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Sample records for accurate segmentation results

  1. 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

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

    PubMed

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

    2014-09-01

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

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

    PubMed

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

    2009-09-01

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

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

    NASA Astrophysics Data System (ADS)

    Wellhausen, Jens

    2003-12-01

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

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

    PubMed

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

    2010-04-01

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

  6. 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

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

    PubMed

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

    2015-03-01

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

  8. [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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  12. 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

  13. 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.

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

    PubMed

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

    2016-06-01

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

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

    PubMed

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

    2012-01-01

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

  16. 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.

  17. 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

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

    NASA Astrophysics Data System (ADS)

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

    2013-10-01

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

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

    PubMed

    Wu, Jianping; Chen, Ke; Gao, Xiaohui

    2013-06-01

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

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

    PubMed

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

    2013-10-21

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

  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. 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

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

    PubMed

    Giachetti, Andrea; Ballerini, Lucia; Trucco, Emanuele

    2014-07-01

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

  4. 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.

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

    PubMed

    Touil, Asma; Kalti, Karim

    2016-08-01

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

  6. Fuzzy fusion of results of medical image segmentation

    NASA Astrophysics Data System (ADS)

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

    1999-05-01

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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

  9. 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

  10. 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

  11. Differences in the Association between Segment and Language: Early Bilinguals Pattern with Monolinguals and Are Less Accurate than Late Bilinguals.

    PubMed

    Blanco, Cynthia P; Bannard, Colin; Smiljanic, Rajka

    2016-01-01

    Early bilinguals often show as much sensitivity to L2-specific contrasts as monolingual speakers of the L2, but most work on cross-language speech perception has focused on isolated segments, and typically only on neighboring vowels or stop contrasts. In tasks that include sounds in context, listeners' success is more variable, so segment discrimination in isolation may not adequately represent the phonetic detail in stored representations. The current study explores the relationship between language experience and sensitivity to segmental cues in context by comparing the categorization patterns of monolingual English listeners and early and late Spanish-English bilinguals. Participants categorized nonce words containing different classes of English- and Spanish-specific sounds as being more English-like or more Spanish-like; target segments included phonemic cues, cues for which there is no analogous sound in the other language, or phonetic cues, cues for which English and Spanish share the category but for which each language varies in its phonetic implementation. Listeners' language categorization accuracy and reaction times were analyzed. Our results reveal a largely uniform categorization pattern across listener groups: Spanish cues were categorized more accurately than English cues, and phonemic cues were easier for listeners to categorize than phonetic cues. There were no differences in the sensitivity of monolinguals and early bilinguals to language-specific cues, suggesting that the early bilinguals' exposure to Spanish did not fundamentally change their representations of English phonology. However, neither did the early bilinguals show more sensitivity than the monolinguals to Spanish sounds. The late bilinguals however, were significantly more accurate than either of the other groups. These findings indicate that listeners with varying exposure to English and Spanish are able to use language-specific cues in a nonce-word language categorization task

  12. 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

  13. 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. PMID:21353575

  14. 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.

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

    PubMed

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

    2016-01-01

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

  16. Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging.

    PubMed

    Lebenberg, Jessica; Lalande, Alain; Clarysse, Patrick; Buvat, Irene; Casta, Christopher; Cochet, Alexandre; Constantinidès, Constantin; Cousty, Jean; de Cesare, Alain; Jehan-Besson, Stephanie; Lefort, Muriel; Najman, Laurent; Roullot, Elodie; Sarry, Laurent; Tilmant, Christophe; Frouin, Frederique; Garreau, Mireille

    2015-01-01

    This work aimed at combining different segmentation approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method. Then, we used an unsupervised statistical evaluation, the extended Regression Without Truth (eRWT) that ranks different methods according to their accuracy in estimating a specific biomarker in a population. The segmentation accuracy was evaluated by estimating six cardiac function parameters resulting from the left ventricle contour delineation using a public cardiac cine MRI database. Eight different segmentation methods, including three expert delineations and five automated methods, were considered, and sixteen combinations of the automated methods using STAPLE were investigated. The supervised and unsupervised evaluations demonstrated that in most cases, STAPLE results provided better estimates than individual automated segmentation methods. Overall, combining different automated segmentation methods improved the reliability of the segmentation result compared to that obtained using an individual method and could achieve the accuracy of an expert. PMID:26287691

  17. A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study.

    PubMed

    Kalpathy-Cramer, Jayashree; Zhao, Binsheng; Goldgof, Dmitry; Gu, Yuhua; Wang, Xingwei; Yang, Hao; Tan, Yongqiang; Gillies, Robert; Napel, Sandy

    2016-08-01

    Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 μl to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies. PMID:26847203

  18. 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

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

    PubMed Central

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

    2015-01-01

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

  20. 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…

  1. Probabilistic Segmentation of Mass Spectrometry (MS) Images Helps Select Important Ions and Characterize Confidence in the Resulting Segments.

    PubMed

    Bemis, Kyle D; Harry, April; Eberlin, Livia S; Ferreira, Christina R; van de Ven, Stephanie M; Mallick, Parag; Stolowitz, Mark; Vitek, Olga

    2016-05-01

    Mass spectrometry imaging is a powerful tool for investigating the spatial distribution of chemical compounds in a biological sample such as tissue. Two common goals of these experiments are unsupervised segmentation of images into newly discovered homogeneous segments and supervised classification of images into predefined classes. In both cases, the important secondary goals are to characterize the uncertainty associated with the segmentation and with the classification and to characterize the spectral features that define each segment or class. Recent analysis methods have focused on the spatial structure of the data to improve results. However, they either do not address these secondary goals or do this with separate post hoc procedures.We introduce spatial shrunken centroids, a statistical model-based framework for both supervised classification and unsupervised segmentation. It takes as input sets of previously detected, aligned, quantified, and normalized spectral features and expresses both spatial and multivariate nature of the data using probabilistic modeling. It selects informative subsets of spectral features that define each unsupervised segment or supervised class and quantifies and visualizes the uncertainty in spatial segmentations and in tissue classification. In the unsupervised setting, it also guides the choice of an appropriate number of segments. We demonstrate the usefulness of this framework in a supervised human renal cell carcinoma experimental dataset and several unsupervised experimental datasets, including a pig fetus cross-section, three rodent brains, and a controlled image with known ground truth. This framework is available for use within the open-source R package Cardinal as part of a full pipeline for the processing, visualization, and statistical analysis of mass spectrometry imaging experiments. PMID:26796117

  2. 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.

  3. Fast and robust segmentation of solar EUV images: algorithm and results for solar cycle 23

    NASA Astrophysics Data System (ADS)

    Barra, V.; Delouille, V.; Kretzschmar, M.; Hochedez, J.-F.

    2009-10-01

    Context: The study of the variability of the solar corona and the monitoring of coronal holes, quiet sun and active regions are of great importance in astrophysics as well as for space weather and space climate applications. Aims: In a previous work, we presented the spatial possibilistic clustering algorithm (SPoCA). This is a multi-channel unsupervised spatially-constrained fuzzy clustering method that automatically segments solar extreme ultraviolet (EUV) images into regions of interest. The results we reported on SoHO-EIT images taken from February 1997 to May 2005 were consistent with previous knowledge in terms of both areas and intensity estimations. However, they presented some artifacts due to the method itself. Methods: Herein, we propose a new algorithm, based on SPoCA, that removes these artifacts. We focus on two points: the definition of an optimal clustering with respect to the regions of interest, and the accurate definition of the cluster edges. We moreover propose methodological extensions to this method, and we illustrate these extensions with the automatic tracking of active regions. Results: The much improved algorithm can decompose the whole set of EIT solar images over the 23rd solar cycle into regions that can clearly be identified as quiet sun, coronal hole and active region. The variations of the parameters resulting from the segmentation, i.e. the area, mean intensity, and relative contribution to the solar irradiance, are consistent with previous results and thus validate the decomposition. Furthermore, we find indications for a small variation of the mean intensity of each region in correlation with the solar cycle. Conclusions: The method is generic enough to allow the introduction of other channels or data. New applications are now expected, e.g. related to SDO-AIA data.

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

    PubMed

    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

  5. 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

  6. 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.

  7. Results of a comparison study using tomographic and segmented gamma scanner technology

    SciTech Connect

    Hurd, J. R.; Estep, R. J.; Dittrich, S.; Grimes, L. V.; Gomez, C. D.

    2004-01-01

    In order to support the many on-going research and programmatic activities at Los Alamos Plutonium and Chemistry and Metallurgy Research (CMR) Facilities in as accurate, efficient, and cost-effective manner possible, every reasonable effort is made to equip the nondestructive assay (NDA) laboratories with the most modern and technologically advanced instrumentation available. Recently, new state-of-the-art tomographic gamma scanner (TGS) instruments were installed to replace aging and outmoded segmented gamma scanner (SGS) instruments. Through the implementation of a translation axis, in addition to the vertical and rotation axes of the SGS, the TGS technique is able to employ axial tomography to determine the spatial distribution and quantity of nuclear material using high-resolution gamma-ray spectroscopy. Because the attenuation matrix and source distributions are known more accurately than with the SGS technology, biases due to matrix and source distributions should be reduced. In principle, a single calibration should suffice for the determination of isotopic mass for a wide range of material and matrix types. A number of questions naturally arise concerning these purported advantages of the TGS. Perhaps the most fundamental of these is to understand how the TGS measurement results compare with those of a typical SGS on the same well-characterized standards differing in matrix and material type. To that end, the TGS operating parameters were optimized to assay 55-gallon drum waste identical to that measured by our SGS. The calibration and measurement results on these standards, placed in typical low-density waste matrices, are presented and discussed. These results should enable more confident use of the TGS as well as point the way toward even more studies to enable more effective employment of the new TGS technology.

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

    PubMed

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

    2014-01-01

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

  9. 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

  10. Accurate Analytic Results for the Steady State Distribution of the Eigen Model

    NASA Astrophysics Data System (ADS)

    Huang, Guan-Rong; Saakian, David B.; Hu, Chin-Kun

    2016-04-01

    Eigen model of molecular evolution is popular in studying complex biological and biomedical systems. Using the Hamilton-Jacobi equation method, we have calculated analytic equations for the steady state distribution of the Eigen model with a relative accuracy of O(1/N), where N is the length of genome. Our results can be applied for the case of small genome length N, as well as the cases where the direct numerics can not give accurate result, e.g., the tail of distribution.

  11. Accurate Navier-Stokes results for the hypersonic flow over a spherical nosetip

    SciTech Connect

    Blottner, F.G.

    1989-01-01

    The unsteady thin-layer Navier-Stokes equations for a perfect gas are solved with a linearized block Alternating Direction Implicit finite-difference solution procedure. Solution errors due to numerical dissipation added to the governing equations are evaluated. Errors in the numerical predictions on three different grids are determined where Richardson extrapolation is used to estimate the exact solution. Accurate computational results are tabulated for the hypersonic laminar flow over a spherical body which can be used as a benchmark test case. Predictions obtained from the code are in good agreement with inviscid numerical results and experimental data. 9 refs., 11 figs., 3 tabs.

  12. Spondylosis deformans and diffuse idiopathic skeletal hyperostosis (dish) resulting in adjacent segment disease.

    PubMed

    Ortega, Maria; Gonçalves, Rita; Haley, Allison; Wessmann, Annette; Penderis, Jacques

    2012-01-01

    Spondylosis deformans and diffuse idiopathic skeletal hyperostosis (DISH) are usually incidental findings and in most dogs are either asymptomatic or associated with mild clinical signs. Severe spondylosis deformans and DISH can result in complete bony fusion of consecutive vertebral segments. One of the recognised complications following vertebral fusion in human patients is the development of adjacent segment disease, which is defined as degenerative changes, most commonly degenerative intervertebral disc disease, in the mobile vertebral segment neighboring a region of complete vertebral fusion. A similar syndrome following cervical fusion in dogs has been termed the domino effect. The purpose of this retrospective study was to investigate the hypothesis that vertebral fusion occurring secondary to spondylosis deformans or DISH in dogs would protect fused intervertebral disc spaces from undergoing degeneration, but result in adjacent segment disease at neighbouring unfused intervertebral disc spaces. Eight dogs with clinical signs of thoracolumbar myelopathy, magnetic resonance imaging of the thoracolumbar vertebral column, and spondylosis deformans or DISH producing fusion of > or = 2 consecutive intervertebral disc spaces were evaluated. Vertebral fusion of > or = 2 consecutive intervertebral disc spaces was correlated (P = 0.0017) with adjacent segment disease at the neighbouring unfused intervertebral disc space. Vertebral fusion appeared to protect fused intervertebral disc spaces from undergoing degeneration (P < 0.0001). Adjacent segment disease should be considered in dogs with severe spondylosis deformans or DISH occurring in conjunction with a thoracolumbar myelopathy. PMID:22734148

  13. [Results of endovascular interventions in patients with occlusive stenotic lesions of arteries of the aortoiliac segment].

    PubMed

    Karpenko, A A; Starodubtsev, V B; Ignatenko, P V; Rabtsun, A A; Mitrofanov, V O

    2016-01-01

    Presented herein are the results of endovascular interventions performed in a total of 220 patients with chronic ischaemia of lower limbs and occlusive and stenotic lesions of the aortoiliac arterial segment. Group One patients (n=155) underwent angioplasty with stenting (a total of 186 interventions performed) and Group Two patients (n=65) were subjected to recanalization of the occlusion zone with stenting (65 interventions). The remote results were assessed in all patients within the terms of up to 4 years. In Group One patients, restenosis of the stented segments within the mentioned terms of follow up was revealed in 11 (7.1%) cases, thrombosis - in 5 (3.2%) cases. In Group Two patients restenosis was detected in 3 (4.6%) cases and thrombosis of the stented segment in 6 (9.2%) cases. In the both groups, restenosis >50% or thrombosis of the stented segment developed significantly more often with the length of the stented segment exceeding 100 mm (p=0.01 in Group One and p=0.0077 in Group Two). Primary patency of the stented segments at 12 and 24 months after the intervention in Group One amounted to 97.5±1.5 and 92.3±3.3% and in Group Two 92.7±3.6 and 81.9±6.6%, respectively. A conclusion was made that endovascular interventions may be a method of choice in occlusive and stenotic lesions of the aortoiliac-segment arteries. Extended length of the lesion of iliac-segment arteries (more than 100 mm) deteriorates the rates of primary patency after stenting. PMID:27336338

  14. 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

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

    PubMed

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

    2014-01-01

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

  16. Change in ST segment elevation 60 minutes after thrombolytic initiation predicts clinical outcome as accurately as later electrocardiographic changes

    PubMed Central

    Purcell, I; Newall, N; Farrer, M

    1997-01-01

    Objective—To compare prospectively the prognostic accuracy of a 50% decrease in ST segment elevation on standard 12-lead electrocardiograms (ECGs) recorded at 60, 90, and 180 minutes after thrombolysis initiation in acute myocardial infarction.
Design—Consecutive sample prospective cohort study.
Setting—A single coronary care unit in the north of England.
Patients—190 consecutive patients receiving thrombolysis for first acute myocardial infarction.
Interventions—Thrombolysis at baseline.
Main outcome measures—Cardiac mortality and left ventricular size and function assessed 36 days later.
Results—Failure of ST segment elevation to resolve by 50% in the single lead of maximum ST elevation or the sum ST elevation of all infarct related ECG leads at each of the times studied was associated with a significantly higher mortality, larger left ventricular volume, and lower ejection fraction. There was some variation according to infarct site with only the 60 minute ECG predicting mortality after inferior myocardial infarction and only in anterior myocardial infarction was persistent ST elevation associated with worse left ventricular function. The analysis of the lead of maximum ST elevation at 60 minutes from thrombolysis performed as well as later ECGs in receiver operating characteristic curves for predicting clinical outcome.
Conclusion—The standard 12-lead ECG at 60 minutes predicts clinical outcome as accurately as later ECGs after thrombolysis for first acute myocardial infarction.

 Keywords: myocardial infarction;  thrombolysis;  ST segment elevation PMID:9415005

  17. 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…

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

    PubMed

    Skrzypiec, Daniel M; Nagel, Katrin; Sellenschloh, Kay; Klein, Anke; Püschel, Klaus; Morlock, Michael M; Huber, Gerd

    2016-08-01

    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

  19. 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

  20. 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.

  1. Preliminary results in large bone segmentation from 3D freehand ultrasound

    NASA Astrophysics Data System (ADS)

    Fanti, Zian; Torres, Fabian; Arámbula Cosío, Fernando

    2013-11-01

    Computer Assisted Orthopedic Surgery (CAOS) requires a correct registration between the patient in the operating room and the virtual models representing the patient in the computer. In order to increase the precision and accuracy of the registration a set of new techniques that eliminated the need to use fiducial markers have been developed. The majority of these newly developed registration systems are based on costly intraoperative imaging systems like Computed Tomography (CT scan) or Magnetic resonance imaging (MRI). An alternative to these methods is the use of an Ultrasound (US) imaging system for the implementation of a more cost efficient intraoperative registration solution. In order to develop the registration solution with the US imaging system, the bone surface is segmented in both preoperative and intraoperative images, and the registration is done using the acquire surface. In this paper, we present the a preliminary results of a new approach to segment bone surface from ultrasound volumes acquired by means 3D freehand ultrasound. The method is based on the enhancement of the voxels that belongs to surface and its posterior segmentation. The enhancement process is based on the information provided by eigenanalisis of the multiscale 3D Hessian matrix. The preliminary results shows that from the enhance volume the final bone surfaces can be extracted using a singular value thresholding.

  2. Production of the IXO glass segmented mirrors by hot slumping with pressure assistance: tests and results

    NASA Astrophysics Data System (ADS)

    Proserpio, L.; Ghigo, M.; Basso, S.; Conconi, P.; Citterio, O.; Civitani, M.; Negri, R.; Pagano, G.; Pareschi, G.; Salmaso, B.; Spiga, D.; Tagliaferri, G.; Terzi, L.; Zambra, A.; Parodi, G.; Martelli, F.; Bavdaz, M.; Wille, E.

    2011-09-01

    The large dimensions of the future X-ray telescopes, with diameters ranging from 3.5 m and up to several meters, will require the adoption of segmented optics and hence the development of new technologies for their manufacturing. These technologies are based on lightweight materials and structures to comply with the mass constrains imposed by the launcher. The Astronomical Observatory of Brera (INAF-OAB) is involved in the development of a glass shaping technology for the production of grazing incidence segmented optics to be employed onboard the next generation of Xray Observatories. This technique, named "Hot slumping technology with pressure", is based on the viscosity change of the glass with the temperature: by applying a suitable thermal cycle the viscosity of the glass is decreased enough to allow its slumping on a mould so to replicate its shape without significantly degrade its surface finishing. Following this replication approach, it is possible to obtain, with the same mould, a number of equal mirror segments that will be integrated and aligned in the telescope aperture so to create a mirror shell in configuration Wolter I. The entire study has been financed by ESA in the context of the International X-ray Observatory (IXO) mission with the aim of developing a back-up technology for the IXO mirror manufacturing. The study started in 2009 and it is scheduled to finish in 2012 with the production of representative module prototypes, named POC and XOU_BB. After a brief review of past results, this paper reports the latest advancement in the slumping of Schott glass D263 foils on Fused Silica and Zerodur moulds and its status as for summer 2011.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  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. 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.

  10. 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.

  11. Direct volume estimation without segmentation

    NASA Astrophysics Data System (ADS)

    Zhen, X.; Wang, Z.; Islam, A.; Bhaduri, M.; Chan, I.; Li, S.

    2015-03-01

    Volume estimation plays an important role in clinical diagnosis. For example, cardiac ventricular volumes including left ventricle (LV) and right ventricle (RV) are important clinical indicators of cardiac functions. Accurate and automatic estimation of the ventricular volumes is essential to the assessment of cardiac functions and diagnosis of heart diseases. Conventional methods are dependent on an intermediate segmentation step which is obtained either manually or automatically. However, manual segmentation is extremely time-consuming, subjective and highly non-reproducible; automatic segmentation is still challenging, computationally expensive, and completely unsolved for the RV. Towards accurate and efficient direct volume estimation, our group has been researching on learning based methods without segmentation by leveraging state-of-the-art machine learning techniques. Our direct estimation methods remove the accessional step of segmentation and can naturally deal with various volume estimation tasks. Moreover, they are extremely flexible to be used for volume estimation of either joint bi-ventricles (LV and RV) or individual LV/RV. We comparatively study the performance of direct methods on cardiac ventricular volume estimation by comparing with segmentation based methods. Experimental results show that direct estimation methods provide more accurate estimation of cardiac ventricular volumes than segmentation based methods. This indicates that direct estimation methods not only provide a convenient and mature clinical tool for cardiac volume estimation but also enables diagnosis of cardiac diseases to be conducted in a more efficient and reliable way.

