctive image locations. Our segmentation algorithm consists of three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ label maps. We introduce generative models for the inference of keypoint labels and for image segmentation, where keypoint matches are treated as a latent random variable and are marginalized out as part of the algorithm. We report segmentation results for abdominal organs in whole-body CT and in contrast-enhanced CT images. The accuracy of our method compares favorably to common multi-atlas segmentation while offering a speed-up of about three orders of magnitude. Furthermore, keypoint transfer requires no training phase or registration to an atlas. The algorithm’s robustness enables the segmentation of scans with highly variable field-of-view. PMID:26221677

  18. Keypoint Transfer Segmentation.

    PubMed

    Wachinger, C; Toews, M; Langs, G; Wells, W; Golland, P

    2015-01-01

    We present an image segmentation method that transfers label maps of entire organs from the training images to the novel image to be segmented. The transfer is based on sparse correspondences between keypoints that represent automatically identified distinctive image locations. Our segmentation algorithm consists of three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ label maps. We introduce generative models for the inference of keypoint labels and for image segmentation, where keypoint matches are treated as a latent random variable and are marginalized out as part of the algorithm. We report segmentation results for abdominal organs in whole-body CT and in contrast-enhanced CT images. The accuracy of our method compares favorably to common multi-atlas segmentation while offering a speed-up of about three orders of magnitude. Furthermore, keypoint transfer requires no training phase or registration to an atlas. The algorithm's robustness enables the segmentation of scans with highly variable field-of-view.

  19. Ten Year Operating Test Results and Post-Test Analysis of a 1/10 Segment Stirling Sodium Heat Pipe, Phase III

    NASA Technical Reports Server (NTRS)

    Rosenfeld, John, H; Minnerly, Kenneth, G; Dyson, Christopher, M.

    2012-01-01

    High-temperature heat pipes are being evaluated for use in energy conversion applications such as fuel cells, gas turbine re-combustors, Stirling cycle heat sources; and with the resurgence of space nuclear power both as reactor heat removal elements and as radiator elements. Long operating life and reliable performance are critical requirements for these applications. Accordingly, long-term materials compatibility is being evaluated through the use of high-temperature life test heat pipes. Thermacore, Inc., has carried out a sodium heat pipe 10-year life test to establish long-term operating reliability. Sodium heat pipes have demonstrated favorable materials compatibility and heat transport characteristics at high operating temperatures in air over long time periods. A representative one-tenth segment Stirling Space Power Converter heat pipe with an Inconel 718 envelope and a stainless steel screen wick has operated for over 87,000 hr (10 yr) at nearly 700 C. These life test results have demonstrated the potential for high-temperature heat pipes to serve as reliable energy conversion system components for power applications that require long operating lifetime with high reliability. Detailed design specifications, operating history, and post-test analysis of the heat pipe and sodium working fluid are described.

  20. Robust Cell Segmentation for Histological Images of Glioblastoma

    PubMed Central

    Kong, Jun; Zhang, Pengyue; Liang, Yanhui; Teodoro, George; Brat, Daniel J.; Wang, Fusheng

    2016-01-01

    Glioblastoma (GBM) is a malignant brain tumor with uniformly dismal prognosis. Quantitative analysis of GBM cells is an important avenue to extract latent histologic disease signatures to correlate with molecular underpinnings and clinical outcomes. As a prerequisite, a robust and accurate cell segmentation is required. In this paper, we present an automated cell segmentation method that can satisfactorily address segmentation of overlapped cells commonly seen in GBM histology specimens. This method first detects cells with seed connectivity, distance constraints, image edge map, and a shape-based voting image. Initialized by identified seeds, cell boundaries are deformed with an improved variational level set method that can handle clumped cells. We test our method on 40 histological images of GBM with human annotations. The validation results suggest that our cell segmentation method is promising and represents an advance in quantitative cancer research.

  1. Centerline correction of incorrectly segmented coronary arteries in CT angiography

    NASA Astrophysics Data System (ADS)

    Fu, Ling; Kang, Yan

    2013-03-01

    For computer-aided diagnosis of cardiovascular diseases, accurately extracted centerlines of coronary arteries are important. However, centerlines extracted from incorrectly segmented vessels are usually unsatisfactory. For this reason, we propose two automatic centerline correction methods in this paper. First, a method based on the local volume comparison and the morphological comparison is presented to remove false centerlines from over-segmented tissues. Second, another method based on the judgment of vessel identity and the gradient-SDF (source distance field) calculation is presented to add missing centerlines of under-segmented vessels. We have validated the proposed centerline correction methods on real CT angiographic datasets of coronary arteries. The quantitative evaluation results show that the proposed methods can effectively correct centerline errors arising from erroneous vessel segmentation in most cases.

  2. Semisupervised segmentation of MRI stroke studies

    NASA Astrophysics Data System (ADS)

    Soltanian-Zadeh, Hamid; Windham, Joe P.; Robbins, Linda

    1997-04-01

    Fast, accurate, and reproducible image segmentation is vital to the diagnosis, treatment, and evaluation of many medical situations. We present development and application of a semi-supervised method for segmenting normal and abnormal brain tissues from magnetic resonance images (MRI) of stroke patients. The method does not require manual drawing of the tissue boundaries. It is therefore faster and more reproducible than conventional methods. The steps of the new method are as follows: (1) T2- and T1-weighted MR images are co-registered using a head and hat approach. (2) Intracranial brain volume is segmented from the skull, scalp, and background using a multi-resolution edge tracking algorithm. (3) Additive noise is suppressed (image is restored) using a non-linear edge-preserving filter which preserves partial volume information on average. (4) Image nonuniformities are corrected using a modified lowpass filtering approach. (5) The resulting images are segmented using a self organizing data analysis technique which is similar in principle to the K-means clustering but includes a set of additional heuristic merging and splitting procedures to generate a meaningful segmentation. (6) Segmented regions are labeled white matter, gray matter, CSF, partial volumes of normal tissues, zones of stroke, or partial volumes between stroke and normal tissues. (7) Previous steps are repeated for each slice of the brain and the volume of each tissue type is estimated from the results. Details and significance of each step are explained. Experimental results using a simulation, a phantom, and selected clinical cases are presented.

  3. Example based lesion segmentation

    NASA Astrophysics Data System (ADS)

    Roy, Snehashis; He, Qing; Carass, Aaron; Jog, Amod; Cuzzocreo, Jennifer L.; Reich, Daniel S.; Prince, Jerry; Pham, Dzung

    2014-03-01

    Automatic and accurate detection of white matter lesions is a significant step toward understanding the progression of many diseases, like Alzheimer's disease or multiple sclerosis. Multi-modal MR images are often used to segment T2 white matter lesions that can represent regions of demyelination or ischemia. Some automated lesion segmentation methods describe the lesion intensities using generative models, and then classify the lesions with some combination of heuristics and cost minimization. In contrast, we propose a patch-based method, in which lesions are found using examples from an atlas containing multi-modal MR images and corresponding manual delineations of lesions. Patches from subject MR images are matched to patches from the atlas and lesion memberships are found based on patch similarity weights. We experiment on 43 subjects with MS, whose scans show various levels of lesion-load. We demonstrate significant improvement in Dice coefficient and total lesion volume compared to a state of the art model-based lesion segmentation method, indicating more accurate delineation of lesions.

  4. Development of image segmentation methods for intracranial aneurysms.

    PubMed

    Sen, Yuka; Qian, Yi; Avolio, Alberto; Morgan, Michael

    2013-01-01

    Though providing vital means for the visualization, diagnosis, and quantification of decision-making processes for the treatment of vascular pathologies, vascular segmentation remains a process that continues to be marred by numerous challenges. In this study, we validate eight aneurysms via the use of two existing segmentation methods; the Region Growing Threshold and Chan-Vese model. These methods were evaluated by comparison of the results obtained with a manual segmentation performed. Based upon this validation study, we propose a new Threshold-Based Level Set (TLS) method in order to overcome the existing problems. With divergent methods of segmentation, we discovered that the volumes of the aneurysm models reached a maximum difference of 24%. The local artery anatomical shapes of the aneurysms were likewise found to significantly influence the results of these simulations. In contrast, however, the volume differences calculated via use of the TLS method remained at a relatively low figure, at only around 5%, thereby revealing the existence of inherent limitations in the application of cerebrovascular segmentation. The proposed TLS method holds the potential for utilisation in automatic aneurysm segmentation without the setting of a seed point or intensity threshold. This technique will further enable the segmentation of anatomically complex cerebrovascular shapes, thereby allowing for more accurate and efficient simulations of medical imagery.

  5. Unsupervised Cardiac Image Segmentation via Multiswarm Active Contours with a Shape Prior

    PubMed Central

    Cruz-Aceves, I.; Avina-Cervantes, J. G.; Lopez-Hernandez, J. M.; Garcia-Hernandez, M. G.; Ibarra-Manzano, M. A.

    2013-01-01

    This paper presents a new unsupervised image segmentation method based on particle swarm optimization and scaled active contours with shape prior. The proposed method uses particle swarm optimization over a polar coordinate system to perform the segmentation task, increasing the searching capability on medical images with respect to different interactive segmentation techniques. This method is used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, where the shape prior is acquired by cardiologists, and it is utilized as the initial active contour. Moreover, to assess the performance of the cardiac medical image segmentations obtained by the proposed method and by the interactive techniques regarding the regions delineated by experts, a set of validation metrics has been adopted. The experimental results are promising and suggest that the proposed method is capable of segmenting human heart and ventricular areas accurately, which can significantly help cardiologists in clinical decision support. PMID:24198850

  6. A novel atlas-based approach for MRI prostate segmentation using multiscale points of interest

    NASA Astrophysics Data System (ADS)

    Álvarez, Charlens; Martínez, Fabio; Romero, Eduardo

    2013-11-01

    Accurate segmentation of the prostate and organs at risk is the fundamental guide for planning any radiotherapy. Such task is currently performed using a manual delineation of the organ on the MRI, a highly time consuming responsibility which in addition introduces inter and intra expert variability. Automatic MRI segmentation is a very challenging goal because of the large organ variability and the proximity of the neighboring organs. This work presents an automatic atlas-based segmentation strategy that selects the most probable template from a database using a robust multiscale similarity analysis. Once that probable template is selected, the associated segmentation is non-rigidly registered to the new MRI. The proposed method takes advantage of both the interindividual shape variation and intra-individual salient point representation. Results show that the method produces reliable segmentations, obtaining an average Dice Coefficient of 72% when comparing with the expert manual segmentation under a leave-one-out scheme with the training database.

  7. Semi-automatic knee cartilage segmentation

    NASA Astrophysics Data System (ADS)

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

    2006-03-01

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

  8. CPCs with segmented absorbers

    SciTech Connect

    Keita, M.; Robertson, H.S. )

    1991-01-01

    One of the most promising means of improving the performance of solar thermal collectors is to reduce the energy lost by the hot absorber. One way to do this, not currently part of the technology, is to recognize that since the absorber is usually not irradiated uniformly, it is therefore possible to construct an absorber of thermally isolated segments, circulate the fluid in sequence from low to high irradiance segments, and reduce loss by improving effective concentration. This procedure works even for ideal concentrators, without violating Winston's theorem. Two equivalent CPC collectors with single and segmented absorber were constructed and compared under actual operating conditions. The results showed that the daily thermal efficiency of the collector with segmented absorber is higher (about 13%) than that of the collector with nonsegmented absorber.

  9. Accurate quantum chemical calculations

    NASA Technical Reports Server (NTRS)

    Bauschlicher, Charles W., Jr.; Langhoff, Stephen R.; Taylor, Peter R.

    1989-01-01

    An important goal of quantum chemical calculations is to provide an understanding of chemical bonding and molecular electronic structure. A second goal, the prediction of energy differences to chemical accuracy, has been much harder to attain. First, the computational resources required to achieve such accuracy are very large, and second, it is not straightforward to demonstrate that an apparently accurate result, in terms of agreement with experiment, does not result from a cancellation of errors. Recent advances in electronic structure methodology, coupled with the power of vector supercomputers, have made it possible to solve a number of electronic structure problems exactly using the full configuration interaction (FCI) method within a subspace of the complete Hilbert space. These exact results can be used to benchmark approximate techniques that are applicable to a wider range of chemical and physical problems. The methodology of many-electron quantum chemistry is reviewed. Methods are considered in detail for performing FCI calculations. The application of FCI methods to several three-electron problems in molecular physics are discussed. A number of benchmark applications of FCI wave functions are described. Atomic basis sets and the development of improved methods for handling very large basis sets are discussed: these are then applied to a number of chemical and spectroscopic problems; to transition metals; and to problems involving potential energy surfaces. Although the experiences described give considerable grounds for optimism about the general ability to perform accurate calculations, there are several problems that have proved less tractable, at least with current computer resources, and these and possible solutions are discussed.

  10. Accurate Evaluation of Quantum Integrals

    NASA Technical Reports Server (NTRS)

    Galant, D. C.; Goorvitch, D.; Witteborn, Fred C. (Technical Monitor)

    1995-01-01

    Combining an appropriate finite difference method with Richardson's extrapolation results in a simple, highly accurate numerical method for solving a Schrodinger's equation. Important results are that error estimates are provided, and that one can extrapolate expectation values rather than the wavefunctions to obtain highly accurate expectation values. We discuss the eigenvalues, the error growth in repeated Richardson's extrapolation, and show that the expectation values calculated on a crude mesh can be extrapolated to obtain expectation values of high accuracy.

  11. Easy-interactive and quick psoriasis lesion segmentation

    NASA Astrophysics Data System (ADS)

    Ma, Guoli; He, Bei; Yang, Wenming; Shu, Chang

    2013-12-01

    This paper proposes an interactive psoriasis lesion segmentation algorithm based on Gaussian Mixture Model (GMM). Psoriasis is an incurable skin disease and affects large population in the world. PASI (Psoriasis Area and Severity Index) is the gold standard utilized by dermatologists to monitor the severity of psoriasis. Computer aid methods of calculating PASI are more objective and accurate than human visual assessment. Psoriasis lesion segmentation is the basis of the whole calculating. This segmentation is different from the common foreground/background segmentation problems. Our algorithm is inspired by GrabCut and consists of three main stages. First, skin area is extracted from the background scene by transforming the RGB values into the YCbCr color space. Second, a rough segmentation of normal skin and psoriasis lesion is given. This is an initial segmentation given by thresholding a single gaussian model and the thresholds are adjustable, which enables user interaction. Third, two GMMs, one for the initial normal skin and one for psoriasis lesion, are built to refine the segmentation. Experimental results demonstrate the effectiveness of the proposed algorithm.

  12. An outlier filtering approach for machine sourced weak segmentations

    NASA Astrophysics Data System (ADS)

    Margolin, Elza; Furst, Jacob; Raicu, Daniela

    2015-03-01

    Analysis of medical images by radiologists is often a time consuming and costly process. Computer Aided diagnosis (CAD) systems can be used to provide second opinion and assist radiologists in diagnosis. Crowdsourcing has been a tool used by many domains in order to generate fast and cheap labels. However, exposing medical images to a large crowd is problematic. Our system takes a machine sourcing approach to CADs - that is, the idea that multiple weak segmentations can be used to create predictions with the same quality as an expert. We propose that the use of a large amount of weak segmentations can simulate crowdsourcing to create accurate predictions of semantic characteristics. In addition, we investigated the idea that outliers filtering will better performance of this CAD. Three segmentation algorithms were employed to create 20 weak segmentations. Low quality segmentations were then removed using outlier filtering method. Segmentations were then classified using an ensemble classifier to create final predictions. It was found that this CAD preformed just as well or better than a radiologist. In contrast, the removal of outliers from the set of segmentations does not improve the result further.

  13. Hippocampus segmentation using locally weighted prior based level set

    NASA Astrophysics Data System (ADS)

    Achuthan, Anusha; Rajeswari, Mandava

    2015-12-01

    Segmentation of hippocampus in the brain is one of a major challenge in medical image segmentation due to its' imaging characteristics, with almost similar intensity between another adjacent gray matter structure, such as amygdala. The intensity similarity has causes the hippocampus to have weak or fuzzy boundaries. With this main challenge being demonstrated by hippocampus, a segmentation method that relies on image information alone may not produce accurate segmentation results. Therefore, it is needed an assimilation of prior information such as shape and spatial information into existing segmentation method to produce the expected segmentation. Previous studies has widely integrated prior information into segmentation methods. However, the prior information has been utilized through a global manner integration, and this does not reflect the real scenario during clinical delineation. Therefore, in this paper, a locally integrated prior information into a level set model is presented. This work utilizes a mean shape model to provide automatic initialization for level set evolution, and has been integrated as prior information into the level set model. The local integration of edge based information and prior information has been implemented through an edge weighting map that decides at voxel level which information need to be observed during a level set evolution. The edge weighting map shows which corresponding voxels having sufficient edge information. Experiments shows that the proposed integration of prior information locally into a conventional edge-based level set model, known as geodesic active contour has shown improvement of 9% in averaged Dice coefficient.

  14. Multi-atlas segmentation with augmented features for cardiac MR images.

    PubMed

    Bai, Wenjia; Shi, Wenzhe; Ledig, Christian; Rueckert, Daniel

    2015-01-01

    Multi-atlas segmentation infers the target image segmentation by combining prior anatomical knowledge encoded in multiple atlases. It has been quite successfully applied to medical image segmentation in the recent years, resulting in highly accurate and robust segmentation for many anatomical structures. However, to guide the label fusion process, most existing multi-atlas segmentation methods only utilise the intensity information within a small patch during the label fusion process and may neglect other useful information such as gradient and contextual information (the appearance of surrounding regions). This paper proposes to combine the intensity, gradient and contextual information into an augmented feature vector and incorporate it into multi-atlas segmentation. Also, it explores the alternative to the K nearest neighbour (KNN) classifier in performing multi-atlas label fusion, by using the support vector machine (SVM) for label fusion instead. Experimental results on a short-axis cardiac MR data set of 83 subjects have demonstrated that the accuracy of multi-atlas segmentation can be significantly improved by using the augmented feature vector. The mean Dice metric of the proposed segmentation framework is 0.81 for the left ventricular myocardium on this data set, compared to 0.79 given by the conventional multi-atlas patch-based segmentation (Coupé et al., 2011; Rousseau et al., 2011). A major contribution of this paper is that it demonstrates that the performance of non-local patch-based segmentation can be improved by using augmented features.

  15. A Modelling Framework for estimating Road Segment Based On-Board Vehicle Emissions

    NASA Astrophysics Data System (ADS)

    Lin-Jun, Yu; Ya-Lan, Liu; Zhong-Ren, Peng; Meng Meng, Liu; Yu-Huan, Ren

    2014-03-01

    Traditional traffic emission inventory models aim to provide overall emissions at regional level which cannot meet planners' demand for detailed and accurate traffic emissions information at the road segment level. Therefore, a road segment-based emission model for estimating light duty vehicle emissions is proposed, where floating car technology is used to collect information of traffic condition of roads. The employed analysis framework consists of three major modules: the Average Speed and the Average Acceleration Module (ASAAM), the Traffic Flow Estimation Module (TFEM) and the Traffic Emission Module (TEM). The ASAAM is used to obtain the average speed and the average acceleration of the fleet on each road segment using FCD. The TFEM is designed to estimate the traffic flow of each road segment in a given period, based on the speed-flow relationship and traffic flow spatial distribution. Finally, the TEM estimates emissions from each road segment, based on the results of previous two modules. Hourly on-road light-duty vehicle emissions for each road segment in Shenzhen's traffic network are obtained using this analysis framework. The temporal-spatial distribution patterns of the pollutant emissions of road segments are also summarized. The results show high emission road segments cluster in several important regions in Shenzhen. Also, road segments emit more emissions during rush hours than other periods. The presented case study demonstrates that the proposed approach is feasible and easy-to-use to help planners make informed decisions by providing detailed road segment-based emission information.

  16. An efficient iris segmentation approach

    NASA Astrophysics Data System (ADS)

    Gomai, Abdu; El-Zaart, A.; Mathkour, H.

    2011-10-01

    Iris recognition system became a reliable system for authentication and verification tasks. It consists of five stages: image acquisition, iris segmentation, iris normalization, feature encoding, and feature matching. Iris segmentation stage is one of the most important stages. It plays an essential role to locate the iris efficiently and accurately. In this paper, we present a new approach for iris segmentation using image processing technique. This approach is composed of four main parts. (1) Eliminating reflections of light on the eye image based on inverting the color of the grayscale image, filling holes in the intensity image, and inverting the color of the intensity image to get the original grayscale image without any reflections. (2) Pupil boundary detection based on dividing an eye image to nine sub-images and finding the minimum value of the mean intensity for each sub-image to get a suitable threshold value of pupil. (3) Enhancing the contrast of outer iris boundary using exponential operator to have sharp variation. (4) Outer iris boundary localization based on applying a gray threshold and morphological operations on the rectangular part of an eye image including the pupil and the outer boundaries of iris to find the small radius of outer iris boundary from the center of pupil. The proposed approach has been tested on CASIA v1.0 iris image database and other collected iris image database. The experimental results show that the approach is able to detect pupil and outer iris boundary with high accuracy results approximately 100% and reduce time consuming.

  17. Automated segmentation of porcine airway wall layers using optical coherence tomography: comparison with manual segmentation and histology

    NASA Astrophysics Data System (ADS)

    Kirby, Miranda; Lee, Anthony M. D.; Candido, Tara; MacAulay, Calum; Lane, Pierre; Lam, Stephen; Coxson, Harvey O.

    2014-03-01

    The objective was to develop an automated optical coherence tomography (OCT) segmentation method. We evaluated three ex-vivo porcine airway specimens; six non-sequential OCT images were selected from each airway specimen. Histology was also performed for each airway and histology images were co-registered to OCT images for comparison. Manual segmentation of the airway luminal area, mucosa area, submucosa area and the outer airway wall area were performed for histology and OCT images. Automated segmentation of OCT images employed a despecking filter for pre-processing, a hessian-based filter for lumen and outer airway wall area segmentation, and K-means clustering for mucosa and submucosa area segmentation. Bland-Altman analysis indicated that there was very little bias between automated OCT segmentation and histology measurements for the airway lumen area (bias=-6%, 95% CI=-21%-8%), mucosa area, (bias=-4%, 95% CI=-14%-5%), submucosa area (bias=7%, 95% CI=-7%-20%) and outer airway wall area segmentation results (bias=-5%, 95% CI=-14%-5%). We also compared automated and manual OCT segmentation and Bland-Altman analysis indicated that there was negligible bias between luminal area (bias=4%, 95% CI=1%-8%), mucosa area (bias=-3%, 95% CI=-6%-1%), submucosa area (bias=-2%, 95% CI=-10%-6%) and the outer airway wall (bias=-3%, 95% CI=-13%-6%). The automated segmentation method for OCT airway imaging developed here allows for accurate and precise segmentation of the airway wall components, suggesting that translation of this method to in vivo human airway analysis would allow for longitudinal and serial studies.

  18. Automatic colon segmentation with dual scan CT colonography.

    PubMed

    Li, Hong; Santago, Peter

    2005-03-01

    We present a fully automated three-dimensional (3-D) segmentation algorithm to extract the colon lumen surface in CT colonography. Focusing on significant-size polyp detection, we target at an efficient algorithm that maximizes overall colon coverage, minimizes the extracolonic components, maintains local shape accuracy, and achieves high segmentation speed. Two-dimensional (2-D) image processing techniques are employed first, resulting in automatic seed placement and better colon coverage. This is followed by near-air threshold 3-D region-growing using an improved marching-cubes algorithm, which provides fast and accurate surface generation. The algorithm constructs a well-organized vertex-triangle structure that uniquely employs a hash table method, yielding an order of magnitude speed improvement. We segment two scans, prone and supine, independently and with the goal of improved colon coverage. Both segmentations would be available for subsequent polyp detection systems. Segmenting and analyzing both scans improves surface coverage by at least 6% over supine or prone alone. According to subjective evaluation, the average coverage is about 87.5% of the entire colon. Employing near-air threshold and elongation criteria, only 6% of the data sets include extracolonic components (EC) in the segmentation. The observed surface shape accuracy of the segmentation is adequate for significant-size (6 mm) polyp detection, which is also verified by the results of the prototype detection algorithm. The segmentation takes less than 5 minutes on an AMD 1-GHz single-processor PC, which includes reading the volume data and writing the surface results. The surface-based segmentation algorithm is practical for subsequent polyp detection algorithms in that it produces high coverage, has a low EC rate, maintains local shape accuracy, and has a computational efficiency that makes real-time polyp detection possible. A fully automatic or computer-aided polyp detection system using this

  19. Semi-automatic Segmentation for Prostate Interventions

    PubMed Central

    Mahdavi, S. Sara; Chng, Nick; Spadinger, Ingrid; Morris, William J.; Salcudean, Septimiu E.

    2011-01-01

    In this paper we report and characterize a semi-automatic prostate segmentation method for prostate brachytherapy. Based on anatomical evidence and requirements of the treatment procedure, a warped and tapered ellipsoid was found suitable as the a priori 3D shape of the prostate. By transforming the acquired endorectal transverse images of the prostate into ellipses, the shape fitting problem was cast into a convex problem which can be solved efficiently. The average whole gland error between volumes created from manual and semi-automatic contours from 21 patients was 6.63±0.9%. For use in brachytherapy treatment planning, the resulting contours were modified, if deemed necessary, by radiation oncologists prior to treatment. The average whole gland volume error between the volumes computed from semi-automatic contours and those computed from modified contours, from 40 patients, was 5.82±4.15%. The amount of bias in the physicians’ delineations when given an initial semi-automatic contour was measured by comparing the volume error between 10 prostate volumes computed from manual contours with those of modified contours. This error was found to be 7.25±0.39% for the whole gland. Automatic contouring reduced subjectivity, as evidenced by a decrease in segmentation inter- and intra-observer variability from 4.65% and 5.95% for manual segmentation to 3.04% and 3.48% for semi-automatic segmentation, respectively. We characterized the performance of the method relative to the reference obtained from manual segmentation by using a novel approach that divides the prostate region into nine sectors. We analyzed each sector independently as the requirements for segmentation accuracy depend on which region of the prostate is considered. The measured segmentation time is 14±1 seconds with an additional 32±14 seconds for initialization. By assuming 1–3 minutes for modification of the contours, if necessary, a total segmentation time of less than 4 minutes is required

  20. A label field fusion bayesian model and its penalized maximum rand estimator for image segmentation.

    PubMed

    Mignotte, Max

    2010-06-01

    This paper presents a novel segmentation approach based on a Markov random field (MRF) fusion model which aims at combining several segmentation results associated with simpler clustering models in order to achieve a more reliable and accurate segmentation result. The proposed fusion model is derived from the recently introduced probabilistic Rand measure for comparing one segmentation result to one or more manual segmentations of the same image. This non-parametric measure allows us to easily derive an appealing fusion model of label fields, easily expressed as a Gibbs distribution, or as a nonstationary MRF model defined on a complete graph. Concretely, this Gibbs energy model encodes the set of binary constraints, in terms of pairs of pixel labels, provided by each segmentation results to be fused. Combined with a prior distribution, this energy-based Gibbs model also allows for definition of an interesting penalized maximum probabilistic rand estimator with which the fusion of simple, quickly estimated, segmentation results appears as an interesting alternative to complex segmentation models existing in the literature. This fusion framework has been successfully applied on the Berkeley image database. The experiments reported in this paper demonstrate that the proposed method is efficient in terms of visual evaluation and quantitative performance measures and performs well compared to the best existing state-of-the-art segmentation methods recently proposed in the literature.

  1. Geometry Guided Segmentation

    NASA Astrophysics Data System (ADS)

    Dunn, Stanley M.; Liang, Tajen

    1989-03-01

    Our overall goal is to develop an image understanding system for automatically interpreting dental radiographs. This paper describes the module that integrates the intrinsic image data to form the region adjacency graph that represents the image. The specific problem is to develop a robust method for segmenting the image into small regions that do not overlap anatomical boundaries. Classical algorithms for finding homogeneous regions (i.e., 2 class segmentation or connected components) will not always yield correct results since blurred edges can cause adjacent anatomical regions to be labeled as one region. This defect is a problem in this and other applications where an object count is necessary. Our solution to the problem is to guide the segmentation by intrinsic properties of the constituent objects. The module takes a set of intrinsic images as arguments. A connected components-like algorithm is performed, but the connectivity relation is not 4- or 8-neighbor connectivity in binary images; the connectivity is defined in terms of the intrinsic image data. We shall describe both the classical method and the modified segmentation procedures, and present experiments using both algorithms. Our experiments show that for the dental radiographs a segmentation using gray level data in conjunction with edges of the surfaces of teeth give a robust and reliable segmentation.

  2. On the Automated Segmentation of Epicardial and Mediastinal Cardiac Adipose Tissues Using Classification Algorithms.

    PubMed

    Rodrigues, Érick Oliveira; Cordeiro de Morais, Felipe Fernandes; Conci, Aura

    2015-01-01

    The quantification of fat depots on the surroundings of the heart is an accurate procedure for evaluating health risk factors correlated with several diseases. However, this type of evaluation is not widely employed in clinical practice due to the required human workload. This work proposes a novel technique for the automatic segmentation of cardiac fat pads. The technique is based on applying classification algorithms to the segmentation of cardiac CT images. Furthermore, we extensively evaluate the performance of several algorithms on this task and discuss which provided better predictive models. Experimental results have shown that the mean accuracy for the classification of epicardial and mediastinal fats has been 98.4% with a mean true positive rate of 96.2%. On average, the Dice similarity index, regarding the segmented patients and the ground truth, was equal to 96.8%. Therfore, our technique has achieved the most accurate results for the automatic segmentation of cardiac fats, to date.

  3. Robotic application of a dynamic resultant force vector using real-time load-control: simulation of an ideal follower load on Cadaveric L4-L5 segments.

    PubMed

    Bennett, Charles R; Kelly, Brian P

    2013-08-09

    Standard in-vitro spine testing methods have focused on application of isolated and/or constant load components while the in-vivo spine is subject to multiple components that can be resolved into resultant dynamic load vectors. To advance towards more in-vivo like simulations the objective of the current study was to develop a methodology to apply robotically-controlled, non-zero, real-time dynamic resultant forces during flexion-extension on human lumbar motion segment units (MSU) with initial application towards simulation of an ideal follower load (FL) force vector. A proportional-integral-derivative (PID) controller with custom algorithms coordinated the motion of a Cartesian serial manipulator comprised of six axes each capable of position- or load-control. Six lumbar MSUs (L4-L5) were tested with continuously increasing sagittal plane bending to 8 Nm while force components were dynamically programmed to deliver a resultant 400 N FL that remained normal to the moving midline of the intervertebral disc. Mean absolute load-control tracking errors between commanded and experimental loads were computed. Global spinal ranges of motion and sagittal plane inter-body translations were compared to previously published values for non-robotic applications. Mean TEs for zero-commanded force and moment axes were 0.7 ± 0.4N and 0.03 ± 0.02 Nm, respectively. For non-zero force axes mean TEs were 0.8 ± 0.8 N, 1.3 ± 1.6 Nm, and 1.3 ± 1.6N for Fx, Fz, and the resolved ideal follower load vector FL(R), respectively. Mean extension and flexion ranges of motion were 2.6° ± 1.2° and 5.0° ± 1.7°, respectively. Relative vertebral body translations and rotations were very comparable to data collected with non-robotic systems in the literature. The robotically coordinated Cartesian load controlled testing system demonstrated robust real-time load-control that permitted application of a real-time dynamic non-zero load vector during flexion-extension. For single MSU

  4. Two Time Point MS Lesion Segmentation in Brain MRI: An Expectation-Maximization Framework

    PubMed Central

    Jain, Saurabh; Ribbens, Annemie; Sima, Diana M.; Cambron, Melissa; De Keyser, Jacques; Wang, Chenyu; Barnett, Michael H.; Van Huffel, Sabine; Maes, Frederik; Smeets, Dirk

    2016-01-01

    Purpose: Lesion volume is a meaningful measure in multiple sclerosis (MS) prognosis. Manual lesion segmentation for computing volume in a single or multiple time points is time consuming and suffers from intra and inter-observer variability. Methods: In this paper, we present MSmetrix-long: a joint expectation-maximization (EM) framework for two time point white matter (WM) lesion segmentation. MSmetrix-long takes as input a 3D T1-weighted and a 3D FLAIR MR image and segments lesions in three steps: (1) cross-sectional lesion segmentation of the two time points; (2) creation of difference image, which is used to model the lesion evolution; (3) a joint EM lesion segmentation framework that uses output of step (1) and step (2) to provide the final lesion segmentation. The accuracy (Dice score) and reproducibility (absolute lesion volume difference) of MSmetrix-long is evaluated using two datasets. Results: On the first dataset, the median Dice score between MSmetrix-long and expert lesion segmentation was 0.63 and the Pearson correlation coefficient (PCC) was equal to 0.96. On the second dataset, the median absolute volume difference was 0.11 ml. Conclusions: MSmetrix-long is accurate and consistent in segmenting MS lesions. Also, MSmetrix-long compares favorably with the publicly available longitudinal MS lesion segmentation algorithm of Lesion Segmentation Toolbox. PMID:28066162

  5. Two Time Point MS Lesion Segmentation in Brain MRI: An Expectation-Maximization Framework.

    PubMed

    Jain, Saurabh; Ribbens, Annemie; Sima, Diana M; Cambron, Melissa; De Keyser, Jacques; Wang, Chenyu; Barnett, Michael H; Van Huffel, Sabine; Maes, Frederik; Smeets, Dirk

    2016-01-01

    Purpose: Lesion volume is a meaningful measure in multiple sclerosis (MS) prognosis. Manual lesion segmentation for computing volume in a single or multiple time points is time consuming and suffers from intra and inter-observer variability. Methods: In this paper, we present MSmetrix-long: a joint expectation-maximization (EM) framework for two time point white matter (WM) lesion segmentation. MSmetrix-long takes as input a 3D T1-weighted and a 3D FLAIR MR image and segments lesions in three steps: (1) cross-sectional lesion segmentation of the two time points; (2) creation of difference image, which is used to model the lesion evolution; (3) a joint EM lesion segmentation framework that uses output of step (1) and step (2) to provide the final lesion segmentation. The accuracy (Dice score) and reproducibility (absolute lesion volume difference) of MSmetrix-long is evaluated using two datasets. Results: On the first dataset, the median Dice score between MSmetrix-long and expert lesion segmentation was 0.63 and the Pearson correlation coefficient (PCC) was equal to 0.96. On the second dataset, the median absolute volume difference was 0.11 ml. Conclusions: MSmetrix-long is accurate and consistent in segmenting MS lesions. Also, MSmetrix-long compares favorably with the publicly available longitudinal MS lesion segmentation algorithm of Lesion Segmentation Toolbox.

  6. Groundtruth approach to accurate quantitation of fluorescence microarrays

    SciTech Connect

    Mascio-Kegelmeyer, L; Tomascik-Cheeseman, L; Burnett, M S; van Hummelen, P; Wyrobek, A J

    2000-12-01

    To more accurately measure fluorescent signals from microarrays, we calibrated our acquisition and analysis systems by using groundtruth samples comprised of known quantities of red and green gene-specific DNA probes hybridized to cDNA targets. We imaged the slides with a full-field, white light CCD imager and analyzed them with our custom analysis software. Here we compare, for multiple genes, results obtained with and without preprocessing (alignment, color crosstalk compensation, dark field subtraction, and integration time). We also evaluate the accuracy of various image processing and analysis techniques (background subtraction, segmentation, quantitation and normalization). This methodology calibrates and validates our system for accurate quantitative measurement of microarrays. Specifically, we show that preprocessing the images produces results significantly closer to the known ground-truth for these samples.

  7. Microearthquakes beneath the Hydrothermal Vent Fields on the Endeavour Segment of the Juan de Fuca Ridge: Results from the Keck Seismic/Hydrothermal Observatory

    NASA Astrophysics Data System (ADS)

    Bowman, D.; Parker, J.; Wilcock, W.; Hooft, E.; Barclay, A.; Toomey, D.; McGill, P.; Stakes, D.; Schmidt, C.; Patel, H.

    2005-12-01

    The W.M. Keck Foundation is supporting the operation of a small seismic network in the vicinity of the hydrothermal vent fields on the central portion of the Endeavour Segment of the Juan de Fuca Ridge. This is part of a program to conduct prototype seafloor observatory experiments to monitor the relationships between episodic deformation, fluid venting and microbial productivity at oceanic plate boundaries. The Endeavour seismic network was installed in the summer of 2003 and comprises seven GEOSense three-component short-period corehole seismometers and one buried Guralp CMG-1T broadband seismometer. A preliminary analysis of the first year of data was undertaken as part of an undergraduate research apprenticeship class taught at the University of Washington's Friday Harbor Laboratories and additional analysis has since been completed by two of the apprentices and by two IRIS undergraduate interns. Over 12,000 earthquakes were located along the ridge-axis of the Endeavour, of which ~3,000 occur within or near the network and appear to be associated with the hydrothermal systems. The levels of seismicity are strongly correlated with the intensity of venting with particularly high rates of seismicity beneath the Main and High Rise Fields and substantially lower rates to the north beneath the relatively inactive Salty Dawg and Sasquatch fields. We have used both HYPOINVERSE and a grid search algorithm to investigate the distribution of focal depths assuming a variety of one-dimensional velocity models. The preliminary results show that the majority of earthquakes occur within a narrow depth range and may represent an intense zone of seismicity within a reaction overlying the axial magma chamber at ~2.5 km depth. However, the mean focal depth is strongly dependent on the relative weights assigned to the S arrivals. We infer from the inspection of residuals that no combination of the P- and S-wave velocity models we have so far investigated are fully consistent with

  8. Influence of nuclei segmentation on breast cancer malignancy classification

    NASA Astrophysics Data System (ADS)

    Jelen, Lukasz; Fevens, Thomas; Krzyzak, Adam

    2009-02-01

    Breast Cancer is one of the most deadly cancers affecting middle-aged women. Accurate diagnosis and prognosis are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides and its influence on malignancy classification. Classification of malignancy plays a very important role during the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable information about the cancer malignancy grade which helps to choose an appropriate treatment. This process involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important. In this work we compare three powerful segmentation approaches and test their impact on the classification of breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c-means segmentation and textural segmentation based on co-occurrence matrix. Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes four different classifiers were trained and tested with previously extracted features. The compared classifiers are Multilayer Perceptron (MLP), Self-Organizing Maps (SOM), Principal Component-based Neural Network (PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the best results over the three compared approaches and leads to a good feature extraction with a lowest average error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron with an error rate of 3.07% using fuzzy c-means segmentation.

  9. Low-complexity topological derivative-based segmentation.

    PubMed

    Cho, Choong Sang; Lee, Sangkeun

    2015-02-01

    Topological derivative has been employed for image segmentation and restoration. The topological derivative-based segmentation uses two sparse matrices, and the computational complexity of the segmentation grows up dramatically as the image size increases due to the size of the sparse matrix. Therefore, to provide a fast and accurate segmentation with low complexity, an effective scheme is proposed with keeping the same segmentation performance. To further reduce the computational complexity, the parallel processing structure for the proposed scheme is designed and implemented on graphics processing unit (GPU). In particular, to reduce the computational cost of generating and multiplying sparse matrices that are squared symmetric, the 2D filters consisting of the coefficients at nonborder regions of sparse matrices are defined, and the multiplication is converted into a convolution filtering. In addition, to design a parallel processing for the segmentation with the proposed scheme on a GPU, an image is divided into several blocks and they are processed in parallel. Experimental results show that the proposed scheme for topological derivative-based segmentation reduces the computational complexity ~ 908 times, and the complexity of the proposed scheme is reduced ~ 17 times more from the parallel structure. In particular, the higher efficiency can be obtained from large sized images because the complexity of the proposed scheme does not depend on the image size. Moreover, the proposed scheme can provide almost identical segmentation result with the original sparse matrix-based approach. Therefore, we believe that the proposed scheme can be a useful tool for efficient topological derivative-based segmentation.

  10. Automated MRI Segmentation for Individualized Modeling of Current Flow in the Human Head

    PubMed Central

    Huang, Yu; Dmochowski, Jacek P.; Su, Yuzhuo; Datta, Abhishek; Rorden, Christopher; Parra, Lucas C.

    2013-01-01

    Objective High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography (HD-EEG) require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images (MRI) requires labor-intensive manual segmentation, even when leveraging available automated segmentation tools. Also, accurate placement of many high-density electrodes on individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process. Approach A fully automated segmentation technique based on Statical Parametric Mapping 8 (SPM8), including an improved tissue probability map (TPM) and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on 4 healthy subjects and 7 stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets. Main results The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view (FOV) extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly

  11. Automated MRI segmentation for individualized modeling of current flow in the human head

    NASA Astrophysics Data System (ADS)

    Huang, Yu; Dmochowski, Jacek P.; Su, Yuzhuo; Datta, Abhishek; Rorden, Christopher; Parra, Lucas C.

    2013-12-01

    Objective. High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of the brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images requires labor-intensive manual segmentation, even when utilizing available automated segmentation tools. Also, accurate placement of many high-density electrodes on an individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process. Approach. A fully automated segmentation technique based on Statical Parametric Mapping 8, including an improved tissue probability map and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on four healthy subjects and seven stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets.Main results. The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly.Significance. Fully

  12. A Wavelet Neural Network for SAR Image Segmentation

    PubMed Central

    Wen, Xian-Bin; Zhang, Hua; Wang, Fa-Yu

    2009-01-01

    This paper proposes a wavelet neural network (WNN) for SAR image segmentation by combining the wavelet transform and an artificial neural network. The WNN combines the multiscale analysis ability of the wavelet transform and the classification capability of the artificial neural network by setting the wavelet function as the transfer function of the neural network. Several SAR images are segmented by the network whose transfer functions are the Morlet and Mexihat functions, respectively. The experimental results show the proposed method is very effective and accurate. PMID:22400005

  13. Automatic segmentation of blood vessels from dynamic MRI datasets.

    PubMed

    Kubassova, Olga

    2007-01-01

    In this paper we present an approach for blood vessel segmentation from dynamic contrast-enhanced MRI datasets of the hand joints acquired from patients with active rheumatoid arthritis. Exclusion of the blood vessels is needed for accurate visualisation of the activation events and objective evaluation of the degree of inflammation. The segmentation technique is based on statistical modelling motivated by the physiological properties of the individual tissues, such as speed of uptake and concentration of the contrast agent; it incorporates Markov random field probabilistic framework and principal component analysis. The algorithm was tested on 60 temporal slices and has shown promising results.

  14. Segmentation of fluorescence microscopy cell images using unsupervised mining.

    PubMed

    Du, Xian; Dua, Sumeet

    2010-05-28

    The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu's threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu's threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications.

  15. Lung Lesion Extraction Using a Toboggan Based Growing Automatic Segmentation Approach.

    PubMed

    Song, Jiangdian; Yang, Caiyun; Fan, Li; Wang, Kun; Yang, Feng; Liu, Shiyuan; Tian, Jie

    2016-01-01

    The accurate segmentation of lung lesions from computed tomography (CT) scans is important for lung cancer research and can offer valuable information for clinical diagnosis and treatment. However, it is challenging to achieve a fully automatic lesion detection and segmentation with acceptable accuracy due to the heterogeneity of lung lesions. Here, we propose a novel toboggan based growing automatic segmentation approach (TBGA) with a three-step framework, which are automatic initial seed point selection, multi-constraints 3D lesion extraction and the final lesion refinement. The new approach does not require any human interaction or training dataset for lesion detection, yet it can provide a high lesion detection sensitivity (96.35%) and a comparable segmentation accuracy with manual segmentation (P > 0.05), which was proved by a series assessments using the LIDC-IDRI dataset (850 lesions) and in-house clinical dataset (121 lesions). We also compared TBGA with commonly used level set and skeleton graph cut methods, respectively. The results indicated a significant improvement of segmentation accuracy . Furthermore, the average time consumption for one lesion segmentation was under 8 s using our new method. In conclusion, we believe that the novel TBGA can achieve robust, efficient and accurate lung lesion segmentation in CT images automatically.

  16. 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.

  17. Segmentation of ribs in digital chest radiographs

    NASA Astrophysics Data System (ADS)

    Cong, Lin; Guo, Wei; Li, Qiang

    2016-03-01

    Ribs and clavicles in posterior-anterior (PA) digital chest radiographs often overlap with lung abnormalities such as nodules, and cause missing of these abnormalities, it is therefore necessary to remove or reduce the ribs in chest radiographs. The purpose of this study was to develop a fully automated algorithm to segment ribs within lung area in digital radiography (DR) for removal of the ribs. The rib segmentation algorithm consists of three steps. Firstly, a radiograph was pre-processed for contrast adjustment and noise removal; second, generalized Hough transform was employed to localize the lower boundary of the ribs. In the third step, a novel bilateral dynamic programming algorithm was used to accurately segment the upper and lower boundaries of ribs simultaneously. The width of the ribs and the smoothness of the rib boundaries were incorporated in the cost function of the bilateral dynamic programming for obtaining consistent results for the upper and lower boundaries. Our database consisted of 93 DR images, including, respectively, 23 and 70 images acquired with a DR system from Shanghai United-Imaging Healthcare Co. and from GE Healthcare Co. The rib localization algorithm achieved a sensitivity of 98.2% with 0.1 false positives per image. The accuracy of the detected ribs was further evaluated subjectively in 3 levels: "1", good; "2", acceptable; "3", poor. The percentages of good, acceptable, and poor segmentation results were 91.1%, 7.2%, and 1.7%, respectively. Our algorithm can obtain good segmentation results for ribs in chest radiography and would be useful for rib reduction in our future study.

  18. Automated segmentation of dental CBCT image with prior-guided sequential random forests

    SciTech Connect

    Wang, Li; Gao, Yaozong; Shi, Feng; Li, Gang; Chen, Ken-Chung; Tang, Zhen; Xia, James J. E-mail: JXia@HoustonMethodist.org; Shen, Dinggang E-mail: JXia@HoustonMethodist.org

    2016-01-15

    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 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT. Methods: In this paper, the authors present a new automatic segmentation method to address these problems. Specifically, the authors first employ a majority voting method to estimate the initial segmentation probability maps of both mandible and maxilla based on multiple aligned expert-segmented CBCT images. These probability maps provide an important prior guidance for CBCT segmentation. The authors then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of random forest classifier that can select discriminative features for segmentation. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of random forest classifier. By iteratively training the subsequent random forest classifier using both the original CBCT features and the updated segmentation probability maps, a sequence of classifiers can be derived for accurate segmentation of CBCT images. Results: Segmentation results on CBCTs of 30 subjects were both quantitatively and qualitatively validated based on manually labeled ground truth. The average Dice ratios of mandible and maxilla by the authors’ method were 0.94 and 0.91, respectively, which are significantly better than the state-of-the-art method based on sparse representation (p-value < 0.001). Conclusions: The authors have developed and validated a novel fully automated method

  19. Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology.

    PubMed

    Srinivasan, Pratul P; Heflin, Stephanie J; Izatt, Joseph A; Arshavsky, Vadim Y; Farsiu, Sina

    2014-02-01

    Accurate quantification of retinal layer thicknesses in mice as seen on optical coherence tomography (OCT) is crucial for the study of numerous ocular and neurological diseases. However, manual segmentation is time-consuming and subjective. Previous attempts to automate this process were limited to high-quality scans from mice with no missing layers or visible pathology. This paper presents an automatic approach for segmenting retinal layers in spectral domain OCT images using sparsity based denoising, support vector machines, graph theory, and dynamic programming (S-GTDP). Results show that this method accurately segments all present retinal layer boundaries, which can range from seven to ten, in wild-type and rhodopsin knockout mice as compared to manual segmentation and has a more accurate performance as compared to the commercial automated Diver segmentation software.

  20. Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology

    PubMed Central

    Srinivasan, Pratul P.; Heflin, Stephanie J.; Izatt, Joseph A.; Arshavsky, Vadim Y.; Farsiu, Sina

    2014-01-01

    Accurate quantification of retinal layer thicknesses in mice as seen on optical coherence tomography (OCT) is crucial for the study of numerous ocular and neurological diseases. However, manual segmentation is time-consuming and subjective. Previous attempts to automate this process were limited to high-quality scans from mice with no missing layers or visible pathology. This paper presents an automatic approach for segmenting retinal layers in spectral domain OCT images using sparsity based denoising, support vector machines, graph theory, and dynamic programming (S-GTDP). Results show that this method accurately segments all present retinal layer boundaries, which can range from seven to ten, in wild-type and rhodopsin knockout mice as compared to manual segmentation and has a more accurate performance as compared to the commercial automated Diver segmentation software. PMID:24575332

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

    PubMed

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

    2015-12-21

    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. 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.

  3. Fuzzy local Gaussian mixture model for brain MR image segmentation.

    PubMed

    Ji, Zexuan; Xia, Yong; Sun, Quansen; Chen, Qiang; Xia, Deshen; Feng, David Dagan

    2012-05-01

    Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.

  4. Image segmentation based on kernel PCA and shape prior

    NASA Astrophysics Data System (ADS)

    Wan, Xiaoping; Boukerroui, Djamal; Cocquerez, Jean-Pierre

    2011-06-01

    The introduction of shape priori in the segmentation model ameliorates effectively the poor segmentation result due to the using of the image information alone to segment the image including noise, occlusion, or missing parts. But the presentation of shape via Principal Component Analysis (PCA) brings on the limitation of the similarity between the objet and the prior shape. In this paper, we proposed using Kernel PCA (KPCA) to capture the shape information - the variability. KPCA can present better shape prior knowledge. The model based on KPCA allows segmenting the object with nonlinear transformation or a quite difference with the priori shape. Moreover, since the shape model is incorporated into the deformable model, our segmentation model includes the image term and the shape term to balance the influence of the global image information and the shape prior knowledge in proceed of segmentation. Our model and the model based on PCA both are applied to synthetic images and CT medical images. The comparative results show that KPCA can more accurately identify the object with large deformation or from the noised seriously background.

  5. Adaptive segmentation of nuclei in H&S stained tendon microscopy

    NASA Astrophysics Data System (ADS)

    Chuang, Bo-I.; Wu, Po-Ting; Hsu, Jian-Han; Jou, I.-Ming; Su, Fong-Chin; Sun, Yung-Nien

    2015-12-01

    Tendiopathy is a popular clinical issue in recent years. In most cases like trigger finger or tennis elbow, the pathology change can be observed under H and E stained tendon microscopy. However, the qualitative analysis is too subjective and thus the results heavily depend on the observers. We develop an automatic segmentation procedure which segments and counts the nuclei in H and E stained tendon microscopy fast and precisely. This procedure first determines the complexity of images and then segments the nuclei from the image. For the complex images, the proposed method adopts sampling-based thresholding to segment the nuclei. While for the simple images, the Laplacian-based thresholding is employed to re-segment the nuclei more accurately. In the experiments, the proposed method is compared with the experts outlined results. The nuclei number of proposed method is closed to the experts counted, and the processing time of proposed method is much faster than the experts'.

  6. [Medical image segmentation based on guided filtering and multi-atlas].

    PubMed

    Wen, Rui; Chen, Hongwen; Zhang, Lei; Lu, Zhentai

    2015-08-01

    A novel medical automatic image segmentation strategy based on guided filtering and multi-atlas is proposed to achieve accurate, smooth, robust, and reliable segmentation. This framework consists of 4 elements: the multi-atlas registration, which uses the atlas prior information; the label fusion, in which the similarity measure of the registration is used as the weight to fuse the warped label; the guided filtering, which uses the local information of the target image to correct the registration errors; and the threshold approaches used to obtain the segment result. The experimental results showed part among the 15 brain MRI images used to segment the hippocampus region, the proposed method achieved a median Dice coefficient of 86% on the left hippocampus and 87.4% on the right hippocampus. Compared with the traditional label fusion algorithm, the proposed algorithm outperforms the common brain image segmentation methods with a good efficiency and accuracy.

  7. Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information.

    PubMed

    Xia, Yong; Eberl, Stefan; Wen, Lingfeng; Fulham, Michael; Feng, David Dagan

    2012-01-01

    Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images.

  8. Classification and segmentation of intracardiac masses in cardiac tumor echocardiograms.

    PubMed

    Strzelecki, Michal; Materka, Andrzej; Drozdz, Jaroslaw; Krzeminska-Pakula, Maria; Kasprzak, Jaroslaw D

    2006-03-01

    network. Thirdly, geometrical test object parameters were estimated and compared to its true values. The experiment was repeated also for ultrasound images, which represented rectangular cross-section of synthetic sponge submerged in water. In addition, classification and segmentation of selected benign tumor echocardiograms were performed. Oscillator network was used with network weights defined for both whole texture region and texture boundary detection for the tumor segmentation. The latter method provides much faster segmentation with the similar accuracy. The obtained segmentation results were discussed and compared to the artificial neural network classifier. Finally, it was demonstrated that the network of synchronized oscillators is a reliable tool for the segmentation of the selected intracardiac masses, since it gives a relatively accurate location of analyzed tissues. The advantage of the proposed method is its resistance to changes of the visual information in the analyzed image and to noise and artifacts, often present in echocardiograms.

  9. Modelling Study at Kutlular Copper FIELD with Spat This Study, Evaluation Steps of Copper Mine Field SP Data Are Shown How to Reach More Accurate Results for SP Inversion Method.

    NASA Astrophysics Data System (ADS)

    Sahin, O. K.; Asci, M.

    2014-12-01

    At this study, determination of theoretical parameters for inversion process of Trabzon-Sürmene-Kutlular ore bed anomalies was examined. Making a decision of which model equation can be used for inversion is the most important step for the beginning. It is thought that will give a chance to get more accurate results. So, sections were evaluated with sphere-cylinder nomogram. After that, same sections were analyzed with cylinder-dike nomogram to determine the theoretical parameters for inversion process for every single model equations. After comparison of results, we saw that only one of them was more close to parameters of nomogram evaluations. But, other inversion result parameters were different from their nomogram parameters.

  10. Accurate spectral color measurements

    NASA Astrophysics Data System (ADS)

    Hiltunen, Jouni; Jaeaeskelaeinen, Timo; Parkkinen, Jussi P. S.

    1999-08-01

    Surface color measurement is of importance in a very wide range of industrial applications including paint, paper, printing, photography, textiles, plastics and so on. For a demanding color measurements spectral approach is often needed. One can measure a color spectrum with a spectrophotometer using calibrated standard samples as a reference. Because it is impossible to define absolute color values of a sample, we always work with approximations. The human eye can perceive color difference as small as 0.5 CIELAB units and thus distinguish millions of colors. This 0.5 unit difference should be a goal for the precise color measurements. This limit is not a problem if we only want to measure the color difference of two samples, but if we want to know in a same time exact color coordinate values accuracy problems arise. The values of two instruments can be astonishingly different. The accuracy of the instrument used in color measurement may depend on various errors such as photometric non-linearity, wavelength error, integrating sphere dark level error, integrating sphere error in both specular included and specular excluded modes. Thus the correction formulas should be used to get more accurate results. Another question is how many channels i.e. wavelengths we are using to measure a spectrum. It is obvious that the sampling interval should be short to get more precise results. Furthermore, the result we get is always compromise of measuring time, conditions and cost. Sometimes we have to use portable syste or the shape and the size of samples makes it impossible to use sensitive equipment. In this study a small set of calibrated color tiles measured with the Perkin Elmer Lamda 18 and the Minolta CM-2002 spectrophotometers are compared. In the paper we explain the typical error sources of spectral color measurements, and show which are the accuracy demands a good colorimeter should have.

  11. Automatic and hierarchical segmentation of the human skeleton in CT images.

    PubMed

    Fu, Yabo; Liu, Shi; Li, Harold; Yang, Deshan

    2017-04-07

    Accurate segmentation of each bone of the human skeleton is useful in many medical disciplines. The results of bone segmentation could facilitate bone disease diagnosis and post-treatment assessment, and support planning and image guidance for many treatment modalities including surgery and radiation therapy. As a medium level medical image processing task, accurate bone segmentation can facilitate automatic internal organ segmentation by providing stable structural reference for inter- or intra-patient registration and internal organ localization. Even though bones in CT images can be visually observed with minimal difficulty due to the high image contrast between the bony structures and surrounding soft tissues, automatic and precise segmentation of individual bones is still challenging due to the many limitations of the CT images. The common limitations include low signal-to-noise ratio, insufficient spatial resolution, and indistinguishable image intensity between spongy bones and soft tissues. In this study, a novel and automatic method is proposed to segment all the major individual bones of the human skeleton above the upper legs in CT images based on an articulated skeleton atlas. The reported method is capable of automatically segmenting 62 major bones, including 24 vertebrae and 24 ribs, by traversing a hierarchical anatomical tree and by using both rigid and deformable image registration. The degrees of freedom of femora and humeri are modeled to support patients in different body and limb postures. The segmentation results are evaluated using the Dice coefficient and point-to-surface error (PSE) against manual segmentation results as the ground-truth. The results suggest that the reported method can automatically segment and label the human skeleton into detailed individual bones with high accuracy. The overall average Dice coefficient is 0.90. The average PSEs are 0.41 mm for the mandible, 0.62 mm for cervical vertebrae, 0.92 mm for thoracic

  12. Automatic and hierarchical segmentation of the human skeleton in CT images

    NASA Astrophysics Data System (ADS)

    Fu, Yabo; Liu, Shi; Li, H. Harold; Yang, Deshan

    2017-04-01

    Accurate segmentation of each bone of the human skeleton is useful in many medical disciplines. The results of bone segmentation could facilitate bone disease diagnosis and post-treatment assessment, and support planning and image guidance for many treatment modalities including surgery and radiation therapy. As a medium level medical image processing task, accurate bone segmentation can facilitate automatic internal organ segmentation by providing stable structural reference for inter- or intra-patient registration and internal organ localization. Even though bones in CT images can be visually observed with minimal difficulty due to the high image contrast between the bony structures and surrounding soft tissues, automatic and precise segmentation of individual bones is still challenging due to the many limitations of the CT images. The common limitations include low signal-to-noise ratio, insufficient spatial resolution, and indistinguishable image intensity between spongy bones and soft tissues. In this study, a novel and automatic method is proposed to segment all the major individual bones of the human skeleton above the upper legs in CT images based on an articulated skeleton atlas. The reported method is capable of automatically segmenting 62 major bones, including 24 vertebrae and 24 ribs, by traversing a hierarchical anatomical tree and by using both rigid and deformable image registration. The degrees of freedom of femora and humeri are modeled to support patients in different body and limb postures. The segmentation results are evaluated using the Dice coefficient and point-to-surface error (PSE) against manual segmentation results as the ground-truth. The results suggest that the reported method can automatically segment and label the human skeleton into detailed individual bones with high accuracy. The overall average Dice coefficient is 0.90. The average PSEs are 0.41 mm for the mandible, 0.62 mm for cervical vertebrae, 0.92 mm for thoracic

  13. Trunk segment numbers and sequential segmentation in myriapods.

    PubMed

    Fusco, Giuseppe

    2005-01-01

    Sequential segmentation from a posterior "proliferative zone" is considered to be the primitive mechanism of segmentation in arthropods. Several studies of embryonic and post-embryonic development and gene expression suggest that this occurs in all major arthropod taxa. Sequential segmentation is often associated with the idea of posterior production of body units that accumulate along the main body axis. However, the precise mechanism of sequential segmentation has not been identified yet, and, while searching for the genetic circuitry able to generate a first periodic pattern in the embryo, we can at least outline the distinctive role in segmentation of a proliferative zone. A perusal of myriapod segmentation patterns suggests that these patterns result from multi-layered developmental processes, where gene expression and epigenetic mechanisms interact in a nonstrictly hierarchical way. The posterior zone is possibly a zone of periodic signal production, but, in general, the resulting segmental pattern is not completely attributable to the activity of the signal generator. In this sense, a posterior proliferative zone would be more a "segmental organizer" than a "segment generator."

  14. Medical image segmentation using level set and watershed transform

    NASA Astrophysics Data System (ADS)

    Zhu, Fuping; Tian, Jie

    2003-07-01

    One of the most popular level set algorithms is the so-called fast marching method. In this paper, a medical image segmentation algorithm is proposed based on the combination of fast marching method and watershed transformation. First, the original image is smoothed using nonlinear diffusion filter, then the smoothed image is over-segmented by the watershed algorithm. Last, the image is segmented automatically using the modified fast marching method. Due to introducing over-segmentation, the arrival time the seeded point to the boundary of region should be calculated. For other pixels inside the region of the seeded point, the arrival time is not calculated because of the region homogeneity. So the algorithm"s speed improves greatly. Moreover, the speed function is redefined based on the statistical similarity degree of the nearby regions. We also extend our algorithm to 3D circumstance and segment medical image series. Experiments show that the algorithm can fast and accurately obtain segmentation results of medical images.

  15. Automatic MRI 2D brain segmentation using graph searching technique.

    PubMed

    Pedoia, Valentina; Binaghi, Elisabetta

    2013-09-01

    Accurate and efficient segmentation of the whole brain in magnetic resonance (MR) images is a key task in many neuroscience and medical studies either because the whole brain is the final anatomical structure of interest or because the automatic extraction facilitates further analysis. The problem of segmenting brain MRI images has been extensively addressed by many researchers. Despite the relevant achievements obtained, automated segmentation of brain MRI imagery is still a challenging problem whose solution has to cope with critical aspects such as anatomical variability and pathological deformation. In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The segmentation of the whole brain over the entire volume is accomplished slice by slice, automatically detecting frames including eyes. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment. Several experiments were performed to assess the performance of the algorithm on a varied set of MRI images obtaining good results in terms of accuracy and stability.

  16. 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.

  17. Phasing a segmented telescope

    NASA Astrophysics Data System (ADS)

    Paykin, Irina; Yacobi, Lee; Adler, Joan; Ribak, Erez N.

    2015-02-01

    A crucial part of segmented or multiple-aperture systems is control of the optical path difference between the segments or subapertures. In order to achieve optimal performance we have to phase subapertures to within a fraction of the wavelength, and this requires high accuracy of positioning for each subaperture. We present simulations and hardware realization of a simulated annealing algorithm in an active optical system with sparse segments. In order to align the optical system we applied the optimization algorithm to the image itself. The main advantage of this method over traditional correction methods is that wave-front-sensing hardware and software are no longer required, making the optical and mechanical system much simpler. The results of simulations and laboratory experiments demonstrate the ability of this optimization algorithm to correct both piston and tip-tilt errors.

  18. Repair of segmental load-bearing bone defect by autologous mesenchymal stem cells and plasma-derived fibrin impregnated ceramic block results in early recovery of limb function.

    PubMed

    Ng, Min Hwei; Duski, Suryasmi; Tan, Kok Keong; Yusof, Mohd Reusmaazran; Low, Kiat Cheong; Rose, Isa Mohamed; Mohamed, Zahiah; Bin Saim, Aminuddin; Idrus, Ruszymah Bt Hj

    2014-01-01

    Calcium phosphate-based bone substitutes have not been used to repair load-bearing bone defects due to their weak mechanical property. In this study, we reevaluated the functional outcomes of combining ceramic block with osteogenic-induced mesenchymal stem cells and platelet-rich plasma (TEB) to repair critical-sized segmental tibial defect. Comparisons were made with fresh marrow-impregnated ceramic block (MIC) and partially demineralized allogeneic bone block (ALLO). Six New Zealand White female rabbits were used in each study group and three rabbits with no implants were used as negative controls. By Day 90, 4/6 rabbits in TEB group and 2/6 in ALLO and MIC groups resumed normal gait pattern. Union was achieved significantly faster in TEB group with a radiological score of 4.50 ± 0.78 versus ALLO (1.06 ± 0.32), MIC (1.28 ± 0.24), and negative controls (0). Histologically, TEB group scored the highest percentage of new bone (82% ± 5.1%) compared to ALLO (5% ± 2.5%) and MIC (26% ± 5.2%). Biomechanically, TEB-treated tibiae achieved the highest compressive strength (43.50 ± 12.72 MPa) compared to those treated with ALLO (15.15 ± 3.57 MPa) and MIC (23.28 ± 6.14 MPa). In conclusion, TEB can repair critical-sized segmental load-bearing bone defects and restore limb function.

  19. 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.

  20. Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials.

    PubMed

    Yan, Zhennan; Zhang, Shaoting; Tan, Chaowei; Qin, Hongxing; Belaroussi, Boubakeur; Yu, Hui Jing; Miller, Colin; Metaxas, Dimitris N

    2015-04-01

    Automated assessment of hepatic fat-fraction is clinically important. A robust and precise segmentation would enable accurate, objective and consistent measurement of hepatic fat-fraction for disease quantification, therapy monitoring and drug development. However, segmenting the liver in clinical trials is a challenging task due to the variability of liver anatomy as well as the diverse sources the images were acquired from. In this paper, we propose an automated and robust framework for liver segmentation and assessment. It uses single statistical atlas registration to initialize a robust deformable model to obtain fine segmentation. Fat-fraction map is computed by using chemical shift based method in the delineated region of liver. This proposed method is validated on 14 abdominal magnetic resonance (MR) volumetric scans. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance comparing with two other atlas-based methods. Experimental results demonstrate the promises of our assessment framework.