  12. Stable creeping fault segments can become destructive as a result of dynamic weakening.

    PubMed

    Noda, Hiroyuki; Lapusta, Nadia

    2013-01-24

    Faults in Earth's crust accommodate slow relative motion between tectonic plates through either similarly slow slip or fast, seismic-wave-producing rupture events perceived as earthquakes. These types of behaviour are often assumed to be separated in space and to occur on two different types of fault segment: one with stable, rate-strengthening friction and the other with rate-weakening friction that leads to stick-slip. The 2011 Tohoku-Oki earthquake with moment magnitude M(w) = 9.0 challenged such assumptions by accumulating its largest seismic slip in the area that had been assumed to be creeping. Here we propose a model in which stable, rate-strengthening behaviour at low slip rates is combined with coseismic weakening due to rapid shear heating of pore fluids, allowing unstable slip to occur in segments that can creep between events. The model parameters are based on laboratory measurements on samples from the fault of the M(w) 7.6 1999 Chi-Chi earthquake. The long-term slip behaviour of the model, which we examine using a unique numerical approach that includes all wave effects, reproduces and explains a number of both long-term and coseismic observations-some of them seemingly contradictory-about the faults at which the Tohoku-Oki and Chi-Chi earthquakes occurred, including there being more high-frequency radiation from areas of lower slip, the largest seismic slip in the Tohoku-Oki earthquake having occurred in a potentially creeping segment, the overall pattern of previous events in the area and the complexity of the Tohoku-Oki rupture. The implication that earthquake rupture may break through large portions of creeping segments, which are at present considered to be barriers, requires a re-evaluation of seismic hazard in many areas. PMID:23302798

  13. 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)

  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. 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.

  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. 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. PMID:24968872

  18. 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

  19. Long-term results of oral valganciclovir for treatment of anterior segment inflammation secondary to cytomegalovirus infection

    PubMed Central

    Wong, Victoria WY; Chan, Carmen KM; Leung, Dexter YL; Lai, Timothy YY

    2012-01-01

    Background The purpose of this study was to assess the efficacy of oral valganciclovir in the treatment of anterior segment inflammation caused by cytomegalovirus (CMV) infection. Methods Consecutive patients with anterior segment inflammation due to CMV causing anterior uveitis or corneal endotheliitis treated with oral valganciclovir were reviewed. Diagnosis of CMV infection was confirmed by polymerase chain reaction of the aqueous aspirate prior to commencement of oral valganciclovir. All patients were treated with an oral loading dose of 900 mg valganciclovir twice daily for at least 2 weeks, followed by an additional 450 mg valganciclovir twice-daily maintenance therapy. Changes in visual acuity, intraocular pressure (IOP), use of antiglaucomatous eye drops, and recurrence were analyzed. Results Thirteen eyes of 11 patients were followed for a mean of 17.2 months. Two patients had bilateral corneal endotheliitis. All eyes had absence of anterior segment inflammation within 3 weeks after treatment. Following treatment, the mean logMAR visual acuity improved significantly from 0.58 at baseline to 0.37 at the last follow-up (P = 0.048). The mean IOP and number of antiglaucomatous eye drops also decreased significantly (P = 0.021 and P = 0.004, respectively). Five (38.5%) eyes had recurrence of anterior uveitis after valganciclovir was stopped and required retreatment with oral valganciclovir. Conclusion Oral valganciclovir appeared to be effective in controlling CMV anterior uveitis, resulting in visual improvement and IOP reduction following control of inflammation. However, despite the initial clinical response in all cases, recurrence after cessation of oral valganciclovir could occur. PMID:22553419

  20. Iterative contextual CV model for liver segmentation

    NASA Astrophysics Data System (ADS)

    Ji, Hongwei; He, Jiangping; Yang, Xin

    2014-01-01

    In this paper, we propose a novel iterative active contour algorithm, i.e. Iterative Contextual CV Model (ICCV), and apply it to automatic liver segmentation from 3D CT images. ICCV is a learning-based method and can be divided into two stages. At the first stage, i.e. the training stage, given a set of abdominal CT training images and the corresponding manual liver labels, our task is to construct a series of self-correcting classifiers by learning a mapping between automatic segmentations (in each round) and manual reference segmentations via context features. At the second stage, i.e. the segmentation stage, first the basic CV model is used to segment the image and subsequently Contextual CV Model (CCV), which combines the image information and the current shape model, is iteratively performed to improve the segmentation result. The current shape model is obtained by inputting the previous automatic segmentation result into the corresponding self-correcting classifier. The proposed method is evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results show that we obtain more and more accurate segmentation results by the iterative steps and satisfying results are obtained after about six iterations. Also, our method is comparable to the state-of-the-art work on liver segmentation.

  1. 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

  2. A multiatlas segmentation using graph cuts with applications to liver segmentation in CT scans.

    PubMed

    Platero, Carlos; Tobar, M Carmen

    2014-01-01

    An atlas-based segmentation approach is presented that combines low-level operations, an affine probabilistic atlas, and a multiatlas-based segmentation. The proposed combination provides highly accurate segmentation due to registrations and atlas selections based on the regions of interest (ROIs) and coarse segmentations. Our approach shares the following common elements between the probabilistic atlas and multiatlas segmentation: (a) the spatial normalisation and (b) the segmentation method, which is based on minimising a discrete energy function using graph cuts. The method is evaluated for the segmentation of the liver in computed tomography (CT) images. Low-level operations define a ROI around the liver from an abdominal CT. We generate a probabilistic atlas using an affine registration based on geometry moments from manually labelled data. Next, a coarse segmentation of the liver is obtained from the probabilistic atlas with low computational effort. Then, a multiatlas segmentation approach improves the accuracy of the segmentation. Both the atlas selections and the nonrigid registrations of the multiatlas approach use a binary mask defined by coarse segmentation. We experimentally demonstrate that this approach performs better than atlas selections and nonrigid registrations in the entire ROI. The segmentation results are comparable to those obtained by human experts and to other recently published results. PMID:25276219

  3. A Multiatlas Segmentation Using Graph Cuts with Applications to Liver Segmentation in CT Scans

    PubMed Central

    2014-01-01

    An atlas-based segmentation approach is presented that combines low-level operations, an affine probabilistic atlas, and a multiatlas-based segmentation. The proposed combination provides highly accurate segmentation due to registrations and atlas selections based on the regions of interest (ROIs) and coarse segmentations. Our approach shares the following common elements between the probabilistic atlas and multiatlas segmentation: (a) the spatial normalisation and (b) the segmentation method, which is based on minimising a discrete energy function using graph cuts. The method is evaluated for the segmentation of the liver in computed tomography (CT) images. Low-level operations define a ROI around the liver from an abdominal CT. We generate a probabilistic atlas using an affine registration based on geometry moments from manually labelled data. Next, a coarse segmentation of the liver is obtained from the probabilistic atlas with low computational effort. Then, a multiatlas segmentation approach improves the accuracy of the segmentation. Both the atlas selections and the nonrigid registrations of the multiatlas approach use a binary mask defined by coarse segmentation. We experimentally demonstrate that this approach performs better than atlas selections and nonrigid registrations in the entire ROI. The segmentation results are comparable to those obtained by human experts and to other recently published results. PMID:25276219

  4. 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.

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

    NASA Astrophysics Data System (ADS)

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

    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).

  6. 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.

  7. LiF TLD-100 as a Dosimeter in High Energy Proton Beam Therapy-Can It Yield Accurate Results?

    SciTech Connect

    Zullo, John R. Kudchadker, Rajat J.; Zhu, X. Ronald; Sahoo, Narayan; Gillin, Michael T.

    2010-04-01

    In the region of high-dose gradients at the end of the proton range, the stopping power ratio of the protons undergoes significant changes, allowing for a broad spectrum of proton energies to be deposited within a relatively small volume. Because of the potential linear energy transfer dependence of LiF TLD-100 (thermolumescent dosimeter), dose measurements made in the distal fall-off region of a proton beam may be less accurate than those made in regions of low-dose gradients. The purpose of this study is to determine the accuracy and precision of dose measured using TLD-100 for a pristine Bragg peak, particularly in the distal fall-off region. All measurements were made along the central axis of an unmodulated 200-MeV proton beam from a Probeat passive beam-scattering proton accelerator (Hitachi, Ltd., Tokyo, Japan) at varying depths along the Bragg peak. Measurements were made using TLD-100 powder flat packs, placed in a virtual water slab phantom. The measurements were repeated using a parallel plate ionization chamber. The dose measurements using TLD-100 in a proton beam were accurate to within {+-}5.0% of the expected dose, previously seen in our past photon and electron measurements. The ionization chamber and the TLD relative dose measurements agreed well with each other. Absolute dose measurements using TLD agreed with ionization chamber measurements to within {+-} 3.0 cGy, for an exposure of 100 cGy. In our study, the differences in the dose measured by the ionization chamber and those measured by TLD-100 were minimal, indicating that the accuracy and precision of measurements made in the distal fall-off region of a pristine Bragg peak is within the expected range. Thus, the rapid change in stopping power ratios at the end of the range should not affect such measurements, and TLD-100 may be used with confidence as an in vivo dosimeter for proton beam therapy.

  8. Evaluation of ultrasonic biomicroscopy results in anterior eye segment before and after cataract surgery

    PubMed Central

    Simsek, Ali; Ciftci, Süleyman

    2012-01-01

    Background The aim of this study was to assess the value of ultrasonic biomicroscopy in reporting decreases in intraocular pressure resulting from changes in anterior chamber depth and angle after phacoemulsification and intracapsular lens implantation in patients with cataract. Methods This prospective interventional case series included 50 eyes of 50 consecutive subjects operated at the same center. Patients with eye disease affecting visual acuity, a history of eye surgery, corneal surface irregularities, a pupil diameter < 5 mm after preoperative dilation, aged younger than 35 years, posterior capsule perforation, iris dialysis during surgery, intensive postoperative corneal edema, and inability to attend adequate follow-up were excluded. Intraocular pressure, anterior chamber depth and angle, and corneal thickness were measured before and one month after surgery. Results The mean preoperative intraocular pressure was 14 mmHg and postoperatively was 11 mmHg. Mean anterior chamber depth preoperatively was 2.8 mm and increased to 3.7 mm postoperatively. The mean anterior chamber angle was measured as 27° preoperatively and as 42° postoperatively. Conclusion After phacoemulsification and intracapsular lens implantation, ultrasonic biomicroscopy showed that the iris diaphragm had shifted backwards, widening the angle of the anterior chamber and decreasing intraocular pressure. PMID:23204837

  9. Position sensors for segmented mirror

    NASA Astrophysics Data System (ADS)

    Rozière, Didier; Buous, Sébastien; Courteville, Alain

    2004-09-01

    There are currently several projects for giant telescopes with segmented mirrors under way. These future telescopes will have their primary mirror made of several thousand segments. The main advantage of segmentation is that it enables the active control of the whole mirror, so as to suppress the deformations of the support structure due to the wind, gravity, thermal inhomogeneities etc. ..., thus getting the best possible stigmatism. However, providing active control of segmented mirrors requires numerous accurate edges sensors. It is acknowledged that capacitance-based technology nowadays offers the best metrological performances-to-cost ratio. As the leader in capacitive technology, FOGALE nanotech offers an original concept which reduces the cost of instrumentation, sensors and electronics, while keeping a very high level of performances with a manufacturing process completely industrialised. We present here the sensors developed for the Segment Alignment Measurement System (SAMS) of the Southern African Large Telescope (SALT). This patented solution represents an important improvement in terms of cost, to market the Position Sensors for Segmented Mirrors of ELTs, whilst maintaining a very high performance level. We present here the concept, the laboratory qualification, and the first trials on the 7 central segments of SALT. The laboratory results are good, and we are now working on the on-site implementation to improve the immunity of the sensors to environment.

  10. 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

  11. Surgical results of dynamic nonfusion stabilization with the Segmental Spinal Correction System for degenerative lumbar spinal diseases with instability: Minimum 2-year follow-up

    PubMed Central

    Ohta, Hideki; Matsumoto, Yoshiyuki; Morishita, Yuichirou; Sakai, Tsubasa; Huang, George; Kida, Hirotaka; Takemitsu, Yoshiharu

    2011-01-01

    Background When spinal fusion is applied to degenerative lumbar spinal disease with instability, adjacent segment disorder will be an issue in the future. However, decompression alone could cause recurrence of spinal canal stenosis because of increased instability on operated segments and lead to revision surgery. Covering the disadvantages of both procedures, we applied nonfusion stabilization with the Segmental Spinal Correction System (Ulrich Medical, Ulm, Germany) and decompression. Methods The surgical results of 52 patients (35 men and 17 women) with a minimum 2-year follow-up were analyzed: 10 patients with lumbar spinal canal stenosis, 15 with lumbar canal stenosis with disc herniation, 20 with degenerative spondylolisthesis, 6 with disc herniation, and 1 with lumbar discopathy. Results The Japanese Orthopaedic Association score was improved, from 14.4 ± 5.3 to 25.5 ± 2.8. The improvement rate was 76%. Range of motion of the operated segments was significantly decreased, from 9.6° ± 4.2° to 2.0° ± 1.8°. Only 1 patient had adjacent segment disease that required revision surgery. There was only 1 screw breakage, but the patient was asymptomatic. Conclusions Over a minimum 2-year follow-up, the results of nonfusion stabilization with the Segmental Spinal Correction System for unstable degenerative lumbar disease were good. It is necessary to follow up the cases with a focus on adjacent segment disorders in the future. PMID:25802671

  12. 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. PMID:27122050

  13. Risk of adjacent-segment disease requiring surgery after short lumbar fusion: results of the French Spine Surgery Society Series.

    PubMed

    Scemama, Caroline; Magrino, Baptiste; Gillet, Philippe; Guigui, Pierre

    2016-07-01

    OBJECTIVE Adjacent-segment disease (ASD) is an increasingly problematic complication following lumbar fusion surgery. The purpose of the current study was to determine the risk of ASD requiring surgical treatment after short lumbar or lumbosacral fusion. Primary spinal disease and surgical factors associated with an increased risk of revision were also investigated. METHODS This was a retrospective cohort study using the French Spine Surgery Society clinical data that included 3338 patients, with an average follow-up duration of 7 years (range 4-10 years). Clinical ASD requiring surgery was the principal judgment criterion; the length of follow-up time and initial spinal disease were also recorded. Kaplan-Meier survival analysis was performed. The correlation between primary spinal disease and surgery with an increased risk of revision was investigated. RESULTS During the follow-up period, 186 patients required revision surgery for ASD (5.6%). The predicted risk of ASD requiring revision surgery was 1.7% (95% CI 1.3%-2.2%) at 2 years, 3.8% (95% CI 4.9%-6.7%) at 4 years, 5.7% (95% CI 4.9%-6.7%) at 6 years, and 9% (95% CI 8.7%-10.6%) at 8 years. Initial spinal disease affected the risk of ASD requiring surgery (p = 0.0003). The highest risk was observed for degenerative spondylolisthesis. CONCLUSIONS ASD requiring revision surgery was predicted in 5.6% of patients 7 years after index short lumbar spinal fusion in the French Spine Surgery Society retrospective series. An increased risk of ASD requiring revision surgery associated with initial spinal disease showed the significance of the influence of natural degenerative history on adjacent-segment pathology. PMID:26967992

  14. Additional correction for energy transfer efficiency calculation in filter-based Förster resonance energy transfer microscopy for more accurate results

    NASA Astrophysics Data System (ADS)

    Sun, Yuansheng; Periasamy, Ammasi

    2010-03-01

    Förster resonance energy transfer (FRET) microscopy is commonly used to monitor protein interactions with filter-based imaging systems, which require spectral bleedthrough (or cross talk) correction to accurately measure energy transfer efficiency (E). The double-label (donor+acceptor) specimen is excited with the donor wavelength, the acceptor emission provided the uncorrected FRET signal and the donor emission (the donor channel) represents the quenched donor (qD), the basis for the E calculation. Our results indicate this is not the most accurate determination of the quenched donor signal as it fails to consider the donor spectral bleedthrough (DSBT) signals in the qD for the E calculation, which our new model addresses, leading to a more accurate E result. This refinement improves E comparisons made with lifetime and spectral FRET imaging microscopy as shown here using several genetic (FRET standard) constructs, where cerulean and venus fluorescent proteins are tethered by different amino acid linkers.

  15. Preliminary results on crust and upper mantle structure in the Rungwe Volcanic Province, Tanzania from the SEGMeNT project

    NASA Astrophysics Data System (ADS)

    Kachingwe, M.; Nyblade, A.; Ebinger, C. J.; Shillington, D. J.; Gaherty, J. B.; Mbogoni, G. J.; Mulibo, G. D.; Ferdinand-Wambura, R.; Kamihanda, G.

    2014-12-01

    While well-developed magmatic and tectonic segmentation is observed in late-stage rifts, little is known about the controls on the initiation and development of magmatism and segmentation in young rifts. The Lake Malawi/Nyasa region in the East African Rift System (EARS) is a region of early rifting that exhibits tectonic segmentation and little volcanism. The only magmatism in this region occurs in an accommodation zone between segments at the northern end of the lake, in the Rungwe Volcanic Province, rather than in the segment center as observed in mid-ocean ridges and late-stage rifts. This phenomenon is also observed elsewhere in EARS and in other young rifts, but the origin and distribution of magma at depth and its role in extension and segmentation is unknown. In the initial phase of the SEGMeNT project, 14 broadband seismometers were deployed in the Rungwe Volcanic Province during August 2013. Data from teleseismic events recorded on these stations, in combination with data from previous seismic networks deployed in southern Tanzania, are being used to develop preliminary models of crust and upper mantle structure for elucidating the role of magmatism in early-stage rifting. Crustal structure is being investigated using H-k stacking of receiver functions and upper mantle structure is being modeled tomographically using P and S arrival times.

  16. Simultaneous segmentation and statistical label fusion

    NASA Astrophysics Data System (ADS)

    Asman, Andrew J.; Landman, Bennett A.

    2012-02-01

    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. 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

  18. Assessment of a sponge layer as a non-reflective boundary treatment with highly accurate gust–airfoil interaction results

    NASA Astrophysics Data System (ADS)

    Crivellini, A.

    2016-02-01

    This paper deals with the numerical performance of a sponge layer as a non-reflective boundary condition. This technique is well known and widely adopted, but only recently have the reasons for a sponge failure been recognised, in analysis by Mani. For multidimensional problems, the ineffectiveness of the method is due to the self-reflections of the sponge occurring when it interacts with an oblique acoustic wave. Based on his theoretical investigations, Mani gives some useful guidelines for implementing effective sponge layers. However, in our opinion, some practical indications are still missing from the current literature. Here, an extensive numerical study of the performance of this technique is presented. Moreover, we analyse a reduced sponge implementation characterised by undamped partial differential equations for the velocity components. The main aim of this paper relies on the determination of the minimal width of the layer, as well as of the corresponding strength, required to obtain a reflection error of no more than a few per cent of that observed when solving the same problem on the same grid, but without employing the sponge layer term. For this purpose, a test case of computational aeroacoustics, the single airfoil gust response problem, has been addressed in several configurations. As a direct consequence of our investigation, we present a well documented and highly validated reference solution for the far-field acoustic intensity, a result that is not well established in the literature. Lastly, the proof of the accuracy of an algorithm for coupling sub-domains solved by the linear and non-liner Euler governing equations is given. This result is here exploited to adopt a linear-based sponge layer even in a non-linear computation.

  19. Automatic setae segmentation from Chaetoceros microscopic images.

    PubMed

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

    2014-09-01

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

  20. 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. PMID:26277730

  1. 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.

  2. 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

  3. Segment alignment control system

    NASA Technical Reports Server (NTRS)

    Aubrun, JEAN-N.; Lorell, Ken R.

    1988-01-01

    The segmented primary mirror for the LDR will require a special segment alignment control system to precisely control the orientation of each of the segments so that the resulting composite reflector behaves like a monolith. The W.M. Keck Ten Meter Telescope will utilize a primary mirror made up of 36 actively controlled segments. Thus the primary mirror and its segment alignment control system are directly analogous to the LDR. The problems of controlling the segments in the face of disturbances and control/structures interaction, as analyzed for the TMT, are virtually identical to those for the LDR. The two systems are briefly compared.

  4. 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

  5. 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)

  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. Integrated segmentation of cellular structures

    NASA Astrophysics Data System (ADS)

    Ajemba, Peter; Al-Kofahi, Yousef; Scott, Richard; Donovan, Michael; Fernandez, Gerardo

    2011-03-01

    Automatic segmentation of cellular structures is an essential step in image cytology and histology. Despite substantial progress, better automation and improvements in accuracy and adaptability to novel applications are needed. In applications utilizing multi-channel immuno-fluorescence images, challenges include misclassification of epithelial and stromal nuclei, irregular nuclei and cytoplasm boundaries, and over and under-segmentation of clustered nuclei. Variations in image acquisition conditions and artifacts from nuclei and cytoplasm images often confound existing algorithms in practice. In this paper, we present a robust and accurate algorithm for jointly segmenting cell nuclei and cytoplasm using a combination of ideas to reduce the aforementioned problems. First, an adaptive process that includes top-hat filtering, Eigenvalues-of-Hessian blob detection and distance transforms is used to estimate the inverse illumination field and correct for intensity non-uniformity in the nuclei channel. Next, a minimum-error-thresholding based binarization process and seed-detection combining Laplacian-of-Gaussian filtering constrained by a distance-map-based scale selection is used to identify candidate seeds for nuclei segmentation. The initial segmentation using a local maximum clustering algorithm is refined using a minimum-error-thresholding technique. Final refinements include an artifact removal process specifically targeted at lumens and other problematic structures and a systemic decision process to reclassify nuclei objects near the cytoplasm boundary as epithelial or stromal. Segmentation results were evaluated using 48 realistic phantom images with known ground-truth. The overall segmentation accuracy exceeds 94%. The algorithm was further tested on 981 images of actual prostate cancer tissue. The artifact removal process worked in 90% of cases. The algorithm has now been deployed in a high-volume histology analysis application.

  8. 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.

  9. Accelerating sub-pixel marker segmentation using GPU

    NASA Astrophysics Data System (ADS)

    Handel, Holger

    2009-02-01

    Sub-pixel accurate marker segmentation is an important task for many computer vision systems. The 3D-positions of markers are used in control loops to determine the position of machine tools or robot end-effectors. Accurate segmentation of the marker position in the image plane is crucial for accurate reconstruction. Many subpixel segmentation algorithms are computationally intensive, especially when the number of markers increases. Modern graphics hardware with its massively parallel architecture provides a powerful tool for many image segmentation tasks. Especially, the time consuming sub-pixel refinement steps in marker segmentation can benefit from the recent progress. This article presents an implementation of a sub-pixel marker segmentation framework using the GPU to accelerate the processing time. The image segmentation chain consists of two stages. The first is a pre-processing stage which segments the initial position of the marker with pixel accuracy, the second stage refines the initial marker position to sub-pixel accuracy. Both stages are implemented as shader programs on the GPU. The flexible architecture allows it to combine different pre-processing and sub-pixel refinement algorithms. Experimental results show that significant speed-up can be achieved compared to CPU implementations, especially when the number of markers increases.

  10. 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.

  11. Color image segmentation

    NASA Astrophysics Data System (ADS)

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

    1994-03-01

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

  12. Collaborative multi organ segmentation by integrating deformable and graphical models.

    PubMed

    Uzunbaş, Mustafa Gökhan; Chen, Chao; Zhang, Shaoting; Poh, Kilian M; Li, Kang; Metaxas, Dimitris

    2013-01-01

    Organ segmentation is a challenging problem on which significant progress has been made. Deformable models (DM) and graphical models (GM) are two important categories of optimization based image segmentation methods. Efforts have been made on integrating two types of models into one framework. However, previous methods are not designed for segmenting multiple organs simultaneously and accurately. In this paper, we propose a hybrid multi organ segmentation approach by integrating DM and GM in a coupled optimization framework. Specifically, we show that region-based deformable models can be integrated with Markov Random Fields (MRF), such that multiple models' evolutions are driven by a maximum a posteriori (MAP) inference. It brings global and local deformation constraints into a unified framework for simultaneous segmentation of multiple objects in an image. We validate this proposed method on two challenging problems of multi organ segmentation, and the results are promising. PMID:24579136

  13. Uncertainty-aware guided volume segmentation.

    PubMed

    Prassni, Jörg-Stefan; Ropinski, Timo; Hinrichs, Klaus

    2010-01-01

    Although direct volume rendering is established as a powerful tool for the visualization of volumetric data, efficient and reliable feature detection is still an open topic. Usually, a tradeoff between fast but imprecise classification schemes and accurate but time-consuming segmentation techniques has to be made. Furthermore, the issue of uncertainty introduced with the feature detection process is completely neglected by the majority of existing approaches.In this paper we propose a guided probabilistic volume segmentation approach that focuses on the minimization of uncertainty. In an iterative process, our system continuously assesses uncertainty of a random walker-based segmentation in order to detect regions with high ambiguity, to which the user's attention is directed to support the correction of potential misclassifications. This reduces the risk of critical segmentation errors and ensures that information about the segmentation's reliability is conveyed to the user in a dependable way. In order to improve the efficiency of the segmentation process, our technique does not only take into account the volume data to be segmented, but also enables the user to incorporate classification information. An interactive workflow has been achieved by implementing the presented system on the GPU using the OpenCL API. Our results obtained for several medical data sets of different modalities, including brain MRI and abdominal CT, demonstrate the reliability and efficiency of our approach. PMID:20975176

  14. 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.

  15. [Head and Neck Tumor Segmentation Based on Augmented Gradient Level Set Method].

    PubMed

    Zhang, Qiongmin; Zhang, Jing; Wang, Mintang; He, Ling; Men, Yi; Wei, Jun; Haung, Hua

    2015-08-01

    To realize the accurate positioning and quantitative volume measurement of tumor in head and neck tumor CT images, we proposed a level set method based on augmented gradient. With the introduction of gradient information in the edge indicator function, our proposed level set model is adaptive to different intensity variation, and achieves accurate tumor segmentation. The segmentation result has been used to calculate tumor volume. In large volume tumor segmentation, the proposed level set method can reduce manual intervention and enhance the segmentation accuracy. Tumor volume calculation results are close to the gold standard. From the experiment results, the augmented gradient based level set method has achieved accurate head and neck tumor segmentation. It can provide useful information to computer aided diagnosis. PMID:26710464

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

    PubMed

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

    2013-01-01

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

  17. Segmentation of stereo terrain images

    NASA Astrophysics Data System (ADS)

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

    2000-06-01

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

  18. Stereotaxy-based regional brain volumetry applied to segmented MRI: validation and results in deficit and nondeficit schizophrenia.

    PubMed

    Quarantelli, Mario; Larobina, Michele; Volpe, Umberto; Amati, Giovanni; Tedeschi, Enrico; Ciarmiello, Andrea; Brunetti, Arturo; Galderisi, Silvana; Alfano, Bruno

    2002-09-01

    A method for postprocessing of segmented routine brain MRI studies providing automated definition of major structures (frontal, parietal, occipital, and temporal lobes; cerebellar hemispheres; and lateral ventricles) according to the Talairach atlas is presented. The method was applied to MRI studies from 25 normal subjects (NV), 14 patients with deficit schizophrenia (DS), and 14 with nondeficit schizophrenia (NDS), to evaluate their gray matter and CSF regional volumes. The two patient groups did not differ in mean age at illness onset, duration of illness, severity of psychotic symptoms, or disorganization; DS had more severe avolition and worse social functioning than NDS. For validation purposes, brain structures were manually outlined on original MR images in 10 studies, thus obtaining reference measures. Manual and automated measures were repeated 1 month apart to measure reproducibilities of both methods. The automated method required less than 1 min/operator per study vs more than 30 min for manual assessment. Mean absolute difference per structure between the two techniques was 4.8 ml. Overall reproducibility did not significantly differ between the two methods. In subjects with schizophrenia, a significant decrease in GM and increase in CSF were found. GM loss was confined to frontal and temporal lobes. Lateral ventricles were significantly larger bilaterally in NDS compared to NV and only on the right in NDS compared to DS. The finding of greater structural brain abnormalities in NDS adds to the evidence that deficit schizophrenia does not represent just the more severe end of the schizophrenia continuum. PMID:12482090

  19. Segmental neurofibromatosis.

    PubMed

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

    2014-07-01

    Segmental neurofibromatosis is a rare disorder, characterized by neurofibromas or cafι-au-lait macules limited to one region of the body. Its occurrence on the face is extremely rare and only few cases of segmental neurofibromatosis over the face have been described so far. We present a case of segmental neurofibromatosis involving the buccal mucosa, tongue, cheek, ear, and neck on the right side of the face. PMID:25565748

  20. 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

  1. Robust system for human airway-tree segmentation

    NASA Astrophysics Data System (ADS)

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

    2008-03-01

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

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

    PubMed Central

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

    2009-01-01

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

  3. A Fast and Accurate Monte Carlo EAS Simulation Scheme in the GZK Energy Region and Some Results for the TA experiment

    NASA Astrophysics Data System (ADS)

    Cohen, F.; Kasahara, K.

    As described in an accompanying paper (kasahara), full M.C simulation of air showers in the GZK region is possible by a distributed-parallel processing method. However, this still needs a long computation time even with ~50 to ~100 cpu's which may be available in many pc cluster environments. Air showers always fluctuate event to event largely, and only 1 or few events are not appropriate for practical application. However, we may note that the fluctuations appear only in the longitudinal development; if we look into the ingredients (energy spectrum, angular distribution, arrival time distribution etc and their correlations) at the same "age" of the shower, they are almost the same (or at least can be scaled; e.g, for the lateral distribution, we may use appropriate Moliere length ). In some cases (for muons and hadrons), we may use another parameter instead of the "age". Based on this fact, we developed a new fast and accurate M.C simulation scheme which utilizes a database in which full M.C results are stored (FDD). We generate a number of air showers by using the usual thin sampling method. The thin sampling is sometimes very dangerous when we discuss detailed ingredient (say,lateral distribution, energy spectrum, their correlations etc) but is safely employed to see the total number of particles in the longitudinal development (LDD; we can generate ~1000 LDD showers by 50 cpu's in a day). Then, for a given 1 particular such an event at a certain depth, we can extract every details from FDD by a correspondence rule such as the one using "age" etc. We describe the method, its current status and show some results for the TA experiment.

  4. Segmental neurofibromatosis.

    PubMed

    Toy, Brian

    2003-10-01

    Segmental neurofibromatosis is a rare variant of neurofibromatosis in which skin lesions are confined to a circumscribed body segment. A case of a 72-year-old woman with this condition is presented. Clinical features and genetic evidence are reviewed. PMID:14594599

  5. Active Segmentation

    PubMed Central

    Mishra, Ajay; Aloimonos, Yiannis

    2009-01-01

    The human visual system observes and understands a scene/image by making a series of fixations. Every fixation point lies inside a particular region of arbitrary shape and size in the scene which can either be an object or just a part of it. We define as a basic segmentation problem the task of segmenting that region containing the fixation point. Segmenting the region containing the fixation is equivalent to finding the enclosing contour- a connected set of boundary edge fragments in the edge map of the scene - around the fixation. This enclosing contour should be a depth boundary. We present here a novel algorithm that finds this bounding contour and achieves the segmentation of one object, given the fixation. The proposed segmentation framework combines monocular cues (color/intensity/texture) with stereo and/or motion, in a cue independent manner. The semantic robots of the immediate future will be able to use this algorithm to automatically find objects in any environment. The capability of automatically segmenting objects in their visual field can bring the visual processing to the next level. Our approach is different from current approaches. While existing work attempts to segment the whole scene at once into many areas, we segment only one image region, specifically the one containing the fixation point. Experiments with real imagery collected by our active robot and from the known databases 1 demonstrate the promise of the approach. PMID:20686671

  6. 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

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

    PubMed Central

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

    2013-01-01

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

  8. 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

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

    PubMed Central

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

    2014-01-01

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

  10. 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

  11. 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.

  12. Mean Polyp per Patient Is an Accurate and Readily Obtainable Surrogate for Adenoma Detection Rate: Results from an Opportunistic Screening Colonoscopy Program

    PubMed Central

    Delavari, Alireza; Salimzadeh, Hamideh; Bishehsari, Faraz; Sobh Rakhshankhah, Elham; Delavari, Farnaz; Moossavi, Shirin; Khosravi, Pejman; Nasseri-Moghaddam, Siavosh; Merat, Shahin; Ansari, Reza; Vahedi, Homayoon; Shahbazkhani, Bijan; Saberifiroozi, Mehdi; Sotoudeh, Masoud; Malekzadeh, Reza

    2015-01-01

    BACKGROUND The incidence of colorectal cancer is rising in several developing countries. In the absence of integrated endoscopy and pathology databases, adenoma detection rate (ADR), as a validated quality indicator of screening colonoscopy, is generally difficult to obtain in practice. We aimed to measure the correlation of polyp-related indicators with ADR in order to identify the most accurate surrogate(s) of ADR in routine practice. METHODS We retrospectively reviewed the endoscopic and histopathological findings of patients who underwent colonoscopy at a tertiary gastrointestinal clinic. The overall ADR and advanced-ADR were calculated using patient-level data. The Pearson’s correlation coefficient (r) was applied to measure the strength of the correlation between the quality metrics obtained by endoscopists. RESULTS A total of 713 asymptomatic adults aged 50 and older who underwent their first-time screening colonoscopy were included in this study. The ADR and advanced-ADR were 33.00% (95% CI: 29.52-36.54) and 13.18% (95% CI: 10.79-15.90), respectively. We observed good correlations between polyp detection rate (PDR) and ADR (r=0.93), and mean number of polyp per patient (MPP) and ADR (r=0.88) throughout the colon. There was a positive, yet insignificant correlation between advanced ADRs and non-advanced ADRs (r=0.42, p=0.35). CONCLUSION MPP is strongly correlated with ADR, and can be considered as a reliable and readily obtainable proxy for ADR in opportunistic screening colonoscopy programs. PMID:26609349

  13. 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.

  14. Interactive Tooth Separation from Dental Model Using Segmentation Field.

    PubMed

    Li, Zhongyi; Wang, Hao

    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

  15. 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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2013-01-01

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

  18. Simplified labeling process for medical image segmentation.

    PubMed

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

    2012-01-01

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

  19. Rayleigh-wave imaging of upper-mantle shear velocities beneath the Malawi Rift; Preliminary results from the SEGMeNT experiment

    NASA Astrophysics Data System (ADS)

    Accardo, N. J.; Gaherty, J. B.; Shillington, D. J.; Nyblade, A.; Ebinger, C. J.; Mbogoni, G. J.; Chindandali, P. R. N.; Mulibo, G. D.; Ferdinand-Wambura, R.; Kamihanda, G.

    2015-12-01

    The Malawi Rift (MR) is an immature rift located at the southern tip of the Western branch of the East African Rift System (EARS). Pronounced border faults and tectonic segmentation are seen within the upper crust. Surface volcanism in the region is limited to the Rungwe volcanic province located north of Lake Malawi (Nyasa). However, the distribution of extension and magma at depth in the crust and mantle lithosphere is unknown. As the Western Rift of the EARS is largely magma-poor except for discrete volcanic provinces, the MR presents the ideal location to elucidate the role of magmatism in early-stage rifting and the manifestation of segmentation at depth. This study investigates the shear velocity of the crust and mantle lithosphere beneath the MR to constrain the thermal structure, the amount of total crustal and lithospheric thinning, and the presence and distribution of magmatism beneath the rift. Utilizing 55 stations from the SEGMeNT (Study of Extension and maGmatism in Malawi aNd Tanzania) passive-source seismic experiment operating in Malawi and Tanzania, we employed a multi-channel cross-correlation algorithm to obtain inter-station phase and amplitude information from Rayleigh wave observations between 20 and 80 s period. We retrieve estimates of phase velocity between 9-20 s period from ambient noise cross-correlograms in the frequency domain via Aki's formula. We invert phase velocity measurements to obtain estimates of shear velocity (Vs) between 50-200 km depth. Preliminary results reveal a striking low-velocity zone (LVZ) beneath the Rungwe volcanic province with Vs ~4.2-4.3 km/s in the uppermost mantle. Low velocities extend along the entire strike of Lake Malawi and to the west where a faster velocity lid (~4.5 km/s) is imaged. These preliminary results will be extended by incorporating broadband data from seven "lake"-bottom seismometers (LBS) to be retrieved from Lake Malawi in October of this year. The crust and mantle modeling will be

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

    PubMed

    Martino, Vanessa B; Sabljic, Thomas; Deschamps, Paula; Green, Rebecca M; Akula, Monica; Peacock, Erica; Ball, Alexander; Williams, Trevor; West-Mays, Judith A

    2016-08-01

    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

  1. 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

  2. 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.

  3. 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

  4. A method for the evaluation of thousands of automated 3D stem cell segmentations.

    PubMed

    Bajcsy, P; Simon, M; Florczyk, S J; Simon, C G; Juba, D; Brady, M C

    2015-12-01

    There is no segmentation method that performs perfectly with any dataset in comparison to human segmentation. Evaluation procedures for segmentation algorithms become critical for their selection. The problems associated with segmentation performance evaluations and visual verification of segmentation results are exaggerated when dealing with thousands of three-dimensional (3D) image volumes because of the amount of computation and manual inputs needed. We address the problem of evaluating 3D segmentation performance when segmentation is applied to thousands of confocal microscopy images (z-stacks). Our approach is to incorporate experimental imaging and geometrical criteria, and map them into computationally efficient segmentation algorithms that can be applied to a very large number of z-stacks. This is an alternative approach to considering existing segmentation methods and evaluating most state-of-the-art algorithms. We designed a methodology for 3D segmentation performance characterization that consists of design, evaluation and verification steps. The characterization integrates manual inputs from projected surrogate 'ground truth' of statistically representative samples and from visual inspection into the evaluation. The novelty of the methodology lies in (1) designing candidate segmentation algorithms by mapping imaging and geometrical criteria into algorithmic steps, and constructing plausible segmentation algorithms with respect to the order of algorithmic steps and their parameters, (2) evaluating segmentation accuracy using samples drawn from probability distribution estimates of candidate segmentations and (3) minimizing human labour needed to create surrogate 'truth' by approximating z-stack segmentations with 2D contours from three orthogonal z-stack projections and by developing visual verification tools. We demonstrate the methodology by applying it to a dataset of 1253 mesenchymal stem cells. The cells reside on 10 different types of biomaterial

  5. Quantum reactive scattering in three dimensions using hyperspherical (APH) coordinates. IV. Discrete variable representation (DVR) basis functions and the analysis of accurate results for F+H2

    NASA Astrophysics Data System (ADS)

    Bačić, Z.; Kress, J. D.; Parker, G. A.; Pack, R. T.

    1990-02-01

    Accurate 3D coupled channel calculations for total angular momentum J=0 for the reaction F+H2→HF+H using a realistic potential energy surface are analyzed. The reactive scattering is formulated using the hyperspherical (APH) coordinates of Pack and Parker. The adiabatic basis functions are generated quite efficiently using the discrete variable representation method. Reaction probabilities for relative collision energies of up to 17.4 kcal/mol are presented. To aid in the interpretation of the resonances and quantum structure observed in the calculated reaction probabilities, we analyze the phases of the S matrix transition elements, Argand diagrams, time delays and eigenlifetimes of the collision lifetime matrix. Collinear (1D) and reduced dimensional 3D bending corrected rotating linear model (BCRLM) calculations are presented and compared with the accurate 3D calculations.

  6. 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

  7. In-plane effects on segmented-mirror control.

    PubMed

    MacMynowski, Douglas G; Roberts, Lewis C; Shelton, J Chris; Chanan, Gary; Bonnet, Henri

    2012-04-20

    Extremely large optical telescopes are being designed with primary mirrors composed of hundreds of segments. The "out-of-plane" piston, tip, and tilt degrees of freedom of each segment are actively controlled using feedback from relative height measurements between neighboring segments. The "in-plane" segment translations and clocking (rotation) are not actively controlled; however, in-plane motions affect the active control problem in several important ways, and thus need to be considered. We extend earlier analyses by constructing the "full" interaction matrix that relates the height, gap, and shear motion at sensor locations to all six degrees of freedom of segment motion, and use this to consider three effects. First, in-plane segment clocking results in height discontinuities between neighboring segments that can lead to a global control system response. Second, knowledge of the in-plane motion is required both to compensate for this effect and to compensate for sensor installation errors, and thus, we next consider the estimation of in-plane motion and the associated noise propagation characteristics. In-plane motion can be accurately estimated using measurements of the gap between segments, but with one unobservable mode in which every segment clocks by an equal amount. Finally, we examine whether in-plane measurements (gap and/or shear) can be used to estimate out-of-plane segment motion; these measurements can improve the noise multiplier for the "focus-mode" of the segmented-mirror array, which involves pure dihedral angle changes between segments and is not observable with only height measurements. PMID:22534898

  8. Hybrid image segmentation using watersheds

    NASA Astrophysics Data System (ADS)

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

    1996-02-01

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

  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. 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.

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

    PubMed Central

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

    2014-01-01

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

  12. Preliminary evaluation of the second hot dry rock geothermal energy reservoir: results of phase 1, run segment 4

    SciTech Connect

    Murphy, H.D.; Aamodt, R.L.; Albright, J.N.