  1. Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study

    PubMed Central

    Maier, Oskar; Schröder, Christoph; Forkert, Nils Daniel; Martinetz, Thomas; Handels, Heinz

    2015-01-01

    Motivation Ischemic stroke, triggered by an obstruction in the cerebral blood supply, leads to infarction of the affected brain tissue. An accurate and reproducible automatic segmentation is of high interest, since the lesion volume is an important end-point for clinical trials. However, various factors, such as the high variance in lesion shape, location and appearance, render it a difficult task. Methods In this article, nine classification methods (e.g. Generalized Linear Models, Random Decision Forests and Convolutional Neural Networks) are evaluated and compared with each other using 37 multiparametric MRI datasets of ischemic stroke patients in the sub-acute phase in terms of their accuracy and reliability for ischemic stroke lesion segmentation. Within this context, a multi-spectral classification approach is compared against mono-spectral classification performance using only FLAIR MRI datasets and two sets of expert segmentations are used for inter-observer agreement evaluation. Results and Conclusion The results of this study reveal that high-level machine learning methods lead to significantly better segmentation results compared to the rather simple classification methods, pointing towards a difficult non-linear problem. The overall best segmentation results were achieved by a Random Decision Forest and a Convolutional Neural Networks classification approach, even outperforming all previously published results. However, none of the methods tested in this work are capable of achieving results in the range of the human observer agreement and the automatic ischemic stroke lesion segmentation remains a complicated problem that needs to be explored in more detail to improve the segmentation results. PMID:26672989

  2. A Robust and Fast Method for Sidescan Sonar Image Segmentation Using Nonlocal Despeckling and Active Contour Model.

    PubMed

    Huo, Guanying; Yang, Simon X; Li, Qingwu; Zhou, Yan

    2017-04-01

    Sidescan sonar image segmentation is a very important issue in underwater object detection and recognition. In this paper, a robust and fast method for sidescan sonar image segmentation is proposed, which deals with both speckle noise and intensity inhomogeneity that may cause considerable difficulties in image segmentation. The proposed method integrates the nonlocal means-based speckle filtering (NLMSF), coarse segmentation using k -means clustering, and fine segmentation using an improved region-scalable fitting (RSF) model. The NLMSF is used before the segmentation to effectively remove speckle noise while preserving meaningful details such as edges and fine features, which can make the segmentation easier and more accurate. After despeckling, a coarse segmentation is obtained by using k -means clustering, which can reduce the number of iterations. In the fine segmentation, to better deal with possible intensity inhomogeneity, an edge-driven constraint is combined with the RSF model, which can not only accelerate the convergence speed but also avoid trapping into local minima. The proposed method has been successfully applied to both noisy and inhomogeneous sonar images. Experimental and comparative results on real and synthetic sonar images demonstrate that the proposed method is robust against noise and intensity inhomogeneity, and is also fast and accurate.

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

    SciTech Connect

    Fritscher, Karl D. Sharp, Gregory; Peroni, Marta; Zaffino, Paolo; Spadea, Maria Francesca; Schubert, Rainer

    2014-05-15

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

  4. Automatic segmentation of radiographic fiducial and seeds from X-ray images in prostate brachytherapy.

    PubMed

    Kuo, Nathanael; Deguet, Anton; Song, Danny Y; Burdette, Everette C; Prince, Jerry L; Lee, Junghoon

    2012-01-01

    Prostate brachytherapy guided by transrectal ultrasound is a common treatment option for early stage prostate cancer. Prostate cancer accounts for 28% of cancer cases and 11% of cancer deaths in men with 217,730 estimated new cases and 32,050 estimated deaths in 2010 in the United States alone. The major current limitation is the inability to reliably localize implanted radiation seeds spatially in relation to the prostate. Multimodality approaches that incorporate X-ray for seed localization have been proposed, but they require both accurate tracking of the imaging device and segmentation of the seeds. Some use image-based radiographic fiducials to track the X-ray device, but manual intervention is needed to select proper regions of interest for segmenting both the tracking fiducial and the seeds, to evaluate the segmentation results, and to correct the segmentations in the case of segmentation failure, thus requiring a significant amount of extra time in the operating room. In this paper, we present an automatic segmentation algorithm that simultaneously segments the tracking fiducial and brachytherapy seeds, thereby minimizing the need for manual intervention. In addition, through the innovative use of image processing techniques such as mathematical morphology, Hough transforms, and RANSAC, our method can detect and separate overlapping seeds that are common in brachytherapy implant images. Our algorithm was validated on 55 phantom and 206 patient images, successfully segmenting both the fiducial and seeds with a mean seed segmentation rate of 96% and sub-millimeter accuracy.

  5. Automatic segmentation of radiographic fiducial and seeds from X-ray images in prostate brachytherapy

    PubMed Central

    Kuo, Nathanael; Deguet, Anton; Song, Danny Y.; Burdette, Everette C.; Prince, Jerry L.; Lee, Junghoon

    2011-01-01

    Prostate brachytherapy guided by transrectal ultrasound is a common treatment option for early stage prostate cancer. Prostate cancer accounts for 28% of cancer cases and 11% of cancer deaths in men with 217,730 estimated new cases and 32,050 estimated deaths in 2010 in the United States alone. The major current limitation is the inability to reliably localize implanted radiation seeds spatially in relation to the prostate. Multimodality approaches that incorporate X-ray for seed localization have been proposed, but they require both accurate tracking of the imaging device and segmentation of the seeds. Some use image-based radiographic fiducials to track the X-ray device, but manual intervention is needed to select proper regions of interest for segmenting both the tracking fiducial and the seeds, to evaluate the segmentation results, and to correct the segmentations in the case of segmentation failure, thus requiring a significant amount of extra time in the operating room. In this paper, we present an automatic segmentation algorithm that simultaneously segments the tracking fiducial and brachytherapy seeds, thereby minimizing the need for manual intervention. In addition, through the innovative use of image processing techniques such as mathematical morphology, Hough transforms, and RANSAC, our method can detect and separate overlapping seeds that are common in brachytherapy implant images. Our algorithm was validated on 55 phantom and 206 patient images, successfully segmenting both the fiducial and seeds with a mean seed segmentation rate of 96% and sub-millimeter accuracy. PMID:21802975

  6. 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.

  7. Multi-scales region segmentation for ROI separation in digital mammograms

    NASA Astrophysics Data System (ADS)

    Zhang, Dapeng; Zhang, Di; Li, Yue; Wang, Wei

    2017-02-01

    Mammography is currently the most effective imaging modality used by radiologists for the screening of breast cancer. Segmentation is one of the key steps in the process of developing anatomical models for calculation of safe medical dose of radiation. This paper explores the potential of the statistical region merging segmentation technique for Breast segmentation in digital mammograms. First, the mammograms are pre-processing for regions enhancement, then the enhanced images are segmented using SRM with multi scales, finally these segmentations are combined for region of interest (ROI) separation and edge detection. The proposed algorithm uses multi-scales region segmentation in order to: separate breast region from background region, region edge detection and ROIs separation. The experiments are performed using a data set of mammograms from different patients, demonstrating the validity of the proposed criterion. Results show that, the statistical region merging segmentation algorithm actually can work on the segmentation of medical image and more accurate than another methods. And the outcome shows that the technique has a great potential to become a method of choice for segmentation of mammograms.

  8. Intraparenchymal hemorrhage segmentation from clinical head CT of patients with traumatic brain injury

    NASA Astrophysics Data System (ADS)

    Roy, Snehashis; Wilkes, Sean; Diaz-Arrastia, Ramon; Butman, John A.; Pham, Dzung L.

    2015-03-01

    Quantification of hemorrhages in head computed tomography (CT) images from patients with traumatic brain injury (TBI) has potential applications in monitoring disease progression and better understanding of the patho-physiology of TBI. Although manual segmentations can provide accurate measures of hemorrhages, the processing time and inter-rater variability make it infeasible for large studies. In this paper, we propose a fully automatic novel pipeline for segmenting intraparenchymal hemorrhages (IPH) from clinical head CT images. Unlike previous methods of model based segmentation or active contour techniques, we rely on relevant and matching examples from already segmented images by trained raters. The CT images are first skull-stripped. Then example patches from an "atlas" CT and its manual segmentation are used to learn a two-class sparse dictionary for hemorrhage and normal tissue. Next, for a given "subject" CT, a subject patch is modeled as a sparse convex combination of a few atlas patches from the dictionary. The same convex combination is applied to the atlas segmentation patches to generate a membership for the hemorrhages at each voxel. Hemorrhages are segmented from 25 subjects with various degrees of TBI. Results are compared with segmentations obtained from an expert rater. A median Dice coefficient of 0.85 between automated and manual segmentations is achieved. A linear fit between automated and manual volumes show a slope of 1.0047, indicating a negligible bias in volume estimation.

  9. Reduced field-of-view DTI segmentation of cervical spine tissue.

    PubMed

    Tang, Lihua; Wen, Ying; Zhou, Zhenyu; von Deneen, Karen M; Huang, Dehui; Ma, Lin

    2013-11-01

    The number of diffusion tensor imaging (DTI) studies regarding the human spine has considerably increased and it is challenging because of the spine's small size and artifacts associated with the most commonly used clinical imaging method. A novel segmentation method based on the reduced field-of-view (rFOV) DTI dataset is presented in cervical spinal canal cerebrospinal fluid, spinal cord grey matter and white matter classification in both healthy volunteers and patients with neuromyelitis optica (NMO) and multiple sclerosis (MS). Due to each channel based on high resolution rFOV DTI images providing complementary information on spinal tissue segmentation, we want to choose a different contribution map from multiple channel images. Via principal component analysis (PCA) and a hybrid diffusion filter with a continuous switch applied on fourteen channel features, eigen maps can be obtained and used for tissue segmentation based on the Bayesian discrimination method. Relative to segmentation by a pair of expert readers, all of the automated segmentation results in the experiment fall in the good segmentation area and performed well, giving an average segmentation accuracy of about 0.852 for cervical spinal cord grey matter in terms of volume overlap. Furthermore, this has important applications in defining more accurate human spinal cord tissue maps when fusing structural data with diffusion data. rFOV DTI and the proposed automatic segmentation outperform traditional manual segmentation methods in classifying MR cervical spinal images and might be potentially helpful for detecting cervical spine diseases in NMO and MS.

  10. Segmentation and Impoverished Youth.

    ERIC Educational Resources Information Center

    Friedman, Judith J.; Friedman, Samuel R.

    1986-01-01

    The following characteristics of jobs held by impoverished youth who applied for a job training program were examined: (1) benefits; (2) skills; (3) career ladders; and (4) unionization. Results imply that segmentation models are not fruitful as guides to labor market experiences of youth at the bottom of wage scale. Other studies were also…

  11. Beam hardening artifacts in micro-computed tomography scanning can be reduced by X-ray beam filtration and the resulting images can be used to accurately measure BMD.

    PubMed

    Meganck, Jeffrey A; Kozloff, Kenneth M; Thornton, Michael M; Broski, Stephen M; Goldstein, Steven A

    2009-12-01

    Bone mineral density (BMD) measurements are critical in many research studies investigating skeletal integrity. For pre-clinical research, micro-computed tomography (microCT) has become an essential tool in these studies. However, the ability to measure the BMD directly from microCT images can be biased by artifacts, such as beam hardening, in the image. This three-part study was designed to understand how the image acquisition process can affect the resulting BMD measurements and to verify that the BMD measurements are accurate. In the first part of this study, the effect of beam hardening-induced cupping artifacts on BMD measurements was examined. In the second part of this study, the number of bones in the X-ray path and the sampling process during scanning was examined. In the third part of this study, microCT-based BMD measurements were compared with ash weights to verify the accuracy of the measurements. The results indicate that beam hardening artifacts of up to 32.6% can occur in sample sizes of interest in studies investigating mineralized tissue and affect mineral density measurements. Beam filtration can be used to minimize these artifacts. The results also indicate that, for murine femora, the scan setup can impact densitometry measurements for both cortical and trabecular bone and morphologic measurements of trabecular bone. Last, when a scan setup that minimized all of these artifacts was used, the microCT-based measurements correlated well with ash weight measurements (R(2)=0.983 when air was excluded), indicating that microCT can be an accurate tool for murine bone densitometry.

  12. Multi-atlas learner fusion: An efficient segmentation approach for large-scale data.

    PubMed

    Asman, Andrew J; Huo, Yuankai; Plassard, Andrew J; Landman, Bennett A

    2015-12-01

    We propose multi-atlas learner fusion (MLF), a framework for rapidly and accurately replicating the highly accurate, yet computationally expensive, multi-atlas segmentation framework based on fusing local learners. In the largest whole-brain multi-atlas study yet reported, multi-atlas segmentations are estimated for a training set of 3464 MR brain images. Using these multi-atlas estimates we (1) estimate a low-dimensional representation for selecting locally appropriate example images, and (2) build AdaBoost learners that map a weak initial segmentation to the multi-atlas segmentation result. Thus, to segment a new target image we project the image into the low-dimensional space, construct a weak initial segmentation, and fuse the trained, locally selected, learners. The MLF framework cuts the runtime on a modern computer from 36 h down to 3-8 min - a 270× speedup - by completely bypassing the need for deformable atlas-target registrations. Additionally, we (1) describe a technique for optimizing the weak initial segmentation and the AdaBoost learning parameters, (2) quantify the ability to replicate the multi-atlas result with mean accuracies approaching the multi-atlas intra-subject reproducibility on a testing set of 380 images, (3) demonstrate significant increases in the reproducibility of intra-subject segmentations when compared to a state-of-the-art multi-atlas framework on a separate reproducibility dataset, (4) show that under the MLF framework the large-scale data model significantly improve the segmentation over the small-scale model under the MLF framework, and (5) indicate that the MLF framework has comparable performance as state-of-the-art multi-atlas segmentation algorithms without using non-local information.

  13. Brain tissue segmentation in 4D CT using voxel classification

    NASA Astrophysics Data System (ADS)

    van den Boom, R.; Oei, M. T. H.; Lafebre, S.; Oostveen, L. J.; Meijer, F. J. A.; Steens, S. C. A.; Prokop, M.; van Ginneken, B.; Manniesing, R.

    2012-02-01

    A method is proposed to segment anatomical regions of the brain from 4D computer tomography (CT) patient data. The method consists of a three step voxel classification scheme, each step focusing on structures that are increasingly difficult to segment. The first step classifies air and bone, the second step classifies vessels and the third step classifies white matter, gray matter and cerebrospinal fluid. As features the time averaged intensity value and the temporal intensity change value were used. In each step, a k-Nearest-Neighbor classifier was used to classify the voxels. Training data was obtained by placing regions of interest in reconstructed 3D image data. The method has been applied to ten 4D CT cerebral patient data. A leave-one-out experiment showed consistent and accurate segmentation results.

  14. Bacterial foraging based edge detection for cell image segmentation.

    PubMed

    Pan, Yongsheng; Zhou, Tao; Xia, Yong

    2015-01-01

    Edge detection is the most popular and common choices for cell image segmentation, in which local searching strategies are commonly used. In spite of their computational efficiency, traditional edge detectors, however, may either produce discontinued edges or rely heavily on initializations. In this paper, we propose a bacterial foraging based edge detection (BFED) algorithm for cell image segmentation. We model the gradients of intensities as the nutrient concentration and propel bacteria to forage along nutrient-rich locations via mimicking the behavior of Escherichia coli, including the chemotaxis, swarming, reproduction, elimination and dispersal. As a nature-inspired evolutionary technique, this algorithm can identify the desired edges and mark them as the tracks of bacteria. We have evaluated the proposed algorithm against the Canny, SUSAN, Verma's and an active contour model (ACM) based edge detectors on both synthetic and real cell images. Our results suggest that the BFED algorithm can identify boundaries more effectively and provide more accurate cell image segmentation.

  15. 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.

  16. Automatic segmentation of mandible in panoramic x-ray

    PubMed Central

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

    2015-01-01

    Abstract. As the panoramic x-ray is the most common extraoral radiography in dentistry, segmentation of its anatomical structures facilitates diagnosis and registration of dental records. This study presents a fast and accurate method for automatic segmentation of mandible in panoramic x-rays. In the proposed four-step algorithm, a superior border is extracted through horizontal integral projections. A modified Canny edge detector accompanied by morphological operators extracts the inferior border of the mandible body. The exterior borders of ramuses are extracted through a contour tracing method based on the average model of mandible. The best-matched template is fetched from the atlas of mandibles to complete the contour of left and right processes. The algorithm was tested on a set of 95 panoramic x-rays. Evaluating the results against manual segmentations of three expert dentists showed that the method is robust. It achieved an average performance of >93% in Dice similarity, specificity, and sensitivity. PMID:26587551

  17. Spatiotemporal video segmentation based on graphical models.

    PubMed

    Wang, Yang; Loe, Kia-Fock; Tan, Tele; Wu, Jian-Kang

    2005-07-01

    This paper proposes a probabilistic framework for spatiotemporal segmentation of video sequences. Motion information, boundary information from intensity segmentation, and spatial connectivity of segmentation are unified in the video segmentation process by means of graphical models. A Bayesian network is presented to model interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notion of the Markov random field is used to encourage the formation of continuous regions. Given consecutive frames, the conditional joint probability density of the three fields is maximized in an iterative way. To effectively utilize boundary information from the intensity segmentation, distance transformation is employed in local objective functions. Experimental results show that the method is robust and generates spatiotemporally coherent segmentation results. Moreover, the proposed video segmentation approach can be viewed as the compromise of previous motion based approaches and region merging approaches.

  18. Fully-automated approach to hippocampus segmentation using a graph-cuts algorithm combined with atlas-based segmentation and morphological opening.

    PubMed

    Kwak, Kichang; Yoon, Uicheul; Lee, Dong-Kyun; Kim, Geon Ha; Seo, Sang Won; Na, Duk L; Shim, Hack-Joon; Lee, Jong-Min

    2013-09-01

    The hippocampus has been known to be an important structure as a biomarker for Alzheimer's disease (AD) and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. In this study, an automated hippocampal segmentation method based on a graph-cuts algorithm combined with atlas-based segmentation and morphological opening was proposed. First of all, the atlas-based segmentation was applied to define initial hippocampal region for a priori information on graph-cuts. The definition of initial seeds was further elaborated by incorporating estimation of partial volume probabilities at each voxel. Finally, morphological opening was applied to reduce false positive of the result processed by graph-cuts. In the experiments with twenty-seven healthy normal subjects, the proposed method showed more reliable results (similarity index=0.81±0.03) than the conventional atlas-based segmentation method (0.72±0.04). Also as for segmentation accuracy which is measured in terms of the ratios of false positive and false negative, the proposed method (precision=0.76±0.04, recall=0.86±0.05) produced lower ratios than the conventional methods (0.73±0.05, 0.72±0.06) demonstrating its plausibility for accurate, robust and reliable segmentation of hippocampus.

  19. Repair of Segmental Load-Bearing Bone Defect by Autologous Mesenchymal Stem Cells and Plasma-Derived Fibrin Impregnated Ceramic Block Results in Early Recovery of Limb Function

    PubMed Central

    Ng, Min Hwei; Duski, Suryasmi; Tan, Kok Keong; Yusof, Mohd Reusmaazran; Low, Kiat Cheong; Mohamed Rose, Isa; Mohamed, Zahiah; Bin Saim, Aminuddin; Idrus, Ruszymah Bt Hj

    2014-01-01

    Calcium phosphate-based bone substitutes have not been used to repair load-bearing bone defects due to their weak mechanical property. In this study, we reevaluated the functional outcomes of combining ceramic block with osteogenic-induced mesenchymal stem cells and platelet-rich plasma (TEB) to repair critical-sized segmental tibial defect. Comparisons were made with fresh marrow-impregnated ceramic block (MIC) and partially demineralized allogeneic bone block (ALLO). Six New Zealand White female rabbits were used in each study group and three rabbits with no implants were used as negative controls. By Day 90, 4/6 rabbits in TEB group and 2/6 in ALLO and MIC groups resumed normal gait pattern. Union was achieved significantly faster in TEB group with a radiological score of 4.50 ± 0.78 versus ALLO (1.06 ± 0.32), MIC (1.28 ± 0.24), and negative controls (0). Histologically, TEB group scored the highest percentage of new bone (82% ± 5.1%) compared to ALLO (5% ± 2.5%) and MIC (26% ± 5.2%). Biomechanically, TEB-treated tibiae achieved the highest compressive strength (43.50 ± 12.72 MPa) compared to those treated with ALLO (15.15 ± 3.57 MPa) and MIC (23.28 ± 6.14 MPa). In conclusion, TEB can repair critical-sized segmental load-bearing bone defects and restore limb function. PMID:25165699

  20. Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images.

    PubMed

    Wang, Yuliang; Zhang, Zaicheng; Wang, Huimin; Bi, Shusheng

    2015-01-01

    Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.

  1. 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.

  2. Discriminative parameter estimation for random walks segmentation.

    PubMed

    Baudin, Pierre-Yves; Goodman, Danny; Kumrnar, Puneet; Azzabou, Noura; Carlier, Pierre G; Paragios, Nikos; Kumar, M Pawan

    2013-01-01

    The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.

  3. 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.

  4. 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.

  5. Accurate monotone cubic interpolation

    NASA Technical Reports Server (NTRS)

    Huynh, Hung T.

    1991-01-01

    Monotone piecewise cubic interpolants are simple and effective. They are generally third-order accurate, except near strict local extrema where accuracy degenerates to second-order due to the monotonicity constraint. Algorithms for piecewise cubic interpolants, which preserve monotonicity as well as uniform third and fourth-order accuracy are presented. The gain of accuracy is obtained by relaxing the monotonicity constraint in a geometric framework in which the median function plays a crucial role.

  6. A fast atlas pre-selection procedure for multi-atlas based brain segmentation.

    PubMed

    Ma, Jingbo; Ma, Heather T; Li, Hengtong; Ye, Chenfei; Wu, Dan; Tang, Xiaoying; Miller, Michael; Mori, Susumu

    2015-01-01

    Multi-atlas based MR image segmentation has been recognized as a quantitative analysis approach for brain. For such purpose, atlas databases keep increasing to include various anatomical characteristics of human brain. Atlas pre-selection becomes a necessary step for efficient and accurate automated segmentation of human brain images. In this study, we proposed a method of atlas pre-selection for target image segmentation on the MriCloud platform, which is a state-of-the-art multi-atlas based segmentation tool. In the MRIcloud pipeline, segmentation of lateral ventricle (LV) label is generated as an additional input in the segmentation pipeline. Under this circumstance, similarity of the LV label between target image and atlases was adopted as the atlas ranking scheme. Dice overlap coefficient was calculated and taken as the quantitative measure for atlas ranking. Segmentation results based on the proposed method were compared with that based on atlas pre-selection by mutual information (MI) between images. The final segmentation results showed a comparable accuracy of the proposed method with that from MI based atlas pre-selection. However, the computation load for the atlas pre-selection was speeded up by about 20 times compared to MI based pre-selection. The proposed method provides a promising assistance for quantitative analysis of brain images.

  7. AB290. SPR-17 Prosthesis insertion into segmented biomechanics simulation models

    PubMed Central

    Hoyte, Lennox; Lisle, Curtis

    2016-01-01

    Objective Biomechanical simulation requires accurate representation of the geometry of the structures to be studied. Specifically, when simulating the interaction of implanted prosthetics with surrounding tissues, the geometric and mechanical properties of the prosthesis and the surrounding tissues need to be adequately represented. The present work describes methods for inserting test prostheses into magnetic resonance imaging (MRI) derived geometric models of the pelvic floor structures, in order to create realistic simulation models. Methods We modified an existing public domain image analysis software tool to allow placement and segmentation of arbitrarily shaped 3D objects into the output segmented geometry of a pelvic MRI image dataset. The tool was applied to create composite segmented geometry and 3D models of the MRI derived pelvic floor structures with the inserted prostheses in the intended anatomic locations, suitable for biomechanical simulation model creation. Results Segmentations of the organs in the source pelvic MRI datasets were created, showing the segmented embedded prostheses in the planned location. Three dimensional reconstructions of the segmented datasets were generated, which were viewable from multiple angles, and the ability to turn on and off all tissue and prosthesis layers was demonstrated. Conclusions We created a software application for inserting prostheses into segmented MRI based datasets. The output segmentations were suitable for input into a soft-tissue simulation tool suite, which generated simulation results suitable for analysis. This tool has the potential to enable patient specific, iterative surgical planning of prolapse repair strategies. Funding Source(s) None

  8. Body segment inertial parameters and low back load in individuals with central adiposity.

    PubMed

    Pryce, Robert; Kriellaars, Dean

    2014-09-22

    There is a paucity of information regarding the impact of central adiposity on the inertial characteristics of body segments. Deriving low back loads during lifting requires accurate estimate of inertial parameters. The purpose was to determine the body segment inertial parameters of people with central adiposity using a photogrammetric technique, and then to evaluate the impact on lumbar spine loading. Five participants with central adiposity (waist:hip ratio>0.9, waist circumference>102 cm) were compared to a normal BMI group. A 3D wireframe model of the surface topography was constructed, partitioned into 8 body segments and then body segment inertial parameters were calculated using volumetric integration assuming uniform segment densities for the segments. Central adiposity dependent increases in body segment parameters ranged from 12 to 400%, varying across segments (greatest for trunk) and parameters. The increase in mass distribution to the trunk was accompanied by an anterior and inferior shift of the centre of mass. A proximal shift in centre of mass was detected for the extremities, along with a reduction in mass distribution to the lower extremity. L5/S1 torques (392 vs 263 Nm) and compressive forces (5918 vs 3986 N) were substantially elevated in comparison to the normal BMI group, as well as in comparison to torques and forces predicted using published BSIP equations. Central adiposity resulted in substantial but non-uniform increases in inertial parameters resulting in task specific increases in torque and compressive loads arising from different inertial and physical components.

  9. LSD: a fast line segment detector with a false detection control.

    PubMed

    Grompone von Gioi, Rafael; Jakubowicz, Jérémie; Morel, Jean-Michel; Randall, Gregory

    2010-04-01

    We propose a linear-time line segment detector that gives accurate results, a controlled number of false detections, and requires no parameter tuning. This algorithm is tested and compared to state-of-the-art algorithms on a wide set of natural images.

  10. Motion-based morphological segmentation of wildlife video

    NASA Astrophysics Data System (ADS)

    Thomas, Naveen M.; Canagarajah, Nishan

    2005-03-01

    Segmentation of objects in a video sequence is a key stage in most content-based retrieval systems. By further analysing the behaviour of these objects, it is possible to extract semantic information suitable for higher level content analysis. Since interesting content in a video is usually provided by moving objects, motion is a key feature to be used for pre content analysis segmentation. A motion based segmentation algorithm is presented in this paper that is both efficient and robust. The algorithm is also robust to the type of camera motion. The framework presented consists of three stages. These are the motion estimation stage, foreground detection stage and the refinement stage. An iteration of the first two stages, adaptively altering the motion estimation parameters each time, results in a joint segmentation and motion estimation approach that is extremely fast and accurate. Two dimensional histograms are used as a tool to carry out the foreground detection. The last stage uses morphological approaches as well as a prediction of foreground regions in future frames to further refine the segmentation. In this paper, results obtained from traditional approaches are compared with that of the proposed framework in the wildlife domain.

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

    PubMed Central

    Heerink, Wout J.; Bergé, Stefaan J.; Maal, Thomas J. J.

    2014-01-01

    Objective To present and validate a semi-automatic segmentation protocol to enable an accurate 3D reconstruction of the mandibular condyles using cone beam computed tomography (CBCT). Materials and Methods Approval from the regional medical ethics review board was obtained for this study. Bilateral mandibular condyles in ten CBCT datasets of patients were segmented using the currently proposed semi-automatic segmentation protocol. This segmentation protocol combined 3D region-growing and local thresholding algorithms. The segmentation of a total of twenty condyles was performed by two observers. The Dice-coefficient and distance map calculations were used to evaluate the accuracy and reproducibility of the segmented and 3D rendered condyles. Results The mean inter-observer Dice-coefficient was 0.98 (range [0.95–0.99]). An average 90th percentile distance of 0.32 mm was found, indicating an excellent inter-observer similarity of the segmented and 3D rendered condyles. No systematic errors were observed in the currently proposed segmentation protocol. Conclusion The novel semi-automated segmentation protocol is an accurate and reproducible tool to segment and render condyles in 3D. The implementation of this protocol in the clinical practice allows the CBCT to be used as an imaging modality for the quantitative analysis of condylar morphology. PMID:25401954

  12. Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy.

    PubMed

    Aljabar, P; Heckemann, R A; Hammers, A; Hajnal, J V; Rueckert, D

    2009-07-01

    Quantitative research in neuroimaging often relies on anatomical segmentation of human brain MR images. Recent multi-atlas based approaches provide highly accurate structural segmentations of the brain by propagating manual delineations from multiple atlases in a database to a query subject and combining them. The atlas databases which can be used for these purposes are growing steadily. We present a framework to address the consequent problems of scale in multi-atlas segmentation. We show that selecting a custom subset of atlases for each query subject provides more accurate subcortical segmentations than those given by non-selective combination of random atlas subsets. Using a database of 275 atlases, we tested an image-based similarity criterion as well as a demographic criterion (age) in a leave-one-out cross-validation study. Using a custom ranking of the database for each subject, we combined a varying number n of atlases from the top of the ranked list. The resulting segmentations were compared with manual reference segmentations using Dice overlap. Image-based selection provided better segmentations than random subsets (mean Dice overlap 0.854 vs. 0.811 for the estimated optimal subset size, n=20). Age-based selection resulted in a similar marked improvement. We conclude that selecting atlases from large databases for atlas-based brain image segmentation improves the accuracy of the segmentations achieved. We show that image similarity is a suitable selection criterion and give results based on selecting atlases by age that demonstrate the value of meta-information for selection.

  13. A region-based segmentation of tumour from brain CT images using nonlinear support vector machine classifier.

    PubMed

    Nanthagopal, A Padma; Rajamony, R Sukanesh

    2012-07-01

    The proposed system provides new textural information for segmenting tumours, efficiently and accurately and with less computational time, from benign and malignant tumour images, especially in smaller dimensions of tumour regions of computed tomography (CT) images. Region-based segmentation of tumour from brain CT image data is an important but time-consuming task performed manually by medical experts. The objective of this work is to segment brain tumour from CT images using combined grey and texture features with new edge features and nonlinear support vector machine (SVM) classifier. The selected optimal features are used to model and train the nonlinear SVM classifier to segment the tumour from computed tomography images and the segmentation accuracies are evaluated for each slice of the tumour image. The method is applied on real data of 80 benign, malignant tumour images. The results are compared with the radiologist labelled ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and the overlap similarity measure dice metric. From the analysis and performance measures such as segmentation accuracy and dice metric, it is inferred that better segmentation accuracy and higher dice metric are achieved with the normalized cut segmentation method than with the fuzzy c-means clustering method.

  14. 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.

  15. Automated seeding-based nuclei segmentation in nonlinear optical microscopy.

    PubMed

    Medyukhina, Anna; Meyer, Tobias; Heuke, Sandro; Vogler, Nadine; Dietzek, Benjamin; Popp, Jürgen

    2013-10-01

    Nonlinear optical (NLO) microscopy based, e.g., on coherent anti-Stokes Raman scattering (CARS) or two-photon-excited fluorescence (TPEF) is a fast label-free imaging technique, with a great potential for biomedical applications. However, NLO microscopy as a diagnostic tool is still in its infancy; there is a lack of robust and durable nuclei segmentation methods capable of accurate image processing in cases of variable image contrast, nuclear density, and type of investigated tissue. Nonetheless, such algorithms specifically adapted to NLO microscopy present one prerequisite for the technology to be routinely used, e.g., in pathology or intraoperatively for surgical guidance. In this paper, we compare the applicability of different seeding and boundary detection methods to NLO microscopic images in order to develop an optimal seeding-based approach capable of accurate segmentation of both TPEF and CARS images. Among different methods, the Laplacian of Gaussian filter showed the best accuracy for the seeding of the image, while a modified seeded watershed segmentation was the most accurate in the task of boundary detection. The resulting combination of these methods followed by the verification of the detected nuclei performs high average sensitivity and specificity when applied to various types of NLO microscopy images.

  16. Concepts and Results of New Method for Accurate Ground and In-Flight Calibration of the Particle Spectrometers of the Fast Plasma Investigation on NASA's Magnetospheric MultiScale Mission

    NASA Astrophysics Data System (ADS)

    Gliese, U.; Gershman, D. J.; Dorelli, J.; Avanov, L. A.; Barrie, A. C.; Clark, G. B.; Kujawski, J. T.; Mariano, A. J.; Coffey, V. N.; Tucker, C. J.; Chornay, D. J.; Cao, N. T.; Zeuch, M. A.; Dickson, C.; Smith, D. L.; Salo, C.; MacDonald, E.; Kreisler, S.; Jacques, A. D.; Giles, B. L.; Pollock, C. J.

    2015-12-01

    The Fast Plasma Investigation (FPI) on NASA's Magnetospheric MultiScale (MMS) mission employs 16 Dual Electron Spectrometers and 16 Dual Ion Spectrometers with 4 of each type on each of 4 spacecraft to enable fast (30 ms for electrons; 150 ms for ions) and spatially differentiated measurements of the full 3D particle velocity distributions. This approach presents a new and challenging aspect to the calibration and operation of these instruments on ground and in flight. The response uniformity, the reliability of their calibration and the approach to handling any temporal evolution of these calibrated characteristics all assume enhanced importance in this application, where we attempt to understand the meaning of particle distributions within the ion and electron diffusion regions of magnetically reconnecting plasmas. We have developed a detailed model of the spectrometer detection system, its behavior and its signal, crosstalk and noise sources. Based on this, we have devised a new calibration method that enables accurate and repeatable measurement of micro-channel plate (MCP) gain, signal loss due to variation in MCP gain and crosstalk effects in one single measurement. The foundational concepts of this new calibration method, named threshold scan, are presented. It is shown how this method has been successfully applied both on ground and in-flight to achieve highly accurate and precise calibration of all 64 spectrometers. Calibration parameters that will evolve in flight are determined daily providing a robust characterization of sensor suite performance, as a basis for both in-situ hardware adjustment and data processing to scientific units, throughout mission lifetime. This is shown to be very desirable as the instruments will produce higher quality raw science data that will require smaller post-acquisition data-corrections using results from in-flight derived pitch angle distribution measurements and ground calibration measurements. The practical application

  17. Active Contours Driven by Multi-Feature Gaussian Distribution Fitting Energy with Application to Vessel Segmentation.

    PubMed

    Wang, Lei; Zhang, Huimao; He, Kan; Chang, Yan; Yang, Xiaodong

    2015-01-01

    Active contour models are of great importance for image segmentation and can extract smooth and closed boundary contours of the desired objects with promising results. However, they cannot work well in the presence of intensity inhomogeneity. Hence, a novel region-based active contour model is proposed by taking image intensities and 'vesselness values' from local phase-based vesselness enhancement into account simultaneously to define a novel multi-feature Gaussian distribution fitting energy in this paper. This energy is then incorporated into a level set formulation with a regularization term for accurate segmentations. Experimental results based on publicly available STructured Analysis of the Retina (STARE) demonstrate our model is more accurate than some existing typical methods and can successfully segment most small vessels with varying width.

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

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

    2016-01-01

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

  20. A coronary artery segmentation method based on multiscale analysis and region growing.

    PubMed

    Kerkeni, Asma; Benabdallah, Asma; Manzanera, Antoine; Bedoui, Mohamed Hedi

    2016-03-01

    Accurate coronary artery segmentation is a fundamental step in various medical imaging applications such as stenosis detection, 3D reconstruction and cardiac dynamics assessing. In this paper, a multiscale region growing (MSRG) method for coronary artery segmentation in 2D X-ray angiograms is proposed. First, a region growing rule incorporating both vesselness and direction information in a unique way is introduced. Then an iterative multiscale search based on this criterion is performed. Selected points in each step are considered as seeds for the following step. By combining vesselness and direction information in the growing rule, this method is able to avoid blockage caused by low vesselness values in vascular regions, which in turn, yields continuous vessel tree. Performing the process in a multiscale fashion helps to extract thin and peripheral vessels often missed by other segmentation methods. Quantitative evaluation performed on real angiography images shows that the proposed segmentation method identifies about 80% of the total coronary artery tree in relatively easy images and 70% in challenging cases with a mean precision of 82% and outperforms others segmentation methods in terms of sensitivity. The MSRG segmentation method was also implemented with different enhancement filters and it has been shown that the Frangi filter gives better results. The proposed segmentation method has proven to be tailored for coronary artery segmentation. It keeps an acceptable performance when dealing with challenging situations such as noise, stenosis and poor contrast.

  1. Multi-atlas pancreas segmentation: Atlas selection based on vessel structure.

    PubMed

    Karasawa, Ken'ichi; Oda, Masahiro; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Chu, Chengwen; Zheng, Guoyan; Rueckert, Daniel; Mori, Kensaku

    2017-03-31

    Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentation scheme, which has recently been used in different approaches in the literature to achieve more accurate and robust segmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, the pancreas has large inter-patient variability in its position, size and shape. Moreover, the CT intensity of the pancreas closely resembles adjacent tissues, rendering its segmentation a challenging task. Due to this, conventional intensity-based atlas selection for pancreas segmentation often fails to select atlases that are similar in pancreas position and shape to those of the unlabeled target volume. In this paper, we propose a new atlas selection strategy based on vessel structure around the pancreatic tissue and demonstrate its application to a multi-atlas pancreas segmentation. Our method utilizes vessel structure around the pancreas to select atlases with high pancreatic resemblance to the unlabeled volume. Also, we investigate two types of applications of the vessel structure information to the atlas selection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes. The experimental results showed that our approach can segment the pancreas with an average Jaccard index of 66.3% and an average Dice overlap coefficient of 78.5%.

  2. Semi-automatic segmentation for 3D motion analysis of the tongue with dynamic MRI.

    PubMed

    Lee, Junghoon; Woo, Jonghye; Xing, Fangxu; Murano, Emi Z; Stone, Maureen; Prince, Jerry L

    2014-12-01

    Dynamic MRI has been widely used to track the motion of the tongue and measure its internal deformation during speech and swallowing. Accurate segmentation of the tongue is a prerequisite step to define the target boundary and constrain the tracking to tissue points within the tongue. Segmentation of 2D slices or 3D volumes is challenging because of the large number of slices and time frames involved in the segmentation, as well as the incorporation of numerous local deformations that occur throughout the tongue during motion. In this paper, we propose a semi-automatic approach to segment 3D dynamic MRI of the tongue. The algorithm steps include seeding a few slices at one time frame, propagating seeds to the same slices at different time frames using deformable registration, and random walker segmentation based on these seed positions. This method was validated on the tongue of five normal subjects carrying out the same speech task with multi-slice 2D dynamic cine-MR images obtained at three orthogonal orientations and 26 time frames. The resulting semi-automatic segmentations of a total of 130 volumes showed an average dice similarity coefficient (DSC) score of 0.92 with less segmented volume variability between time frames than in manual segmentations.

  3. Robust Prostate Segmentation Using Intrinsic Properties of TRUS Images.

    PubMed

    Wu, Pengfei; Liu, Yiguang; Li, Yongzhong; Liu, Bingbing

    2015-06-01

    Accurate segmentation is usually crucial in transrectal ultrasound (TRUS) image based prostate diagnosis; however, it is always hampered by heavy speckles. Contrary to the traditional view that speckles are adverse to segmentation, we exploit intrinsic properties induced by speckles to facilitate the task, based on the observations that sizes and orientations of speckles provide salient cues to determine the prostate boundary. Since the speckle orientation changes in accordance with a statistical prior rule, rotation-invariant texture feature is extracted along the orientations revealed by the rule. To address the problem of feature changes due to different speckle sizes, TRUS images are split into several arc-like strips. In each strip, every individual feature vector is sparsely represented, and representation residuals are obtained. The residuals, along with the spatial coherence inherited from biological tissues, are combined to segment the prostate preliminarily via graph cuts. After that, the segmentation is fine-tuned by a novel level sets model, which integrates (1) the prostate shape prior, (2) dark-to-light intensity transition near the prostate boundary, and (3) the texture feature just obtained. The proposed method is validated on two 2-D image datasets obtained from two different sonographic imaging systems, with the mean absolute distance on the mid gland images only 1.06±0.53 mm and 1.25±0.77 mm, respectively. The method is also extended to segment apex and base images, producing competitive results over the state of the art.

  4. Residual Deconvolutional Networks for Brain Electron Microscopy Image Segmentation.

    PubMed

    Fakhry, Ahmed; Zeng, Tao; Ji, Shuiwang

    2017-02-01

    Accurate reconstruction of anatomical connections between neurons in the brain using electron microscopy (EM) images is considered to be the gold standard for circuit mapping. A key step in obtaining the reconstruction is the ability to automatically segment neurons with a precision close to human-level performance. Despite the recent technical advances in EM image segmentation, most of them rely on hand-crafted features to some extent that are specific to the data, limiting their ability to generalize. Here, we propose a simple yet powerful technique for EM image segmentation that is trained end-to-end and does not rely on prior knowledge of the data. Our proposed residual deconvolutional network consists of two information pathways that capture full-resolution features and contextual information, respectively. We showed that the proposed model is very effective in achieving the conflicting goals in dense output prediction; namely preserving full-resolution predictions and including sufficient contextual information. We applied our method to the ongoing open challenge of 3D neurite segmentation in EM images. Our method achieved one of the top results on this open challenge. We demonstrated the generality of our technique by evaluating it on the 2D neurite segmentation challenge dataset where consistently high performance was obtained. We thus expect our method to generalize well to other dense output prediction problems.

  5. Cochlea segmentation using iterated random walks with shape prior

    NASA Astrophysics Data System (ADS)

    Ruiz Pujadas, Esmeralda; Kjer, Hans Martin; Vera, Sergio; Ceresa, Mario; González Ballester, Miguel Ángel

    2016-03-01

    Cochlear implants can restore hearing to deaf or partially deaf patients. In order to plan the intervention, a model from high resolution µCT images is to be built from accurate cochlea segmentations and then, adapted to a patient-specific model. Thus, a precise segmentation is required to build such a model. We propose a new framework for segmentation of µCT cochlear images using random walks where a region term is combined with a distance shape prior weighted by a confidence map to adjust its influence according to the strength of the image contour. Then, the region term can take advantage of the high contrast between the background and foreground and the distance prior guides the segmentation to the exterior of the cochlea as well as to less contrasted regions inside the cochlea. Finally, a refinement is performed preserving the topology using a topological method and an error control map to prevent boundary leakage. We tested the proposed approach with 10 datasets and compared it with the latest techniques with random walks and priors. The experiments suggest that this method gives promising results for cochlea segmentation.

  6. Spectral clustering algorithms for ultrasound image segmentation.

    PubMed

    Archip, Neculai; Rohling, Robert; Cooperberg, Peter; Tahmasebpour, Hamid; Warfield, Simon K

    2005-01-01

    Image segmentation algorithms derived from spectral clustering analysis rely on the eigenvectors of the Laplacian of a weighted graph obtained from the image. The NCut criterion was previously used for image segmentation in supervised manner. We derive a new strategy for unsupervised image segmentation. This article describes an initial investigation to determine the suitability of such segmentation techniques for ultrasound images. The extension of the NCut technique to the unsupervised clustering is first described. The novel segmentation algorithm is then performed on simulated ultrasound images. Tests are also performed on abdominal and fetal images with the segmentation results compared to manual segmentation. Comparisons with the classical NCut algorithm are also presented. Finally, segmentation results on other types of medical images are shown.

  7. Three-dimensional coupled-object segmentation using symmetry and tissue type information.

    PubMed

    Bijari, Payam B; Akhondi-Asl, Alireza; Soltanian-Zadeh, Hamid

    2010-04-01

    This paper presents an automatic method for segmentation of brain structures using their symmetry and tissue type information. The proposed method generates segmented structures that have homogenous tissues. It benefits from general symmetry of the brain structures in the two hemispheres. It also benefits from the tissue regions generated by fuzzy c-means clustering. All in all, the proposed method can be described as a dynamic knowledge-based method that eliminates the need for statistical shape models of the structures while generating accurate segmentation results. The proposed approach is implemented in MATLAB and tested on the Internet Brain Segmentation Repository (IBSR) datasets. To this end, it is applied to the segmentation of caudate and ventricles three-dimensionally in magnetic resonance images (MRI) of the brain. Impacts of each of the steps of the proposed approach are demonstrated through experiments. It is shown that the proposed method generates accurate segmentation results that are insensitive to initialization and parameter selection. The proposed method is compared to four previous methods illustrating advantages and limitations of each method.

  8. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation.

    PubMed

    Depa, Michal; Sabuncu, Mert R; Holmvang, Godtfred; Nezafat, Reza; Schmidt, Ehud J; Golland, Polina

    Automatic segmentation of the heart's left atrium offers great benefits for planning and outcome evaluation of atrial ablation procedures. However, the high anatomical variability of the left atrium presents significant challenges for atlas-guided segmentation. In this paper, we demonstrate an automatic method for left atrium segmentation using weighted voting label fusion and a variant of the demons registration algorithm adapted to handle images with different intensity distributions. We achieve accurate automatic segmentation that is robust to the high anatomical variations in the shape of the left atrium in a clinical dataset of MRA images.

  9. Data-driven interactive 3D medical image segmentation based on structured patch model.

    PubMed

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

    2013-01-01

    In this paper, we present a novel three dimensional interactive medical image segmentation method based on high level knowledge of training set. Since the interactive system should provide intermediate results to an user quickly, insufficient low level models are used for most of previous methods. To exploit the high level knowledge within a short time, we construct a structured patch model that consists of multiple corresponding patch sets. The structured patch model includes the spatial relationships between neighboring patch sets and the prior knowledge of the corresponding patch set on each local region. The spatial relationships accelerate the search of corresponding patch in test time, while the prior knowledge improves the segmentation accuracy. The proposed framework provides not only fast editing tool, but the incremental learning system through adding the segmentation result to the training set. Experiments demonstrate that the proposed method is useful for fast and accurate segmentation of target objects from the multiple medical images.

  10. Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

    NASA Technical Reports Server (NTRS)

    Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

    2011-01-01

    The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for 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. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

  11. Segmenting the mental health care market.

    PubMed

    Stone, T R; Warren, W E; Stevens, R E

    1990-03-01

    The authors report the results of a segmentation study of the mental health care market. A random sample of 387 residents of a western city were interviewed by telephone. Cluster analysis of the data identified six market segments. Each is described according to the mental health care services to which it is most sensitive. Implications for targeting the segments are discussed.

  12. Semi-automated 3D segmentation of major tracts in the rat brain: comparing DTI with standard histological methods.

    PubMed

    Gyengesi, Erika; Calabrese, Evan; Sherrier, Matthew C; Johnson, G Allan; Paxinos, George; Watson, Charles

    2014-03-01

    Researchers working with rodent models of neurological disease often require an accurate map of the anatomical organization of the white matter of the rodent brain. With the increasing popularity of small animal MRI techniques, including diffusion tensor imaging (DTI), there is considerable interest in rapid segmentation methods of neurological structures for quantitative comparisons. DTI-derived tractography allows simple and rapid segmentation of major white matter tracts, but the anatomic accuracy of these computer-generated fibers is open to question and has not been rigorously evaluated in the rat brain. In this study, we examine the anatomic accuracy of tractography-based segmentation in the adult rat brain. We analysed 12 major white matter pathways using semi-automated tractography-based segmentation alongside manual segmentation of Gallyas silver-stained histology sections. We applied four fiber-tracking algorithms to the DTI data-two integration methods and two deflection methods. In many cases, tractography-based segmentation closely matched histology-based segmentation; however different tractography algorithms produced dramatically different results. Results suggest that certain white matter pathways are more amenable to tractography-based segmentation than others. We believe that these data will help researchers decide whether it is appropriate to use tractography-based segmentation of white matter structures for quantitative DTI-based analysis of neurologic disease models.

  13. Multi-object model-based multi-atlas segmentation for rodent brains using dense discrete correspondences

    NASA Astrophysics Data System (ADS)

    Lee, Joohwi; Kim, Sun Hyung; Styner, Martin

    2016-03-01

    The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra- subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool.

  14. Multi-Object Model-based Multi-Atlas Segmentation for Rodent Brains using Dense Discrete Correspondences

    PubMed Central

    Lee, Joohwi; Kim, Sun Hyung; Styner, Martin

    2016-01-01

    The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra-subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool. PMID:27065200

  15. Segmenting the Femoral Head and Acetabulum in the Hip Joint Automatically Using a Multi-Step Scheme

    NASA Astrophysics Data System (ADS)

    Wang, Ji; Cheng, Yuanzhi; Fu, Yili; Zhou, Shengjun; Tamura, Shinichi

    We describe a multi-step approach for automatic segmentation of the femoral head and the acetabulum in the hip joint from three dimensional (3D) CT images. Our segmentation method consists of the following steps: 1) construction of the valley-emphasized image by subtracting valleys from the original images; 2) initial segmentation of the bone regions by using conventional techniques including the initial threshold and binary morphological operations from the valley-emphasized image; 3) further segmentation of the bone regions by using the iterative adaptive classification with the initial segmentation result; 4) detection of the rough bone boundaries based on the segmented bone regions; 5) 3D reconstruction of the bone surface using the rough bone boundaries obtained in step 4) by a network of triangles; 6) correction of all vertices of the 3D bone surface based on the normal direction of vertices; 7) adjustment of the bone surface based on the corrected vertices. We evaluated our approach on 35 CT patient data sets. Our experimental results show that our segmentation algorithm is more accurate and robust against noise than other conventional approaches for automatic segmentation of the femoral head and the acetabulum. Average root-mean-square (RMS) distance from manual reference segmentations created by experienced users was approximately 0.68mm (in-plane resolution of the CT data).

  16. Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification

    PubMed Central

    Liu, Hui; Zhang, Cai-Ming; Su, Zhi-Yuan; Wang, Kai; Deng, Kai

    2015-01-01

    The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms. PMID:25945120

  17. The segmentation of the CT image based on k clustering and graph-cut

    NASA Astrophysics Data System (ADS)

    Chen, Yuke; Wu, Xiaoming; Yang, Rongqian; Ou, Shanxin; Cai, Ken; Chen, Hai

    2011-11-01

    Computed tomography angiography (CTA) is widely used to assess heart disease, like coronary artery disease. In order to complete the auto-segmentation of cardiac image of dual-source CT (DSCT) and extract the structure of heart accurately, this paper proposes a hybrid segmentation method based on k clustering and Graph-Cuts (GC). It identifies the initial label of pixels by this method. Based on this, it creates the energy function of the label with the knowledge of anatomic construction of heart and constructs the network diagram. Finally, it minimizes the energy function by the method of max-flow/min-cut theorem and picks up region of interest. The experiment results indicate that the robust, accurate segmentation of the cardiac DSCT image can be realized by combining Graph-Cut and k clustering algorithm.

  18. Rigid shape matching by segmentation averaging.

    PubMed

    Wang, Hongzhi; Oliensis, John

    2010-04-01

    We use segmentations to match images by shape. The new matching technique does not require point-to-point edge correspondence and is robust to small shape variations and spatial shifts. To address the unreliability of segmentations computed bottom-up, we give a closed form approximation to an average over all segmentations. Our method has many extensions, yielding new algorithms for tracking, object detection, segmentation, and edge-preserving smoothing. For segmentation, instead of a maximum a posteriori approach, we compute the "central" segmentation minimizing the average distance to all segmentations of an image. For smoothing, instead of smoothing images based on local structures, we smooth based on the global optimal image structures. Our methods for segmentation, smoothing, and object detection perform competitively, and we also show promising results in shape-based tracking.

  19. Segmental arterial mediolysis.

    PubMed

    Chao, Christine P

    2009-09-01

    Segmental arterial mediolysis (SAM) is a nonatherosclerotic, noninflammatory arteriopathy, which is characterized by dissecting aneurysms resulting from lysis of the outer media of the arterial wall. The most common presentation is abdominal pain and hemorrhage in the elderly. Computed tomography (CT) and angiography imaging findings overlap with various vasculitides and include segmental changes of aneurysm and stenosis. A key distinguishing feature is the presence of dissections, the principle morphologic expression of SAM. Differentiation and exclusion of an inflammatory arteritis is crucial in appropriate management, as immunosuppressants generally used for treatment of vasculitis may be ineffective or even worsen the vasculopathy. Although the disease can be self-limiting without treatment or with conservative medical therapy, the acute process carries a 50% mortality rate and may necessitate urgent surgical and/or endovascular therapy. Prompt recognition and diagnosis are therefore of utmost importance in appropriate management of this rare entity.

  20. Volumetric CT-based segmentation of NSCLC using 3D-Slicer

    PubMed Central

    Velazquez, Emmanuel Rios; Parmar, Chintan; Jermoumi, Mohammed; Mak, Raymond H.; van Baardwijk, Angela; Fennessy, Fiona M.; Lewis, John H.; De Ruysscher, Dirk; Kikinis, Ron; Lambin, Philippe; Aerts, Hugo J. W. L.

    2013-01-01

    Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the “gold standard”. The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability (p = 0.0003) and smaller uncertainty areas (p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81–0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck. PMID:24346241

  1. Pupil Tracking for Real-Time Motion Corrected Anterior Segment Optical Coherence Tomography

    PubMed Central

    Carrasco-Zevallos, Oscar M.; Nankivil, Derek; Viehland, Christian; Keller, Brenton; Izatt, Joseph A.

    2016-01-01

    Volumetric acquisition with anterior segment optical coherence tomography (ASOCT) is necessary to obtain accurate representations of the tissue structure and to account for asymmetries of the anterior eye anatomy. Additionally, recent interest in imaging of anterior segment vasculature and aqueous humor flow resulted in application of OCT angiography techniques to generate en face and 3D micro-vasculature maps of the anterior segment. Unfortunately, ASOCT structural and vasculature imaging systems do not capture volumes instantaneously and are subject to motion artifacts due to involuntary eye motion that may hinder their accuracy and repeatability. Several groups have demonstrated real-time tracking for motion-compensated in vivo OCT retinal imaging, but these techniques are not applicable in the anterior segment. In this work, we demonstrate a simple and low-cost pupil tracking system integrated into a custom swept-source OCT system for real-time motion-compensated anterior segment volumetric imaging. Pupil oculography hardware coaxial with the swept-source OCT system enabled fast detection and tracking of the pupil centroid. The pupil tracking ASOCT system with a field of view of 15 x 15 mm achieved diffraction-limited imaging over a lateral tracking range of +/- 2.5 mm and was able to correct eye motion at up to 22 Hz. Pupil tracking ASOCT offers a novel real-time motion compensation approach that may facilitate accurate and reproducible anterior segment imaging. PMID:27574800

  2. Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization.

    PubMed

    Rathke, Fabian; Schmidt, Stefan; Schnörr, Christoph

    2014-07-01

    With the introduction of spectral-domain optical coherence tomography (OCT), resulting in a significant increase in acquisition speed, the fast and accurate segmentation of 3-D OCT scans has become evermore important. This paper presents a novel probabilistic approach, that models the appearance of retinal layers as well as the global shape variations of layer boundaries. Given an OCT scan, the full posterior distribution over segmentations is approximately inferred using a variational method enabling efficient probabilistic inference in terms of computationally tractable model components: Segmenting a full 3-D volume takes around a minute. Accurate segmentations demonstrate the benefit of using global shape regularization: We segmented 35 fovea-centered 3-D volumes with an average unsigned error of 2.46 ± 0.22 μm as well as 80 normal and 66 glaucomatous 2-D circular scans with errors of 2.92 ± 0.5 μm and 4.09 ± 0.98 μm respectively. Furthermore, we utilized the inferred posterior distribution to rate the quality of the segmentation, point out potentially erroneous regions and discriminate normal from pathological scans. No pre- or postprocessing was required and we used the same set of parameters for all data sets, underlining the robustness and out-of-the-box nature of our approach.

  3. Sequential Registration-Based Segmentation of the Prostate Gland in MR Image Volumes.

    PubMed

    Khalvati, Farzad; Salmanpour, Aryan; Rahnamayan, Shahryar; Haider, Masoom A; Tizhoosh, H R

    2016-04-01

    Accurate and fast segmentation and volume estimation of the prostate gland in magnetic resonance (MR) images are necessary steps in the diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semi-automated segmentation of individual slices in T2-weighted MR image sequences. The proposed sequential registration-based segmentation (SRS) algorithm, which was inspired by the clinical workflow during medical image contouring, relies on inter-slice image registration and user interaction/correction to segment the prostate gland without the use of an anatomical atlas. It automatically generates contours for each slice using a registration algorithm, provided that the user edits and approves the marking in some previous slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid). Five radiation oncologists participated in the study where they contoured the prostate MR (T2-weighted) images of 15 patients both manually and using the SRS algorithm. Compared to the manual segmentation, on average, the SRS algorithm reduced the contouring time by 62% (a speedup factor of 2.64×) while maintaining the segmentation accuracy at the same level as the intra-user agreement level (i.e., Dice similarity coefficient of 91 versus 90%). The proposed algorithm exploits the inter-slice similarity of volumetric MR image series to achieve highly accurate results while significantly reducing the contouring time.

  4. Volumetric CT-based segmentation of NSCLC using 3D-Slicer

    NASA Astrophysics Data System (ADS)

    Velazquez, Emmanuel Rios; Parmar, Chintan; Jermoumi, Mohammed; Mak, Raymond H.; van Baardwijk, Angela; Fennessy, Fiona M.; Lewis, John H.; de Ruysscher, Dirk; Kikinis, Ron; Lambin, Philippe; Aerts, Hugo J. W. L.

    2013-12-01

    Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the ``gold standard''. The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability (p = 0.0003) and smaller uncertainty areas (p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81-0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck.

  5. Automatic segmentation of nerve structures in ultrasound images using Graph Cuts and Gaussian processes.

    PubMed

    Gil González, Julián; Álvarez, Mauricio A; Orozco, Álvaro A

    2015-01-01

    Peripheral Nerve Blocking (PNB), is a procedure used for performing regional anesthesia, that comprises the administration of anesthetic in the proximity of a nerve. Several techniques have been used with the purpose of locating nerve structures when the PNB procedure is performed: anatomical surface landmarks, elicitation of paresthesia, nerve stimulation and ultrasound imaging. Among those, ultrasound imaging has gained great attention because it is not invasive and offers an accurate location of the nerve and the structures around it. However, the segmentation of nerve structures in ultrasound images is a difficult task for the specialist, since such images are affected by echo perturbations and speckle noise. The development of systems for the automatic segmentation of nerve structures can aid the specialist for locating nerve structures accurately. In this paper we present a methodology for the automatic segmentation of nerve structures in ultrasound images. An initial step is carried out using Graph Cut segmentation in order to generate regions of interest; we then use machine learning techniques with the aim of segmenting the nerve structure; here, a specific non-linear Wavelet transform is used for the feature extraction stage, and Gaussian processes for the classification step. The methodology performance is measured in terms of accuracy and the dice coefficient. Results show that the implemented methodology can be used for automatically segmenting nerve structures.

  6. Recent Advancements in Retinal Vessel Segmentation.

    PubMed

    L Srinidhi, Chetan; Aparna, P; Rajan, Jeny

    2017-04-01

    Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. For the last two decades, a tremendous amount of research has been dedicated in developing automated methods for segmentation of blood vessels from retinal fundus images. Despite the fact, segmentation of retinal vessels still remains a challenging task due to the presence of abnormalities, varying size and shape of the vessels, non-uniform illumination and anatomical variability between subjects. In this paper, we carry out a systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years. The objectives of this study are as follows: first, we discuss the most crucial preprocessing steps that are involved in accurate segmentation of vessels. Second, we review most recent state-of-the-art retinal vessel segmentation techniques which are classified into different categories based on their main principle. Third, we quantitatively analyse these methods in terms of its sensitivity, specificity, accuracy, area under the curve and discuss newly introduced performance metrics in current literature. Fourth, we discuss the advantages and limitations of the existing segmentation techniques. Finally, we provide an insight into active problems and possible future directions towards building successful computer-aided diagnostic system.

  7. Incorporating parameter uncertainty in Bayesian segmentation models: application to hippocampal subfield volumetry.

    PubMed

    Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Van Leemput, Koen

    2012-01-01

    Many successful segmentation algorithms are based on Bayesian models in which prior anatomical knowledge is combined with the available image information. However, these methods typically have many free parameters that are estimated to obtain point estimates only, whereas a faithful Bayesian analysis would also consider all possible alternate values these parameters may take. In this paper, we propose to incorporate the uncertainty of the free parameters in Bayesian segmentation models more accurately by using Monte Carlo sampling. We demonstrate our technique by sampling atlas warps in a recent method for hippocampal subfield segmentation, and show a significant improvement in an Alzheimer's disease classification task. As an additional benefit, the method also yields informative "error bars" on the segmentation results for each of the individual sub-structures.

  8. A robust and fast line segment detector based on top-down smaller eigenvalue analysis

    NASA Astrophysics Data System (ADS)

    Liu, Dong; Wang, Yongtao; Tang, Zhi; Lu, Xiaoqing

    2014-01-01

    In this paper, we propose a robust and fast line segment detector, which achieves accurate results with a controlled number of false detections and requires no parameter tuning. It consists of three steps: first, we propose a novel edge point chaining method to extract Canny edge segments (i.e., contiguous chains of Canny edge points) from the input image; second, we propose a top-down scheme based on smaller eigenvalue analysis to extract line segments within each obtained edge segment; third, we employ Desolneux et al.'s method to reject false detections. Experiments demonstrate that it is very efficient and more robust than two state of the art methods—LSD and EDLines.

  9. A novel breast ultrasound image segmentation algorithm based on neutrosophic similarity score and level set.

    PubMed

    Guo, Yanhui; Şengür, Abdulkadir; Tian, Jia-Wei

    2016-01-01

    Breast ultrasound (BUS) image segmentation is a challenging task due to the speckle noise, poor quality of the ultrasound images and size and location of the breast lesions. In this paper, we propose a new BUS image segmentation algorithm based on neutrosophic similarity score (NSS) and level set algorithm. At first, the input BUS image is transferred to the NS domain via three membership subsets T, I and F, and then, a similarity score NSS is defined and employed to measure the belonging degree to the true tumor region. Finally, the level set method is used to segment the tumor from the background tissue region in the NSS image. Experiments have been conducted on a variety of clinical BUS images. Several measurements are used to evaluate and compare the proposed method's performance. The experimental results demonstrate that the proposed method is able to segment the BUS images effectively and accurately.

  10. 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

  11. 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.

  12. Marker based watershed to segment dim infrared target through image enhancement

    NASA Astrophysics Data System (ADS)

    Zhou, Fugen; Bai, Xiangzhi; Xie, Yongchun; Jin, Ting

    2008-10-01

    A novel marker based watershed through image enhancement is proposed to segment the dim infrared target. The dim infrared target is firstly enhanced by CB top-hat transformation and image quantization. Then, the accurate marker of the target can be easily obtained through image binarisation and marker filtering. To calculate an efficient gradient image of the dim target for the watershed segmentation, the gradient image is firstly calculated through Sobel operator and then efficiently enhanced through pseudo top-hat transformation and gradient quantization. Because of the enhancement of the dim target and the gradient image, the watershed can efficiently segment the dim infrared image. Experimental results show that the proposed algorithm is much efficient for dim infrared target segmentation.

  13. BIOACCESSIBILITY TESTS ACCURATELY ESTIMATE ...

    EPA Pesticide Factsheets

    Hazards of soil-borne Pb to wild birds may be more accurately quantified if the bioavailability of that Pb is known. To better understand the bioavailability of Pb to birds, we measured blood Pb concentrations in Japanese quail (Coturnix japonica) fed diets containing Pb-contaminated soils. Relative bioavailabilities were expressed by comparison with blood Pb concentrations in quail fed a Pb acetate reference diet. Diets containing soil from five Pb-contaminated Superfund sites had relative bioavailabilities from 33%-63%, with a mean of about 50%. Treatment of two of the soils with P significantly reduced the bioavailability of Pb. The bioaccessibility of the Pb in the test soils was then measured in six in vitro tests and regressed on bioavailability. They were: the “Relative Bioavailability Leaching Procedure” (RBALP) at pH 1.5, the same test conducted at pH 2.5, the “Ohio State University In vitro Gastrointestinal” method (OSU IVG), the “Urban Soil Bioaccessible Lead Test”, the modified “Physiologically Based Extraction Test” and the “Waterfowl Physiologically Based Extraction Test.” All regressions had positive slopes. Based on criteria of slope and coefficient of determination, the RBALP pH 2.5 and OSU IVG tests performed very well. Speciation by X-ray absorption spectroscopy demonstrated that, on average, most of the Pb in the sampled soils was sorbed to minerals (30%), bound to organic matter 24%, or present as Pb sulfate 18%. Ad

  14. Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling

    PubMed Central

    Valverde, Sergi; Oliver, Arnau; Roura, Eloy; Pareto, Deborah; Vilanova, Joan C.; Ramió-Torrentà, Lluís; Sastre-Garriga, Jaume; Montalban, Xavier; Rovira, Àlex; Lladó, Xavier

    2015-01-01

    Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here, we analyzed the % of error introduced by automating the lesion segmentation and filling processes in the tissue segmentation of 70 clinically isolated syndrome patient images. First of all, images were processed using the LST and SLS toolkits with different pipeline combinations that differed in either automated or manual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM) and white matter (WM) using SPM8, and compared with the same images where expert lesion annotations were filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error in GM and WM volume on images of MS patients, and performed similarly to the images where expert lesion annotations were masked before segmentation. In all the pipelines, the amount of misclassified lesion voxels was the main cause in the observed error in GM and WM volume. However, the % of error was significantly lower when automatically estimated lesions were filled and not masked before segmentation. These results are relevant and suggest that LST and SLS toolboxes allow the performance of accurate brain tissue volume measurements without any kind of manual intervention, which can be convenient not only in terms of time and economic costs, but also to avoid the inherent intra/inter variability between manual annotations. PMID:26740917

  15. Adaptive Breast Radiation Therapy Using Modeling of Tissue Mechanics: A Breast Tissue Segmentation Study

    SciTech Connect

    Juneja, Prabhjot; Harris, Emma J.; Kirby, Anna M.; Evans, Philip M.