    1980-05-01

    Results of the preliminary assessment of the second hot dry rock reservoir at the Fenton Hill field site are presented. This second reservoir was created by fracturing a deeper interval of granite rock located at a depth of 2.93 km (9620 ft) in the same wellbore pair used in the creation of the first reservoir; no additional redrilling was required. The new fracture system has a vertical extent of at least 320 m (1050 ft), suggesting that the combined heat-transfer area of the old and new fracture systems is 11 times that of the old system. The virgin rock temperature at the bottom of the deeper interval was 197/sup 0/C (386/sup 0/F). Water at a flow rate of 6 l/s (100 gpm) was circulated through the reservoir for a period of 23 days. Downhole measurements of the water temperature at the reservoir outlet, as well as temperatures inferred from geothermometry, showed that the thermal drawdown of the reservoir was negligible and preliminary estimates indicate that the minimum effective heat-transfer area of the new reservoir is 45,000 m/sup 2/ (480,000 ft/sup 2/), which is six times larger than the first reservoir. The following are presented: operational plan, reservoir geometry and flow paths, flow impedance, geochemistry, heat extraction, dye tracer flow distribution studies, and seismicity. (MHR)

  13. 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

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

    NASA Astrophysics Data System (ADS)

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

    2012-01-01

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

  15. 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

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

    PubMed

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

    2015-01-01

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

  17. Uterine fibroid segmentation and volume measurement on MRI

    NASA Astrophysics Data System (ADS)

    Yao, Jianhua; Chen, David; Lu, Wenzhu; Premkumar, Ahalya

    2006-03-01

    Uterine leiomyomas are the most common pelvic tumors in females. The efficacy of medical treatment is gauged by shrinkage of the size of these tumors. In this paper, we present a method to robustly segment the fibroids on MRI and accurately measure the 3D volume. Our method is based on a combination of fast marching level set and Laplacian level set. With a seed point placed inside the fibroid region, a fast marching level set is first employed to obtain a rough segmentation, followed by a Laplacian level set to refine the segmentation. We devised a scheme to automatically determine the parameters for the level set function and the sigmoid function based on pixel statistics around the seed point. The segmentation is conducted on three concurrent views (axial, coronal and sagittal), and a combined volume measurement is computed to obtain a more reliable measurement. We carried out extensive tests on 13 patients, 25 MRI studies and 133 fibroids. The segmentation result was validated against manual segmentation defined by experts. The average segmentation sensitivity (true positive fraction) among all fibroids was 84.6%, and the average segmentation specificity (1-false positive fraction) was 84.3%.

  18. 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.

  19. Effective phonocardiogram segmentation using time statistics and nonlinear prediction

    NASA Astrophysics Data System (ADS)

    Sridharan, Rajeswari; Janet, J.

    2010-02-01

    In the fields of image processing, signal processing and recognition, image Segmentation is an efficient method for segmenting the phonocardiograph signals (PCG) is offered. Primarily, inter-beat segmentation is approved and carried out by means of DII lead of the ECG recording for identifying the happenings of the very first heart sound (S1). Then, the intra-beat segmentation is attained by the use of recurrence time statistics (RTS), and that is very sensitive to variations of the renovated attractor in a state space derived from nonlinear dynamic analysis. Apart from this if the segmentation with RTS is unsuccessful, a special segmentation is proposed using threshold that is extracted from the high frequency rate decomposition and the feature extraction of the disorder is classified based on the murmur sounds. In the Inter-beat segmentation process the accuracy was 100% of the over all PCG recording. Taking into account a different level of PCG beats were strongly concerned by different types of cardiac murmurs and intra-beat segmentation are give up for an accurate result.

  20. 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)

  1. 3-D segmentation of human sternum in lung MDCT images.

    PubMed

    Pazokifard, Banafsheh; Sowmya, Arcot

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Niu, Shiwei; Ren, Kan

    2015-10-01

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

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

    PubMed

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

    2016-09-01

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

  4. 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. PMID:26961983

  5. Renal compartment segmentation in DCE-MRI images.

    PubMed

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

    2016-08-01

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

  6. Segmented combustor

    NASA Technical Reports Server (NTRS)

    Halila, Ely E. (Inventor)

    1994-01-01

    A combustor liner segment includes a panel having four sidewalls forming a rectangular outer perimeter. A plurality of integral supporting lugs are disposed substantially perpendicularly to the panel and extend from respective ones of the four sidewalls. A plurality of integral bosses are disposed substantially perpendicularly to the panel and extend from respective ones of the four sidewalls, with the bosses being shorter than the lugs. In one embodiment, the lugs extend through supporting holes in an annular frame for mounting the liner segments thereto, with the bosses abutting the frame for maintaining a predetermined spacing therefrom.

  7. Five-year results from the prospective European multicentre cohort study on radiofrequency segmental thermal ablation for incompetent great saphenous veins

    PubMed Central

    Proebstle, T M; Alm, B J; Göckeritz, O; Wenzel, C; Noppeney, T; Lebard, C; Sessa, C; Creton, D; Pichot, O

    2015-01-01

    Background This was a prospective study of radiofrequency segmental thermal ablation (RFA) for the treatment of incompetent varicose great saphenous veins (GSVs). The present report describes long-term follow-up at 5 years. Methods The 5-year follow-up of this multicentre European study included assessment of the Venous Clinical Severity Score (VCSS), and GSV occlusion and reflux on duplex imaging. Results A total of 225 patients had 295 GSVs treated by RFA, achieving an initial vein occlusion rate of 100 per cent. With 80·0 per cent compliance, Kaplan–Meier analyses showed a GSV occlusion rate of 91·9 per cent and a reflux-free rate of 94·9 per cent at 5 years. Among the 15 GSVs noted with reflux during follow-up, only three showed full recanalization of the GSV at 1 week, 6 months and 3 years. Of the 12 legs with partial recanalization, reflux originated at the saphenofemoral junction in ten, with a mean length of the patent segment of 5·8 (range 3·2–10) cm; only six patients were symptomatic. Mean(s.d.) VCSS scores improved from 3·9(2·1) at baseline to 0·6(1·2), 0·9(1·3) and 1·3(1·7) at 1, 3 and 5 years. Conclusion At 5 years RFA proved to be an efficient endovenous treatment for incompetent GSVs in terms of sustained clinical and anatomical success for the vast majority of treated patients. PMID:25627262

  8. 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.

  9. Clinically accurate fetal ECG parameters acquired from maternal abdominal sensors

    PubMed Central

    CLIFFORD, Gari; SAMENI, Reza; WARD, Mr. Jay; ROBINSON, Julian; WOLFBERG, Adam J.

    2011-01-01

    OBJECTIVE To evaluate the accuracy of a novel system for measuring fetal heart rate and ST-segment changes using non-invasive electrodes on the maternal abdomen. STUDY DESIGN Fetal ECGs were recorded using abdominal sensors from 32 term laboring women who had a fetal scalp electrode (FSE) placed for a clinical indication. RESULTS Good quality data for FHR estimation was available in 91.2% of the FSE segments, and 89.9% of the abdominal electrode segments. The root mean square (RMS) error between the FHR data calculated by both methods over all processed segments was 0.36 beats per minute. ST deviation from the isoelectric point ranged from 0 to 14.2% of R-wave amplitude. The RMS error between the ST change calculated by both methods averaged over all processed segments was 3.2%. CONCLUSION FHR and ST change acquired from the maternal abdomen is highly accurate and on average is clinically indistinguishable from FHR and ST change calculated using FSE data. PMID:21514560

  10. Everolimus-eluting bioresorbable stent vs. durable polymer everolimus-eluting metallic stent in patients with ST-segment elevation myocardial infarction: results of the randomized ABSORB ST-segment elevation myocardial infarction—TROFI II trial

    PubMed Central

    Sabaté, Manel; Windecker, Stephan; Iñiguez, Andres; Okkels-Jensen, Lisette; Cequier, Angel; Brugaletta, Salvatore; Hofma, Sjoerd H.; Räber, Lorenz; Christiansen, Evald Høi; Suttorp, Maarten; Pilgrim, Thomas; Anne van Es, Gerrit; Sotomi, Yohei; García-García, Hector M.; Onuma, Yoshinobu; Serruys, Patrick W.

    2016-01-01

    Aims Patients with ST-segment elevation myocardial infarction (STEMI) feature thrombus-rich lesions with large necrotic core, which are usually associated with delayed arterial healing and impaired stent-related outcomes. The use of bioresorbable vascular scaffolds (Absorb) has the potential to overcome these limitations owing to restoration of native vessel lumen and physiology at long term. The purpose of this randomized trial was to compare the arterial healing response at short term, as a surrogate for safety and efficacy, between the Absorb and the metallic everolimus-eluting stent (EES) in patients with STEMI. Methods and results ABSORB-STEMI TROFI II was a multicentre, single-blind, non-inferiority, randomized controlled trial. Patients with STEMI who underwent primary percutaneous coronary intervention were randomly allocated 1:1 to treatment with the Absorb or EES. The primary endpoint was the 6-month optical frequency domain imaging healing score (HS) based on the presence of uncovered and/or malapposed stent struts and intraluminal filling defects. Main secondary endpoint included the device-oriented composite endpoint (DOCE) according to the Academic Research Consortium definition. Between 06 January 2014 and 21 September 2014, 191 patients (Absorb [n = 95] or EES [n = 96]; mean age 58.6 years old; 17.8% females) were enrolled at eight centres. At 6 months, HS was lower in the Absorb arm when compared with EES arm [1.74 (2.39) vs. 2.80 (4.44); difference (90% CI) −1.06 (−1.96, −0.16); Pnon-inferiority <0.001]. Device-oriented composite endpoint was also comparably low between groups (1.1% Absorb vs. 0% EES). One case of definite subacute stent thrombosis occurred in the Absorb arm (1.1% vs. 0% EES; P = ns). Conclusion Stenting of culprit lesions with Absorb in the setting of STEMI resulted in a nearly complete arterial healing which was comparable with that of metallic EES at 6 months. These findings provide the basis for further exploration in

  11. Proximal femur segmentation in conventional pelvic x ray.

    PubMed

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

    2008-06-01

    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 approximately 13 pixels, average point-to-boundary error approximately 7 pixels). Using a template-based initialization based on a registration technique, a successful segmentation rate of approximately 89% is achieved, with an average point-to-point error approximately 12 pixels, and an average point-to-boundary error approximately 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. PMID:18649479

  12. 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.

  13. 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.

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

    PubMed Central

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  16. Interactive explorations of hierarchical segmentations

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    1992-01-01

    The authors report on the implementation of an interactive tool, called HSEGEXP, to interactively explore the hierarchical segmentation produced by the iterative parallel region growing (IPRG) algorithm to select the best segmentation result. This combination of the HSEGEXP tool with the IPRG algorithm amounts to a computer-assisted image segmentation system guided by human interaction. The initial application of the HSEGEXP tool is in the refinement of ground reference data based on the IPRG/HSEGEXP segmentation of the corresponding remotely sensed image data. The HSEGEXP tool is being used to help evaluate the effectiveness of an automatic 'best' segmentation process under development.

  17. 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.

  18. 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.

  19. Automatic brain segmentation in rhesus monkeys

    NASA Astrophysics Data System (ADS)

    Styner, Martin; Knickmeyer, Rebecca; Joshi, Sarang; Coe, Christopher; Short, Sarah J.; Gilmore, John

    2007-03-01

    Many neuroimaging studies are applied to primates as pathologies and environmental exposures can be studied in well-controlled settings and environment. In this work, we present a framework for both the semi-automatic creation of a rhesus monkey atlas and a fully automatic segmentation of brain tissue and lobar parcellation. We determine the atlas from training images by iterative, joint deformable registration into an unbiased average image. On this atlas, probabilistic tissue maps and a lobar parcellation. The atlas is then applied via affine, followed by deformable registration. The affinely transformed atlas is employed for a joint T1/T2 based tissue classification. The deformed atlas parcellation masks the tissue segmentations to define the parcellation. Other regional definitions on the atlas can also straightforwardly be used as segmentation. We successfully built average atlas images for the T1 and T2 datasets using a developmental training datasets of 18 cases aged 16-34 months. The atlas clearly exhibits an enhanced signal-to-noise ratio compared to the original images. The results further show that the cortical folding variability in our data is highly limited. Our segmentation and parcellation procedure was successfully re-applied to all training images, as well as applied to over 100 additional images. The deformable registration was able to identify corresponding cortical sulcal borders accurately. Even though the individual methods used in this segmentation framework have been applied before on human data, their combination is novel, as is their adaptation and application to rhesus monkey MRI data. The reduced variability present in the primate data results in a segmentation pipeline that exhibits high stability and anatomical accuracy.

  20. [Segmental neurofibromatosis].

    PubMed

    Zulaica, A; Peteiro, C; Pereiro, M; Pereiro Ferreiros, M; Quintas, C; Toribio, J

    1989-01-01

    Four cases of segmental neurofibromatosis (SNF) are reported. It is a rare entity considered to be a localized variant of neurofibromatosis (NF)-Riccardi's type V. Two cases are male and two female. The lesions are located to the head in a patient and the other three cases in the trunk. No family history nor transmission to progeny were manifested. The rest of the organs are undamaged. PMID:2502696

  1. An Integrative Method for Accurate Comparative Genome Mapping

    PubMed Central

    Swidan, Firas; Rocha, Eduardo P. C; Shmoish, Michael; Pinter, Ron Y

    2006-01-01

    We present MAGIC, an integrative and accurate method for comparative genome mapping. Our method consists of two phases: preprocessing for identifying “maximal similar segments,” and mapping for clustering and classifying these segments. MAGIC's main novelty lies in its biologically intuitive clustering approach, which aims towards both calculating reorder-free segments and identifying orthologous segments. In the process, MAGIC efficiently handles ambiguities resulting from duplications that occurred before the speciation of the considered organisms from their most recent common ancestor. We demonstrate both MAGIC's robustness and scalability: the former is asserted with respect to its initial input and with respect to its parameters' values. The latter is asserted by applying MAGIC to distantly related organisms and to large genomes. We compare MAGIC to other comparative mapping methods and provide detailed analysis of the differences between them. Our improvements allow a comprehensive study of the diversity of genetic repertoires resulting from large-scale mutations, such as indels and duplications, including explicitly transposable and phagic elements. The strength of our method is demonstrated by detailed statistics computed for each type of these large-scale mutations. MAGIC enabled us to conduct a comprehensive analysis of the different forces shaping prokaryotic genomes from different clades, and to quantify the importance of novel gene content introduced by horizontal gene transfer relative to gene duplication in bacterial genome evolution. We use these results to investigate the breakpoint distribution in several prokaryotic genomes. PMID:16933978

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

    NASA Astrophysics Data System (ADS)

    Tian, Zhiqiang; Liu, LiZhi; Fei, Baowei

    2015-03-01

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

  3. Automated method for RNFL segmentation in spectral domain OCT

    NASA Astrophysics Data System (ADS)

    Paranjape, Amit S.; Elmaanaoui, Badr; Dewelle, Jordan; Rylander, H. Grady; Milner, Thomas E.

    2008-02-01

    We introduce a method based on optical reflectivity changes to segment the retinal nerve fiber layer (RNFL) in images recorded using swept source spectral domain optical coherence tomography (OCT). The segmented image is used to determine the RNFL thickness. Simple filtering followed by edge detecting techniques can successfully be applied to segment the RNFL from recorded images and estimate RNFL thickness. The method is computationally more efficient than previously reported approaches. Higher computational efficiency allows faster segmentation and provides the ophthalmologist segmented retinal images that better utilize advantages of spectral domain OCT instrumentation. OCT B-scan and fundus images of the retina are recorded for 5 patients. The segmentation method is applied on B-scan images recorded from all patients. An expert ophthalmologist separately demarcates the RNFL layer in the OCT images from the same patients in each B-scan image. Results from automated image processing software are compared to the boundary demarcated by the expert ophthalmologist. The absolute error between the boundaries demarcated by the expert and the algorithm is expressed in terms of area and is used as an error metric. Ability of the algorithm to accurately segment the RNFL in comparison with an expert ophthalmologist is reported.

  4. 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.

  5. 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.

  6. 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.

  7. Adjacent segment disease.

    PubMed

    Virk, Sohrab S; Niedermeier, Steven; Yu, Elizabeth; Khan, Safdar N

    2014-08-01

    EDUCATIONAL OBJECTIVES As a result of reading this article, physicians should be able to: 1. Understand the forces that predispose adjacent cervical segments to degeneration. 2. Understand the challenges of radiographic evaluation in the diagnosis of cervical and lumbar adjacent segment disease. 3. Describe the changes in biomechanical forces applied to adjacent segments of lumbar vertebrae with fusion. 4. Know the risk factors for adjacent segment disease in spinal fusion. Adjacent segment disease (ASD) is a broad term encompassing many complications of spinal fusion, including listhesis, instability, herniated nucleus pulposus, stenosis, hypertrophic facet arthritis, scoliosis, and vertebral compression fracture. The area of the cervical spine where most fusions occur (C3-C7) is adjacent to a highly mobile upper cervical region, and this contributes to the biomechanical stress put on the adjacent cervical segments postfusion. Studies have shown that after fusion surgery, there is increased load on adjacent segments. Definitive treatment of ASD is a topic of continuing research, but in general, treatment choices are dictated by patient age and degree of debilitation. Investigators have also studied the risk factors associated with spinal fusion that may predispose certain patients to ASD postfusion, and these data are invaluable for properly counseling patients considering spinal fusion surgery. Biomechanical studies have confirmed the added stress on adjacent segments in the cervical and lumbar spine. The diagnosis of cervical ASD is complicated given the imprecise correlation of radiographic and clinical findings. Although radiological and clinical diagnoses do not always correlate, radiographs and clinical examination dictate how a patient with prolonged pain is treated. Options for both cervical and lumbar spine ASD include fusion and/or decompression. Current studies are encouraging regarding the adoption of arthroplasty in spinal surgery, but more long

  8. 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.

  9. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2011-03-01

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

  11. Accuracy of patient specific organ-dose estimates obtained using an automated image segmentation algorithm

    NASA Astrophysics Data System (ADS)

    Gilat-Schmidt, Taly; Wang, Adam; Coradi, Thomas; Haas, Benjamin; Star-Lack, Josh

    2016-03-01

    The overall goal of this work is to develop a rapid, accurate and fully automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using a deterministic Boltzmann Transport Equation solver and automated CT segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. The investigated algorithm uses a combination of feature-based and atlas-based methods. A multiatlas approach was also investigated. We hypothesize that the auto-segmentation algorithm is sufficiently accurate to provide organ dose estimates since random errors at the organ boundaries will average out when computing the total organ dose. To test this hypothesis, twenty head-neck CT scans were expertly segmented into nine regions. A leave-one-out validation study was performed, where every case was automatically segmented with each of the remaining cases used as the expert atlas, resulting in nineteen automated segmentations for each of the twenty datasets. The segmented regions were applied to gold-standard Monte Carlo dose maps to estimate mean and peak organ doses. The results demonstrated that the fully automated segmentation algorithm estimated the mean organ dose to within 10% of the expert segmentation for regions other than the spinal canal, with median error for each organ region below 2%. In the spinal canal region, the median error was 7% across all data sets and atlases, with a maximum error of 20%. The error in peak organ dose was below 10% for all regions, with a median error below 4% for all organ regions. The multiple-case atlas reduced the variation in the dose estimates and additional improvements may be possible with more robust multi-atlas approaches. Overall, the results support potential feasibility of an automated segmentation algorithm to provide accurate organ dose estimates.

  12. 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.

  13. 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

  14. Implicit active contours for automatic brachytherapy seed segmentation in fluoroscopy

    NASA Astrophysics Data System (ADS)

    Moult, Eric; Burdette, Clif; Song, Danny; Fichtinger, Gabor; Fallavollita, Pascal

    2012-02-01

    Motivation: In prostate brachytherapy, intra-operative dosimetry would be ideal to allow for rapid evaluation of the implant quality while the patient is still in the treatment position. Such a mechanism, however, requires 3-D visualization of the currently deposited seeds relative to the prostate. Thus, accurate, robust, and fully-automatic seed segmentation is of critical importance in achieving intra-operative dosimetry. Methodology: Implanted brachytherapy seeds are segmented by utilizing a region-based implicit active contour approach. Overlapping seed clusters are then resolved using a simple yet effective declustering technique. Results: Ground-truth seed coordinates were obtained via a published segmentation technique. A total of 248 clinical C-arm images from 16 patients were used to validate the proposed algorithm resulting in a 98.4% automatic detection rate with a corresponding 2.5% false-positive rate. The overall mean centroid error between the ground-truth and automatic segmentations was measured to be 0.42 pixels, while the mean centroid error for overlapping seed clusters alone was measured to be 0.67 pixels. Conclusion: Based on clinical data evaluation and validation, robust, accurate, and fully-automatic brachytherapy seed segmentation can be achieved through the implicit active contour framework and subsequent seed declustering method.

  15. Segmental neurofibromatosis.

    PubMed

    Sobjanek, Michał; Dobosz-Kawałko, Magdalena; Michajłowski, Igor; Pęksa, Rafał; Nowicki, Roman

    2014-12-01

    Segmental neurofibromatosis or type V neurofibromatosis is a rare genodermatosis characterized by neurofibromas, café-au-lait spots and neurofibromas limited to a circumscribed body region. The disease may be associated with systemic involvement and malignancies. The disorder has not been reported yet in the Polish medical literature. A 63-year-old Caucasian woman presented with a 20-year history of multiple, flesh colored, dome-shaped, soft to firm nodules situated in the right lumbar region. A histopathologic evaluation of three excised tumors revealed neurofibromas. No neurological and ophthalmologic symptoms of neurofibromatosis were diagnosed. PMID:25610358

  16. Segmental neurofibromatosis.

    PubMed

    Adigun, Chris G; Stein, Jennifer

    2011-01-01

    A 59-year-old man presented for evaluation and excision of non-tender, fleshy nodules that were arranged in a dermatomal distribution from the left side of the chest to the left axilla. A biopsy specimen of a nodule was consistent with a neurofibroma. Owing to the lack of other cutaneous findings, the lack of a family history of neurofibromatosis, and the dermatomal distribution of the neurofibromas, this patient met the criteria for a diagnosis of segmental neurofibromatosis (SNF) according to Riccardi's definition of SNF and classification of neurofibromatosis. Because the patient has no complications of neurofibromatosis 1 no medical treatment is required. PMID:22031651

  17. Segmental neurofibromatosis

    PubMed Central

    Dobosz-Kawałko, Magdalena; Michajłowski, Igor; Pęksa, Rafał; Nowicki, Roman

    2014-01-01

    Segmental neurofibromatosis or type V neurofibromatosis is a rare genodermatosis characterized by neurofibromas, café-au-lait spots and neurofibromas limited to a circumscribed body region. The disease may be associated with systemic involvement and malignancies. The disorder has not been reported yet in the Polish medical literature. A 63-year-old Caucasian woman presented with a 20-year history of multiple, flesh colored, dome-shaped, soft to firm nodules situated in the right lumbar region. A histopathologic evaluation of three excised tumors revealed neurofibromas. No neurological and ophthalmologic symptoms of neurofibromatosis were diagnosed. PMID:25610358

  18. Automatic segmentation of bones from digital hand radiographs

    NASA Astrophysics Data System (ADS)

    Liu, Brent J.; Taira, Ricky K.; Shim, Hyeonjoon; Keaton, Patricia

    1995-05-01

    The purpose of this paper is to develop a robust and accurate method that automatically segments phalangeal and epiphyseal bones from digital pediatric hand radiographs exhibiting various stages of growth. The algorithm uses an object-oriented approach comprising several stages beginning with the most general objects to be segmented, such as the outline of the hand from background, and proceeding in a succession of stages to the most specific object, such as a specific phalangeal bone from a digit of the hand. Each stage carries custom operators unique to the needs of that specific stage which will aid in more accurate results. The method is further aided by a knowledge base where all model contours and other information such as age, race, and sex, are stored. Shape models, 1-D wrist profiles, as well as an interpretation tree are used to map model and data contour segments. Shape analysis is performed using an arc-length orientation transform. The method is tested on close to 340 phalangeal and epiphyseal objects to be segmented from 17 cases of pediatric hand images obtained from our clinical PACS. Patient age ranges from 2 - 16 years. A pediatric radiologist preliminarily assessed the results of the object contours and were found to be accurate to within 95% for cases with non-fused bones and to within 85% for cases with fused bones. With accurate and robust results, the method can be applied toward areas such as the determination of bone age, the development of a normal hand atlas, and the characterization of many congenital and acquired growth diseases. Furthermore, this method's architecture can be applied to other image segmentation problems.

  19. 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.

  20. 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.

  1. Results from the HARPS-N 2014 Campaign to Estimate Accurately the Densities of Planets Smaller than 2.5 Earth Radii

    NASA Astrophysics Data System (ADS)

    Charbonneau, David; Harps-N Collaboration

    2015-01-01

    Although the NASA Kepler Mission has determined the physical sizes of hundreds of small planets, and we have in many cases characterized the star in detail, we know virtually nothing about the planetary masses: There are only 7 planets smaller than 2.5 Earth radii for which there exist published mass estimates with a precision better than 20 percent, the bare minimum value required to begin to distinguish between different models of composition.HARPS-N is an ultra-stable fiber-fed high-resolution spectrograph optimized for the measurement of very precise radial velocities. We have 80 nights of guaranteed time per year, of which half are dedicated to the study of small Kepler planets.In preparation for the 2014 season, we compared all available Kepler Objects of Interest to identify the ones for which our 40 nights could be used most profitably. We analyzed the Kepler light curves to constrain the stellar rotation periods, the lifetimes of active regions on the stellar surface, and the noise that would result in our radial velocities. We assumed various mass-radius relations to estimate the observing time required to achieve a mass measurement with a precision of 15%, giving preference to stars that had been well characterized through asteroseismology. We began by monitoring our long list of targets. Based on preliminary results we then selected our final short list, gathering typically 70 observations per target during summer 2014.These resulting mass measurements will have a signifcant impact on our understanding of these so-called super-Earths and small Neptunes. They would form a core dataset with which the international astronomical community can meaningfully seek to understand these objects and their formation in a quantitative fashion.HARPS-N was funded by the Swiss Space Office, the Harvard Origin of Life Initiative, the Scottish Universities Physics Alliance, the University of Geneva, the Smithsonian Astrophysical Observatory, the Italian National

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

    PubMed Central

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

    2014-01-01

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

  3. 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

  4. 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

  5. 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.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-06-01

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

  8. Rearrangement and expression of the human {Psi}C{lambda}6 gene segment results in a surface Ig receptor with a truncated light chain constant region

    SciTech Connect

    Stiernholm, N.B.J.; Verkoczy, L.K.; Berinstein, N.L.

    1995-05-01

    The constant region of the human Ig{lambda} locus consists of seven tandemly organized J-C gene segments. Although it has been established that the J-C{lambda}1, J-C{lambda}2, J-C{lambda}3, and J-C{lambda}7 gene segments are functional, and code for the four distinct Ig{lambda} isotypes found in human serum, the J-C{lambda}4, J-C{lambda}5, and J-C{lambda}6 gene segments are generally considered to be pseudogenes. Although one example of a functional J-C{lambda}6 gene segment has been documented, in the majority of cases, J-C{lambda}6 is rendered nonfunctional by virtue of a single duplication of four nucleotides, creating a premature translational arrest. We show here that rearrangements to the J-C{lambda}6 gene segment do occur, and that such a rearrangement encodes an Ig{lambda} protein that lacks the terminal end of the constant region. We also show that this truncated protein is expressed on the surface with the IgH chain, creating an unusual surface Ig (sIg) receptor (sIg{triangle}CL). Cells that express this receptor on the surface do so at significantly reduced levels compared with clonally related variants, which express sIg receptors with conventional Ig{lambda} L chains. However, the effects of sIg cross-linking on tyrosine phosphorylation and surface expression of the CD25 and CD71 Ags are similar in cells that express conventional sIg receptors and in those that express sIg{triangle}CL receptors, suggesting that the latter could possibly function as an Ag receptor. 35 refs., 7 figs.

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

    PubMed

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

    2006-01-01

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

  10. 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'.

  11. 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.

  12. Accurate Evaluation of Quantum Integrals

    NASA Technical Reports Server (NTRS)

    Galant, David C.; Goorvitch, D.

    1994-01-01

    Combining an appropriate finite difference method with Richardson's extrapolation results in a simple, highly accurate numerical method for solving a Schr\\"{o}dinger'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.

  13. 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.

  14. Bootstrapping structured page segmentation

    NASA Astrophysics Data System (ADS)

    Ma, Huanfeng; Doermann, David S.

    2003-01-01

    In this paper, we present an approach to the bootstrap learning of a page segmentation model. The idea evolves from attempts to segment dictionaries that often have a consistent page structure, and is extended to the segmentation of more general structured documents. In cases of highly regular structure, the layout can be learned from examples of only a few pages. The system is first trained using a small number of samples, and a larger test set is processed based on the training result. After making corrections to a selected subset of the test set, these corrected samples are combined with the original training samples to generate bootstrap samples. The newly created samples are used to retrain the system, refine the learned features and resegment the test samples. This procedure is applied iteratively until the learned parameters are stable. Using this approach, we do not need to initially provide a large set of training samples. We have applied this segmentation to many structured documents such as dictionaries, phone books, spoken language transcripts, and obtained satisfying segmentation performance.

  15. 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. PMID:16476535

  16. Generating anatomically accurate finite element meshes for electrical impedance tomography of the human head

    NASA Astrophysics Data System (ADS)

    Yang, Bin; Xu, Canhua; Dai, Meng; Fu, Feng; Dong, Xiuzhen

    2013-07-01

    For electrical impedance tomography (EIT) of brain, the use of anatomically accurate and patient-specific finite element (FE) mesh has been shown to confer significant improvements in the quality of image reconstruction. But, given the lack of a rapid method to achieve the accurate anatomic geometry of the head, the generation of patient-specifc mesh is time-comsuming. In this paper, a modified fuzzy c-means algorithm based on non-local means method is performed to implement the segmentation of different layers in the head based on head CT images. This algorithm showed a better effect, especially an accurate recognition of the ventricles and a suitable performance dealing with noise. And the FE mesh established according to the segmentation results is validated in computational simulation. So a rapid practicable method can be provided for the generation of patient-specific FE mesh of the human head that is suitable for brain EIT.

  17. 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

  18. 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.

  19. Phasing a segmented telescope.

    PubMed

    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. PMID:25768631

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

    NASA Astrophysics Data System (ADS)

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

    1996-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-02-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2007-08-01

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

  3. The feasibility of atlas-based automatic segmentation of MRI for H&N radiotherapy planning.

    PubMed

    Wardman, Kieran; Prestwich, Robin J D; Gooding, Mark J; Speight, Richard J

    2016-01-01

    Atlas-based autosegmentation is an established tool for segmenting structures for CT-planned head and neck radiotherapy. MRI is being increasingly integrated into the planning process. The aim of this study is to assess the feasibility of MRI-based, atlas-based autosegmentation for organs at risk (OAR) and lymph node levels, and to compare the segmentation accuracy with CT-based autosegmentation. Fourteen patients with locally advanced head and neck cancer in a prospective imaging study underwent a T1-weighted MRI and a PET-CT (with dedicated contrast-enhanced CT) in an immobilization mask. Organs at risk (orbits, parotids, brainstem, and spinal cord) and the left level II lymph node region were manually delineated on the CT and MRI separately. A 'leave one out' approach was used to automatically segment structures onto the remaining images separately for CT and MRI. Contour comparison was performed using multiple positional metrics: Dice index, mean distance to conformity (MDC), sensitivity index (Se Idx), and inclusion index (Incl Idx). Automatic segmentation using MRI of orbits, parotids, brainstem, and lymph node level was acceptable with a DICE coefficient of 0.73-0.91, MDC 2.0-5.1mm, Se Idx 0.64-0.93, Incl Idx 0.76-0.93. Segmentation of the spinal cord was poor (Dice coefficient 0.37). The process of automatic segmentation was significantly better on MRI compared to CT for orbits, parotid glands, brainstem, and left lymph node level II by multiple positional metrics; spinal cord segmentation based on MRI was inferior compared with CT. Accurate atlas-based automatic segmentation of OAR and lymph node levels is feasible using T1-MRI; segmentation of the spinal cord was found to be poor. Comparison with CT-based automatic segmentation suggests that the process is equally as, or more accurate, using MRI. These results support further translation of MRI-based segmentation methodology into clinicalpractice. PMID:27455480

  4. 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.

  5. NNLOPS accurate associated HW production

    NASA Astrophysics Data System (ADS)

    Astill, William; Bizon, Wojciech; Re, Emanuele; Zanderighi, Giulia

    2016-06-01

    We present a next-to-next-to-leading order accurate description of associated HW production consistently matched to a parton shower. The method is based on reweighting events obtained with the HW plus one jet NLO accurate calculation implemented in POWHEG, extended with the MiNLO procedure, to reproduce NNLO accurate Born distributions. Since the Born kinematics is more complex than the cases treated before, we use a parametrization of the Collins-Soper angles to reduce the number of variables required for the reweighting. We present phenomenological results at 13 TeV, with cuts suggested by the Higgs Cross section Working Group.

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  7. Wood adhesion cell segmentation scheme based on GVF-Snake model

    NASA Astrophysics Data System (ADS)

    Zhao, Lei; Ma, Yan

    2010-08-01

    In order to extract the characteristic parameters of the wood cells accurately, this paper presents an efficient scheme for wood cell segmentation. This scheme is mainly based on GVF-Snake model and the method of image thinning. Firstly, computing the Category Roundness of every connectivity domain is done in order to get the degree of adhesion. Secondly, image thinning helps to get the skeleton of the cell. Finally, according to the location coordinates of skeleton and contour, it can determine the location of segmentation. Experimental results demonstrate the scheme for precise extraction with limited human intervention; it can also determine the correct edge of segmentation. Comparatively speaking, the inaccuracy is rather limited.

  8. Ear segmentation using histogram based K-means clustering and Hough transformation under CVL dataset

    NASA Astrophysics Data System (ADS)

    Liu, Heng; Liu, Dekai

    2009-10-01

    Under CVL dataset, we provide an image segmentation approach based on adaptive histogram based K-means clustering and fast Hough transformation. This work firstly analyzes the characteristics of ear images in CVL face dataset. According to the analysis, we then use adaptive histogram based K-means clustering method to threshold ear images and then roughly segment the ear parts. After ear contour extraction, with boundary determination through vertical project, Hough transformation is utilized to locate the ear contour accurately. The experimental results and comparisons with other segmentation methods show our approach is effective.

  9. 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

  10. 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.

  11. Automatic segmentation of the facial nerve and chorda tympani in pediatric CT scans

    PubMed Central

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

    2011-01-01

    Purpose: Cochlear implant surgery is used to implant an electrode array in the cochlea to treat hearing loss. The authors recently introduced a minimally invasive image-guided technique termed percutaneous cochlear implantation. This approach achieves access to the cochlea by drilling a single linear channel from the outer skull into the cochlea via the facial recess, a region bounded by the facial nerve and chorda tympani. To exploit existing methods for computing automatically safe drilling trajectories, the facial nerve and chorda tympani need to be segmented. The goal of this work is to automatically segment the facial nerve and chorda tympani in pediatric CT scans. Methods: The authors have proposed an automatic technique to achieve the segmentation task in adult patients that relies on statistical models of the structures. These models contain intensity and shape information along the central axes of both structures. In this work, the authors attempted to use the same method to segment the structures in pediatric scans. However, the authors learned that substantial differences exist between the anatomy of children and that of adults, which led to poor segmentation results when an adult model is used to segment a pediatric volume. Therefore, the authors built a new model for pediatric cases and used it to segment pediatric scans. Once this new model was built, the authors employed the same segmentation method used for adults with algorithm parameters that were optimized for pediatric anatomy. Results: A validation experiment was conducted on 10 CT scans in which manually segmented structures were compared to automatically segmented structures. The mean, standard deviation, median, and maximum segmentation errors were 0.23, 0.17, 0.18, and 1.27 mm, respectively. Conclusions: The results indicate that accurate segmentation of the facial nerve and chorda tympani in pediatric scans is achievable, thus suggesting that safe drilling trajectories can also be computed

  12. 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. PMID:24962337

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

    PubMed

    Boix, Macarena; Cantó, Begoña

    2013-04-01

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

  14. 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.

  15. 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.

  16. 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

  17. Active learning based segmentation of Crohns disease from abdominal MRI.

    PubMed

    Mahapatra, Dwarikanath; Vos, Franciscus M; Buhmann, Joachim M

    2016-05-01

    This paper proposes a novel active learning (AL) framework, and combines it with semi supervised learning (SSL) for segmenting Crohns disease (CD) tissues from abdominal magnetic resonance (MR) images. Robust fully supervised learning (FSL) based classifiers require lots of labeled data of different disease severities. Obtaining such data is time consuming and requires considerable expertise. SSL methods use a few labeled samples, and leverage the information from many unlabeled samples to train an accurate classifier. AL queries labels of most informative samples and maximizes gain from the labeling effort. Our primary contribution is in designing a query strategy that combines novel context information with classification uncertainty and feature similarity. Combining SSL and AL gives a robust segmentation method that: (1) optimally uses few labeled samples and many unlabeled samples; and (2) requires lower training time. Experimental results show our method achieves higher segmentation accuracy than FSL methods with fewer samples and reduced training effort. PMID:27040833

  18. 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.

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

    PubMed

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

    2015-10-01

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

  20. 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.

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

    PubMed

    Ni, Bo; He, Fazhi; Yuan, ZhiYong

    2015-12-01

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

  2. 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. PMID:26363845

  3. Automatic brain tumor segmentation

    NASA Astrophysics Data System (ADS)

    Clark, Matthew C.; Hall, Lawrence O.; Goldgof, Dmitry B.; Velthuizen, Robert P.; Murtaugh, F. R.; Silbiger, Martin L.

    1998-06-01

    A system that automatically segments and labels complete glioblastoma-multiform tumor volumes in magnetic resonance images of the human brain is presented. The magnetic resonance images consist of three feature images (T1- weighted, proton density, T2-weighted) and are processed by a system which integrates knowledge-based techniques with multispectral analysis and is independent of a particular magnetic resonance scanning protocol. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intra-cranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intra-cranial region, with region analysis used in performing the final tumor labeling. This system has been trained on eleven volume data sets and tested on twenty-two unseen volume data sets acquired from a single magnetic resonance imaging system. The knowledge-based tumor segmentation was compared with radiologist-verified `ground truth' tumor volumes and results generated by a supervised fuzzy clustering algorithm. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.

  4. [Toxic anterior segment syndrome].

    PubMed

    Cornut, P-L; Chiquet, C

    2011-01-01

    Toxic anterior segment syndrome (TASS) is a general term used to describe acute, sterile postoperative inflammation due to a non-infectious substance that accidentally enters the anterior segment at the time of surgery and mimics infectious endophthalmitis. TASS most commonly occurs acutely following anterior segment surgery, typically 12-72h after cataract extraction. Anterior segment inflammation is usually quite severe with hypopyon. Endothelial cell damage is common, resulting in diffuse corneal edema. No bacterium is isolated from ocular samples. The causes of TASS are numerous and difficult to isolate. Any device or substance used during the surgery or in the immediate postoperative period may be implicated. The major known causes include: preservatives in ophthalmic solutions, denatured ophthalmic viscosurgical devices, bacterial endotoxin, and intraocular lens-induced inflammation. Clinical features of infectious and non-infectious inflammation are initially indistinguishable and TASS is usually diagnosed and treated as acute endophthalmitis. It usually improves with local steroid treatment but may result in chronic elevation of intraocular pressure or irreversible corneal edema due to permanent damage of trabecular meshwork or endothelial cells. PMID:21176994

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

    PubMed Central

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

    2009-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

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

  7. 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.

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

    NASA Astrophysics Data System (ADS)

    Chen, Yuehuan; Wan, Justin W. L.

    2015-03-01

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

  9. 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.

  10. Optimal retinal cyst segmentation from OCT images

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  11. 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.

  12. Algorithms for Accurate and Fast Plotting of Contour Surfaces in 3D Using Hexahedral Elements

    NASA Astrophysics Data System (ADS)

    Singh, Chandan; Saini, Jaswinder Singh

    2016-07-01

    In the present study, Fast and accurate algorithms for the generation of contour surfaces in 3D are described using hexahedral elements which are popular in finite element analysis. The contour surfaces are described in the form of groups of boundaries of contour segments and their interior points are derived using the contour equation. The locations of contour boundaries and the interior points on contour surfaces are as accurate as the interpolation results obtained by hexahedral elements and thus there are no discrepancies between the analysis and visualization results.

  13. Algorithms for Accurate and Fast Plotting of Contour Surfaces in 3D Using Hexahedral Elements

    NASA Astrophysics Data System (ADS)

    Singh, Chandan; Saini, Jaswinder Singh

    2016-05-01

    In the present study, Fast and accurate algorithms for the generation of contour surfaces in 3D are described using hexahedral elements which are popular in finite element analysis. The contour surfaces are described in the form of groups of boundaries of contour segments and their interior points are derived using the contour equation. The locations of contour boundaries and the interior points on contour surfaces are as accurate as the interpolation results obtained by hexahedral elements and thus there are no discrepancies between the analysis and visualization results.

  14. Semi-Automatic Segmentation Software for Quantitative Clinical Brain Glioblastoma Evaluation

    PubMed Central

    Zhu, Y; Young, G; Xue, Z; Huang, R; You, H; Setayesh, K; Hatabu, H; Cao, F; Wong, S.T.