    2012-11-01

    Purpose: To validate and compare the accuracy of breast tissue segmentation methods applied to computed tomography (CT) scans used for radiation therapy planning and to study the effect of tissue distribution on the segmentation accuracy for the purpose of developing models for use in adaptive breast radiation therapy. Methods and Materials: Twenty-four patients receiving postlumpectomy radiation therapy for breast cancer underwent CT imaging in prone and supine positions. The whole-breast clinical target volume was outlined. Clinical target volumes were segmented into fibroglandular and fatty tissue using the following algorithms: physical density thresholding; interactive thresholding; fuzzy c-means with 3 classes (FCM3) and 4 classes (FCM4); and k-means. The segmentation algorithms were evaluated in 2 stages: first, an approach based on the assumption that the breast composition should be the same in both prone and supine position; and second, comparison of segmentation with tissue outlines from 3 experts using the Dice similarity coefficient (DSC). Breast datasets were grouped into nonsparse and sparse fibroglandular tissue distributions according to expert assessment and used to assess the accuracy of the segmentation methods and the agreement between experts. Results: Prone and supine breast composition analysis showed differences between the methods. Validation against expert outlines found significant differences (P<.001) between FCM3 and FCM4. Fuzzy c-means with 3 classes generated segmentation results (mean DSC = 0.70) closest to the experts' outlines. There was good agreement (mean DSC = 0.85) among experts for breast tissue outlining. Segmentation accuracy and expert agreement was significantly higher (P<.005) in the nonsparse group than in the sparse group. Conclusions: The FCM3 gave the most accurate segmentation of breast tissues on CT data and could therefore be used in adaptive radiation therapy-based on tissue modeling. Breast tissue segmentation

  16. Interactive breast cancer segmentation based on relevance feedback: from user-centered design to evaluation

    NASA Astrophysics Data System (ADS)

    Gouze, A.; Kieffer, S.; Van Brussel, C.; Moncarey, R.; Grivegnée, A.; Macq, B.

    2009-02-01

    Computer systems play an important role in medical imaging industry since radiologists depend on it for visualization, interpretation, communication and archiving. In particular, computer-aided diagnosis (CAD) systems help in lesion detection tasks. This paper presents the design and the development of an interactive segmentation tool for breast cancer screening and diagnosis. The tool conception is based upon a user-centered approach in order to ensure that the application is of real benefit to radiologists. The analysis of user expectations, workflow and decision-making practices give rise to the need for an interactive reporting system based on the BIRADS, that would not only include the numerical features extracted from the segmentation of the findings in a structured manner, but also support human relevance feedback as well. This way, the numerical results from segmentation can be either validated by end-users or enhanced thanks to domain-experts subjective interpretation. Such a domain-expert centered system requires the segmentation to be sufficiently accurate and locally adapted, and the features to be carefully selected in order to best suit user's knowledge and to be of use in enhancing segmentation. Improving segmentation accuracy with relevance feedback and providing radiologists with a user-friendly interface to support image analysis are the contributions of this work. The preliminary result is first the tool conception, and second the improvement of the segmentation precision.

  17. Combined texture feature analysis of segmentation and classification of benign and malignant tumour CT slices.

    PubMed

    Padma, A; Sukanesh, R

    2013-01-01

    A computer software system is designed for the segmentation and classification of benign from malignant tumour slices in brain computed tomography (CT) images. This paper presents a method to find and select both the dominant run length and co-occurrence texture features of region of interest (ROI) of the tumour region of each slice to be segmented by Fuzzy c means clustering (FCM) and evaluate the performance of support vector machine (SVM)-based classifiers in classifying benign and malignant tumour slices. Two hundred and six tumour confirmed CT slices are considered in this study. A total of 17 texture features are extracted by a feature extraction procedure, and six features are selected using Principal Component Analysis (PCA). This study constructed the SVM-based classifier with the selected features and by comparing the segmentation results with the experienced radiologist labelled ground truth (target). Quantitative analysis between ground truth and segmented tumour is presented in terms of segmentation accuracy, segmentation error and overlap similarity measures such as the Jaccard index. The classification performance of the SVM-based classifier with the same selected features is also evaluated using a 10-fold cross-validation method. The proposed system provides some newly found texture features have an important contribution in classifying benign and malignant tumour slices efficiently and accurately with less computational time. The experimental results showed that the proposed system is able to achieve the highest segmentation and classification accuracy effectiveness as measured by jaccard index and sensitivity and specificity.

  18. Combining watershed and graph cuts methods to segment organs at risk in radiotherapy

    NASA Astrophysics Data System (ADS)

    Dolz, Jose; Kirisli, Hortense A.; Viard, Romain; Massoptier, Laurent

    2014-03-01

    Computer-aided segmentation of anatomical structures in medical images is a valuable tool for efficient radiation therapy planning (RTP). As delineation errors highly affect the radiation oncology treatment, it is crucial to delineate geometric structures accurately. In this paper, a semi-automatic segmentation approach for computed tomography (CT) images, based on watershed and graph-cuts methods, is presented. The watershed pre-segmentation groups small areas of similar intensities in homogeneous labels, which are subsequently used as input for the graph-cuts algorithm. This methodology does not require of prior knowledge of the structure to be segmented; even so, it performs well with complex shapes and low intensity. The presented method also allows the user to add foreground and background strokes in any of the three standard orthogonal views - axial, sagittal or coronal - making the interaction with the algorithm easy and fast. Hence, the segmentation information is propagated within the whole volume, providing a spatially coherent result. The proposed algorithm has been evaluated using 9 CT volumes, by comparing its segmentation performance over several organs - lungs, liver, spleen, heart and aorta - to those of manual delineation from experts. A Dicés coefficient higher than 0.89 was achieved in every case. That demonstrates that the proposed approach works well for all the anatomical structures analyzed. Due to the quality of the results, the introduction of the proposed approach in the RTP process will be a helpful tool for organs at risk (OARs) segmentation.

  19. Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms.

    PubMed

    Yang, Miin-Shen; Lin, Karen Chia-Ren; Liu, Hsiu-Chih; Lirng, Jiing-Feng

    2007-02-01

    In this article, we propose batch-type learning vector quantization (LVQ) segmentation techniques for the magnetic resonance (MR) images. Magnetic resonance imaging (MRI) segmentation is an important technique to differentiate abnormal and normal tissues in MR image data. The proposed LVQ segmentation techniques are compared with the generalized Kohonen's competitive learning (GKCL) methods, which were proposed by Lin et al. [Magn Reson Imaging 21 (2003) 863-870]. Three MRI data sets of real cases are used in this article. The first case is from a 2-year-old girl who was diagnosed with retinoblastoma in her left eye. The second case is from a 55-year-old woman who developed complete left side oculomotor palsy immediately after a motor vehicle accident. The third case is from an 84-year-old man who was diagnosed with Alzheimer disease (AD). Our comparisons are based on sensitivity of algorithm parameters, the quality of MRI segmentation with the contrast-to-noise ratio and the accuracy of the region of interest tissue. Overall, the segmentation results from batch-type LVQ algorithms present good accuracy and quality of the segmentation images, and also flexibility of algorithm parameters in all the comparison consequences. The results support that the proposed batch-type LVQ algorithms are better than the previous GKCL algorithms. Specifically, the proposed fuzzy-soft LVQ algorithm works well in segmenting AD MRI data set to accurately measure the hippocampus volume in AD MR images.

  20. Locally-constrained Boundary Regression for Segmentation of Prostate and Rectum in the Planning CT Images

    PubMed Central

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

    2015-01-01

    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

  1. 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.

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

    PubMed

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

    2014-09-01

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

  3. 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.

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

    SciTech Connect

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

    2014-03-15

    results significantly (p < 0.01) based on DSC (6.72%) and SD of contour-to-contour distances (0.08 cm) and decreased the 95% confidence intervals of the bladder volume differences. Moreover, expanding the shape model improved the segmentation results significantly (p < 0.01) based on DSC and SD of contour-to-contour distances. Conclusions: This patient-specific shape model based automatic bladder segmentation method on CBCT is accurate and generic. Our segmentation method only needs two pretreatment imaging data sets as prior knowledge, is independent of patient gender and patient treatment position and has the possibility to manually adapt the segmentation locally.

  5. Reduplication facilitates early word segmentation.

    PubMed

    Ota, Mitsuhiko; Skarabela, Barbora

    2017-02-06

    This study explores the possibility that early word segmentation is aided by infants' tendency to segment words with repeated syllables ('reduplication'). Twenty-four nine-month-olds were familiarized with passages containing one novel reduplicated word and one novel non-reduplicated word. Their central fixation times in response to these as well as new reduplicated and non-reduplicated words introduced at test showed that familiarized reduplicated words were segmented better than familiarized non-reduplicated words. These results demonstrate that infants are predisposed to segment words with repeated phonological elements, and suggest that register-specific words in infant-directed speech may have evolved in response to this learning bias.

  6. Metrology of IXO Mirror Segments

    NASA Technical Reports Server (NTRS)

    Chan, Kai-Wing

    2011-01-01

    For future x-ray astrophysics mission that demands optics with large throughput and excellent angular resolution, many telescope concepts build around assembling thin mirror segments in a Wolter I geometry, such as that originally proposed for the International X-ray Observatory. The arc-second resolution requirement posts unique challenges not just for fabrication, mounting but also for metrology of these mirror segments. In this paper, we shall discuss the metrology of these segments using normal incidence metrological method with interferometers and null lenses. We present results of the calibration of the metrology systems we are currently using, discuss their accuracy and address the precision in measuring near-cylindrical mirror segments and the stability of the measurements.

  7. Spatial scale of motion segmentation from speed cues

    NASA Technical Reports Server (NTRS)

    Mestre, D. R.; Masson, G. S.; Stone, L. S.

    2001-01-01

    For the accurate perception of multiple, potentially overlapping, surfaces or objects, the visual system must distinguish different local motion vectors and selectively integrate similar motion vectors over space to segment the retinal image properly. We recently showed that large differences in speed are required to yield a percept of motion transparency. In the present study, to investigate the spatial scale of motion segmentation from speed cues alone, we measured the speed-segmentation threshold (the minimum speed difference required for 75% performance accuracy) for 'corrugated' random-dot patterns, i.e. patterns in which dots with two different speeds were alternately placed in adjacent bars of variable width. In a first experiment, we found that, at large bar widths, a smaller speed difference was required to segment and perceive the corrugated pattern of moving dots, while at small bar-widths, a larger speed difference was required to segment the two speeds and perceive two transparent surfaces of moving dots. Both the perceptual and segmentation performance transitions occurred at a bar width of around 0.4 degrees. In a second experiment, speed-segmentation thresholds were found to increase sharply when dots with different speeds were paired within a local pooling area. The critical pairing distance was about 0.2 degrees in the fovea and increased linearly with stimulus eccentricity. However, across the range of eccentricities tested (up to 15 degrees ), the critical pairing distance did not change much and remained close to the receptive field size of neurons within the primate primary visual cortex. In a third experiment, increasing dot density changed the relationship between speed-segmentation thresholds and bar width. Thresholds decreased for large bar widths, but increased for small bar widths. All of these results are well fit by a simple stochastic model, which estimates the probabilities of having identical or different motion vectors within a

  8. Prognostic value of clinical variables at presentation in patients with non-ST-segment elevation acute coronary syndromes: results of the Proyecto de Estudio del Pronóstico de la Angina (PEPA).

    PubMed

    López de Sá, Esteban; López-Sendón, José; Anguera, Ignasi; Bethencourt, Armando; Bosch, Xavier

    2002-11-01

    Patients with suspected non-ST-segment elevation acute coronary syndromes (NSTEACS) constitute a heterogeneous population with variable outcomes. Risk stratification in this population of patients is difficult due to the complexity in patient risk profile. We conducted this study to characterize the value of clinical and electrocardiographic variables for risk stratification in an unselected population of consecutive patients with NSTEACS on admission. Thirty-five clinical and electrocardiographic variables at presentation in the emergency room of 18 hospitals were prospectively analyzed in 4,115 patients with NSTEACS and related with the outcomes at 90 days. We also developed a risk score using the variables found to be independent predictors of ischemic events to facilitate risk stratification. Cardiovascular mortality was 4.3% and the rate for the outcome of either cardiovascular death or nonfatal myocardial infarction was 6.9%. The only independent predictors of mortality were age, diabetes, peripheral vascular disease, postinfarction angina, Killip class > or = 2, ST-segment depression, and elevation of cardiac markers. A risk profile using the variables found to be independent predictors of events was calculated for cardiovascular mortality and for the combination of either death or nonfatal myocardial infarction. Event rates increased significantly in all subgroups of patients based on the number of independent risk factors as the risk score increased. Using these factors, 90-day mortality ranged from as low as 0.4% in patients with no risk factors to 21.1% for those with more than 4 risk factors. In conclusion, simple clinical and electrocardiographic data obtained at hospital admission allow an accurate risk stratification of patients with NSTEACS. In the PEPA registry, simple variables easy to obtain at admission appear to be a valuable tool in discerning between patients at very low and very high risk according to the cluster of factors for each patient

  9. Factorization-based texture segmentation

    SciTech Connect

    Yuan, Jiangye; Wang, Deliang; Cheriyadat, Anil M.

    2015-06-17

    This study introduces a factorization-based approach that efficiently segments textured images. We use local spectral histograms as features, and construct an M × N feature matrix using M-dimensional feature vectors in an N-pixel image. Based on the observation that each feature can be approximated by a linear combination of several representative features, we factor the feature matrix into two matrices-one consisting of the representative features and the other containing the weights of representative features at each pixel used for linear combination. The factorization method is based on singular value decomposition and nonnegative matrix factorization. The method uses local spectral histograms to discriminate region appearances in a computationally efficient way and at the same time accurately localizes region boundaries. Finally, the experiments conducted on public segmentation data sets show the promise of this simple yet powerful approach.

  10. Factorization-based texture segmentation

    DOE PAGES

    Yuan, Jiangye; Wang, Deliang; Cheriyadat, Anil M.

    2015-06-17

    This study introduces a factorization-based approach that efficiently segments textured images. We use local spectral histograms as features, and construct an M × N feature matrix using M-dimensional feature vectors in an N-pixel image. Based on the observation that each feature can be approximated by a linear combination of several representative features, we factor the feature matrix into two matrices-one consisting of the representative features and the other containing the weights of representative features at each pixel used for linear combination. The factorization method is based on singular value decomposition and nonnegative matrix factorization. The method uses local spectral histogramsmore » to discriminate region appearances in a computationally efficient way and at the same time accurately localizes region boundaries. Finally, the experiments conducted on public segmentation data sets show the promise of this simple yet powerful approach.« less

  11. 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

  12. Lung segmentation from HRCT using united geometric active contours

    NASA Astrophysics Data System (ADS)

    Liu, Junwei; Li, Chuanfu; Xiong, Jin; Feng, Huanqing

    2007-12-01

    Accurate lung segmentation from high resolution CT images is a challenging task due to various detail tracheal structures, missing boundary segments and complex lung anatomy. One popular method is based on gray-level threshold, however its results are usually rough. A united geometric active contours model based on level set is proposed for lung segmentation in this paper. Particularly, this method combines local boundary information and region statistical-based model synchronously: 1) Boundary term ensures the integrality of lung tissue.2) Region term makes the level set function evolve with global characteristic and independent on initial settings. A penalizing energy term is introduced into the model, which forces the level set function evolving without re-initialization. The method is found to be much more efficient in lung segmentation than other methods that are only based on boundary or region. Results are shown by 3D lung surface reconstruction, which indicates that the method will play an important role in the design of computer-aided diagnostic (CAD) system.

  13. 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%.

  14. Robust Retinal Blood Vessel Segmentation Based on Reinforcement Local Descriptions

    PubMed Central

    Li, Meng; Ma, Zhenshen; Liu, Chao; Han, Zhe

    2017-01-01

    Retinal blood vessels segmentation plays an important role for retinal image analysis. In this paper, we propose robust retinal blood vessel segmentation method based on reinforcement local descriptions. A novel line set based feature is firstly developed to capture local shape information of vessels by employing the length prior of vessels, which is robust to intensity variety. After that, local intensity feature is calculated for each pixel, and then morphological gradient feature is extracted for enhancing the local edge of smaller vessel. At last, line set based feature, local intensity feature, and morphological gradient feature are combined to obtain the reinforcement local descriptions. Compared with existing local descriptions, proposed reinforcement local description contains more local information of local shape, intensity, and edge of vessels, which is more robust. After feature extraction, SVM is trained for blood vessel segmentation. In addition, we also develop a postprocessing method based on morphological reconstruction to connect some discontinuous vessels and further obtain more accurate segmentation result. Experimental results on two public databases (DRIVE and STARE) demonstrate that proposed reinforcement local descriptions outperform the state-of-the-art method. PMID:28194407

  15. A Scalable Framework For Segmenting Magnetic Resonance Images

    PubMed Central

    Hore, Prodip; Goldgof, Dmitry B.; Gu, Yuhua; Maudsley, Andrew A.; Darkazanli, Ammar

    2009-01-01

    A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data. PMID:20046893

  16. Robust Retinal Blood Vessel Segmentation Based on Reinforcement Local Descriptions.

    PubMed

    Li, Meng; Ma, Zhenshen; Liu, Chao; Zhang, Guang; Han, Zhe

    2017-01-01

    Retinal blood vessels segmentation plays an important role for retinal image analysis. In this paper, we propose robust retinal blood vessel segmentation method based on reinforcement local descriptions. A novel line set based feature is firstly developed to capture local shape information of vessels by employing the length prior of vessels, which is robust to intensity variety. After that, local intensity feature is calculated for each pixel, and then morphological gradient feature is extracted for enhancing the local edge of smaller vessel. At last, line set based feature, local intensity feature, and morphological gradient feature are combined to obtain the reinforcement local descriptions. Compared with existing local descriptions, proposed reinforcement local description contains more local information of local shape, intensity, and edge of vessels, which is more robust. After feature extraction, SVM is trained for blood vessel segmentation. In addition, we also develop a postprocessing method based on morphological reconstruction to connect some discontinuous vessels and further obtain more accurate segmentation result. Experimental results on two public databases (DRIVE and STARE) demonstrate that proposed reinforcement local descriptions outperform the state-of-the-art method.

  17. Gaussian mixtures on tensor fields for segmentation: applications to medical imaging.

    PubMed

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

    2011-01-01

    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.

  18. 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

  19. A minimal path searching approach for active shape model (ASM)-based segmentation of the lung

    NASA Astrophysics Data System (ADS)

    Guo, Shengwen; Fei, Baowei

    2009-02-01

    We are developing a minimal path searching method for active shape model (ASM)-based segmentation for detection of lung boundaries on digital radiographs. With the conventional ASM method, the position and shape parameters of the model points are iteratively refined and the target points are updated by the least Mahalanobis distance criterion. We propose an improved searching strategy that extends the searching points in a fan-shape region instead of along the normal direction. A minimal path (MP) deformable model is applied to drive the searching procedure. A statistical shape prior model is incorporated into the segmentation. In order to keep the smoothness of the shape, a smooth constraint is employed to the deformable model. To quantitatively assess the ASM-MP segmentation, we compare the automatic segmentation with manual segmentation for 72 lung digitized radiographs. The distance error between the ASM-MP and manual segmentation is 1.75 +/- 0.33 pixels, while the error is 1.99 +/- 0.45 pixels for the ASM. Our results demonstrate that our ASM-MP method can accurately segment the lung on digital radiographs.

  20. Neural network for image segmentation

    NASA Astrophysics Data System (ADS)

    Skourikhine, Alexei N.; Prasad, Lakshman; Schlei, Bernd R.

    2000-10-01

    Image analysis is an important requirement of many artificial intelligence systems. Though great effort has been devoted to inventing efficient algorithms for image analysis, there is still much work to be done. It is natural to turn to mammalian vision systems for guidance because they are the best known performers of visual tasks. The pulse- coupled neural network (PCNN) model of the cat visual cortex has proven to have interesting properties for image processing. This article describes the PCNN application to the processing of images of heterogeneous materials; specifically PCNN is applied to image denoising and image segmentation. Our results show that PCNNs do well at segmentation if we perform image smoothing prior to segmentation. We use PCNN for obth smoothing and segmentation. Combining smoothing and segmentation enable us to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. This approach makes image processing based on PCNN more automatic in our application and also results in better segmentation.

  1. Accounting for segment correlations in segmented gamma-ray scans

    SciTech Connect

    Sheppard, G.A.; Prettyman, T.H.; Piquette, E.C.

    1994-08-01

    In a typical segmented gamma-ray scanner (SGS), the detector`s field of view is collimated so that a complete horizontal slice or segment of the desired thickness is visible. Ordinarily, the collimator is not deep enough to exclude gamma rays emitted from sample volumes above and below the segment aligned with the collimator. This can lead to assay biases, particularly for certain radioactive-material distributions. Another consequence of the collimator`s low aspect ratio is that segment assays at the top and bottom of the sample are biased low because the detector`s field of view is not filled. This effect is ordinarily countered by placing the sample on a low-Z pedestal and scanning one or more segment thicknesses below and above the sample. This takes extra time, however, We have investigated a number of techniques that both account for correlated segments and correct for end effects in SGS assays. Also, we have developed an algorithm that facilitates estimates of assay precision. Six calculation methods have been compared by evaluating the results of thousands of simulated, assays for three types of gamma-ray source distribution and ten masses. We will report on these computational studies and their experimental verification.

  2. 3D segmentation of lung CT data with graph-cuts: analysis of parameter sensitivities

    NASA Astrophysics Data System (ADS)

    Cha, Jung won; Dunlap, Neal; Wang, Brian; Amini, Amir

    2016-03-01

    Lung boundary image segmentation is important for many tasks including for example in development of radiation treatment plans for subjects with thoracic malignancies. In this paper, we describe a method and parameter settings for accurate 3D lung boundary segmentation based on graph-cuts from X-ray CT data1. Even though previously several researchers have used graph-cuts for image segmentation, to date, no systematic studies have been performed regarding the range of parameter that give accurate results. The energy function in the graph-cuts algorithm requires 3 suitable parameter settings: K, a large constant for assigning seed points, c, the similarity coefficient for n-links, and λ, the terminal coefficient for t-links. We analyzed the parameter sensitivity with four lung data sets from subjects with lung cancer using error metrics. Large values of K created artifacts on segmented images, and relatively much larger value of c than the value of λ influenced the balance between the boundary term and the data term in the energy function, leading to unacceptable segmentation results. For a range of parameter settings, we performed 3D image segmentation, and in each case compared the results with the expert-delineated lung boundaries. We used simple 6-neighborhood systems for n-link in 3D. The 3D image segmentation took 10 minutes for a 512x512x118 ~ 512x512x190 lung CT image volume. Our results indicate that the graph-cuts algorithm was more sensitive to the K and λ parameter settings than to the C parameter and furthermore that amongst the range of parameters tested, K=5 and λ=0.5 yielded good results.

  3. Transitional lumbosacral segment with unilateral transverse process anomaly (Castellvi type 2A) resulting in extraforaminal impingement of the spinal nerve: a pathoanatomical study of four specimens and report of two clinical cases.

    PubMed

    Weber, Jochen; Ernestus, Ralf-Ingo

    2010-04-01

    The spinal nerve can be pinched between the transverse process of the fifth lumbar vertebra and the sacral ala. The patients are divided into two types: elderly persons with degenerative scoliosis and somewhat younger adults with isthmic spondylolisthesis. For the first time, we describe extraforaminal impingement of the spinal nerve in transitional lumbosacral segment with unilateral transverse process anomaly. Selective nerve root blocks were performed in two clinical cases. One patient underwent nerve root decompression via a posterior approach. One year after operation, this patient reported no radicular or lumbar pain. The pathoanatomical study demonstrated pseudoarthrosis between the transverse process and the ala of the sacrum and showed dysplastic facet joints at the level below the transitional vertebra in all specimens. Furthermore, we present the oldest illustration of this pathological condition, published in a book by Carl Wenzel in 1824. Extraforaminal entrapment of the spinal nerve in transitional lumbosacral segment with unilateral transverse process anomaly can cause radiculopathy, and osteophytes are the cause of the entrapment. Dysplastic facet joints on the level below the transitional vertebra could be one reason for "micromotion" resulting in pseudoarthrosis with osteophytes. Sciatica relief was obtained by means of selective nerve root blocks or posterior decompression via a dorsomedial approach.

  4. Station Tour: Russian Segment

    NASA Video Gallery

    Expedition 33 Commander Suni Williams concludes her tour of the International Space Station with a visit to the Russian segment, which includes Zarya, the first segment of the station launched in 1...

  5. Linked statistical shape models for multi-modal segmentation: application to prostate CT-MR segmentation in radiotherapy planning

    NASA Astrophysics Data System (ADS)

    Chowdhury, Najeeb; Chappelow, Jonathan; Toth, Robert; Kim, Sung; Hahn, Stephen; Vapiwala, Neha; Lin, Haibo; Both, Stefan; Madabhushi, Anant

    2011-03-01

    transferred onto the CT image using the LSSM. We compared our fLSSM against another LSSM (xLSSM), where, unlike the fLSSM, expert delineations of the capsule on both MRI and CT were employed in the model building; xLSSM representing the idealized LSSM. We also compared our fLSSM against an exclusive CT-based SSM (ctSSM), built from expert delineations of capsule on CT only. Due to the intensity-driven nature of the segmentation algorithm, the ctSSM was not able segment the prostate. On MRI, the xLSSM and fLSSM yielded almost identical results. On CT, our results suggest that the fLSSM, while not dependent on highly accurate delineations of the capsule on CT, yields comparable results to an idealized LSSM scheme (xLSSM). Hence, the fLSSM provides an accurate alternative to SSMs that require careful SOI delineations that may be difficult or laborious to obtain, while providing concurrent segmentations of the capsule on multiple modalities.

  6. Unsupervised Trajectory Segmentation for Surgical Gesture Recognition in Robotic Training.

    PubMed

    Despinoy, Fabien; Bouget, David; Forestier, Germain; Penet, Cedric; Zemiti, Nabil; Poignet, Philippe; Jannin, Pierre

    2016-06-01

    Dexterity and procedural knowledge are two critical skills that surgeons need to master to perform accurate and safe surgical interventions. However, current training systems do not allow us to provide an in-depth analysis of surgical gestures to precisely assess these skills. Our objective is to develop a method for the automatic and quantitative assessment of surgical gestures. To reach this goal, we propose a new unsupervised algorithm that can automatically segment kinematic data from robotic training sessions. Without relying on any prior information or model, this algorithm detects critical points in the kinematic data that define relevant spatio-temporal segments. Based on the association of these segments, we obtain an accurate recognition of the gestures involved in the surgical training task. We, then, perform an advanced analysis and assess our algorithm using datasets recorded during real expert training sessions. After comparing our approach with the manual annotations of the surgical gestures, we observe 97.4% accuracy for the learning purpose and an average matching score of 81.9% for the fully automated gesture recognition process. Our results show that trainees workflow can be followed and surgical gestures may be automatically evaluated according to an expert database. This approach tends toward improving training efficiency by minimizing the learning curve.

  7. Stress polishing demonstrator for ELT M1 segments and industrialization

    NASA Astrophysics Data System (ADS)

    Hugot, Emmanuel; Bernard, Anaïs.; Laslandes, Marie; Floriot, Johan; Dufour, Thibaut; Fappani, Denis; Combes, Jean Marc; Ferrari, Marc

    2014-07-01

    After two years of research and development under ESO support, LAM and Thales SESO present the results of their experiment for the fast and accurate polishing under stress of ELT 1.5 meter segments as well as the industrialization approach for mass production. Based on stress polishing, this manufacturing method requires the conception of a warping harness able to generate extremely accurate bending of the optical surface of the segments during the polishing. The conception of the warping harness is based on finite element analysis and allowed a fine tuning of each geometrical parameter of the system in order to fit an error budget of 25nm RMS over 300μm of bending peak to valley. The optimisation approach uses the simulated influence functions to extract the system eigenmodes and characterise the performance. The same approach is used for the full characterisation of the system itself. The warping harness has been manufactured, integrated and assembled with the Zerodur 1.5 meter segment on the LAM 2.5meter POLARIS polishing facility. The experiment consists in a cross check of optical and mechanical measurements of the mirrors bending in order to develop a blind process, ie to bypass the optical measurement during the final industrial process. This article describes the optical and mechanical measurements, the influence functions and eigenmodes of the system and the full performance characterisation of the warping harness.

  8. 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.

  9. Segmentation Assisted Food Classification for Dietary Assessment.

    PubMed

    Zhu, Fengqing; Bosch, Marc; Schap, Tusarebecca; Khanna, Nitin; Ebert, David S; Boushey, Carol J; Delp, Edward J

    2011-01-24

    Accurate methods and tools to assess food and nutrient intake are essential for the association between diet and health. Preliminary studies have indicated that the use of a mobile device with a built-in camera to obtain images of the food consumed may provide a less burdensome and more accurate method for dietary assessment. We are developing methods to identify food items using a single image acquired from the mobile device. Our goal is to automatically determine the regions in an image where a particular food is located (segmentation) and correctly identify the food type based on its features (classification or food labeling). Images of foods are segmented using Normalized Cuts based on intensity and color. Color and texture features are extracted from each segmented food region. Classification decisions for each segmented region are made using support vector machine methods. The segmentation of each food region is refined based on feedback from the output of classifier to provide more accurate estimation of the quantity of food consumed.

  10. Segmentation assisted food classification for dietary assessment

    NASA Astrophysics Data System (ADS)

    Zhu, Fengqing; Bosch, Marc; Schap, TusaRebecca; Khanna, Nitin; Ebert, David S.; Boushey, Carol J.; Delp, Edward J.

    2011-03-01

    Accurate methods and tools to assess food and nutrient intake are essential for the association between diet and health. Preliminary studies have indicated that the use of a mobile device with a built-in camera to obtain images of the food consumed may provide a less burdensome and more accurate method for dietary assessment. We are developing methods to identify food items using a single image acquired from the mobile device. Our goal is to automatically determine the regions in an image where a particular food is located (segmentation) and correctly identify the food type based on its features (classification or food labeling). Images of foods are segmented using Normalized Cuts based on intensity and color. Color and texture features are extracted from each segmented food region. Classification decisions for each segmented region are made using support vector machine methods. The segmentation of each food region is refined based on feedback from the output of classifier to provide more accurate estimation of the quantity of food consumed.

  11. Morphometric Relationship between the Cervicothoracic Cord Segments and Vertebral Bodies

    PubMed Central

    Kim, Ji Hoon; Lee, Chul Woo; Chun, Kwon Soo; Shin, Won Han; Bae, Hack-Gun

    2012-01-01

    Objective The objective of this study was to investigate the morphologic characteristics between the vertebral body and the regions of the cervical and thoracic spinal cords where each rootlets branch out. Methods Sixteen adult cadavers (12 males and 4 females) with a mean age of 57.9 (range of 33 to 70 years old) were used in this study. The anatomical relationship between the exit points of the nerve roots from the posterior root entry zone at each spinal cord segment and their corresponding relevant vertebral bodies were also analyzed. Results Vertical span of the posterior root entry zone between the upper and lower rootlet originating from each spinal segment ranged from 10-12 mm. The lengths of the rootlets from their point of origin at the spinal cord to their entrance into the intervertebral foramen were 5.9 mm at the third cervical nerve root and increased to 14.5 mm at the eighth cervical nerve root. At the lower segments of the nerve roots (T3 to T12), the posterior root entry zone of the relevant nerve roots had a corresponding anatomical relationship with the vertebral body that is two segments above. The posterior root entry zones of the sixth (94%) and seventh (81%) cervical nerve roots were located at a vertebral body a segment above from relevant segment. Conclusion Through these investigations, a more accurate diagnosis, the establishment of a better therapeutic plan, and a decrease in surgical complications can be expected when pathologic lesions occur in the spinal cord or vertebral body. PMID:23133729

  12. An active contour model for medical image segmentation with application to brain CT image

    PubMed Central

    Qian, Xiaohua; Wang, Jiahui; Guo, Shuxu; Li, Qiang

    2013-01-01

    Purpose: Cerebrospinal fluid (CSF) segmentation in computed tomography (CT) is a key step in computer-aided detection (CAD) of acute ischemic stroke. Because of image noise, low contrast and intensity inhomogeneity, CSF segmentation has been a challenging task. A region-based active contour model, which is insensitive to contour initialization and robust to intensity inhomogeneity, was developed for segmenting CSF in brain CT images. Methods: The energy function of the region-based active contour model is composed of a range domain kernel function, a space domain kernel function, and an edge indicator function. By minimizing the energy function, the region of edge elements of the target could be automatically identified in images with less dependence on initial contours. The energy function was optimized by means of the deepest descent method with a level set framework. An overlap rate between segmentation results and the reference standard was used to assess the segmentation accuracy. The authors evaluated the performance of the proposed method on both synthetic data and real brain CT images. They also compared the performance level of our method to those of region-scalable fitting (RSF) and global convex segment (GCS) models. Results: For the experiment of CSF segmentation in 67 brain CT images, their method achieved an average overlap rate of 66% compared to the average overlap rates of 16% and 46% from the RSF model and the GCS model, respectively. Conclusions: Their region-based active contour model has the ability to achieve accurate segmentation results in images with high noise level and intensity inhomogeneity. Therefore, their method has great potential in the segmentation of medical images and would be useful for developing CAD schemes for acute ischemic stroke in brain CT images. PMID:23387759

  13. Test Results of LARP 3.6 m Nb3Sn Racetrack Coils Support by Full-length and Segmented Shell Structures

    SciTech Connect

    Muratore, Joseph F.; Ambrosio, Giorgio; Anerella, Michael; Barzi, Emanuela; Bossert, Rodger; Caspi, Shlomo; Cheng, D. W.; Cozzolino, John; Dietderich, Daniel R.; Escallier, John; Feher, Sandor; Felice, Helene; Ferracin, Paolo; Ganetis, George; Ghosh, Arup K.; Gupta, Ramesh C.; Hafalia, A. R.; Hannaford, C. R.; Joshi, Piyush; Kovach, Paul; Lietzke, A. F.; Louie, Wing; Marone, Andrew; McInturff, Al D.; Nobrega, F.; Sabbi, GianLuca; Schmalzle, Jesse; Thomas, Richard; Turrioni, Daniele; Wanderer, Peter

    2008-08-17

    As part of the LHC Accelerator Research Program (LARP) to build a high performance quadrupole magnet with Nb{sub 3}Sn conductor, a pair of 3.6 m-long Nb{sub 3}Sn racetrack coils has been made at Brookhaven National Laboratory (BNL) and installed in two shell-type support structures built by Lawrence Berkeley National Laboratory (LBL). These magnet assemblies have been tested at 4.5 K at BNL to gauge the effect of extended length and prestress on the mechanical performance of the long structure compared to earlier short models. This paper presents the results of quench testing and compares the overall performance of the two versions of the support structure. We also summarize the shell strain measurements and discuss the variation of quench current with ramp rate.

  14. Automatic 2D and 3D segmentation of liver from Computerised Tomography

    NASA Astrophysics Data System (ADS)

    Evans, Alun

    As part of the diagnosis of liver disease, a Computerised Tomography (CT) scan is taken of the patient, which the clinician then uses for assistance in determining the presence and extent of the disease. This thesis presents the background, methodology, results and future work of a project that employs automated methods to segment liver tissue. The clinical motivation behind this work is the desire to facilitate the diagnosis of liver disease such as cirrhosis or cancer, assist in volume determination for liver transplantation, and possibly assist in measuring the effect of any treatment given to the liver. Previous attempts at automatic segmentation of liver tissue have relied on 2D, low-level segmentation techniques, such as thresholding and mathematical morphology, to obtain the basic liver structure. The derived boundary can then be smoothed or refined using more advanced methods. The 2D results presented in this thesis improve greatly on this previous work by using a topology adaptive active contour model to accurately segment liver tissue from CT images. The use of conventional snakes for liver segmentation is difficult due to the presence of other organs closely surrounding the liver this new technique avoids this problem by adding an inflationary force to the basic snake equation, and initialising the snake inside the liver. The concepts underlying the 2D technique are extended to 3D, and results of full 3D segmentation of the liver are presented. The 3D technique makes use of an inflationary active surface model which is adaptively reparameterised, according to its size and local curvature, in order that it may more accurately segment the organ. Statistical analysis of the accuracy of the segmentation is presented for 18 healthy liver datasets, and results of the segmentation of unhealthy livers are also shown. The novel work developed during the course of this project has possibilities for use in other areas of medical imaging research, for example the

  15. SEGMENTAL CLAVICLE FRACTURE

    PubMed Central

    Grossi, Evander Azevedo

    2015-01-01

    The aim here was to present an unusual case of segmental clavicle fracture associated with ipsilateral rib fracture. Although the clavicle is very superficial, undetected cases of both types of fracture may occur, because these patients usually suffer multiple trauma. The case of a patient with a fracture of the diaphysis and lateral extremity of the clavicle is described: the patient was treated surgically and an excellent result was achieved. Similar cases in the literature are reviewed and their management is discussed. PMID:27047835

  16. Crustal and mantle structure and anisotropy beneath the incipient segments of the East African Rift System: Preliminary results from the ongoing SAFARI

    NASA Astrophysics Data System (ADS)

    Yu, Y.; Reed, C. A.; Gao, S. S.; Liu, K. H.; Massinque, B.; Mdala, H. S.; moidaki, M.; Mutamina, D. M.; Atekwana, E. A.; Ingate, S. F.; Reusch, A.; Barstow, N.

    2013-12-01

    Despite the vast wealth of research conducted toward understanding processes associated with continental rifting, the extent of our knowledge is derived primarily from studies focused on mature rift systems, such as the well-developed portions of the East African Rift System (EARS) north of Lake Malawi. To explore the dynamics of early rift evolution, the SAFARI (Seismic Arrays for African Rift Initiation) team deployed 50 PASSCAL broadband seismic stations across the Malawi, Luangwa, and Okavango rifts of the EARS during the summer of 2012. The cumulative length of the profiles is about 2500 km and the planned recording duration is 2 years. Here we present the preliminary results of systematic analyses of data obtained from the first year of acquisition for all 50 stations. A total of 446 high-quality shear-wave splitting measurements using PKS, SKKS, and SKS phases from 84 teleseismic events were used to constrain fast polarization directions and splitting times throughout the region. The Malawi and Okavango rifts are characterized by mostly NE trending fast directions with a mean splitting time of about 1 s. The fast directions on the west side of the Luangwa Rift Zone are parallel to the rift valley, and those on the east side are more N-S oriented. Stacking of approximately 1900 radial receiver functions reveals significant spatial variations of both crustal thickness and the ratio of crustal P and S wave velocities, as well as the thickness of the mantle transition zone. Stations situated within the Malawi rift demonstrate a southward increase in observed crustal thickness, which is consistent with the hypothesis that the Malawi rift originated at the northern end of the rift system and propagated southward. Both the Okavango and Luangwa rifts are associated with thinned crust and increased Vp/Vs, although additional data is required at some stations to enhance the reliability of the observations. Teleseismic P-wave travel-time residuals show a delay of about

  17. Joint Lung CT Image Segmentation: A Hierarchical Bayesian Approach

    PubMed Central

    Cheng, Wenjun; Ma, Luyao; Yang, Tiejun; Liang, Jiali

    2016-01-01

    Accurate lung CT image segmentation is of great clinical value, especially when it comes to delineate pathological regions including lung tumor. In this paper, we present a novel framework that jointly segments multiple lung computed tomography (CT) images via hierarchical Dirichlet process (HDP). In specifics, based on the assumption that lung CT images from different patients share similar image structure (organ sets and relative positioning), we derive a mathematical model to segment them simultaneously so that shared information across patients could be utilized to regularize each individual segmentation. Moreover, compared to many conventional models, the algorithm requires little manual involvement due to the nonparametric nature of Dirichlet process (DP). We validated proposed model upon clinical data consisting of healthy and abnormal (lung cancer) patients. We demonstrate that, because of the joint segmentation fashion, more accurate and consistent segmentations could be obtained. PMID:27611188

  18. Left-Atrial Segmentation From 3-D Ultrasound Using B-Spline Explicit Active Surfaces With Scale Uncoupling.

    PubMed

    Almeida, Nuno; Friboulet, Denis; Sarvari, Sebastian Imre; Bernard, Olivier; Barbosa, Daniel; Samset, Eigil; Dhooge, Jan

    2016-02-01

    Segmentation of the left atrium (LA) of the heart allows quantification of LA volume dynamics which can give insight into cardiac function. However, very little attention has been given to LA segmentation from three-dimensional (3-D) ultrasound (US), most efforts being focused on the segmentation of the left ventricle (LV). The B-spline explicit active surfaces (BEAS) framework has been shown to be a very robust and efficient methodology to perform LV segmentation. In this study, we propose an extension of the BEAS framework, introducing B-splines with uncoupled scaling. This formulation improves the shape support for less regular and more variable structures, by giving independent control over smoothness and number of control points. Semiautomatic segmentation of the LA endocardium using this framework was tested in a setup requiring little user input, on 20 volumetric sequences of echocardiographic data from healthy subjects. The segmentation results were evaluated against manual reference delineations of the LA. Relevant LA morphological and functional parameters were derived from the segmented surfaces, in order to assess the performance of the proposed method on its clinical usage. The results showed that the modified BEAS framework is capable of accurate semiautomatic LA segmentation in 3-D transthoracic US, providing reliable quantification of the LA morphology and function.

  19. 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.

  20. A watershed approach for improving medical image segmentation.

    PubMed

    Zanaty, E A; Afifi, Ashraf

    2013-01-01

    In this paper, a novel watershed approach based on seed region growing and image entropy is presented which could improve the medical image segmentation. The proposed algorithm enables the prior information of seed region growing and image entropy in its calculation. The algorithm starts by partitioning the image into several levels of intensity using watershed multi-degree immersion process. The levels of intensity are the input to a computationally efficient seed region segmentation process which produces the initial partitioning of the image regions. These regions are fed to entropy procedure to carry out a suitable merging which produces the final segmentation. The latter process uses a region-based similarity representation of the image regions to decide whether regions can be merged. The region is isolated from the level and the residual pixels are uploaded to the next level and so on, we recall this process as multi-level process and the watershed is called multi-level watershed. The proposed algorithm is applied to challenging applications: grey matter-white matter segmentation in magnetic resonance images (MRIs). The established methods and the proposed approach are experimented by these applications to a variety of simulating immersion, multi-degree, multi-level seed region growing and multi-level seed region growing with entropy. It is shown that the proposed method achieves more accurate results for medical image oversegmentation.

  1. Robust Optic Nerve Segmentation on Clinically Acquired CT.

    PubMed

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

    2014-03-21

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

  2. Robust optic nerve segmentation on clinically acquired CT

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  3. Level Set Segmentation of Lumbar Vertebrae Using Appearance Models

    NASA Astrophysics Data System (ADS)

    Fritscher, Karl; Leber, Stefan; Schmölz, Werner; Schubert, Rainer

    For the planning of surgical interventions of the spine exact knowledge about 3D shape and the local bone quality of vertebrae are of great importance in order to estimate the anchorage strength of screws or implants. As a prerequisite for quantitative analysis a method for objective and therefore automated segmentation of vertebrae is needed. In this paper a framework for the automatic segmentation of vertebrae using 3D appearance models in a level set framework is presented. In this framework model information as well as gradient information and probabilities of pixel intensities at object edges in the unseen image are used. The method is tested on 29 lumbar vertebrae leading to accurate results, which can be useful for surgical planning and further analysis of the local bone quality.

  4. Prior knowledge driven multiscale segmentation of brain MRI.

    PubMed

    Akselrod-Ballin, Ayelet; Galun, Meirav; Gomori, John Moshe; Brandt, Achi; Basri, Ronen

    2007-01-01

    We present a novel automatic multiscale algorithm applied to segmentation of anatomical structures in brain MRI. The algorithm which is derived from algebraic multigrid, uses a graph representation of the image and performs a coarsening process that produces a full hierarchy of segments. Our main contribution is the incorporation of prior knowledge information into the multiscale framework through a Bayesian formulation. The probabilistic information is based on an atlas prior and on a likelihood function estimated from a manually labeled training set. The significance of our new approach is that the constructed pyramid, reflects the prior knowledge formulated. This leads to an accurate and efficient methodology for detection of various anatomical structures simultaneously. Quantitative validation results on gold standard MRI show the benefit of our approach.

  5. Automatic Segmentation of Invasive Breast Carcinomas from DCE-MRI using Time Series Analysis

    PubMed Central

    Jayender, Jagadaeesan; Chikarmane, Sona; Jolesz, Ferenc A.; Gombos, Eva

    2013-01-01

    Purpose Quantitative segmentation methods based on black-box modeling and pharmacokinetic modeling are highly dependent on imaging pulse sequence, timing of bolus injection, arterial input function, imaging noise and fitting algorithms. To accurately segment invasive ductal carcinomas (IDCs) from dynamic contrast enhanced MRI (DCE-MRI) using time series analysis based on linear dynamic system (LDS) modeling. Methods We modeled the underlying dynamics of the tumor by a LDS and use the system parameters to segment the carcinoma on the DCE-MRI. Twenty-four patients with biopsy-proven IDCs were analyzed. The lesions segmented by the algorithm were compared with an expert radiologist’s segmentation and the output of a commercial software, CADstream. The results are quantified in terms of the accuracy and sensitivity of detecting the lesion and the amount of overlap, measured in terms of the Dice similarity coefficient (DSC). Results The segmentation algorithm detected the tumor with 90% accuracy and 100% sensitivity when compared to the radiologist’s segmentation and 82.1% accuracy and 100% sensitivity when compared to the CADstream output. The overlap of the algorithm output with the radiologist’s segmentation and CADstream output, computed in terms of the DSC was 0.77 and 0.72 respectively. The algorithm also shows robust stability to imaging noise. Simulated imaging noise with zero mean and standard deviation equal to 25% of the base signal intensity was added to the DCE-MRI series. The amount of overlap between the tumor maps generated by the LDS-based algorithm from the noisy and original DCE-MRI was DSC=0.95. Conclusion The time-series analysis based segmentation algorithm provides high accuracy and sensitivity in delineating the regions of enhanced perfusion corresponding to tumor from DCE-MRI. PMID:24115175

  6. Model-based segmentation of the facial nerve and chorda tympani in pediatric CT scans

    NASA Astrophysics Data System (ADS)

    Reda, Fitsum A.; Noble, Jack H.; Rivas, Alejandro; Labadie, Robert F.; Dawant, Benoit M.

    2011-03-01

    In image-guided cochlear implant surgery an electrode array is implanted in the cochlea to treat hearing loss. Access to the cochlea is achieved by drilling from the outer skull to the cochlea through the facial recess, a region bounded by the facial nerve and the chorda tympani. To exploit existing methods for computing automatically safe drilling trajectories, the facial nerve and chorda tympani need to be segmented. The effectiveness of traditional segmentation approaches to achieve this is severely limited because the facial nerve and chorda are small structures (~1 mm and ~0.3 mm in diameter, respectively) and exhibit poor image contrast. We have recently proposed a technique to achieve this task in adult patients, which relies on statistical models of the structures. These models contain intensity and shape information along the central axes of both structures. In this work we use the same method to segment pediatric scans. We show that substantial differences exist between the anatomy of children and the anatomy of adults, which lead to poor segmentation results when an adult model is used to segment a pediatric volume. We have built a new model for pediatric cases and we have applied it to ten scans. A leave-one-out validation experiment was conducted in which manually segmented structures were compared to automatically segmented structures. The maximum segmentation error was 1 mm. This result indicates that accurate segmentation of the facial nerve and chorda in pediatric scans is achievable, thus suggesting that safe drilling trajectories can also be computed automatically.

  7. 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.

  8. [Results of measuring neutrons doses and energy spectra inside Russian segment of the International Space Station in experiment "Matryoshka-R" using bubble detectors during the ISS-24-34 missions].

    PubMed

    Khulapko, S V; Liagushin, V I; Arkhangel'skiĭ, V V; Shurshakov, V A; Smith, M; Ing, H; Machrafi, R; Nikolaev, I V

    2014-01-01

    The paper presents the results of calculating the equivalent dose from and energy spectrum of neutrons in the right-hand crewquarters in module Zvezda of the ISS Russian segment. Dose measurements were made in the period between July, 2010 and November, 2012 (ISS Missions 24-34) by research equipment including the bubble dosimeter as part of experiment "Matryoshka-R". Neutron energy spectra in the crewquarters are in good agreement with what has been calculated for the ISS USOS and, earlier, for the MIR orbital station. The neutron dose rate has been found to amount to 196 +/- 23 microSv/d on Zvezda panel-443 (crewquarters) and 179 +/- 16 microSv/d on the "Shielding shutter" surface in the crewquarters.

  9. Livewire based single still image segmentation

    NASA Astrophysics Data System (ADS)

    Zhang, Jun; Yang, Rong; Liu, Xiaomao; Yue, Hao; Zhu, Hao; Tian, Dandan; Chen, Shu; Li, Yiquan; Tian, Jinwen

    2011-11-01

    In the application of the video contactless measurement, the quality of the image taken from underwater is not very well. It is well known that automatic image segmental method cannot provide acceptable segmentation result with low quality single still image. Snake algorithm can provide better result in this case with the aiding of human. However, sometimes the segmental result of Snake may far from the initial segmental contour drawn by user. Livewire algorithm can keep the location of the seed points that user selected nailed from the beginning to the end. But the contour may have burrs when the image's noise is quite high and the contrast is low. In this paper, we modified the cost function of Livewire algorithm and proposed a new segmentation method that can be used for single still image segmentation with high noise and low contrast.

  10. 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

  11. AUTOMATED CELL COUNTING AND CLUSTER SEGMENTATION USING CONCAVITY DETECTION AND ELLIPSE FITTING TECHNIQUES

    PubMed Central

    Kothari, Sonal; Chaudry, Qaiser; Wang, May D

    2016-01-01

    This paper presents a novel, fast and semi-automatic method for accurate cell cluster segmentation and cell counting of digital tissue image samples. In pathological conditions, complex cell clusters are a prominent feature in tissue samples. Segmentation of these clusters is a major challenge for development of an accurate cell counting methodology. We address the issue of cluster segmentation by following a three step process. The first step involves pre-processing required to obtain the appropriate nuclei cluster boundary image from the RGB tissue samples. The second step involves concavity detection at the edge of a cluster to find the points of overlap between two nuclei. The third step involves segmentation at these concavities by using an ellipse-fitting technique. Once the clusters are segmented, individual nuclei are counted to give the cell count. The method was tested on four different types of cancerous tissue samples and shows promising results with a low percentage error, high true positive rate and low false discovery rate.

  12. Brain-inspired speech segmentation for automatic speech recognition using the speech envelope as a temporal reference

    NASA Astrophysics Data System (ADS)

    Lee, Byeongwook; Cho, Kwang-Hyun

    2016-11-01

    Speech segmentation is a crucial step in automatic speech recognition because additional speech analyses are performed for each framed speech segment. Conventional segmentation techniques primarily segment speech using a fixed frame size for computational simplicity. However, this approach is insufficient for capturing the quasi-regular structure of speech, which causes substantial recognition failure in noisy environments. How does the brain handle quasi-regular structured speech and maintain high recognition performance under any circumstance? Recent neurophysiological studies have suggested that the phase of neuronal oscillations in the auditory cortex contributes to accurate speech recognition by guiding speech segmentation into smaller units at different timescales. A phase-locked relationship between neuronal oscillation and the speech envelope has recently been obtained, which suggests that the speech envelope provides a foundation for multi-timescale speech segmental information. In this study, we quantitatively investigated the role of the speech envelope as a potential temporal reference to segment speech using its instantaneous phase information. We evaluated the proposed approach by the achieved information gain and recognition performance in various noisy environments. The results indicate that the proposed segmentation scheme not only extracts more information from speech but also provides greater robustness in a recognition test.

  13. Brain-inspired speech segmentation for automatic speech recognition using the speech envelope as a temporal reference.

    PubMed

    Lee, Byeongwook; Cho, Kwang-Hyun

    2016-11-23

    Speech segmentation is a crucial step in automatic speech recognition because additional speech analyses are performed for each framed speech segment. Conventional segmentation techniques primarily segment speech using a fixed frame size for computational simplicity. However, this approach is insufficient for capturing the quasi-regular structure of speech, which causes substantial recognition failure in noisy environments. How does the brain handle quasi-regular structured speech and maintain high recognition performance under any circumstance? Recent neurophysiological studies have suggested that the phase of neuronal oscillations in the auditory cortex contributes to accurate speech recognition by guiding speech segmentation into smaller units at different timescales. A phase-locked relationship between neuronal oscillation and the speech envelope has recently been obtained, which suggests that the speech envelope provides a foundation for multi-timescale speech segmental information. In this study, we quantitatively investigated the role of the speech envelope as a potential temporal reference to segment speech using its instantaneous phase information. We evaluated the proposed approach by the achieved information gain and recognition performance in various noisy environments. The results indicate that the proposed segmentation scheme not only extracts more information from speech but also provides greater robustness in a recognition test.

  14. Brain-inspired speech segmentation for automatic speech recognition using the speech envelope as a temporal reference

    PubMed Central

    Lee, Byeongwook; Cho, Kwang-Hyun

    2016-01-01

    Speech segmentation is a crucial step in automatic speech recognition because additional speech analyses are performed for each framed speech segment. Conventional segmentation techniques primarily segment speech using a fixed frame size for computational simplicity. However, this approach is insufficient for capturing the quasi-regular structure of speech, which causes substantial recognition failure in noisy environments. How does the brain handle quasi-regular structured speech and maintain high recognition performance under any circumstance? Recent neurophysiological studies have suggested that the phase of neuronal oscillations in the auditory cortex contributes to accurate speech recognition by guiding speech segmentation into smaller units at different timescales. A phase-locked relationship between neuronal oscillation and the speech envelope has recently been obtained, which suggests that the speech envelope provides a foundation for multi-timescale speech segmental information. In this study, we quantitatively investigated the role of the speech envelope as a potential temporal reference to segment speech using its instantaneous phase information. We evaluated the proposed approach by the achieved information gain and recognition performance in various noisy environments. The results indicate that the proposed segmentation scheme not only extracts more information from speech but also provides greater robustness in a recognition test. PMID:27876875

  15. 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.

  16. Segmented ionization chambers for beam monitoring in hadrontherapy

    NASA Astrophysics Data System (ADS)

    Braccini, Saverio; Cirio, Roberto; Donetti, Marco; Marchetto, Flavio; Pittà, Giuseppe; Lavagno, Marco; La Rosa, Vanessa

    2015-05-01

    Segmented ionization chambers represent a good solution to monitor the position, the intensity and the shape of ion beams in hadrontherapy. Pixel and strip chambers have been developed for both passive scattering and active scanning dose delivery systems. In particular, strip chambers are optimal for pencil beam scanning, allowing for spatial and time resolutions below 0.1 mm and 1 ms, respectively. The MATRIX pixel and the Strip Accurate Monitor for Beam Applications (SAMBA) detectors are described in this paper together with the results of several beam tests and industrial developments based on these prototypes.

  17. Accurate three-dimensional documentation of distinct sites

    NASA Astrophysics Data System (ADS)

    Singh, Mahesh K.; Dutta, Ashish; Subramanian, Venkatesh K.

    2017-01-01

    One of the most critical aspects of documenting distinct sites is acquiring detailed and accurate range information. Several three-dimensional (3-D) acquisition techniques are available, but each has its own limitations. This paper presents a range data fusion method with the aim to enhance the descriptive contents of the entire 3-D reconstructed model. A kernel function is introduced for supervised classification of the range data using a kernelized support vector machine. The classification method is based on the local saliency features of the acquired range data. The range data acquired from heterogeneous range sensors are transformed into a defined common reference frame. Based on the segmentation criterion, the fusion of range data is performed by integrating finer regions of range data acquired from a laser range scanner with the coarser region of Kinect's range data. After fusion, the Delaunay triangulation algorithm is applied to generate the highly accurate, realistic 3-D model of the scene. Finally, experimental results show the robustness of the proposed approach.

  18. MR diffusion-weighted imaging-based subcutaneous tumour volumetry in a xenografted nude mouse model using 3D Slicer: an accurate and repeatable method

    PubMed Central

    Ma, Zelan; Chen, Xin; Huang, Yanqi; He, Lan; Liang, Cuishan; Liang, Changhong; Liu, Zaiyi

    2015-01-01

    Accurate and repeatable measurement of the gross tumour volume(GTV) of subcutaneous xenografts is crucial in the evaluation of anti-tumour therapy. Formula and image-based manual segmentation methods are commonly used for GTV measurement but are hindered by low accuracy and reproducibility. 3D Slicer is open-source software that provides semiautomatic segmentation for GTV measurements. In our study, subcutaneous GTVs from nude mouse xenografts were measured by semiautomatic segmentation with 3D Slicer based on morphological magnetic resonance imaging(mMRI) or diffusion-weighted imaging(DWI)(b = 0,20,800 s/mm2) . These GTVs were then compared with those obtained via the formula and image-based manual segmentation methods with ITK software using the true tumour volume as the standard reference. The effects of tumour size and shape on GTVs measurements were also investigated. Our results showed that, when compared with the true tumour volume, segmentation for DWI(P = 0.060–0.671) resulted in better accuracy than that mMRI(P < 0.001) and the formula method(P < 0.001). Furthermore, semiautomatic segmentation for DWI(intraclass correlation coefficient, ICC = 0.9999) resulted in higher reliability than manual segmentation(ICC = 0.9996–0.9998). Tumour size and shape had no effects on GTV measurement across all methods. Therefore, DWI-based semiautomatic segmentation, which is accurate and reproducible and also provides biological information, is the optimal GTV measurement method in the assessment of anti-tumour treatments. PMID:26489359

  19. 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

  20. Gender identification from high-pass filtered vowel segments: the use of high-frequency energy.

    PubMed

    Donai, Jeremy J; Lass, Norman J

    2015-10-01

    The purpose of this study was to examine the use of high-frequency information for making gender identity judgments from high-pass filtered vowel segments produced by adult speakers. Specifically, the effect of removing lower-frequency spectral detail (i.e., F3 and below) from vowel segments via high-pass filtering was evaluated. Thirty listeners (ages 18-35) with normal hearing participated in the experiment. A within-subjects design was used to measure gender identification for six 250-ms vowel segments (/æ/, /ɪ /, /ɝ/, /ʌ/, /ɔ/, and /u/), produced by ten male and ten female speakers. The results of this experiment demonstrated that despite the removal of low-frequency spectral detail, the listeners were accurate in identifying speaker gender from the vowel segments, and did so with performance significantly above chance. The removal of low-frequency spectral detail reduced gender identification by approximately 16 % relative to unfiltered vowel segments. Classification results using linear discriminant function analyses followed the perceptual data, using spectral and temporal representations derived from the high-pass filtered segments. Cumulatively, these findings indicate that normal-hearing listeners are able to make accurate perceptual judgments regarding speaker gender from vowel segments with low-frequency spectral detail removed via high-pass filtering. Therefore, it is reasonable to suggest the presence of perceptual cues related to gender identity in the high-frequency region of naturally produced vowel signals. Implications of these findings and possible mechanisms for performing the gender identification task from high-pass filtered stimuli are discussed.

  1. A Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation.

    PubMed

    Korez, Robert; Ibragimov, Bulat; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

    2015-08-01

    Automated and semi-automated detection and segmentation of spinal and vertebral structures from computed tomography (CT) images is a challenging task due to a relatively high degree of anatomical complexity, presence of unclear boundaries and articulation of vertebrae with each other, as well as due to insufficient image spatial resolution, partial volume effects, presence of image artifacts, intensity variations and low signal-to-noise ratio. In this paper, we describe a novel framework for automated spine and vertebrae detection and segmentation from 3-D CT images. A novel optimization technique based on interpolation theory is applied to detect the location of the whole spine in the 3-D image and, using the obtained location of the whole spine, to further detect the location of individual vertebrae within the spinal column. The obtained vertebra detection results represent a robust and accurate initialization for the subsequent segmentation of individual vertebrae, which is performed by an improved shape-constrained deformable model approach. The framework was evaluated on two publicly available CT spine image databases of 50 lumbar and 170 thoracolumbar vertebrae. Quantitative comparison against corresponding reference vertebra segmentations yielded an overall mean centroid-to-centroid distance of 1.1 mm and Dice coefficient of 83.6% for vertebra detection, and an overall mean symmetric surface distance of 0.3 mm and Dice coefficient of 94.6% for vertebra segmentation. The results indicate that by applying the proposed automated detection and segmentation framework, vertebrae can be successfully detected and accurately segmented in 3-D from CT spine images.

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

    PubMed

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

    2016-12-01

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

  3. Automated liver segmentation for whole-body low-contrast CT images from PET-CT scanners.

    PubMed

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

    2009-01-01

    Accurate objective automated liver segmentation in PET-CT studies is important to improve the identification and localization of hepatic tumor. However, this segmentation is an extremely challenging task from the low-contrast CT images captured from PET-CT scanners because of the intensity similarity between liver and adjacent loops of bowel, stomach and muscle. In this paper, we propose a novel automated three-stage liver segmentation technique for PET-CT whole body studies, where: 1) the starting liver slice is automatically localized based on the liver - lung relations; 2) the "masking" slice containing the biggest liver section is localized using the ratio of liver ROI size to the right half of abdomen ROI size; 3) the liver segmented from the "masking" slice forms the initial estimation or mask for the automated liver segmentation. Our experimental results from clinical PET-CT studies show that this method can automatically segment the liver for a range of different patients, with consistent objective selection criteria and reproducible accurate results.

  4. A 3-D liver segmentation method with parallel computing for selective internal radiation therapy.

    PubMed

    Goryawala, Mohammed; Guillen, Magno R; Cabrerizo, Mercedes; Barreto, Armando; Gulec, Seza; Barot, Tushar C; Suthar, Rekha R; Bhatt, Ruchir N; Mcgoron, Anthony; Adjouadi, Malek

    2012-01-01

    This study describes a new 3-D liver segmentation method in support of the selective internal radiation treatment as a treatment for liver tumors. This 3-D segmentation is based on coupling a modified k-means segmentation method with a special localized contouring algorithm. In the segmentation process, five separate regions are identified on the computerized tomography image frames. The merit of the proposed method lays in its potential to provide fast and accurate liver segmentation and 3-D rendering as well as in delineating tumor region(s), all with minimal user interaction. Leveraging of multicore platforms is shown to speed up the processing of medical images considerably, making this method more suitable in clinical settings. Experiments were performed to assess the effect of parallelization using up to 442 slices. Empirical results, using a single workstation, show a reduction in processing time from 4.5 h to almost 1 h for a 78% gain. Most important is the accuracy achieved in estimating the volumes of the liver and tumor region(s), yielding an average error of less than 2% in volume estimation over volumes generated on the basis of the current manually guided segmentation processes. Results were assessed using the analysis of variance statistical analysis.

  5. Automatic segmentation of the facial nerve and chorda tympani using image registration and statistical priors

    NASA Astrophysics Data System (ADS)

    Noble, Jack H.; Warren, Frank M.; Labadie, Robert F.; Dawant, Benoit M.

    2008-03-01

    In cochlear implant surgery, an electrode array is permanently implanted in the cochlea to stimulate the auditory nerve and allow deaf people to hear. A minimally invasive surgical technique has recently been proposed--percutaneous cochlear access--in which a single hole is drilled from the skull surface to the cochlea. For the method to be feasible, a safe and effective drilling trajectory must be determined using a pre-operative CT. Segmentation of the structures of the ear would improve trajectory planning safety and efficiency and enable the possibility of automated planning. Two important structures of the ear, the facial nerve and chorda tympani, present difficulties in intensity based segmentation due to their diameter (as small as 1.0 and 0.4 mm) and adjacent inter-patient variable structures of similar intensity in CT imagery. A multipart, model-based segmentation algorithm is presented in this paper that accomplishes automatic segmentation of the facial nerve and chorda tympani. Segmentation results are presented for 14 test ears and are compared to manually segmented surfaces. The results show that mean error in structure wall localization is 0.2 and 0.3 mm for the facial nerve and chorda, proving the method we propose is robust and accurate.