    2012-01-01

    Rationale and Objectives Quantitative measurement provides essential information about disease progression and treatment response in patients with Glioblastoma multiforme (GBM). The goal of this paper is to present and validate a software pipeline for semi-automatic GBM segmentation, called AFINITI (Assisted Follow-up in NeuroImaging of Therapeutic Intervention), using clinical data from GBM patients. Materials and Methods Our software adopts the current state-of-the-art tumor segmentation algorithms and combines them into one clinically usable pipeline. Both the advantages of the traditional voxel-based and the deformable shape-based segmentation are embedded into the software pipeline. The former provides an automatic tumor segmentation scheme based on T1- and T2-weighted MR brain data, and the latter refines the segmentation results with minimal manual input. Results Twenty six clinical MR brain images of GBM patients were processed and compared with manual results. The results can be visualized using the embedded graphic user interface (GUI). Conclusion Validation results using clinical GBM data showed high correlation between the AFINITI results and manual annotation. Compared to the voxel-wise segmentation, AFINITI yielded more accurate results in segmenting the enhanced GBM from multimodality MRI data. The proposed pipeline could be used as additional information to interpret MR brain images in neuroradiology. PMID:22591720

  15. Brain tumor classification and segmentation using sparse coding and dictionary learning.

    PubMed

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

    2016-08-01

    This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods. PMID:26351901

  16. Liver vessel tree segmentation based on a hybrid graph cut / fuzzy connectedness method

    NASA Astrophysics Data System (ADS)

    Chen, Xinjian

    2012-02-01

    In the monitoring of oncological therapy, the prediction of liver tumor growth from consecutive CT scans is an important aspect in deciding the treatment planning. The accurate segmentation of liver vessel tree is fundamental for successful prediction of the tumor growth. In this paper, we report a 3D liver vessel tree segmentation method based on the hybrid graph cut (GC) / fuzzy connectedness (FC) method. GC is a popular image segmentation technique. However, it is not always efficient when segmenting thin elongated objects due to its "shrinking bias". To overcome this problem, we propose to impose an additional connectivity prior, which comes from the FC segmentation results. The proposed method synergistically combines the GC with FC methods. The proposed method consists of two main steps. First, the FC method is applied to initially segment the liver vessel tree, which provided the connectivity prior to the subsequent GC method. Second, the connectivity prior integrated GC method is employed to refine the segmented liver vessel tree. The proposed method was tested on 10 clinical portal venous phase CT data sets. The preliminary results showed the feasibility and efficiency of the proposed method. The accuracy of segmentation on this dataset, expressed in sensitivity, was 60%, 92% and 100% for vessel diameters in the range of 0.5 to 1, 1 to 2 and >2 mm, respectively.

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

    PubMed

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

    2010-04-01

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

  18. 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. PMID:25047741

  19. FIST: a fast interactive segmentation technique

    NASA Astrophysics Data System (ADS)

    Padfield, Dirk; Bhotika, Rahul; Natanzon, Alexander

    2015-03-01

    Radiologists are required to read thousands of patient images every day, and any tools that can improve their workflow to help them make efficient and accurate measurements is of great value. Such an interactive tool must be intuitive to use, and we have found that users are accustomed to clicking on the contour of the object for segmentation and would like the final segmentation to pass through these points. The tool must also be fast to enable real-time interactive feedback. To meet these needs, we present a segmentation workflow that enables an intuitive method for fast interactive segmentation of 2D and 3D objects. Given simple user clicks on the contour of an object in one 2D view, the algorithm generates foreground and background seeds and computes foreground and background distributions that are used to segment the object in 2D. It then propagates the information to the two orthogonal planes in a 3D volume and segments all three 2D views. The automated segmentation is automatically updated as the user continues to add points around the contour, and the algorithm is re-run using the total set of points. Based on the segmented objects in these three views, the algorithm then computes a 3D segmentation of the object. This process requires only limited user interaction to segment complex shapes and significantly improves the workflow of the user.

  20. Fully automated liver segmentation from SPIR image series.

    PubMed

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

    2014-10-01

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

  1. 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

  2. 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.

  3. Fundus optic disc localization and segmentation method based on phase congruency.

    PubMed

    Geng, Lei; Shao, Yi-Ting; Xiao, Zhi-Tao; Zhang, Fang; Wu, Jun; Li, Min; Shan, Chun-Yan

    2014-01-01

    It has been demonstrated that shape, area and depth of the optic disc are relevant indices of diabetic retinopathy. In this paper, we present a new fundus optic disc localization and segmentation method based on phase congruency (PC). Firstly, in order to highlight the optic disc, channel images with the highest contrast between optic disc and background are selected in LAB, YUV, YIQ and HSV spaces respectively. Secondly, with the use of PC, features of four selected channel images can be extracted. Multiplication operation is then used to enhance PC detection results. Thirdly, window scanning and gray accumulating are utilized to locate the optic disc. Finally, iterative OTSU automatic threshold segmentation and Hough transform are performed on location images, before the final optic disc segmentation result can be obtained. The experimental results showed that the proposed method can effectively and accurately perform optic disc location and segmentation. PMID:25227031

  4. 2D and 3D shape based segmentation using deformable models.

    PubMed

    El-Baz, Ayman; Yuksel, Seniha E; Shi, Hongjian; Farag, Aly A; El-Ghar, Mohamed A; Eldiasty, Tarek; Ghoneim, Mohamed A

    2005-01-01

    A novel shape based segmentation approach is proposed by modifying the external energy component of a deformable model. The proposed external energy component depends not only on the gray level of the images but also on the shape information which is obtained from the signed distance maps of objects in a given data set. The gray level distribution and the signed distance map of the points inside and outside the object of interest are accurately estimated by modelling the empirical density function with a linear combination of discrete Gaussians (LCDG) with positive and negative components. Experimental results on the segmentation of the kidneys from low-contrast DCE-MRI and on the segmentation of the ventricles from brain MRI's show how the approach is accurate in segmenting 2-D and 3-D data sets. The 2D results for the kidney segmentation have been validated by a radiologist and the 3D results of the ventricle segmentation have been validated with a geometrical phantom. PMID:16686036

  5. 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.

  6. 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. PMID:24085213

  7. 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

  8. 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. PMID:27540353

  9. Co-Segmentation Guided Hough Transform for Robust Feature Matching.

    PubMed

    Chen, Hsin-Yi; Lin, Yen-Yu; Chen, Bing-Yu

    2015-12-01

    We present an algorithm that integrates image co-segmentation into feature matching, and can robustly yield accurate and dense feature correspondences. Inspired by the fact that correct feature correspondences on the same object typically have coherent transformations, we cast the task of feature matching as a density estimation problem in the homography space. Specifically, we project the homographies of correspondence candidates into the parametric Hough space, in which geometric verification of correspondences can be activated by voting. The precision of matching is then boosted. On the other hand, we leverage image co-segmentation, which discovers object boundaries, to determine relevant voters and speed up Hough voting. In addition, correspondence enrichment can be achieved by inferring the concerted homographies that are propagated between the features within the same segments. The recall is hence increased. In our approach, feature matching and image co-segmentation are tightly coupled. Through an iterative optimization process, more and more correct correspondences are detected owing to object boundaries revealed by co-segmentation. The proposed approach is comprehensively evaluated. Promising experimental results on four datasets manifest its effectiveness. PMID:26539845

  10. Segmentation of confocal microscopic image of insect brain

    NASA Astrophysics Data System (ADS)

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

    2002-05-01

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

  11. 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. PMID:26748040

  12. 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. PMID:25155697

  13. Grading More Accurately

    ERIC Educational Resources Information Center

    Rom, Mark Carl

    2011-01-01

    Grades matter. College grading systems, however, are often ad hoc and prone to mistakes. This essay focuses on one factor that contributes to high-quality grading systems: grading accuracy (or "efficiency"). I proceed in several steps. First, I discuss the elements of "efficient" (i.e., accurate) grading. Next, I present analytical results…

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

    NASA Astrophysics Data System (ADS)

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

    2010-02-01

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

  15. THREE-DIMENSIONAL COUPLED-OBJECT SEGMENTATION USING SYMMETRY AND TISSUE TYPE INFORMATION

    PubMed Central

    Bahmanbijari, Payam; Akhondi-Asl, Alireza; Soltanian-Zadeh, Hamid

    2010-01-01

    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. PMID:19932598

  16. Local Earthquakes on the Endeavour Segment of the Juan de Fuca Ridge: First Seismic Results from the Keck Seismic/Hydrothermal Observatory

    NASA Astrophysics Data System (ADS)

    Wilcock, W. S.; Barclay, A. H.; McGill, P. R.; Stakes, D. S.; Ramirez, T. M.; Toomey, D. R.; Durant, D. T.; Hooft, E. E.; Mulder, T. L.; Ristau, J. P.

    2004-12-01

    The W.M. Keck Foundation is supporting a five-year program to conduct prototype seafloor observatory experiments to monitor the relationships between episodic deformation, fluid venting and microbial productivity on the Endeavour segment of the Juan de Fuca Ridge and at the intersection of the Nootka fault and the Cascadia subduction zone. At the Endeavour, the experiment is sited near the central portion of the segment in a region where the spreading axis is characterized by a 100-m-deep, 500-m-wide axial valley that hosts five high-temperature hydrothermal vent fields spaced 2-3 km apart. The objectives of the experiment are to monitor local and regional seismicity around the vent fields in conjunction with the deployment of sensors and samplers to monitor temporal variations in the physical, chemical and ultimately microbial characteristics of the hydrothermal fluids. The Endeavour seismic network was installed in the summer of 2003 with the ROV ROPOS and comprises seven GEOSense three-component short-period corehole seismometers and one buried Guralp CMG-1T broadband seismometer. Five of the seven short-period seismometers were inserted in horizontal coreholes drilled into seafloor basalts; two were deployed in concrete monuments on the ridge flanks. It is the first seismic network on a mid-ocean ridge in which the sensors are deployed with an ROV beneath the seafloor in order to ensure good coupling and minimize the effects of current-generated noise. In August 2004, we used the ROV Tiburon to service the Endeavour seismic network and recover the first year of data. In addition, we installed a second broadband and three short period seismometers on the Nootka fault and a third broadband seismometer on the Explorer plate. The Endeavour seismic network performed well with all eight instruments recording high-quality data. A preliminary inspection of the data reveals many examples of local, regional and teleseismic earthquakes. One striking characteristic of the

  17. 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.

  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. PMID:20224119

  19. Some metallographic results for brush bristles and brush segments of a shroud ring brush seal tested in a T-700 engine

    NASA Technical Reports Server (NTRS)

    Hendricks, Robert C.; Griffin, Thomas A.; Bobula, George A.; Bill, Robert C.; Hull, David R.; Csavina, Kristine R.

    1994-01-01

    Post-test investigation of a T-700 engine brush seal found regions void of bristles ('yanked out'), regions of bent-over bristles near the inlet, some 'snapped' bristles near the fence, and a more uniform smeared bristle interface between the first and last axial rows of bristles. Several bristles and four brush segments were cut from the brush seal, wax mounted, polished, and analyzed. Metallographic analysis of the bristle near the rub tip showed tungsten-rich phases uniformly distributed throughout the bristle, no apparent change within 1 mu m of the interface, and possibly a small amount of titanium, which would represent a transfer from the rotor. Analysis of the bristle wear face showed nonuniform tungsten, which is indicative of material resolidification. The cut end contained oxides and internal fractures; the worn end was covered with oxide scale. Material losses due to wear and elastoplastic deformation within the shear zone and third-body lubrication effects in the contact zone are discussed.

  20. Unsupervised Performance Evaluation of Image Segmentation

    NASA Astrophysics Data System (ADS)

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

    2006-12-01

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

  1. 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

  2. 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).

  3. 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.

  4. Automatic segmentation of Potyviridae family polyproteins.

    PubMed

    Vargas, Jheyson Faride; Velasco, Jairo Andrés; Alvarez, Gloria Inés; Linares, Diego Luis; Bravo, Enrique

    2015-01-01

    We describe an automatic segmentation method for polyproteins of the viruses belonging to the Potyviridae family. It uses machine learning techniques in order to predict the cleavage site which define the segments in which said polyproteins are cut in their process of functional maturation. The segmentation application is publicly available for use on a website and it can be accessed through the web service interface too. The prediction models have an average sensitivity of 0.79 and a Matthews correlation coefficient average of 0.23. This method is capable of predicting correctly (coinciding with previously published segmentation) the segmentation of sequences which come from Potyvirus and Rymovirus, genera. However accurate prediction capabilities are affected when faced with either atypical sequences or viruses belonging to less common genera in the Potyviridae family. Future work will focus on establishing greater flexibility in this sense. PMID:26642361

  5. 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

  6. Research on a pulmonary nodule segmentation method combining fast self-adaptive FCM and classification.

    PubMed

    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

  7. 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.

  8. Level set segmentation for greenbelts by integrating wavelet texture and priori color knowledge

    NASA Astrophysics Data System (ADS)

    Yang, Tie-jun; Song, Zhi-hui; Jiang, Chuan-xian; Huang, Lin

    2013-09-01

    Segmenting greenbelts quickly and accurately in remote sensing images is an economic and effective method for the statistics of green coverage rate (GCR). Towards the problem of over-reliance on priori knowledge of the traditional level set segmentation model based on max-flow/min-cut Graph Cut principle and weighted Total Variation (GCTV), this paper proposes a level set segmentation method of combining regional texture features and priori knowledge of color and applies it to greenbelt segmentation in urban remote sensing images. For the color of greenbelts is not reliable for segmentation, Gabor wavelet transform is used to extract image texture features. Then we integrate the extracted features into the GCTV model which contains only priori knowledge of color, and use both the prior knowledge and the targets' texture to constrain the evolving of the level set which can solve the problem of over-reliance on priori knowledge. Meanwhile, the convexity of the corresponding energy functional is ensured by using relaxation and threshold method, and primal-dual algorithm with global relabeling is used to accelerate the evolution of the level set. The experiments show that our method can effectively reduce the dependence on priori knowledge of GCTV, and yields more accurate greenbelt segmentation results.

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

    PubMed

    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

  10. 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

  11. Segmentation of ovarian follicles using geometric properties, texture descriptions, and boundary information

    NASA Astrophysics Data System (ADS)

    Robinson, Glynn P.; Chakraborty, Amit; Johnston, Michael; Reuss, M. Lynne; Duncan, James S.

    1996-04-01

    The size and number of follicles present within an ovary may be used as an indicator of fertility in women. Ultrasound is the imaging modality of choice for obtaining information on the follicles as it is inexpensive and readily available. A method of segmenting the follicles and ovary and producing accurate 2D and 3D representation would be of great benefit to a large segment of the population. However, the nature of ultrasound images means that standard approaches to segmentation based on image gradients or detecting regions of homogeneous gray-level alone are inadequate. A semi-automatic method of segmentation which combined a texture based classification for initial segmentation with deformable models to provide descriptions of individual objects is extended by imposing geometric constraints on the relationships between the individual objects present within an image. Since we are interested in segmenting the individual objects over a 3D spatial stack we use the results from one image in the sequence as the initial estimates for the next image. This reduces the need for operator intervention and provides representations of individual objects through the whole sequence. These representations can then be used for accurate measurement of area/volume and for three-dimensional visualization of the relationships between the individual follicles and the enclosing ovary.

  12. 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

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

    PubMed

    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

  14. Bayesian segmentation of hyperspectral images

    NASA Astrophysics Data System (ADS)

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

    2004-11-01

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

  15. Leg mass characteristics of accurate and inaccurate kickers--an Australian football perspective.

    PubMed

    Hart, Nicolas H; Nimphius, Sophia; Cochrane, Jodie L; Newton, Robert U

    2013-01-01

    Athletic profiling provides valuable information to sport scientists, assisting in the optimal design of strength and conditioning programmes. Understanding the influence these physical characteristics may have on the generation of kicking accuracy is advantageous. The aim of this study was to profile and compare the lower limb mass characteristics of accurate and inaccurate Australian footballers. Thirty-one players were recruited from the Western Australian Football League to perform ten drop punt kicks over 20 metres to a player target. Players were separated into accurate (n = 15) and inaccurate (n = 16) groups, with leg mass characteristics assessed using whole body dual energy x-ray absorptiometry (DXA) scans. Accurate kickers demonstrated significantly greater relative lean mass (P ≤ 0.004) and significantly lower relative fat mass (P ≤ 0.024) across all segments of the kicking and support limbs, while also exhibiting significantly higher intra-limb lean-to-fat mass ratios for all segments across both limbs (P ≤ 0.009). Inaccurate kickers also produced significantly larger asymmetries between limbs than accurate kickers (P ≤ 0.028), showing considerably lower lean mass in their support leg. These results illustrate a difference in leg mass characteristics between accurate and inaccurate kickers, highlighting the potential influence these may have on technical proficiency of the drop punt. PMID:23687978

  16. Automatic segmentation and diameter measurement of coronary artery vessels

    NASA Astrophysics Data System (ADS)

    Zhao, Kun; Tang, Zhenyu; Pauli, Josef

    2011-03-01

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

  17. Influence of musical expertise on segmental and tonal processing in Mandarin Chinese.

    PubMed

    Marie, Céline; Delogu, Franco; Lampis, Giulia; Belardinelli, Marta Olivetti; Besson, Mireille

    2011-10-01

    A same-different task was used to test the hypothesis that musical expertise improves the discrimination of tonal and segmental (consonant, vowel) variations in a tone language, Mandarin Chinese. Two four-word sequences (prime and target) were presented to French musicians and nonmusicians unfamiliar with Mandarin, and event-related brain potentials were recorded. Musicians detected both tonal and segmental variations more accurately than nonmusicians. Moreover, tonal variations were associated with higher error rate than segmental variations and elicited an increased N2/N3 component that developed 100 msec earlier in musicians than in nonmusicians. Finally, musicians also showed enhanced P3b components to both tonal and segmental variations. These results clearly show that musical expertise influenced the perceptual processing as well as the categorization of linguistic contrasts in a foreign language. They show positive music-to-language transfer effects and open new perspectives for the learning of tone languages. PMID:20946053

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

    PubMed

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

    2015-01-01

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

  19. 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.

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

    PubMed

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

    2013-04-01

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

  1. 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

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

    PubMed

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

    2014-01-01

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

  3. 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

  4. 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

  5. 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.

  6. 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.

  7. Robust Non-Local Multi-Atlas Segmentation of the Optic Nerve.

    PubMed

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

    2013-03-13

    Labeling or segmentation of structures of interest on medical images plays an essential role in both clinical and scientific understanding of the biological etiology, progression, and recurrence of pathological disorders. Here, we focus on the optic nerve, a structure that plays a critical role in many devastating pathological conditions - including glaucoma, ischemic neuropathy, optic neuritis and multiple-sclerosis. Ideally, existing fully automated procedures would result in accurate and robust segmentation of the optic nerve anatomy. However, current segmentation procedures often require manual intervention due to anatomical and imaging variability. Herein, we propose a framework for robust and fully-automated segmentation of the optic nerve anatomy. First, we provide a robust registration procedure that results in consistent registrations, despite highly varying data in terms of voxel resolution and image field-of-view. Additionally, we demonstrate the efficacy of a recently proposed non-local label fusion algorithm that accounts for small scale errors in registration correspondence. On a dataset consisting of 31 highly varying computed tomography (CT) images of the human brain, we demonstrate that the proposed framework consistently results in accurate segmentations. In particular, we show (1) that the proposed registration procedure results in robust registrations of the optic nerve anatomy, and (2) that the non-local statistical fusion algorithm significantly outperforms several of the state-of-the-art label fusion algorithms. PMID:24478826

  8. Salient Segmentation of Medical Time Series Signals

    PubMed Central

    Woodbridge, Jonathan; Lan, Mars; Sarrafzadeh, Majid; Bui, Alex

    2016-01-01

    Searching and mining medical time series databases is extremely challenging due to large, high entropy, and multidimensional datasets. Traditional time series databases are populated using segments extracted by a sliding window. The resulting database index contains an abundance of redundant time series segments with little to no alignment. This paper presents the idea of “salient segmentation”. Salient segmentation is a probabilistic segmentation technique for populating medical time series databases. Segments with the lowest probabilities are considered salient and are inserted into the index. The resulting index has little redundancy and is composed of aligned segments. This approach reduces index sizes by more than 98% over conventional sliding window techniques. Furthermore, salient segmentation can reduce redundancy in motif discovery algorithms by more than 85%, yielding a more succinct representation of a time series signal.

  9. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2006-03-01

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

  11. Factorization-based texture segmentation

    DOE PAGESBeta

    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

  12. 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.

  13. 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

  14. Sipunculans and segmentation

    PubMed Central

    Kristof, Alen; Brinkmann, Nora

    2009-01-01

    Comparative molecular, developmental and morphogenetic analyses show that the three major segmented animal groups—Lophotrochozoa, Ecdysozoa and Vertebrata—use a wide range of ontogenetic pathways to establish metameric body organization. Even in the life history of a single specimen, different mechanisms may act on the level of gene expression, cell proliferation, tissue differentiation and organ system formation in individual segments. Accordingly, in some polychaete annelids the first three pairs of segmental peripheral neurons arise synchronously, while the metameric commissures of the ventral nervous system form in anterior-posterior progression. Contrary to traditional belief, loss of segmentation may have occurred more often than commonly assumed, as exemplified in the sipunculans, which show remnants of segmentation in larval stages but are unsegmented as adults. The developmental plasticity and potential evolutionary lability of segmentation nourishes the controversy of a segmented bilaterian ancestor versus multiple independent evolution of segmentation in respective metazoan lineages. PMID:19513266

  15. Segmented trapped vortex cavity

    NASA Technical Reports Server (NTRS)

    Grammel, Jr., Leonard Paul (Inventor); Pennekamp, David Lance (Inventor); Winslow, Jr., Ralph Henry (Inventor)

    2010-01-01

    An annular trapped vortex cavity assembly segment comprising includes a cavity forward wall, a cavity aft wall, and a cavity radially outer wall there between defining a cavity segment therein. A cavity opening extends between the forward and aft walls at a radially inner end of the assembly segment. Radially spaced apart pluralities of air injection first and second holes extend through the forward and aft walls respectively. The segment may include first and second expansion joint features at distal first and second ends respectively of the segment. The segment may include a forward subcomponent including the cavity forward wall attached to an aft subcomponent including the cavity aft wall. The forward and aft subcomponents include forward and aft portions of the cavity radially outer wall respectively. A ring of the segments may be circumferentially disposed about an axis to form an annular segmented vortex cavity assembly.