  6. Automatic 3D kidney segmentation based on shape constrained GC-OAAM

    NASA Astrophysics Data System (ADS)

    Chen, Xinjian; Summers, Ronald M.; Yao, Jianhua

    2011-03-01

    The kidney can be classified into three main tissue types: renal cortex, renal medulla and renal pelvis (or collecting system). Dysfunction of different renal tissue types may cause different kidney diseases. Therefore, accurate and efficient segmentation of kidney into different tissue types plays a very important role in clinical research. In this paper, we propose an automatic 3D kidney segmentation method which segments the kidney into the three different tissue types: renal cortex, medulla and pelvis. The proposed method synergistically combines active appearance model (AAM), live wire (LW) and graph cut (GC) methods, GC-OAAM for short. Our method consists of two main steps. First, a pseudo 3D segmentation method is employed for kidney initialization in which the segmentation is performed slice-by-slice via a multi-object oriented active appearance model (OAAM) method. An improved iterative model refinement algorithm is proposed for the AAM optimization, which synergistically combines the AAM and LW method. Multi-object strategy is applied to help the object initialization. The 3D model constraints are applied to the initialization result. Second, the object shape information generated from the initialization step is integrated into the GC cost computation. A multi-label GC method is used to segment the kidney into cortex, medulla and pelvis. The proposed method was tested on 19 clinical arterial phase CT data sets. The preliminary results showed the feasibility and efficiency of the proposed method.

  7. Automated choroidal segmentation method in human eye with 1050nm optical coherence tomography

    NASA Astrophysics Data System (ADS)

    Liu, Cindy; Wang, Ruikang K.

    2014-02-01

    Choroidal thickness (ChT), defined as the distance between the retinal pigment epithelium (RPE) and the choroid-sclera interface (CSI), is highly correlated with various ocular disorders like high myopia, diabetic retinopathy, and central serous chorioretinopathy. Long wavelength Optical Coherence Tomography (OCT) has the ability to penetrate deep to the CSI, making the measurement of the ChT possible. The ability to accurately segment the CSI and RPE is important in extracting clinical information. However, automated CSI segmentation is challenging due to the weak boundary in the lower choroid and inconsistent texture with varied blood vessels. We propose a K-means clustering based automated algorithm, which is effective in segmenting the CSI and RPE. The performance of the method was evaluated using 531 frames from 4 normal subjects. The RPE and CSI segmentation time was about 0.3 seconds per frame, and the average time was around 0.5 seconds per frame with correction among frames, which is faster than reported algorithms. The results from the proposed method are consistent with the manual segmentation results. Further investigation includes the optimization of the algorithm to cover more OCT images captured from patients and the increase of the processing speed and robustness of the segmentation method.

  8. Segmentation of organs at risk in CT volumes of head, thorax, abdomen, and pelvis

    NASA Astrophysics Data System (ADS)

    Han, Miaofei; Ma, Jinfeng; Li, Yan; Li, Meiling; Song, Yanli; Li, Qiang

    2015-03-01

    Accurate segmentation of organs at risk (OARs) is a key step in treatment planning system (TPS) of image guided radiation therapy. We are developing three classes of methods to segment 17 organs at risk throughout the whole body, including brain, brain stem, eyes, mandible, temporomandibular joints, parotid glands, spinal cord, lungs, trachea, heart, livers, kidneys, spleen, prostate, rectum, femoral heads, and skin. The three classes of segmentation methods include (1) threshold-based methods for organs of large contrast with adjacent structures such as lungs, trachea, and skin; (2) context-driven Generalized Hough Transform-based methods combined with graph cut algorithm for robust localization and segmentation of liver, kidneys and spleen; and (3) atlas and registration-based methods for segmentation of heart and all organs in CT volumes of head and pelvis. The segmentation accuracy for the seventeen organs was subjectively evaluated by two medical experts in three levels of score: 0, poor (unusable in clinical practice); 1, acceptable (minor revision needed); and 2, good (nearly no revision needed). A database was collected from Ruijin Hospital, Huashan Hospital, and Xuhui Central Hospital in Shanghai, China, including 127 head scans, 203 thoracic scans, 154 abdominal scans, and 73 pelvic scans. The percentages of "good" segmentation results were 97.6%, 92.9%, 81.1%, 87.4%, 85.0%, 78.7%, 94.1%, 91.1%, 81.3%, 86.7%, 82.5%, 86.4%, 79.9%, 72.6%, 68.5%, 93.2%, 96.9% for brain, brain stem, eyes, mandible, temporomandibular joints, parotid glands, spinal cord, lungs, trachea, heart, livers, kidneys, spleen, prostate, rectum, femoral heads, and skin, respectively. Various organs at risk can be reliably segmented from CT scans by use of the three classes of segmentation methods.

  9. Pulmonary airways tree segmentation from CT examinations using adaptive volume of interest

    NASA Astrophysics Data System (ADS)

    Park, Sang Cheol; Kim, Won Pil; Zheng, Bin; Leader, Joseph K.; Pu, Jiantao; Tan, Jun; Gur, David

    2009-02-01

    Airways tree segmentation is an important step in quantitatively assessing the severity of and changes in several lung diseases such as chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis. It can also be used in guiding bronchoscopy. The purpose of this study is to develop an automated scheme for segmenting the airways tree structure depicted on chest CT examinations. After lung volume segmentation, the scheme defines the first cylinder-like volume of interest (VOI) using a series of images depicting the trachea. The scheme then iteratively defines and adds subsequent VOIs using a region growing algorithm combined with adaptively determined thresholds in order to trace possible sections of airways located inside the combined VOI in question. The airway tree segmentation process is automatically terminated after the scheme assesses all defined VOIs in the iteratively assembled VOI list. In this preliminary study, ten CT examinations with 1.25mm section thickness and two different CT image reconstruction kernels ("bone" and "standard") were selected and used to test the proposed airways tree segmentation scheme. The experiment results showed that (1) adopting this approach affectively prevented the scheme from infiltrating into the parenchyma, (2) the proposed method reasonably accurately segmented the airways trees with lower false positive identification rate as compared with other previously reported schemes that are based on 2-D image segmentation and data analyses, and (3) the proposed adaptive, iterative threshold selection method for the region growing step in each identified VOI enables the scheme to segment the airways trees reliably to the 4th generation in this limited dataset with successful segmentation up to the 5th generation in a fraction of the airways tree branches.

  10. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging.

    PubMed

    García-Lorenzo, Daniel; Francis, Simon; Narayanan, Sridar; Arnold, Douglas L; Collins, D Louis

    2013-01-01

    Magnetic resonance (MR) imaging is often used to characterize and quantify multiple sclerosis (MS) lesions in the brain and spinal cord. The number and volume of lesions have been used to evaluate MS disease burden, to track the progression of the disease and to evaluate the effect of new pharmaceuticals in clinical trials. Accurate identification of MS lesions in MR images is extremely difficult due to variability in lesion location, size and shape in addition to anatomical variability between subjects. Since manual segmentation requires expert knowledge, is time consuming and is subject to intra- and inter-expert variability, many methods have been proposed to automatically segment lesions. The objective of this study was to carry out a systematic review of the literature to evaluate the state of the art in automated multiple sclerosis lesion segmentation. From 1240 hits found initially with PubMed and Google scholar, our selection criteria identified 80 papers that described an automatic lesion segmentation procedure applied to MS. Only 47 of these included quantitative validation with at least one realistic image. In this paper, we describe the complexity of lesion segmentation, classify the automatic MS lesion segmentation methods found, and review the validation methods applied in each of the papers reviewed. Although many segmentation solutions have been proposed, including some with promising results using MRI data obtained on small groups of patients, no single method is widely employed due to performance issues related to the high variability of MS lesion appearance and differences in image acquisition. The challenge remains to provide segmentation techniques that work in all cases regardless of the type of MS, duration of the disease, or MRI protocol, and this within a comprehensive, standardized validation framework. MS lesion segmentation remains an open problem.

  11. Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images.

    PubMed

    Oktay, Ozan; Bai, Wenjia; Guerrero, Ricardo; Rajchl, Martin; de Marvao, Antonio; O'Regan, Declan P; Cook, Stuart A; Heinrich, Mattias P; Glocker, Ben; Rueckert, Daniel

    2017-01-01

    Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D high-resolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-the-art landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy.

  12. Integrating Compact Constraint and Distance Regularization with Level Set for Hepatocellular Carcinoma (HCC) Segmentation on Computed Tomography (CT) Images

    NASA Astrophysics Data System (ADS)

    Gui, Luying; He, Jian; Qiu, Yudong; Yang, Xiaoping

    2017-01-01

    This paper presents a variational level set approach to segment lesions with compact shapes on medical images. In this study, we investigate to address the problem of segmentation for hepatocellular carcinoma which are usually of various shapes, variable intensities, and weak boundaries. An efficient constraint which is called the isoperimetric constraint to describe the compactness of shapes is applied in this method. In addition, in order to ensure the precise segmentation and stable movement of the level set, a distance regularization is also implemented in the proposed variational framework. Our method is applied to segment various hepatocellular carcinoma regions on Computed Tomography images with promising results. Comparison results also prove that the proposed method is more accurate than other two approaches.

  13. On numerically accurate finite element

    NASA Technical Reports Server (NTRS)

    Nagtegaal, J. C.; Parks, D. M.; Rice, J. R.

    1974-01-01

    A general criterion for testing a mesh with topologically similar repeat units is given, and the analysis shows that only a few conventional element types and arrangements are, or can be made suitable for computations in the fully plastic range. Further, a new variational principle, which can easily and simply be incorporated into an existing finite element program, is presented. This allows accurate computations to be made even for element designs that would not normally be suitable. Numerical results are given for three plane strain problems, namely pure bending of a beam, a thick-walled tube under pressure, and a deep double edge cracked tensile specimen. The effects of various element designs and of the new variational procedure are illustrated. Elastic-plastic computation at finite strain are discussed.

  14. 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.

  15. A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT.

    PubMed

    Xu, Ziyue; Bagci, Ulas; Foster, Brent; Mansoor, Awais; Udupa, Jayaram K; Mollura, Daniel J

    2015-08-01

    Inflammatory and infectious lung diseases commonly involve bronchial airway structures and morphology, and these abnormalities are often analyzed non-invasively through high resolution computed tomography (CT) scans. Assessing airway wall surfaces and the lumen are of great importance for diagnosing pulmonary diseases. However, obtaining high accuracy from a complete 3-D airway tree structure can be quite challenging. The airway tree structure has spiculated shapes with multiple branches and bifurcation points as opposed to solid single organ or tumor segmentation tasks in other applications, hence, it is complex for manual segmentation as compared with other tasks. For computerized methods, a fundamental challenge in airway tree segmentation is the highly variable intensity levels in the lumen area, which often causes a segmentation method to leak into adjacent lung parenchyma through blurred airway walls or soft boundaries. Moreover, outer wall definition can be difficult due to similar intensities of the airway walls and nearby structures such as vessels. In this paper, we propose a computational framework to accurately quantify airways through (i) a novel hybrid approach for precise segmentation of the lumen, and (ii) two novel methods (a spatially constrained Markov random walk method (pseudo 3-D) and a relative fuzzy connectedness method (3-D)) to estimate the airway wall thickness. We evaluate the performance of our proposed methods in comparison with mostly used algorithms using human chest CT images. Our results demonstrate that, on publicly available data sets and using standard evaluation criteria, the proposed airway segmentation method is accurate and efficient as compared with the state-of-the-art methods, and the airway wall estimation algorithms identified the inner and outer airway surfaces more accurately than the most widely applied methods, namely full width at half maximum and phase congruency.

  16. Communicative functions integrate segments in prosodies and prosodies in segments.

    PubMed

    Kohler, Klaus J

    2011-01-01

    This paper takes a new look at the traditionally established divide between sounds and prosodies, viewing it as a useful heuristics in language descriptions that focus on the segmental make- up of words. It pleads for a new approach that bridges this reified compartmentalization of speech in a more global communicative perspective. Data are presented from a German perception experiment in the framework of the Semantic Differential that shows interdependence of f0 contours and the spectral characteristics of a following fricative segment, for the expression of semantic functions along the scales questioning - asserting, excited - calm, forceful - not forceful, contrary - agreeable. The results lead to the conclusion that segments shape prosodies and are shaped by them in varying ways in the coding of semantic functions. This implies that the analysis of sentence prosodies needs to integrate the manifestation of segments, just as the analysis of segments needs to consider their prosodic embedding. In communicative interaction, speakers set broad prosodic time windows of varying sizes, and listeners respond to them. So, future phonetic research needs to concentrate on speech analysis in such windows.

  17. GeoSegmenter: A statistically learned Chinese word segmenter for the geoscience domain

    NASA Astrophysics Data System (ADS)

    Huang, Lan; Du, Youfu; Chen, Gongyang

    2015-03-01

    Unlike English, the Chinese language has no space between words. Segmenting texts into words, known as the Chinese word segmentation (CWS) problem, thus becomes a fundamental issue for processing Chinese documents and the first step in many text mining applications, including information retrieval, machine translation and knowledge acquisition. However, for the geoscience subject domain, the CWS problem remains unsolved. Although a generic segmenter can be applied to process geoscience documents, they lack the domain specific knowledge and consequently their segmentation accuracy drops dramatically. This motivated us to develop a segmenter specifically for the geoscience subject domain: the GeoSegmenter. We first proposed a generic two-step framework for domain specific CWS. Following this framework, we built GeoSegmenter using conditional random fields, a principled statistical framework for sequence learning. Specifically, GeoSegmenter first identifies general terms by using a generic baseline segmenter. Then it recognises geoscience terms by learning and applying a model that can transform the initial segmentation into the goal segmentation. Empirical experimental results on geoscience documents and benchmark datasets showed that GeoSegmenter could effectively recognise both geoscience terms and general terms.

  18. Geomorphometirc Segmentation of Shield Deserts by Self-Organizing Maps

    NASA Astrophysics Data System (ADS)

    Foroutan, M.; Kompanizare, M.; Ehsani, A. H.

    2015-12-01

    Shield deserts have developed on ancient crystalline bedrocks and mainly composed of folded and faulted rocks hardened by heat and pressure over millions of years. They were unearthed by erosion and form steep-sided hills and basins filled with sediments. The Sahara, Arabian, southern African, central Kavir and Australian deserts are in this group. Their ranges usually supply groundwater resources or in some regions contain huge oil reservoirs. Geomorphological segmentation of shield deserts is one of the fundamental tools in their land use or site investigation planning as well as in their surface water and groundwater management. In many studies the morphology of shield deserts has been investigated by limited qualitative and subjective methods using limited number of simple parameters such as surface elevation and slope. However the importance of these regions supports the need for their accurate and quantitative morphologic classification. The present study attempts to implement a quantitative method, Self-Organizing Map (SOM), for geomorphological classification of a typical shield desert within Kavir Desert, Iran. The area is tectonically stable and characterized by flat clay pans, playas, well-developed pediments around scattered and low elevation ranges. Twenty-two multi-scale morphometric parameters were derived from the first- to third-orders partial derivatives of the surface elevation. Seven optimized parameters with their proper scales were selected by Artificial Neural Networks, Optimum Index Factor, Davies-Bouldin Index and statistic models. Finally, the area was segmented to seven homogeneous areas by SOM algorithm. The results revealed the most distinguishing parameter set (MDPS) for morphologic segmentation of shield deserts. The same segmentation results through using MDPS for another shield deserts in Australia proves the applicability of MDPS for shield deserts segmentation.

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

    There is a need for robust, fully automated whole body organ segmentation for diagnostic CT. This study investigates and optimizes a Random Forest algorithm for automated organ segmentation; explores the limitations of a Random Forest algorithm applied to the CT environment; and demonstrates segmentation accuracy in a feasibility study of pediatric and adult patients. To the best of our knowledge, this is the first study to investigate a trainable Weka segmentation (TWS) implementation using Random Forest machine-learning as a means to develop a fully automated tissue segmentation tool developed specifically for pediatric and adult examinations in a diagnostic CT environment. Current innovation in computed tomography (CT) is focused on radiomics, patient-specific radiation dose calculation, and image quality improvement using iterative reconstruction, all of which require specific knowledge of tissue and organ systems within a CT image. The purpose of this study was to develop a fully automated Random Forest classifier algorithm for segmentation of neck-chest-abdomen-pelvis CT examinations based on pediatric and adult CT protocols. Seven materials were classified: background, lung/internal air or gas, fat, muscle, solid organ parenchyma, blood/contrast enhanced fluid, and bone tissue using Matlab and the TWS plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance evaluated over a voxel radius of 2 n , (n from 0 to 4), along with noise reduction and edge preserving filters: Gaussian, bilateral, Kuwahara, and anisotropic diffusion. The Random Forest algorithm used 200 trees with 2 features randomly selected per node. The optimized auto-segmentation algorithm resulted in 16 image features including features derived from maximum, mean, variance Gaussian and Kuwahara filters. Dice similarity coefficient (DSC) calculations between manually segmented and Random Forest algorithm segmented images from 21

  20. Accurate tracking of tumor volume change during radiotherapy by CT-CBCT registration with intensity correction

    NASA Astrophysics Data System (ADS)

    Park, Seyoun; Robinson, Adam; Quon, Harry; Kiess, Ana P.; Shen, Colette; Wong, John; Plishker, William; Shekhar, Raj; Lee, Junghoon

    2016-03-01

    In this paper, we propose a CT-CBCT registration method to accurately predict the tumor volume change based on daily cone-beam CTs (CBCTs) during radiotherapy. CBCT is commonly used to reduce patient setup error during radiotherapy, but its poor image quality impedes accurate monitoring of anatomical changes. Although physician's contours drawn on the planning CT can be automatically propagated to daily CBCTs by deformable image registration (DIR), artifacts in CBCT often cause undesirable errors. To improve the accuracy of the registration-based segmentation, we developed a DIR method that iteratively corrects CBCT intensities by local histogram matching. Three popular DIR algorithms (B-spline, demons, and optical flow) with the intensity correction were implemented on a graphics processing unit for efficient computation. We evaluated their performances on six head and neck (HN) cancer cases. For each case, four trained scientists manually contoured the nodal gross tumor volume (GTV) on the planning CT and every other fraction CBCTs to which the propagated GTV contours by DIR were compared. The performance was also compared with commercial image registration software based on conventional mutual information (MI), VelocityAI (Varian Medical Systems Inc.). The volume differences (mean±std in cc) between the average of the manual segmentations and automatic segmentations are 3.70+/-2.30 (B-spline), 1.25+/-1.78 (demons), 0.93+/-1.14 (optical flow), and 4.39+/-3.86 (VelocityAI). The proposed method significantly reduced the estimation error by 9% (B-spline), 38% (demons), and 51% (optical flow) over the results using VelocityAI. Although demonstrated only on HN nodal GTVs, the results imply that the proposed method can produce improved segmentation of other critical structures over conventional methods.

  1. Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino acid contents. 2. Results for wheat, barley, corn, triticale, wheat bran/middlings, rice bran, and sorghum.

    PubMed

    Fontaine, Johannes; Schirmer, Barbara; Hörr, Jutta

    2002-07-03

    Further NIRS calibrations were developed for the accurate and fast prediction of the total contents of methionine, cystine, lysine, threonine, tryptophan, and other essential amino acids, protein, and moisture in the most important cereals and brans or middlings for animal feed production. More than 1100 samples of global origin collected over five years were analyzed for amino acids following the Official Methods of the United States and European Union. Detailed data and graphics are given to characterize the obtained calibration equations. NIRS was validated with 98 independent samples for wheat and 78 samples for corn and compared to amino acid predictions using linear crude protein regression equations. With a few exceptions, validation showed that 70-98% of the amino acid variance in the samples could be explained using NIRS. Especially for lysine and methionine, the most limiting amino acids for farm animals, NIRS can predict contents in cereals much better than crude protein regressions. Through low cost and high speed of analysis NIRS enables the amino acid analysis of many samples in order to improve the accuracy of feed formulation and obtain better quality and lower production costs.

  2. A robust medical image segmentation method using KL distance and local neighborhood information.

    PubMed

    Zheng, Qian; Lu, Zhentai; Yang, Wei; Zhang, Minghui; Feng, Qianjin; Chen, Wufan

    2013-06-01

    In this paper, we propose an improved Chan-Vese (CV) model that uses Kullback-Leibler (KL) distances and local neighborhood information (LNI). Due to the effects of heterogeneity and complex constructions, the performance of level set segmentation is subject to confounding by the presence of nearby structures of similar intensity, preventing it from discerning the exact boundary of the object. Moreover, the CV model cannot usually obtain accurate results in medical image segmentation in cases of optimal configuration of controlling parameters, which requires substantial manual intervention. To overcome the above deficiency, we improve the segmentation accuracy by the usage of KL distance and LNI, thereby introducing the image local characteristics. Performance evaluation of the present method was achieved through experiments on the synthetic images and a series of real medical images. The extensive experimental results showed the superior performance of the proposed method over the state-of-the-art methods, in terms of both robustness and efficiency.

  3. Comparison of three image segmentation techniques for target volume delineation in positron emission tomography.

    PubMed

    Drever, Laura A; Roa, Wilson; McEwan, Alexander; Robinson, Don

    2007-03-09

    Incorporation of positron emission tomography (PET) data into radiotherapy planning is currently under investigation for numerous sites including lung, brain, head and neck, breast, and prostate. Accurate tumor-volume quantification is essential to the proper utilization of the unique information provided by PET. Unfortunately,target delineation within PET currently remains a largely unaddressed problem. We therefore examined the ability of three segmentation methods-thresholding, Sobel edge detection, and the watershed approach-to yield accurate delineation of PET target cross-sections. A phantom study employing well-defined cylindrical and spherical volumes and activity distributions provided an opportunity to assess the relative efficacy with which the three approaches could yield accurate target delineation in PET. Results revealed that threshold segmentation can accurately delineate target cross-sections, but that the Sobel and watershed techniques both consistently fail to correctly identify the size of experimental volumes. The usefulness of threshold-based segmentation is limited, however, by the dependence of the correct threshold (that which returns the correct area at each image slice) on target size.

  4. Bone image segmentation.

    PubMed

    Liu, Z Q; Liew, H L; Clement, J G; Thomas, C D

    1999-05-01

    Characteristics of microscopic structures in bone cross sections carry essential clues in age determination in forensic science and in the study of age-related bone developments and bone diseases. Analysis of bone cross sections represents a major area of research in bone biology. However, traditional approaches in bone biology have relied primarily on manual processes with very limited number of bone samples. As a consequence, it is difficult to reach reliable and consistent conclusions. In this paper we present an image processing system that uses microstructural and relational knowledge present in the bone cross section for bone image segmentation. This system automates the bone image analysis process and is able to produce reliable results based on quantitative measurements from a large number of bone images. As a result, using large databases of bone images to study the correlation between bone structural features and age-related bone developments becomes feasible.

  5. Improvement of a retinal blood vessel segmentation method using the Insight Segmentation and Registration Toolkit (ITK).

    PubMed

    Martinez-Perez, M; Hughes, Alun D; Thom, Simon A; Parker, Kim H

    2007-01-01

    We describe an improved implementation of a segmentation method for retinal blood vessels based on a multi-scale approach and region growing employing modules from the Insight Segmentation and Registration Toolkit (ITK). We present the results of segmentation of retinal blood vessels using this improved method and compare these with results obtained using the original implementation in Matlab, as well as with expert manual segmentations obtained from a public database. We show that the ITK implementation achieves high quality segmentations with markedly improved computational efficiency. The ITK version has greater segmentation accuracy, from 0.94 to 0.96, than the Matlab version due to a decrease in FPR values and it is between 8 and 12 times faster than the original version. Furthermore, the ITK implementation is able to segment high-resolution images in an acceptable timescale.

  6. Mid-ocean ridges: discontinuities, segments and giant cracks.

    PubMed

    Macdonald, K C; Scheirer, D S; Carbotte, S M

    1991-08-30

    Geological observations reveal that mid-ocean ridges are segmented by numerous rigid and nonrigid discontinuities. A hierarchy of segmentation, ranging from large, long-lived segments to others that are small, migratory, and transient, determines the pattern and timing of creation of new ocean floor. To the extent that spreading segments behave like giant cracks in a plate, the crack propagation force at segment tips increases with segment length, which may explain why long segments tend to lengthen and prevail over shorter neighboring segments. Partial melting caused by decompression of the upper mantle due to plate separation and changes in the direction of spreading result in the spawning of new short segments so that a balance of long and short segments is maintained.

  7. Impact assisted segmented cutterhead

    DOEpatents

    Morrell, Roger J.; Larson, David A.; Ruzzi, Peter L.

    1992-01-01

    An impact assisted segmented cutterhead device is provided for cutting various surfaces from coal to granite. The device comprises a plurality of cutting bit segments deployed in side by side relationship to form a continuous cutting face and a plurality of impactors individually associated with respective cutting bit segments. An impactor rod of each impactor connects that impactor to the corresponding cutting bit segment. A plurality of shock mounts dampening the vibration from the associated impactor. Mounting brackets are used in mounting the cutterhead to a base machine.

  8. Event segmentation ability uniquely predicts event memory.

    PubMed

    Sargent, Jesse Q; Zacks, Jeffrey M; Hambrick, David Z; Zacks, Rose T; Kurby, Christopher A; Bailey, Heather R; Eisenberg, Michelle L; Beck, Taylor M

    2013-11-01

    Memory for everyday events plays a central role in tasks of daily living, autobiographical memory, and planning. Event memory depends in part on segmenting ongoing activity into meaningful units. This study examined the relationship between event segmentation and memory in a lifespan sample to answer the following question: Is the ability to segment activity into meaningful events a unique predictor of subsequent memory, or is the relationship between event perception and memory accounted for by general cognitive abilities? Two hundred and eight adults ranging from 20 to 79years old segmented movies of everyday events and attempted to remember the events afterwards. They also completed psychometric ability tests and tests measuring script knowledge for everyday events. Event segmentation and script knowledge both explained unique variance in event memory above and beyond the psychometric measures, and did so as strongly in older as in younger adults. These results suggest that event segmentation is a basic cognitive mechanism, important for memory across the lifespan.

  9. Automatic layer segmentation of H&E microscopic images of mice skin

    NASA Astrophysics Data System (ADS)

    Hussein, Saif; Selway, Joanne; Jassim, Sabah; Al-Assam, Hisham

    2016-05-01

    Mammalian skin is a complex organ composed of a variety of cells and tissue types. The automatic detection and quantification of changes in skin structures has a wide range of applications for biological research. To accurately segment and quantify nuclei, sebaceous gland, hair follicles, and other skin structures, there is a need for a reliable segmentation of different skin layers. This paper presents an efficient segmentation algorithm to segment the three main layers of mice skin, namely epidermis, dermis, and subcutaneous layers. It also segments the epidermis layer into two sub layers, basal and cornified layers. The proposed algorithm uses adaptive colour deconvolution technique on H&E stain images to separate different tissue structures, inter-modes and Otsu thresholding techniques were effectively combined to segment the layers. It then uses a set of morphological and logical operations on each layer to removing unwanted objects. A dataset of 7000 H&E microscopic images of mutant and wild type mice were used to evaluate the effectiveness of the algorithm. Experimental results examined by domain experts have confirmed the viability of the proposed algorithms.

  10. 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.

  11. Co-adding techniques for image-based wavefront sensing for segmented-mirror telescopes

    NASA Astrophysics Data System (ADS)

    Smith, J. S.; Aronstein, David L.; Dean, Bruce H.; Acton, D. S.

    2007-09-01

    Image-based wavefront sensing algorithms are being used to characterize the 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 co-added 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 each PSF frame individually and average the resulting wavefronts. The choice of co-add methodology is particularly noteworthy for segmented-mirror telescopes that are subject to noise that causes uncorrelated motions between groups of segments. Using models and data from the James Webb Space Telescope (JWST) Testbed Telescope (TBT), 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 co-add method. Of particular interest, segment piston is more accurately recovered in "image-plane space" co-adding, while segment tip/tilt is recovered in "pupil-plane space" co-adding.

  12. Optimal reinforcement of training datasets in semi-supervised landmark-based segmentation

    NASA Astrophysics Data System (ADS)

    Ibragimov, Bulat; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

    2015-03-01

    During the last couple of decades, the development of computerized image segmentation shifted from unsupervised to supervised methods, which made segmentation results more accurate and robust. However, the main disadvantage of supervised segmentation is a need for manual image annotation that is time-consuming and subjected to human error. To reduce the need for manual annotation, we propose a novel learning approach for training dataset reinforcement in the area of landmark-based segmentation, where newly detected landmarks are optimally combined with reference landmarks from the training dataset and therefore enriches the training process. The approach is formulated as a nonlinear optimization problem, where the solution is a vector of weighting factors that measures how reliable are the detected landmarks. The detected landmarks that are found to be more reliable are included into the training procedure with higher weighting factors, whereas the detected landmarks that are found to be less reliable are included with lower weighting factors. The approach is integrated into the landmark-based game-theoretic segmentation framework and validated against the problem of lung field segmentation from chest radiographs.

  13. Efficient liver segmentation in CT images based on graph cuts and bottleneck detection.

    PubMed

    Liao, Miao; Zhao, Yu-Qian; Wang, Wei; Zeng, Ye-Zhan; Yang, Qing; Shih, Frank Y; Zou, Bei-Ji

    2016-11-01

    Liver segmentation from abdominal computed tomography (CT) volumes is extremely important for computer-aided liver disease diagnosis and surgical planning of liver transplantation. Due to ambiguous edges, tissue adhesion, and variation in liver intensity and shape across patients, accurate liver segmentation is a challenging task. In this paper, we present an efficient semi-automatic method using intensity, local context, and spatial correlation of adjacent slices for the segmentation of healthy liver regions in CT volumes. An intensity model is combined with a principal component analysis (PCA) based appearance model to exclude complex background and highlight liver region. They are then integrated with location information from neighboring slices into graph cuts to segment the liver in each slice automatically. Finally, a boundary refinement method based on bottleneck detection is used to increase the segmentation accuracy. Our method does not require heavy training process or statistical model construction, and is capable of dealing with complicated shape and intensity variations. We apply the proposed method on XHCSU14 and SLIVER07 databases, and evaluate it by MICCAI criteria and Dice similarity coefficient. Experimental results show our method outperforms several existing methods on liver segmentation.

  14. Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation.

    PubMed

    Freiman, M; Kronman, A; Esses, S J; Joskowicz, L; Sosna, J

    2010-01-01

    We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images. The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field. A non-parametric hybrid shape and intensity model is treated as a latent variable in the energy functional. The latent model and labeling map that minimize the energy functional are then simultaneously computed with an expectation maximization approach. The main advantages of our method are that it does not assume a fixed parametric prior model, which is subjective to inter-patient variability and registration errors, and that it combines both the model and the image information into a unified graph min-cut based segmentation framework. We evaluated our method on 20 kidneys from 10 CT datasets with and without contrast agent for which ground-truth segmentations were generated by averaging three manual segmentations. Our method yields an average volumetric overlap error of 10.95%, and average symmetric surface distance of 0.79 mm. These results indicate that our method is accurate and robust for kidney segmentation.

  15. Fully automated segmentation of left ventricle using dual dynamic programming in cardiac cine MR images

    NASA Astrophysics Data System (ADS)

    Jiang, Luan; Ling, Shan; Li, Qiang

    2016-03-01

    Cardiovascular diseases are becoming a leading cause of death all over the world. The cardiac function could be evaluated by global and regional parameters of left ventricle (LV) of the heart. The purpose of this study is to develop and evaluate a fully automated scheme for segmentation of LV in short axis cardiac cine MR images. Our fully automated method consists of three major steps, i.e., LV localization, LV segmentation at end-diastolic phase, and LV segmentation propagation to the other phases. First, the maximum intensity projection image along the time phases of the midventricular slice, located at the center of the image, was calculated to locate the region of interest of LV. Based on the mean intensity of the roughly segmented blood pool in the midventricular slice at each phase, end-diastolic (ED) and end-systolic (ES) phases were determined. Second, the endocardial and epicardial boundaries of LV of each slice at ED phase were synchronously delineated by use of a dual dynamic programming technique. The external costs of the endocardial and epicardial boundaries were defined with the gradient values obtained from the original and enhanced images, respectively. Finally, with the advantages of the continuity of the boundaries of LV across adjacent phases, we propagated the LV segmentation from the ED phase to the other phases by use of dual dynamic programming technique. The preliminary results on 9 clinical cardiac cine MR cases show that the proposed method can obtain accurate segmentation of LV based on subjective evaluation.

  16. The segmentation of hadron calorimeters

    NASA Astrophysics Data System (ADS)

    Chen, He Sheng

    1987-05-01

    Optimization of the segmentation of large hadron calorimeters is important in order to obtain good resolution for jet physics at minimum construction cost for the next generation of high energy experiments. The principles of the segmentation of hadron calorimeters are discussed. As an example, the Monte Carlo optimization of the segmentation of the L3 hadron calorimeter barrel at CERN is described. Comparisons of results for the reconstructed jet shapes show that the optimum number ADC channels is about 20K for the readout of 450K wires of the proportional chambers. The matching between the sandwiched φ towers and Z towers is the dominant factor for angular resolution. Based on these Monte Carlo simulations, an optimized tower structure is obtained.

  17. Evaluation of Semi-automatic Segmentation Methods for Persistent Ground Glass Nodules on Thin-Section CT Scans

    PubMed Central

    Kim, Young Jae; Lee, Seung Hyun; Park, Chang Min

    2016-01-01

    Objectives This work was a comparative study that aimed to find a proper method for accurately segmenting persistent ground glass nodules (GGN) in thin-section computed tomography (CT) images after detecting them. Methods To do this, we first applied five types of semi-automatic segmentation methods (i.e., level-set-based active contour model, localized region-based active contour model, seeded region growing, K-means clustering, and fuzzy C-means clustering) to preprocessed GGN images, respectively. Then, to measure the similarities, we calculated the Dice coefficient of the segmented area using each semiautomatic method with the result of the manually segmented area by two radiologists. Results Comparison experiments were performed using 40 persistent GGNs. In our experiment, the mean Dice coefficient for each semiautomatic segmentation tool with manually segmented area was 0.808 for the level-set-based active contour model, 0.8001 for the localized region-based active contour model, 0.629 for seeded region growing, 0.7953 for K-means clustering, and 0.7999 for fuzzy C-means clustering, respectively. Conclusions The level-set-based active contour model algorithm showed the best performance, which was most similar to the result of manual segmentation by two radiologists. From the differentiation between the normal parenchyma and the nodule, it was also the most efficient. Effective segmentation methods will be essential for the development of computer-aided diagnosis systems for more accurate early diagnosis and prognosis of lung cancer in thin-section CT images. PMID:27895963

  18. Document segmentation via oblique cuts

    NASA Astrophysics Data System (ADS)

    Svendsen, Jeremy; Branzan-Albu, Alexandra

    2013-01-01

    This paper presents a novel solution for the layout segmentation of graphical elements in Business Intelligence documents. We propose a generalization of the recursive X-Y cut algorithm, which allows for cutting along arbitrary oblique directions. An intermediate processing step consisting of line and solid region removal is also necessary due to presence of decorative elements. The output of the proposed segmentation is a hierarchical structure which allows for the identification of primitives in pie and bar charts. The algorithm was tested on a database composed of charts from business documents. Results are very promising.

  19. Dynamic Aperture-based Solar Loop Segmentation

    NASA Technical Reports Server (NTRS)

    Lee, Jon Kwan; Newman, Timothy S.; Gary, G. Allen

    2006-01-01

    A new method to automatically segment arc-like loop structures from intensity images of the Sun's corona is introduced. The method constructively segments credible loop structures by exploiting the Gaussian-like shape of loop cross-sectional intensity profiles. The experimental results show that the method reasonably segments most of the well-defined loops in coronal images. The method is only the second published automated solar loop segmentation method. Its advantage over the other published method is that it operates independently of supplemental time specific data.

  20. Arbitrary segments of absolute negative mobility

    NASA Astrophysics Data System (ADS)

    Chen, Ruyin; Nie, Linru; Chen, Chongyang; Wang, Chaojie

    2017-01-01

    In previous research work, investigators have reported only one or two segments of absolute negative mobility (ANM) in a periodic potential. In fact, many segments of ANM also occur in the system considered here. We investigate transport of an inertial particle in a gating ratchet periodic potential subjected to a constant bias force. Our numerical results show that its mean velocity can decrease with the bias force increasing, i.e. ANM phenomenon. Furthermore, the ANM can take place arbitrary segments, even up to more than thirty. Intrinsic physical mechanism and conditions for arbitrary segments of ANM to occur are discussed in detail.

  1. A strategy for blood vessels segmentation based on the threshold which combines statistical and scale space filter. Application to the study of angiogenesis.

    PubMed

    Rodríguez, Roberto

    2006-04-01

    This paper presents a strategy for segmenting blood vessels based on the threshold, which combines statistics and scale space filter. By incorporating statistical information, the strategy is capable of reducing over-segmentation. We propose a two-stage strategy which involves: (1) optimal selection of window size and (2) optimal selection of scale. We compared our strategy to two commonly used thresholding techniques. Experimental results showed that our method is much more robust and accurate. Our strategy suggested a modification to Otsu's method. In this application the important information to be extracted from images is only the number of blood vessels present in the images. The proposed segmentation technique is tested on manual segmentation, where segmentation errors less than 3% were observed. The work presented in this paper is a part of a global image analysis process. Therefore, these images will be subject to a further morphometrical analysis in order to diagnose and predict automatically malign tumors.

  2. BEaST: brain extraction based on nonlocal segmentation technique.

    PubMed

    Eskildsen, Simon F; Coupé, Pierrick; Fonov, Vladimir; Manjón, José V; Leung, Kelvin K; Guizard, Nicolas; Wassef, Shafik N; Østergaard, Lasse Riis; Collins, D Louis

    2012-02-01

    Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834±0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781±0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors.

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

    SciTech Connect

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

    2004-02-17

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

  4. 3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed

    PubMed Central

    Atta-Fosu, Thomas; Guo, Weihong; Jeter, Dana; Mizutani, Claudia M.; Stopczynski, Nathan; Sousa-Neves, Rui

    2017-01-01

    Image segmentation is an important process that separates objects from the background and also from each other. Applied to cells, the results can be used for cell counting which is very important in medical diagnosis and treatment, and biological research that is often used by scientists and medical practitioners. Segmenting 3D confocal microscopy images containing cells of different shapes and sizes is still challenging as the nuclei are closely packed. The watershed transform provides an efficient tool in segmenting such nuclei provided a reasonable set of markers can be found in the image. In the presence of low-contrast variation or excessive noise in the given image, the watershed transform leads to over-segmentation (a single object is overly split into multiple objects). The traditional watershed uses the local minima of the input image and will characteristically find multiple minima in one object unless they are specified (marker-controlled watershed). An alternative to using the local minima is by a supervised technique called seeded watershed, which supplies single seeds to replace the minima for the objects. Consequently, the accuracy of a seeded watershed algorithm relies on the accuracy of the predefined seeds. In this paper, we present a segmentation approach based on the geometric morphological properties of the ‘landscape’ using curvatures. The curvatures are computed as the eigenvalues of the Shape matrix, producing accurate seeds that also inherit the original shape of their respective cells. We compare with some popular approaches and show the advantage of the proposed method. PMID:28280723

  5. Automatic segmentation of white matter lesions on magnetic resonance images of the brain by using an outlier detection strategy.

    PubMed

    Wang, Rui; Li, Chao; Wang, Jie; Wei, Xiaoer; Li, Yuehua; Hui, Chun; Zhu, Yuemin; Zhang, Su

    2014-12-01

    White matter lesions (WMLs) are commonly observed on the magnetic resonance (MR) images of normal elderly in association with vascular risk factors, such as hypertension or stroke. An accurate WML detection provides significant information for disease tracking, therapy evaluation, and normal aging research. In this article, we present an unsupervised WML segmentation method that uses Gaussian mixture model to describe the intensity distribution of the normal brain tissues and detects the WMLs as outliers to the normal brain tissue model based on extreme value theory. The detection of WMLs is performed by comparing the probability distribution function of a one-sided normal distribution and a Gumbel distribution, which is a specific extreme value distribution. The performance of the automatic segmentation is validated on synthetic and clinical MR images with regard to different imaging sequences and lesion loads. Results indicate that the segmentation method has a favorable accuracy competitive with other state-of-the-art WML segmentation methods.

  6. Spatially-Coherent Non-Linear Dimensionality Reduction and Segmentation of Hyper-Spectral Images (PREPRINT)

    DTIC Science & Technology

    2006-06-01

    projection methods such as Principal Component Analysis ( PCA ) [16]. Methods like PCA , Factor analysis, and multidimensional scaling, assume that the...underlying data manifold is linear, which is not necessarily true in the case of hyper-spectral data and most imaging modalities. PCA has been shown not to...accurate classification and segmentation. This spatial coherence is introduced following recent results in the literature of image denoising and texture

  7. A new intelligent method for minerals segmentation in thin sections based on a novel incremental color clustering

    NASA Astrophysics Data System (ADS)

    Izadi, Hossein; Sadri, Javad; Mehran, Nosrat-Agha

    2015-08-01

    Mineral segmentation in thin sections is a challenging, popular, and important research topic in computational geology, mineralogy, and mining engineering. Mineral segmentation in thin sections containing altered minerals, in which there are no evident and close boundaries, is a rather complex process. Most of the thin sections created in industries include altered minerals. However, intelligent mineral segmentation in thin sections containing altered minerals has not been widely investigated in the literature, and the current state of the art algorithms are not able to accurately segment minerals in such thin sections. In this paper, a novel method based on incremental learning for clustering pixels is proposed in order to segment index minerals in both thin sections with and without altered minerals. Our algorithm uses 12 color features that are extracted from thin section images. These features include red, green, blue, hue, saturation and intensity, under plane and cross polarized lights in maximum intensity situation. The proposed method has been tested on 155 igneous samples and the overall accuracy of 92.15% and 85.24% has been obtained for thin sections without altered minerals and thin sections containing altered minerals, respectively. Experimental results indicate that the proposed method outperforms the results of other similar methods in the literature, especially for segmenting thin sections containing altered minerals. The proposed algorithm could be applied in applications which require a real time segmentation or efficient identification map such as petroleum geology, petrography and NASA Mars explorations.

  8. ℓ1/2-norm regularized nonnegative low-rank and sparse affinity graph for remote sensing image segmentation

    NASA Astrophysics Data System (ADS)

    Tian, Shu; Zhang, Ye; Yan, Yiming; Su, Nan

    2016-10-01

    Segmentation of real-world remote sensing images is a challenge due to the complex texture information with high heterogeneity. Thus, graph-based image segmentation methods have been attracting great attention in the field of remote sensing. However, most of the traditional graph-based approaches fail to capture the intrinsic structure of the feature space and are sensitive to noises. A ℓ-norm regularization-based graph segmentation method is proposed to segment remote sensing images. First, we use the occlusion of the random texture model (ORTM) to extract the local histogram features. Then, a ℓ-norm regularized low-rank and sparse representation (LNNLRS) is implemented to construct a ℓ-regularized nonnegative low-rank and sparse graph (LNNLRS-graph), by the union of feature subspaces. Moreover, the LNNLRS-graph has a high ability to discriminate the manifold intrinsic structure of highly homogeneous texture information. Meanwhile, the LNNLRS representation takes advantage of the low-rank and sparse characteristics to remove the noises and corrupted data. Last, we introduce the LNNLRS-graph into the graph regularization nonnegative matrix factorization to enhance the segmentation accuracy. The experimental results using remote sensing images show that when compared to five state-of-the-art image segmentation methods, the proposed method achieves more accurate segmentation results.

  9. Joint optimization of segmentation and shape prior from level-set-based statistical shape model, and its application to the automated segmentation of abdominal organs.

    PubMed

    Saito, Atsushi; Nawano, Shigeru; Shimizu, Akinobu

    2016-02-01

    The goal of this study is to provide a theoretical framework for accurately optimizing the segmentation energy considering all of the possible shapes generated from the level-set-based statistical shape model (SSM). The proposed algorithm solves the well-known open problem, in which a shape prior may not be optimal in terms of an objective functional that needs to be minimized during segmentation. The algorithm allows the selection of an optimal shape prior from among all possible shapes generated from an SSM by conducting a branch-and-bound search over an eigenshape space. The proposed algorithm does not require predefined shape templates or the construction of a hierarchical clustering tree before graph-cut segmentation. It jointly optimizes an objective functional in terms of both the shape prior and segmentation labeling, and finds an optimal solution by considering all possible shapes generated from an SSM. We apply the proposed algorithm to both pancreas and spleen segmentation using multiphase computed tomography volumes, and we compare the results obtained with those produced by a conventional algorithm employing a branch-and-bound search over a search tree of predefined shapes, which were sampled discretely from an SSM. The proposed algorithm significantly improves the segmentation performance in terms of the Jaccard index and Dice similarity index. In addition, we compare the results with the state-of-the-art multiple abdominal organs segmentation algorithm, and confirmed that the performances of both algorithms are comparable to each other. We discuss the high computational efficiency of the proposed algorithm, which was determined experimentally using a normalized number of traversed nodes in a search tree, and the extensibility of the proposed algorithm to other SSMs or energy functionals.

  10. Automatic coronary lumen segmentation with partial volume modeling improves lesions' hemodynamic significance assessment

    NASA Astrophysics Data System (ADS)

    Freiman, M.; Lamash, Y.; Gilboa, G.; Nickisch, H.; Prevrhal, S.; Schmitt, H.; Vembar, M.; Goshen, L.

    2016-03-01

    The determination of hemodynamic significance of coronary artery lesions from cardiac computed tomography angiography (CCTA) based on blood flow simulations has the potential to improve CCTA's specificity, thus resulting in improved clinical decision making. Accurate coronary lumen segmentation required for flow simulation is challenging due to several factors. Specifically, the partial-volume effect (PVE) in small-diameter lumina may result in overestimation of the lumen diameter that can lead to an erroneous hemodynamic significance assessment. In this work, we present a coronary artery segmentation algorithm tailored specifically for flow simulations by accounting for the PVE. Our algorithm detects lumen regions that may be subject to the PVE by analyzing the intensity values along the coronary centerline and integrates this information into a machine-learning based graph min-cut segmentation framework to obtain accurate coronary lumen segmentations. We demonstrate the improvement in hemodynamic significance assessment achieved by accounting for the PVE in the automatic segmentation of 91 coronary artery lesions from 85 patients. We compare hemodynamic significance assessments by means of fractional flow reserve (FFR) resulting from simulations on 3D models generated by our segmentation algorithm with and without accounting for the PVE. By accounting for the PVE we improved the area under the ROC curve for detecting hemodynamically significant CAD by 29% (N=91, 0.85 vs. 0.66, p<0.05, Delong's test) with invasive FFR threshold of 0.8 as the reference standard. Our algorithm has the potential to facilitate non-invasive hemodynamic significance assessment of coronary lesions.

  11. Hospital benefit segmentation.

    PubMed

    Finn, D W; Lamb, C W

    1986-12-01

    Market segmentation is an important topic to both health care practitioners and researchers. The authors explore the relative importance that health care consumers attach to various benefits available in a major metropolitan area hospital. The purposes of the study are to test, and provide data to illustrate, the efficacy of one approach to hospital benefit segmentation analysis.

  12. Cross entropy: a new solver for Markov random field modeling and applications to medical image segmentation.

    PubMed

    Wu, Jue; Chung, Albert C S

    2005-01-01

    This paper introduces a novel solver, namely cross entropy (CE), into the MRF theory for medical image segmentation. The solver, which is based on the theory of rare event simulation, is general and stochastic. Unlike some popular optimization methods such as belief propagation and graph cuts, CE makes no assumption on the form of objective functions and thus can be applied to any type of MRF models. Furthermore, it achieves higher performance of finding more global optima because of its stochastic property. In addition, it is more efficient than other stochastic methods like simulated annealing. We tested the new solver in 4 series of segmentation experiments on synthetic and clinical, vascular and cerebral images. The experiments show that CE can give more accurate segmentation results.

  13. Unsupervised segmentation of the prostate using MR images based on level set with a shape prior.

    PubMed

    Liu, Xin; Langer, D L; Haider, M A; Van der Kwast, T H; Evans, A J; Wernick, M N; Yetik, I S

    2009-01-01

    Prostate cancer is the second leading cause of cancer death in American men. Current prostate MRI can benefit from automated tumor localization to help guide biopsy, radiotherapy and surgical planning. An important step of automated prostate cancer localization is the segmentation of the prostate. In this paper, we propose a fully automatic method for the segmentation of the prostate. We firstly apply a deformable ellipse model to find an ellipse that best fits the prostate shape. Then, this ellipse is used to initiate the level set and constrain the level set evolution with a shape penalty term. Finally, certain post processing methods are applied to refine the prostate boundaries. We apply the proposed method to real diffusion-weighted (DWI) MRI images data to test the performance. Our results show that accurate segmentation can be obtained with the proposed method compared to human readers.

  14. Optimal Surface Segmentation in Volumetric Images—A Graph-Theoretic Approach

    PubMed Central

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

    2008-01-01

    Efficient segmentation of globally optimal surfaces representing object boundaries in volumetric data sets is important and challenging in many medical image analysis applications. We have developed an optimal surface detection method capable of simultaneously detecting multiple interacting surfaces, in which the optimality is controlled by the cost functions designed for individual surfaces and by several geometric constraints defining the surface smoothness and interrelations. The method solves the surface segmentation problem by transforming it into computing a minimum s-t cut in a derived arc-weighted directed graph. The proposed algorithm has a low-order polynomial time complexity and is computationally efficient. It has been extensively validated on more than 300 computer-synthetic volumetric images, 72 CT-scanned data sets of different-sized plexiglas tubes, and tens of medical images spanning various imaging modalities. In all cases, the approach yielded highly accurate results. Our approach can be readily extended to higher-dimensional image segmentation. PMID:16402624

  15. Interactive segmentation of plexiform neurofibroma tissue: method and preliminary performance evaluation.

    PubMed

    Weizman, Lior; Hoch, Lior; Ben Bashat, Dafna; Joskowicz, Leo; Pratt, Li-tal; Constantini, Shlomi; Ben Sira, Liat

    2012-08-01

    Plexiform neurofibromas (PNs) are a major manifestation of neurofibromatosis-1 (NF1), a common genetic disease involving the nervous system. Treatment decisions are mostly based on a gross assessment of changes in tumor using MRI. Accurate volumetric measurements are rarely performed in this kind of tumors mainly due to its great dispersion, size, and multiple locations. This paper presents a semi-automatic method for segmentation of PN from STIR MRI scans. The method starts with a user-based delineation of the tumor area in a single slice and automatically segments the PN lesions in the entire image based on the tumor connectivity. Experimental results on seven datasets, with lesion volumes in the range of 75-690 ml, yielded a mean absolute volume error of 10 % (after manual adjustment) as compared to manual segmentation by an expert radiologist. The mean computation and interaction time was 13 versus 63 min for manual annotation.

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  17. Selective Segmentation for Global Optimization of Depth Estimation in Complex Scenes

    PubMed Central

    Liu, Sheng; Jin, Haiqiang; Mao, Xiaojun; Zhai, Binbin; Zhan, Ye; Feng, Xiaofei

    2013-01-01

    This paper proposes a segmentation-based global optimization method for depth estimation. Firstly, for obtaining accurate matching cost, the original local stereo matching approach based on self-adapting matching window is integrated with two matching cost optimization strategies aiming at handling both borders and occlusion regions. Secondly, we employ a comprehensive smooth term to satisfy diverse smoothness request in real scene. Thirdly, a selective segmentation term is used for enforcing the plane trend constraints selectively on the corresponding segments to further improve the accuracy of depth results from object level. Experiments on the Middlebury image pairs show that the proposed global optimization approach is considerably competitive with other state-of-the-art matching approaches. PMID:23766717

  18. Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation.

    PubMed

    Pereyra, Marcelo; McLaughlin, Stephen

    2017-03-15

    This paper presents a new Bayesian estimation technique for hidden Potts-Markov random fields with unknown regularisation parameters, with application to fast unsupervised K-class image segmentation. The technique is derived by first removing the regularisation parameter from the Bayesian model by marginalisation, followed by a small-variance-asymptotic (SVA) analysis in which the spatial regularisation and the integer-constrained terms of the Potts model are decoupled. The evaluation of this SVA Bayesian estimator is then relaxed into a problem that can be computed efficiently by iteratively solving a convex total-variation denoising problem and a least-squares clustering (K-means) problem, both of which can be solved straightforwardly, even in high-dimensions, and with parallel computing techniques. This leads to a fast fully unsupervised Bayesian image segmentation methodology in which the strength of the spatial regularisation is adapted automatically to the observed image during the inference procedure, and that can be easily applied in large 2D and 3D scenarios or in applications requiring low computing times. Experimental results on synthetic and real images, as well as extensive comparisons with state-ofthe- art algorithms, confirm that the proposed methodology offer extremely fast convergence and produces accurate segmentation results, with the important additional advantage of self-adjusting regularisation parameters.

  19. Hierarchical partial matching and segmentation of interacting cells.

    PubMed

    Wu, Zheng; Gurari, Danna; Wong, Joyce Y; Betke, Margrit

    2012-01-01

    We propose a method that automatically tracks and segments living cells in phase-contrast image sequences, especially for cells that deform and interact with each other or clutter. We formulate the problem as a many-to-one elastic partial matching problem between closed curves. We introduce Double Cyclic Dynamic Time Warping for the scenario where a collision event yields a single boundary that encloses multiple touching cells and that needs to be cut into separate cell boundaries. The resulting individual boundaries may consist of segments to be connected to produce closed curves that match well with the individual cell boundaries before the collision event. We show how to convert this partial-curve matching problem into a shortest path problem that we then solve efficiently by reusing the computed shortest path tree. We also use our shortest path algorithm to fill the gaps between the segments of the target curves. Quantitative results demonstrate the benefit of our method by showing maintained accurate recognition of individual cell boundaries across 8068 images containing multiple cell interactions.

  20. 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

  1. Webpage Segments Classification with Incremental Knowledge Acquisition

    NASA Astrophysics Data System (ADS)

    Guo, Wei; Kim, Yang Sok; Kang, Byeong Ho

    This paper suggests an incremental information extraction method for social network analysis of web publications. For this purpose, we employed an incremental knowledge acquisition method, called MCRDR (Multiple Classification Ripple-Down Rules), to classify web page segments. Our experimental results show that our MCRDR-based web page segments classification system successfully supports easy acquisition and maintenance of information extraction rules.

  2. Labor Market Segmentation and Librarian Salaries.

    ERIC Educational Resources Information Center

    Van House, Nancy A.

    1987-01-01

    Segmented labor market theory is used to explain how the structure of the library labor market may determine salary differences by type of library. Evidence that segmentation exists at intraoccupational levels and the possibility that comparing entire occupations may obscure results are also reported. (Author/CLB)

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  4. Segmentation of Textures Defined on Flat vs. Layered Surfaces using Neural Networks: Comparison of 2D vs. 3D Representations.

    PubMed

    Oh, Sejong; Choe, Yoonsuck

    2007-08-01

    Texture boundary detection (or segmentation) is an important capability in human vision. Usually, texture segmentation is viewed as a 2D problem, as the definition of the problem itself assumes a 2D substrate. However, an interesting hypothesis emerges when we ask a question regarding the nature of textures: What are textures, and why did the ability to discriminate texture evolve or develop? A possible answer to this question is that textures naturally define physically distinct (i.e., occluded) surfaces. Hence, we can hypothesize that 2D texture segmentation may be an outgrowth of the ability to discriminate surfaces in 3D. In this paper, we conducted computational experiments with artificial neural networks to investigate the relative difficulty of learning to segment textures defined on flat 2D surfaces vs. those in 3D configurations where the boundaries are defined by occluding surfaces and their change over time due to the observer's motion. It turns out that learning is faster and more accurate in 3D, very much in line with our expectation. Furthermore, our results showed that the neural network's learned ability to segment texture in 3D transfers well into 2D texture segmentation, bolstering our initial hypothesis, and providing insights on the possible developmental origin of 2D texture segmentation function in human vision.

  5. Automatic knee cartilage segmentation from multi-contrast MR images using support vector machine classification with spatial dependencies.

    PubMed

    Zhang, Kunlei; Lu, Wenmiao; Marziliano, Pina

    2013-12-01

    Accurate segmentation of knee cartilage is required to obtain quantitative cartilage measurements, which is crucial for the assessment of knee pathology caused by musculoskeletal diseases or sudden injuries. This paper presents an automatic knee cartilage segmentation technique which exploits a rich set of image features from multi-contrast magnetic resonance (MR) images and the spatial dependencies between neighbouring voxels. The image features and the spatial dependencies are modelled into a support vector machine (SVM)-based association potential and a discriminative random field (DRF)-based interaction potential. Subsequently, both potentials are incorporated into an inference graphical model such that the knee cartilage segmentation is cast into an optimal labelling problem which can be efficiently solved by loopy belief propagation. The effectiveness of the proposed technique is validated on a database of multi-contrast MR images. The experimental results show that using diverse forms of image and anatomical structure information as the features are helpful in improving the segmentation, and the joint SVM-DRF model is superior to the classification models based solely on DRF or SVM in terms of accuracy when the same features are used. The developed segmentation technique achieves good performance compared with gold standard segmentations and obtained higher average DSC values than the state-of-the-art automatic cartilage segmentation studies.

  6. Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs

    PubMed Central

    Abdullah, Bassem A; Younis, Akmal A; John, Nigel M

    2012-01-01

    In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI. PMID:22741026

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

    PubMed

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

    2016-04-01

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

  8. The functional relationship between ectodermal and mesodermal segmentation in the crustacean, Parhyale hawaiensis.

    PubMed

    Hannibal, Roberta L; Price, Alivia L; Patel, Nipam H

    2012-01-15

    In arthropods, annelids and chordates, segmentation of the body axis encompasses both ectodermal and mesodermal derivatives. In vertebrates, trunk mesoderm segments autonomously and induces segmental arrangement of the ectoderm-derived nervous system. In contrast, in the arthropod Drosophila melanogaster, the ectoderm segments autonomously and mesoderm segmentation is at least partially dependent on the ectoderm. While segmentation has been proposed to be a feature of the common ancestor of vertebrates and arthropods, considering vertebrates and Drosophila alone, it is impossible to conclude whether the ancestral primary segmented tissue was the ectoderm or the mesoderm. Furthermore, much of Drosophila segmentation occurs before gastrulation and thus may not accurately represent the mechanisms of segmentation in all arthropods. To better understand the relationship between segmented germ layers in arthropods, we asked whether segmentation is an intrinsic property of the ectoderm and/or the mesoderm in the crustacean Parhyale hawaiensis by ablating either the ectoderm or the mesoderm and then assaying for segmentation in the remaining tissue layer. We found that the ectoderm segments autonomously. However, mesoderm segmentation requires at least a permissive signal from the ectoderm. Although mesodermal stem cells undergo normal rounds of division in the absence of ectoderm, they do not migrate properly in respect to migration direction and distance. In addition, their progeny neither divide nor express the mesoderm segmentation markers Ph-twist and Ph-Even-skipped. As segmentation is ectoderm-dependent in both Parhyale and holometabola insects, we hypothesize that segmentation is primarily a property of the ectoderm in pancrustacea.

  9. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    SciTech Connect

    Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

    2014-11-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  10. Active Mask Segmentation of Fluorescence Microscope Images

    PubMed Central

    Srinivasa, Gowri; Fickus, Matthew C.; Guo, Yusong; Linstedt, Adam D.; Kovačević, Jelena

    2009-01-01

    We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the “contour” to that of “inside and outside”, or, masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well, and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively as well as quantitatively. PMID:19380268

  11. TH-C-18A-02: Machine Learning and STAPLE Based Simultaneous Longitudinal Segmentation of Bone and Marrow Structures From Dual Energy CT

    SciTech Connect

    Fehr, D; Schmidtlein, C; Hwang, S; Deasy, J; Veeraraghavan, H

    2014-06-15

    Purpose: To develop a fully-automatic longitudinal bone and marrow segmentation method in the pelvic region from dual energy computed tomography (DECT). Methods: We developed a two-step automatic bone and marrow segmentation method for simultaneous longitudinal evaluation of patients with metastatic bone disease using dual energy CT (DECT). Our approach transforms the DECT images into a multi-material decomposition (MMD) model that represents the voxels as a mixture of multiple materials. A support vector machine (SVM) was trained using a single scan. In the first step of the longitudinal segmentation the trained SVM model detects bone and marrow structures on all available longitudinal scans. Segmentation is further refined through active contour segmentation. In the second step, the segmentations from the individual scans are merged by employing the simultaneous truth and performance level estimation (STAPLE) algorithm. The scans are registered using affine and deformable registration. We found that our approach improves the segmentation in all the scans under reliable registration performance between the same scans. Improving registration was not under the scope of this work. Results: We applied our approach to segment bone and marrow in DECT scans in the pelvic regions for multiple patients. Each patient had three to five follow up scans. All the patients in the analysis had artificial metal prostheses which introduced challenges for the registration. Our algorithm achieved reasonable accurate segmentation despite the presence of metal artifacts and high-density oral contrast in neighboring structures. Our approach obtained an overall segmentation accuracy of 80% using DICE metric. Conclusion: We developed a two-step automatic longitudinal segmentation technique for bone and marrow region structures in the pelvic areas from dual energy CT. Our approach achieves robust segmentation despite the presence of confounding structures with similar intensities as the

  12. Underwater color image segmentation method via RGB channel fusion

    NASA Astrophysics Data System (ADS)

    Xuan, Li; Mingjun, Zhang

    2017-02-01

    Aiming at the problem of low segmentation accuracy and high computation time by applying existing segmentation methods for underwater color images, this paper has proposed an underwater color image segmentation method via RGB color channel fusion. Based on thresholding segmentation methods to conduct fast segmentation, the proposed method relies on dynamic estimation of the optimal weights for RGB channel fusion to obtain the grayscale image with high foreground-background contrast and reaches high segmentation accuracy. To verify the segmentation accuracy of the proposed method, the authors have conducted various underwater comparative experiments. The experimental results demonstrate that the proposed method is robust to illumination, and it is superior to existing methods in terms of both segmentation accuracy and computation time. Moreover, a segmentation technique is proposed for image sequences for real-time autonomous underwater vehicle operations.

  13. Touching character segmentation method for Chinese historical documents

    NASA Astrophysics Data System (ADS)

    Sun, Xiaolu; Peng, Liangrui; Ding, Xiaoqing

    2010-01-01

    The OCR technology for Chinese historical documents is still an open problem. As these documents are hand-written or hand-carved in various styles, overlapped and touching characters bring great difficulty for character segmentation module. This paper presents an over-segmentation-based method to handle the overlapped and touching Chinese characters in historic documents. The whole segmentation process includes two parts: over-segmented and segmenting path optimization. In the former part, touching strokes will be found and segmented by analyzing the geometric information of the white and black connected components. The segmentation cost of the touching strokes is estimated with connected components' shape and location, as well as the touching stroke width. The latter part uses local optimization dynamic programming to find best segmenting path. HMM is used to express the multiple choices of segmenting paths, and Viterbi algorithm is used to search local optimal solution. Experimental results on practical Chinese documents show the proposed method is effective.

  14. Combining Multi-atlas Segmentation with Brain Surface Estimation.

    PubMed

    Huo, Yuankai; Carass, Aaron; Resnick, Susan M; Pham, Dzung L; Prince, Jerry L; Landman, Bennett A

    2016-02-27

    Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential techniques for investigating the human brain. The two tasks are typically conducted independently, however, which leads to spatial inconsistencies and hinders further integrated cortical analyses. To obtain self-consistent whole brain segmentations and surfaces, FreeSurfer segregates the subcortical and cortical segmentations before and after the cortical surface reconstruction. However, this "segmentation to surface to parcellation" strategy has shown limitations in various situations. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. To our knowledge, this is the first work that achieves the reliability of state-of-the-art multi-atlas segmentation and labeling methods together with accurate and consistent cortical surface reconstruction. Compared with previous methods, MaCRUISE has three features: (1) MaCRUISE obtains 132 cortical/subcortical labels simultaneously from a single multi-atlas segmentation before reconstructing volume consistent surfaces; (2) Fuzzy tissue memberships are combined with multi-atlas segmentations to address partial volume effects; (3) MaCRUISE reconstructs topologically consistent cortical surfaces by using the sulci locations from multi-atlas segmentation. Two data sets, one consisting of five subjects with expertly traced landmarks and the other consisting of 100 volumes from elderly subjects are used for validation. Compared with CRUISE, MaCRUISE achieves self-consistent whole brain segmentation and cortical reconstruction without compromising on surface accuracy. MaCRUISE is comparably accurat