  16. [Bilateral segmental neurofibromatosis].

    PubMed

    Rose, I; Vakilzadeh, F

    1991-12-01

    Segmental neurofibromatosis is a rare type of neurofibromatosis. We report a case of bilateral manifestation, review the literature on this extremely uncommon variant, and discuss the possible causative mechanisms and the genetic risk of segmental neurofibromatosis. PMID:1765491

  17. 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...

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

    PubMed

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

    2015-12-01

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

  19. Toward automated detection and segmentation of aortic calcifications from radiographs

    NASA Astrophysics Data System (ADS)

    Lauze, François; de Bruijne, Marleen

    2007-03-01

    This paper aims at automatically measuring the extent of calcified plaques in the lumbar aorta from standard radiographs. Calcifications in the abdominal aorta are an important predictor for future cardiovascular morbidity and mortality. Accurate and reproducible measurement of the amount of calcified deposit in the aorta is therefore of great value in disease diagnosis and prognosis, treatment planning, and the study of drug effects. We propose a two-step approach in which first the calcifications are detected by an iterative statistical pixel classification scheme combined with aorta shape model optimization. Subsequently, the detected calcified pixels are used as the initialization for an inpainting based segmentation. We present results on synthetic images from the inpainting based segmentation as well as results on several X-ray images based on the two-steps approach.

  20. 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.

  1. 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%.

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

    PubMed

    Liu, Guoying; Zhang, Yun; Wang, Aimin

    2015-11-01

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

  3. 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.

  4. Gel image segmentation based on discontinuity and region information

    NASA Astrophysics Data System (ADS)

    Wang, Weixing

    2005-10-01

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

  5. 3D active surfaces for liver segmentation in multisequence MRI images.

    PubMed

    Bereciartua, Arantza; Picon, Artzai; Galdran, Adrian; Iriondo, Pedro

    2016-08-01

    Biopsies for diagnosis can sometimes be replaced by non-invasive techniques such as CT and MRI. Surgeons require accurate and efficient methods that allow proper segmentation of the organs in order to ensure the most reliable intervention planning. Automated liver segmentation is a difficult and open problem where CT has been more widely explored than MRI. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise and low contrast. In this paper, we present a novel method for multichannel MRI automatic liver segmentation. The proposed method consists of the minimization of a 3D active surface by means of the dual approach to the variational formulation of the underlying problem. This active surface evolves over a probability map that is based on a new compact descriptor comprising spatial and multisequence information which is further modeled by means of a liver statistical model. This proposed 3D active surface approach naturally integrates volumetric regularization in the statistical model. The advantages of the compact visual descriptor together with the proposed approach result in a fast and accurate 3D segmentation method. The method was tested on 18 healthy liver studies and results were compared to a gold standard made by expert radiologists. Comparisons with other state-of-the-art approaches are provided by means of nine well established quality metrics. The obtained results improve these methodologies, achieving a Dice Similarity Coefficient of 98.59. PMID:27282235

  6. 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

  7. 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.

  8. New method for moving objects segmentation based on human vision perception in infrared video

    NASA Astrophysics Data System (ADS)

    Min, Chaobo

    2013-07-01

    A new method for moving object segmentation based on human vision perception in infrared video is proposed. In this paper, we introduce a new region growing method to achieve the accurate and complete segmentation of the moving objects. At first, the ideal seeds of every moving object are extracted based on the "hole" effect of temporal difference, respectively. At the next step, on the basis of the consideration that human vision system (HVS) is most sensitive to the local contrast between targets and surrounding, we proposed a metric for "good" infrared target segmentation based on human vision perception. And according to this metric, a search method based on fine and rough adjustment is applied to determine the best growing threshold for every moving object. The segmented mask of every moving object is grown from the relevant seeds with the best growing threshold. At last, the segmented masks of all moving objects are merged into a complete segmented mask. Experimental results show that the proposed method is superior and effective on segmentation of moving object in infrared video.

  9. A Minimal Path Searching Approach for Active Shape Model (ASM)-based Segmentation of the Lung.

    PubMed

    Guo, Shengwen; Fei, Baowei

    2009-03-27

    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. PMID:24386531

  10. 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.

  11. 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

  12. 3D segmentation of the true and false lumens on CT aortic dissection images

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

  13. Possible and Impossible Segments.

    ERIC Educational Resources Information Center

    Walker, Rachel; Pullum, Geoffrey K.

    1999-01-01

    Examines the relationship between phonetic possibility and phonological permissibility of segment types. Specific focus is on whether there are any phonetically impossible segments phonologically permissible, and whether there are any phonetically possible segments phonologically impermissable. Examines the case of nasality spreading in Sudanese…

  14. 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.

  15. Accurate measurement of time

    NASA Astrophysics Data System (ADS)

    Itano, Wayne M.; Ramsey, Norman F.

    1993-07-01

    The paper discusses current methods for accurate measurements of time by conventional atomic clocks, with particular attention given to the principles of operation of atomic-beam frequency standards, atomic hydrogen masers, and atomic fountain and to the potential use of strings of trapped mercury ions as a time device more stable than conventional atomic clocks. The areas of application of the ultraprecise and ultrastable time-measuring devices that tax the capacity of modern atomic clocks include radio astronomy and tests of relativity. The paper also discusses practical applications of ultraprecise clocks, such as navigation of space vehicles and pinpointing the exact position of ships and other objects on earth using the GPS.

  16. Efficient threshold for volumetric segmentation

    NASA Astrophysics Data System (ADS)

    Burdescu, Dumitru D.; Brezovan, Marius; Stanescu, Liana; Stoica Spahiu, Cosmin; Ebanca, Daniel

    2015-07-01

    Image segmentation plays a crucial role in effective understanding of digital images. However, the research on the existence of general purpose segmentation algorithm that suits for variety of applications is still very much active. Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. Volumetric image segmentation can simply result an image partition composed by relevant regions, but the most fundamental challenge in segmentation algorithm is to precisely define the volumetric extent of some object, which may be represented by the union of multiple regions. The aim in this paper is to present a new method to detect visual objects from color volumetric images and efficient threshold. We present a unified framework for volumetric image segmentation and contour extraction that uses a virtual tree-hexagonal structure defined on the set of the image voxels. The advantage of using a virtual tree-hexagonal network superposed over the initial image voxels is that it reduces the execution time and the memory space used, without losing the initial resolution of the image.

  17. 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.

  18. 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.

  19. 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. PMID:22128304

  20. 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.

  1. 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. PMID:26513773

  2. Segmentation of endpoint trajectories does not imply segmented control.

    PubMed

    Sternad, D; Schaal, S

    1999-01-01

    While it is generally assumed that complex movements consist of a sequence of simpler units, the quest to define these units of action, or movement primitives, remains an open question. In this context, two hypotheses of movement segmentation of endpoint trajectories in three-dimensional human drawing movements are reexamined: (1) the stroke-based segmentation hypothesis based on the results that the proportionality coefficient of the two-thirds power law changes discontinuously with each new "stroke," and (2) the segmentation hypothesis inferred from the observation of piecewise planar endpoint trajectories of three-dimensional drawing movements. In two experiments human subjects performed a set of elliptical and figure eight patterns of different sizes and orientations using their whole arm in three dimensions. The kinematic characteristics of the endpoint trajectories and the seven joint angles of the arm were analyzed. While the endpoint trajectories produced similar segmentation features to those reported in the literature, analyses of the joint angles show no obvious segmentation but rather continuous oscillatory patterns. By approximating the joint angle data of human subjects with sinusoidal trajectories, and by implementing this model on a 7-degree-of-freedom (DOF) anthropomorphic robot arm, it is shown that such a continuous movement strategy can produce exactly the same features as observed by the above segmentation hypotheses. The origin of this apparent segmentation of endpoint trajectories is traced back to the nonlinear transformations of the forward kinematics of human arms. The presented results demonstrate that principles of discrete movement generation may not be reconciled with those of rhythmic movement as easily as has been previously suggested, while the generalization of nonlinear pattern generators to arm movements can offer an interesting alternative to approach the question of units of action. PMID:9928796

  3. 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

  4. Multi-segment detector

    NASA Technical Reports Server (NTRS)

    George, Peter K. (Inventor)

    1978-01-01

    A plurality of stretcher detector segments are connected in series whereby detector signals generated when a bubble passes thereby are added together. Each of the stretcher detector segments is disposed an identical propagation distance away from passive replicators wherein bubbles are replicated from a propagation path and applied, simultaneously, to the stretcher detector segments. The stretcher detector segments are arranged to include both dummy and active portions thereof which are arranged to permit the geometry of both the dummy and active portions of the segment to be substantially matched.

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

    PubMed Central

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

    2010-01-01

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

  6. 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.

  7. 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.

  8. Statistical segmentation of carotid plaque neovascularization

    NASA Astrophysics Data System (ADS)

    Akkus, Zeynettin; Bosch, Johan G.; Sánchez-Ferrero, Gonzalo V.; Carvalho, Diego D. B.; Renaud, Guillaume; van den Oord, Stijn C. H.; ten Kate, Gerrit L.; Schinkel, Arend F. L.; de Jong, Nico; van der Steen, Antonius F. W.

    2013-03-01

    In several studies, intraplaque neovascularization (IPN) has been linked with plaque vulnerability. The recent development of contrast enhanced ultrasound enables IPN detection, but an accurate quantification of IPN is a big challenge due to noise, motion, subtle contrast response, blooming of contrast and artifacts. We present an algorithm that automatically estimates the location and amount of contrast within the plaque over time. Plaque pixels are initially labeled through an iterative expectation-maximization (EM) algorithm. The used algorithm avoids several drawbacks of standard EM. It is capable of selecting the best number of components in an unsupervised way, based on a minimum message length criterion. Next, neighborhood information using a 5×5 kernel and spatiotemporal behavior are combined with the known characteristics of contrast spots in order to group components, identify artifacts and finalize the classification. Image sequences are divided into 3-seconds subgroups. A pixel is relabeled as an artifact if it is labeled as contrast for more than 1.5 seconds in at least two subgroups. For 10 plaques, automated segmentation results were validated with manual segmentation of contrast in 10 frames per clip. Average Dice index and area ratio were 0.73+/-0.1 (mean+/-SD) and 98.5+/-29.6 (%) respectively. Next, 45 atherosclerotic plaques were analyzed. Time integrated IPN surface area was calculated. Average area of IPN was 3.73+/-3.51 mm2. Average area of 45 plaques was 11.6+/-8.6 mm2. This method based on EM contrast segmentation provides a new way of IPN quantification.

  9. 40 CFR 35.2108 - Phased or segmented treatment works.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... segmented treatment works. Grant funding may be awarded for a phase or segment of a treatment works, subject to the limitations of § 35.2123, although that phase or segment does not result in compliance with... make the treatment works of which the phase or segment is a part operational and comply with...

  10. 40 CFR 35.2108 - Phased or segmented treatment works.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... segmented treatment works. Grant funding may be awarded for a phase or segment of a treatment works, subject to the limitations of § 35.2123, although that phase or segment does not result in compliance with... make the treatment works of which the phase or segment is a part operational and comply with...

  11. 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.

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

    PubMed

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

    2007-01-01

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

  13. 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. PMID:21128090

  14. 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.

  15. 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

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

    NASA Astrophysics Data System (ADS)

    Jiji, G. Wiselin; Dehmeshki, Jamshid

    2014-04-01

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

  17. 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.

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

    PubMed Central

    Sarrafzadeh, Omid; Dehnavi, Alireza Mehri

    2015-01-01

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

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

    SciTech Connect

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

    2010-08-15

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

  20. 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.

  1. 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.

  2. Object-oriented approach to the automatic segmentation of bones from pediatric hand radiographs

    NASA Astrophysics Data System (ADS)

    Shim, Hyeonjoon; Liu, Brent J.; Taira, Ricky K.; Hall, Theodore R.

    1997-04-01

    The purpose of this paper is to develop a robust and accurate method that automatically segments phalangeal and epiphyseal bones from digital pediatric hand radiographs exhibiting various stages of growth. The development of this system draws principles from object-oriented design, model- guided analysis, and feedback control. A system architecture called 'the object segmentation machine' was implemented incorporating these design philosophies. The system is aided by a knowledge base where all model contours and other information such as age, race, and sex, are stored. These models include object structure models, shape models, 1-D wrist profiles, and gray level histogram models. Shape analysis is performed first by using an arc-length orientation transform to break down a given contour into elementary segments and curves. Then an interpretation tree is used as an inference engine to map known model contour segments to data contour segments obtained from the transform. Spatial and anatomical relationships among contour segments work as constraints from shape model. These constraints aid in generating a list of candidate matches. The candidate match with the highest confidence is chosen to be the current intermediate result. Verification of intermediate results are perform by a feedback control loop.

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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. PMID:26026778

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

    PubMed

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

    2015-01-01

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

  10. 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.

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

    PubMed

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

    2016-09-01

    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

  12. CDIS: Circle Density Based Iris Segmentation

    NASA Astrophysics Data System (ADS)

    Gupta, Anand; Kumari, Anita; Kundu, Boris; Agarwal, Isha

    Biometrics is an automated approach of measuring and analysing physical and behavioural characteristics for identity verification. The stability of the Iris texture makes it a robust biometric tool for security and authentication purposes. Reliable Segmentation of Iris is a necessary precondition as an error at this stage will propagate into later stages and requires proper segmentation of non-ideal images having noises like eyelashes, etc. Iris Segmentation work has been done earlier but we feel it lacks in detecting iris in low contrast images, removal of specular reflections, eyelids and eyelashes. Hence, it motivates us to enhance the said parameters. Thus, we advocate a new approach CDIS for Iris segmentation along with new algorithms for removal of eyelashes, eyelids and specular reflections and pupil segmentation. The results obtained have been presented using GAR vs. FAR graphs at the end and have been compared with prior works related to segmentation of iris.

  13. Neurosphere segmentation in brightfield images

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  14. [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. PMID:25089327

  15. 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.

  16. 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.

  17. 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.

  18. Multispectral MRI segmentation of age related white matter changes using a cascade of support vector machines.

    PubMed

    Damangir, Soheil; Manzouri, Amirhossein; Oppedal, Ketil; Carlsson, Stefan; Firbank, Michael J; Sonnesyn, Hogne; Tysnes, Ole-Bjørn; O'Brien, John T; Beyer, Mona K; Westman, Eric; Aarsland, Dag; Wahlund, Lars-Olof; Spulber, Gabriela

    2012-11-15

    White matter changes (WMC) are the focus of intensive research and have been linked to cognitive impairment and depression in the elderly. Cumbersome manual outlining procedures make research on WMC labor intensive and prone to subjective bias. We present a fast, fully automated method for WMC segmentation using a cascade of reduced support vector machines (SVMs) with active learning. Data of 102 subjects was used in this study. Two MRI sequences (T1-weighted and FLAIR) and masks of manually outlined WMC from each subject were used for the image analysis. The segmentation framework comprises pre-processing, classification (training and core segmentation) and post-processing. After pre-processing, the model was trained on two subjects and tested on the remaining 100 subjects. The effectiveness and robustness of the classification was assessed using the receiver operating curve technique. The cascade of SVMs segmentation framework outputted accurate results with high sensitivity (90%) and specificity (99.5%) values, with the manually outlined WMC as reference. An algorithm for the segmentation of WMC is proposed. This is a completely competitive and fast automatic segmentation framework, capable of using different input sequences, without changes or restrictions of the image analysis algorithm. PMID:22921728

  19. Brain MRI segmentation and lesion detection using generalized Gaussian and Rician modeling

    NASA Astrophysics Data System (ADS)

    Wu, Xuqiang; Bricq, Stéphanie; Collet, Christophe

    2011-03-01

    In this paper we propose a mixed noise modeling so as to segment the brain and to detect lesion. Indeed, accurate segmentation of multimodal (T1, T2 and Flair) brain MR images is of great interest for many brain disorders but requires to efficiently manage multivariate correlated noise between available modalities. We addressed this problem in1 by proposing an entirely unsupervised segmentation scheme, taking into account multivariate Gaussian noise, imaging artifacts,intrinsic tissue variation and partial volume effects in a Bayesian framework. Nevertheless, tissue classification remains a challenging task especially when one addresses the lesion detection during segmentation process2 as we did. In order to improve brain segmentation into White and Gray Matter (resp. WM and GM) and cerebro-spinal fluid (CSF), we propose to fit a Rician (RC) density distribution for CSF whereas Generalized Gaussian (GG) models are used to fit the likelihood between model and data corresponding to WM and GM. In this way, we present in this paper promising results showing that in a multimodal segmentation-detection scheme, this model fits better with the data and increases lesion detection rate. One of the main challenges consists in being able to take into account various pdf (Gaussian and non- Gaussian) for correlated noise between modalities and to show that lesion-detection is then clearly improved, probably because non-Gaussian noise better fits to the physic of MRI image acquisition.

  20. 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.

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

    NASA Astrophysics Data System (ADS)

    Li, Ke; Fei, Baowei

    2008-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1998-06-01

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

  4. 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.

  5. 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. PMID:21945694

  6. Segmentation of ultrasonic breast tumors based on homogeneous patch

    PubMed Central

    Gao, Liang; Yang, Wei; Liao, Zhiwu; Liu, Xiaoyun; Feng, Qianjin; Chen, Wufan

    2012-01-01

    Purpose: Accurately segmenting breast tumors in ultrasound (US) images is a difficult problem due to their specular nature and appearance of sonographic tumors. The current paper presents a variant of the normalized cut (NCut) algorithm based on homogeneous patches (HP-NCut) for the segmentation of ultrasonic breast tumors. Methods: A novel boundary-detection function is defined by combining texture and intensity information to find the fuzzy boundaries in US images. Subsequently, based on the precalculated boundary map, an adaptive neighborhood according to image location referred to as a homogeneous patch (HP) is proposed. HPs are guaranteed to spread within the same tissue region; thus, the statistics of primary features within the HPs is more reliable in distinguishing the different tissues and benefits subsequent segmentation. Finally, the fuzzy distribution of textons within HPs is used as final image features, and the segmentation is obtained using the NCut framework. Results: The HP-NCut algorithm was evaluated on a large dataset of 100 breast US images (50 benign and 50 malignant). The mean Hausdorff distance measure, the mean minimum Euclidean distance measure and similarity measure achieved 7.1 pixels, 1.58 pixels, and 86.67%, respectively, for benign tumors while those achieved 10.57 pixels, 1.98 pixels, and 84.41%, respectively, for malignant tumors. Conclusions: The HP-NCut algorithm provided the improvement in accuracy and robustness compared with state-of-the-art methods. A conclusion that the HP-NCut algorithm is suitable for ultrasonic tumor segmentation problems can be drawn. PMID:22755713

  7. Are Patient-Specific Joint and Inertial Parameters Necessary for Accurate Inverse Dynamics Analyses of Gait?

    PubMed Central

    Reinbolt, Jeffrey A.; Haftka, Raphael T.; Chmielewski, Terese L.; Fregly, Benjamin J.

    2013-01-01

    Variations in joint parameter values (axis positions and orientations in body segments) and inertial parameter values (segment masses, mass centers, and moments of inertia) as well as kinematic noise alter the results of inverse dynamics analyses of gait. Three-dimensional linkage models with joint constraints have been proposed as one way to minimize the effects of noisy kinematic data. Such models can also be used to perform gait optimizations to predict post-treatment function given pre-treatment gait data. This study evaluates whether accurate patient-specific joint and inertial parameter values are needed in three-dimensional linkage models to produce accurate inverse dynamics results for gait. The study was performed in two stages. First, we used optimization analyses to evaluate whether patient-specific joint and inertial parameter values can be calibrated accurately from noisy kinematic data, and second, we used Monte Carlo analyses to evaluate how errors in joint and inertial parameter values affect inverse dynamics calculations. Both stages were performed using a dynamic, 27 degree-of-freedom, full-body linkage model and synthetic (i.e., computer generated) gait data corresponding to a nominal experimental gait motion. In general, joint but not inertial parameter values could be found accurately from noisy kinematic data. Root-mean-square (RMS) errors were 3° and 4 mm for joint parameter values and 1 kg, 22 mm, and 74,500 kg*mm2 for inertial parameter values. Furthermore, errors in joint but not inertial parameter values had a significant effect on calculated lower-extremity inverse dynamics joint torques. The worst RMS torque error averaged 4% bodyweight*height (BW*H) due to joint parameter variations but less than 0.25% BW*H due to inertial parameter variations. These results suggest that inverse dynamics analyses of gait utilizing linkage models with joint constraints should calibrate the model’s joint parameter values to obtain accurate joint

  8. FISICO: Fast Image SegmentatIon COrrection

    PubMed Central

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

    2016-01-01

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

  9. Highly Accurate Inverse Consistent Registration: A Robust Approach

    PubMed Central

    Reuter, Martin; Rosas, H. Diana; Fischl, Bruce

    2010-01-01

    The registration of images is a task that is at the core of many applications in computer vision. In computational neuroimaging where the automated segmentation of brain structures is frequently used to quantify change, a highly accurate registration is necessary for motion correction of images taken in the same session, or across time in longitudinal studies where changes in the images can be expected. This paper, inspired by Nestares and Heeger (2000), presents a method based on robust statistics to register images in the presence of differences, such as jaw movement, differential MR distortions and true anatomical change. The approach we present guarantees inverse consistency (symmetry), can deal with different intensity scales and automatically estimates a sensitivity parameter to detect outlier regions in the images. The resulting registrations are highly accurate due to their ability to ignore outlier regions and show superior robustness with respect to noise, to intensity scaling and outliers when compared to state-of-the-art registration tools such as FLIRT (in FSL) or the coregistration tool in SPM. PMID:20637289

  10. 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.

  11. Reliability measure for segmenting algorithms

    NASA Astrophysics Data System (ADS)

    Alvarez, Robert E.

    2004-05-01

    Segmenting is a key initial step in many computer-aided detection (CAD) systems. Our purpose is to develop a method to estimate the reliability of segmenting algorithm results. We use a statistical shape model computed using principal component analysis. The model retains a small number of eigenvectors, or modes, that represent a large fraction of the variance. The residuals between the segmenting result and its projection into the space of retained modes are computed. The sum of the squares of residuals is transformed to a zero-mean, unit standard deviation Gaussian random variable. We also use the standardized scale parameter. The reliability measure is the probability that the transformed residuals and scale parameter are greater than the absolute value of the observed values. We tested the reliability measure with thirty chest x-ray images with "leave-out-one" testing. The Gaussian assumption was verified using normal probability plots. For each image, a statistical shape model was computed from the hand-digitized data of the rest of the images in the training set. The residuals and scale parameter with automated segment results for the image were used to compute the reliability measure in each case. The reliability measure was significantly lower for two images in the training set with unusual lung fields or processing errors. The data and Matlab scripts for reproducing the figures are at http://www.aprendtech.com/papers/relmsr.zip Errors detected by the new reliability measure can be used to adjust processing or warn the user.

  12. 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. PMID:26716720

  13. 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)

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

    PubMed

    Xu, Hongming; Mandal, Mrinal

    2015-08-01

    Segmentation of epidermis areas is an important step towards automatic analysis of skin histopathological images. This paper presents a robust technique for epidermis segmentation in whole slide skin histopathological images. The proposed technique first performs a coarse epidermis segmentation using global thresholding and shape analysis. The epidermis thickness is then estimated by a series of line segments perpendicular to the main axis of the initially segmented epidermis mask. If the segmented epidermis mask has a thickness greater than a predefined threshold, the segmentation is suspected to be inaccurate. A second pass of fine segmentation using k-means algorithm is then carried out over these coarsely segmented result to enhance the performance. Experimental results on 64 different skin histopathological images show that the proposed technique provides a superior performance compared to the existing techniques. PMID:26737135

  15. 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.

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

    SciTech Connect

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

    2005-02-01

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

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

    PubMed

    Wang, Jingyue; Huang, Weizhang

    2016-05-01

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

  18. Sensitivity based segmentation and identification in automatic speech recognition

    NASA Astrophysics Data System (ADS)

    Absher, R.

    1984-03-01

    This research program continued an investigation of sensitivity analysis, and its use in the segmentation and identification of the phonetic units of speech, that was initiated during the 1982 Summer Faculty Research Program. The elements of the sensitivity matrix, which express the relative change in each pole of the speech model to a relative change in each coefficient of the characteristic equation, were evaluated for an expanded set of data which consisted of six vowels contained in single words spoken in a simple carrier phrase by five males with differing dialects. The objectives were to evaluate the sensitivity matrix, interpret its changes during the production of the vowels, and to evaluate inter-speaker variations. It was determined that the sensitivity analysis (1) serves to segment the vowel interval, (2) provides a measure of when a vowel is on target, and (3) should provide sufficient information to identify each particular vowel. Based on the results presented, sensitivity analysis should result in more accurate segmentation and identification of phonemes and should provide a practicable framework for incorporation of acoustic-phonetic variance as well as time and talker normalization.

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

    PubMed

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

    2011-07-01

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

  20. 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

  1. 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.

  2. 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

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

    PubMed

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

    2015-01-01

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

  4. Novel multiresolution mammographic density segmentation using pseudo 3D features and adaptive cluster merging

    NASA Astrophysics Data System (ADS)

    He, Wenda; Juette, Arne; Denton, Erica R. E.; Zwiggelaar, Reyer

    2015-03-01

    Breast cancer is the most frequently diagnosed cancer in women. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective ways to overcome the disease. Successful mammographic density segmentation is a key aspect in deriving correct tissue composition, ensuring an accurate mammographic risk assessment. However, mammographic densities have not yet been fully incorporated with non-image based risk prediction models, (e.g. the Gail and the Tyrer-Cuzick model), because of unreliable segmentation consistency and accuracy. This paper presents a novel multiresolution mammographic density segmentation, a concept of stack representation is proposed, and 3D texture features were extracted by adapting techniques based on classic 2D first-order statistics. An unsupervised clustering technique was employed to achieve mammographic segmentation, in which two improvements were made; 1) consistent segmentation by incorporating an optimal centroids initialisation step, and 2) significantly reduced the number of missegmentation by using an adaptive cluster merging technique. A set of full field digital mammograms was used in the evaluation. Visual assessment indicated substantial improvement on segmented anatomical structures and tissue specific areas, especially in low mammographic density categories. The developed method demonstrated an ability to improve the quality of mammographic segmentation via clustering, and results indicated an improvement of 26% in segmented image with good quality when compared with the standard clustering approach. This in turn can be found useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

  5. Computerized detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): improvement of vessel segmentation

    NASA Astrophysics Data System (ADS)

    Zhou, Chuan; Chan, Heang-Ping; Kuriakose, Jean W.; Chughtai, Aamer; Hadjiiski, Lubomir M.; Wei, Jun; Patel, Smita; Kazerooni, Ella A.

    2011-03-01

    Vessel segmentation is a fundamental step in an automated pulmonary embolism (PE) detection system. The purpose of this study is to improve the segmentation scheme for pulmonary vessels affected by PE and other lung diseases. We have developed a multiscale hierarchical vessel enhancement and segmentation (MHES) method for pulmonary vessel tree extraction based on the analysis of eigenvalues of Hessian matrices. However, it is difficult to segment the pulmonary vessels accurately when the vessel is occluded by PEs and/or surrounded by lymphoid tissues or lung diseases. In this study, we developed a method that combines MHES with level set refinement (MHES-LSR) to improve vessel segmentation accuracy. The level set was designed to propagate the initial object contours to the regions with relatively high gray-level, high gradient, and high compactness as measured by the smoothness of the curvature along vessel boundaries. Two and eight CTPA scans were randomly selected as training and test data sets, respectively. Forty volumes of interest (VOI) containing "representative" vessels were manually segmented by a radiologist experienced in CTPA interpretation and used as reference standard. The results show that, for the 32 test VOIs, the average percentage volume error relative to the reference standard was improved from 31.7+/-10.9% using the MHES method to 7.7+/-4.7% using the MHES-LSR method. The correlation between the computer-segmented vessel volume and the reference standard was improved from 0.954 to 0.986. The accuracy of vessel segmentation was improved significantly (p<0.05). The MHES-LSR method may have the potential to improve PE detection.

  6. 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.

  7. Segmented ion thruster

    NASA Technical Reports Server (NTRS)

    Brophy, John R. (Inventor)

    1993-01-01

    Apparatus and methods for large-area, high-power ion engines comprise dividing a single engine into a combination of smaller discharge chambers (or segments) configured to operate as a single large-area engine. This segmented ion thruster (SIT) approach enables the development of 100-kW class argon ion engines for operation at a specific impulse of 10,000 s. A combination of six 30-cm diameter ion chambers operating as a single engine can process over 100 kW. Such a segmented ion engine can be operated from a single power processor unit.

  8. 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

  9. Volumetric Semantic Segmentation using Pyramid Context Features

    PubMed Central

    Barron, Jonathan T.; Arbeláez, Pablo; Keränen, Soile V. E.; Biggin, Mark D.; Knowles, David W.; Malik, Jitendra

    2015-01-01

    We present an algorithm for the per-voxel semantic segmentation of a three-dimensional volume. At the core of our algorithm is a novel “pyramid context” feature, a descriptive representation designed such that exact per-voxel linear classification can be made extremely efficient. This feature not only allows for efficient semantic segmentation but enables other aspects of our algorithm, such as novel learned features and a stacked architecture that can reason about self-consistency. We demonstrate our technique on 3D fluorescence microscopy data of Drosophila embryos for which we are able to produce extremely accurate semantic segmentations in a matter of minutes, and for which other algorithms fail due to the size and high-dimensionality of the data, or due to the difficulty of the task. PMID:26029008

  10. Accurate thickness measurement of graphene

    NASA Astrophysics Data System (ADS)

    Shearer, Cameron J.; Slattery, Ashley D.; Stapleton, Andrew J.; Shapter, Joseph G.; Gibson, Christopher T.

    2016-03-01

    Graphene has emerged as a material with a vast variety of applications. The electronic, optical and mechanical properties of graphene are strongly influenced by the number of layers present in a sample. As a result, the dimensional characterization of graphene films is crucial, especially with the continued development of new synthesis methods and applications. A number of techniques exist to determine the thickness of graphene films including optical contrast, Raman scattering and scanning probe microscopy techniques. Atomic force microscopy (AFM), in particular, is used extensively since it provides three-dimensional images that enable the measurement of the lateral dimensions of graphene films as well as the thickness, and by extension the number of layers present. However, in the literature AFM has proven to be inaccurate with a wide range of measured values for single layer graphene thickness reported (between 0.4 and 1.7 nm). This discrepancy has been attributed to tip-surface interactions, image feedback settings and surface chemistry. In this work, we use standard and carbon nanotube modified AFM probes and a relatively new AFM imaging mode known as PeakForce tapping mode to establish a protocol that will allow users to accurately determine the thickness of graphene films. In particular, the error in measuring the first layer is reduced from 0.1-1.3 nm to 0.1-0.3 nm. Furthermore, in the process we establish that the graphene-substrate adsorbate layer and imaging force, in particular the pressure the tip exerts on the surface, are crucial components in the accurate measurement of graphene using AFM. These findings can be applied to other 2D materials.

  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. Combining Multi-atlas Segmentation with Brain Surface Estimation

    PubMed Central

    Carass, Aaron; Resnick, Susan M.; Pham, Dzung L.; Prince, Jerry L.; Landman, Bennett A.

    2016-01-01

    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 accurate to FreeSurfer while achieving greater robustness across an elderly

  13. Combining multi-atlas segmentation with brain surface estimation

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

    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 limitation 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 accurate to FreeSurfer while achieving greater robustness across an elderly population.

  14. Automated Segmentation of Soils Using X-ray Tomography

    NASA Astrophysics Data System (ADS)

    Miller, M.; Miller, E.; McKinley, J.

    2014-12-01

    X-ray tomography (CT) has long been a useful tool for three-dimensional imaging of compositionally heterogeneous objects. In the environmental sciences, CT is an efficient tool for the nondestructive inspection of sediment and soil cores. However, in order to extract parameters describing such properties as pore space and solid-phase distribution, the imaged volume must be segmented according to relevant categories. When done manually by image inspection, segmentation produces results that are often inconsistent, and applying the method over multiple images may be impractical. Modern machine learning techniques have been shown to be more accurate than humans at some vision tasks in fields of histology and remote sensing, and those techniques may be useful for environmental samples. We present a technique using deep learning to categorize a tomographic volume into solid and pore regions, while also identifying morphologically similar solid-phase regions within the imaged object. Finally, we show how the composition of these characteristic solid constituents may be estimated by propagating two dimensional XRF data through the segmented volume. This research was funded by the Chemical Imaging Initiative under the Laboratory Directed Research and Development Program at PNNL.

  15. Image segmentation survey

    NASA Technical Reports Server (NTRS)

    Haralick, R. M.

    1982-01-01

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

  16. Segmentation of SAR images

    NASA Technical Reports Server (NTRS)

    Kwok, Ronald

    1989-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2009-02-01

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

  18. Segmented pyroelector detector

    DOEpatents

    Stotlar, S.C.; McLellan, E.J.

    1981-01-21

    A pyroelectric detector is described which has increased voltage output and improved responsivity over equivalent size detectors. The device comprises a plurality of edge-type pyroelectric detectors which have a length which is much greater than the width of the segments between the edge-type electrodes. External circuitry connects the pyroelectric detector segments in parallel to provide a single output which maintains 50 ohm impedance characteristics.

  19. Squaring a Circular Segment

    ERIC Educational Resources Information Center

    Gordon, Russell

    2008-01-01

    Consider a circular segment (the smaller portion of a circle cut off by one of its chords) with chord length c and height h (the greatest distance from a point on the arc of the circle to the chord). Is there a simple formula involving c and h that can be used to closely approximate the area of this circular segment? Ancient Chinese and Egyptian…

  20. Body Segment Inertial Parameters of elite swimmers Using DXA and indirect Methods

    PubMed Central

    Rossi, Marcel; LYTTLE, Andrew; EL-SALLAM, Amar; BENJANUVATRA, Nat; BLANKSBY, Brian

    2013-01-01

    As accurate body segment inertial parameters (BSIPs) are difficult to obtain in motion analysis, this study computed individual BSIPs from DXA scan images. Therefore, by co-registering areal density data with DXA grayscale image, the relationship between pixel color gradient and the mass within the pixel area could be established. Thus, one can calculate BSIPs, including segment mass, center of mass (COM) and moment of inertia about the sagittal axis (Ixx). This technique calculated whole body mass very accurately (%RMSE of < 1.5%) relatively to results of the generic DXA scanner software. The BSIPs of elite male and female swimmers, and young adult Caucasian males (n = 28), were computed using this DXA method and 5 other common indirect estimation methods. A 3D surface scan of each subject enabled mapping of key anthropometric variables required for the 5 indirect estimation methods. Mass, COM and Ixx were calculated for seven body segments (head, trunk, head + trunk, upper arm, forearm, thigh and shank). Between-group comparisons of BSIPs revealed that elite female swimmers had the lowest segment masses of the three groups (p < 0.05). Elite male swimmers recorded the greatest inertial parameters of the trunk and upper arms (p < 0.05). Using the DXA method as the criterion, the five indirect methods produced errors greater than 10% for at least one BSIP in all three populations. Therefore, caution is required when computing BSIPs for elite swimmers via these indirect methods, DXA accurately estimated BSIPs in the frontal plane. Key Points Elite swimmers have significantly different body segment inertial parameters than young adult Caucasian males. The errors computed from indirect BSIP estimation methods are large regardless whether applied to elite swimmers or young adult Caucasian males. No indirect estimation method consistently performed best. PMID:24421737

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

    PubMed

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

    2016-04-01

    Accurate segmentation of organs at risk is an important step in radiotherapy planning. Manual segmentation being a tedious procedure and prone to inter- and intra-observer variability, there is a growing interest in automated segmentation methods. However, automatic methods frequently fail to provide satisfactory result, and post-processing corrections are often needed. Semi-automatic segmentation methods are designed to overcome these problems by combining physicians' expertise and computers' potential. This study evaluates two semi-automatic segmentation methods with different types of user interactions, named the "strokes" and the "contour", to provide insights into the role and impact of human-computer interaction. Two physicians participated in the experiment. In total, 42 case studies were carried out on five different types of organs at risk. For each case study, both the human-computer interaction process and quality of the segmentation results were measured subjectively and objectively. Furthermore, different measures of the process and the results were correlated. A total of 36 quantifiable and ten non-quantifiable correlations were identified for each type of interaction. Among those pairs of measures, 20 of the contour method and 22 of the strokes method were strongly or moderately correlated, either directly or inversely. Based on those correlated measures, it is concluded that: (1) in the design of semi-automatic segmentation methods, user interactions need to be less cognitively challenging; (2) based on the observed workflows and preferences of physicians, there is a need for flexibility in the interface design; (3) the correlated measures provide insights that can be used in improving user interaction design. PMID:26553109

  2. Segment aberration effects on contrast.

    PubMed

    Crossfield, Ian J; Troy, Mitchell

    2007-07-20

    High-contrast imaging, particularly the direct detection of extrasolar planets, is a major science driver for the next generation of telescopes. This science requires the suppression of scattered starlight at extremely high levels and that telescopes be correctly designed today to meet these stringent requirements in the future. The challenge increases in systems with complicated aperture geometries such as obscured, segmented telescopes. Such systems can also require intensive modeling and simulation efforts in order to understand the trade-offs between different optical parameters. The feasibility and development of a contrast prediction tool for use in the design and systems engineering of these telescopes is described. The performance of a particular starlight suppression system on a large segmented telescope is described analytically. These analytical results and the results of a contrast predictor are then compared with the results of a full wave-optics simulation. PMID:17609697

  3. Segmental neurofibromatosis and malignancy.

    PubMed

    Dang, Julie D; Cohen, Philip R

    2010-01-01

    Segmental neurofibromatosis is an uncommon variant of neurofibromatosis type I characterized by neurofibromas and/or café-au-lait macules localized to one sector of the body. Although patients with neurofibromatosis type I have an associated increased risk of certain malignancies, malignancy has only occasionally been reported in patients with segmental neurofibromatosis. The published reports of patients with segmental neurofibromatosis who developed malignancy were reviewed and the characteristics of these patients and their cancers were summarized. Ten individuals (6 women and 4 men) with segmental neurofibromatosis and malignancy have been reported. The malignancies include malignant peripheral nerve sheath tumor (3), malignant melanoma (2), breast cancer (1), colon cancer (1), gastric cancer (1), lung cancer (1), and Hodgkin lymphoma (1). The most common malignancies in patients with segmental neurofibromatosis are derived from neural crest cells: malignant peripheral nerve sheath tumor and malignant melanoma. The incidence of malignancy in patients with segmental neurofibromatosis may approach that of patients with neurofibromatosis type I. PMID:21137621

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

    NASA Astrophysics Data System (ADS)

    Yang, Xiaofeng; Fei, Baowei

    2010-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

  6. Semi-automated segmentation of neuroblastoma nuclei using the gradient energy tensor: a user driven approach

    NASA Astrophysics Data System (ADS)

    Kromp, Florian; Taschner-Mandl, Sabine; Schwarz, Magdalena; Blaha, Johanna; Weiss, Tamara; Ambros, Peter F.; Reiter, Michael

    2015-02-01

    We propose a user-driven method for the segmentation of neuroblastoma nuclei in microscopic fluorescence images involving the gradient energy tensor. Multispectral fluorescence images contain intensity and spatial information about antigene expression, fluorescence in situ hybridization (FISH) signals and nucleus morphology. The latter serves as basis for the detection of single cells and the calculation of shape features, which are used to validate the segmentation and to reject false detections. Accurate segmentation is difficult due to varying staining intensities and aggregated cells. It requires several (meta-) parameters, which have a strong influence on the segmentation results and have to be selected carefully for each sample (or group of similar samples) by user interactions. Because our method is designed for clinicians and biologists, who may have only limited image processing background, an interactive parameter selection step allows the implicit tuning of parameter values. With this simple but intuitive method, segmentation results with high precision for a large number of cells can be achieved by minimal user interaction. The strategy was validated on handsegmented datasets of three neuroblastoma cell lines.

  7. SU-F-BRF-02: Automated Lung Segmentation Method Using Atlas-Based Sparse Shape Composition with a Shape Constrained Deformable Model

    SciTech Connect

    Zhou, J; Yan, Z; Zhang, S; Zhang, B; Lasio, G; Prado, K; D'Souza, W

    2014-06-15

    Purpose: To develop an automated lung segmentation method, which combines the atlas-based sparse shape composition with a shape constrained deformable model in thoracic CT for patients with compromised lung volumes. Methods: Ten thoracic computed tomography scans for patients with large lung tumors were collected and reference lung ROIs in each scan was manually segmented to assess the performance of the method. We propose an automated and robust framework for lung tissue segmentation by using single statistical atlas registration to initialize a robust deformable model in order to perform fine segmentation that includes compromised lung tissue. First, a statistical image atlas with sparse shape composition is constructed and employed to obtain an approximate estimation of lung volume. Next, a robust deformable model with shape prior is initialized from this estimation. Energy terms from ROI edge potential and interior ROI region based potential as well as the initial ROI are combined in this model for accurate and robust segmentation. Results: The proposed segmentation method is applied to segment right lung on three CT scans. The quantitative results of our segmentation method achieved mean dice score of (0.92–0.95), mean accuracy of (0.97,0.98), and mean relative error of (0.10,0.16) with 95% CI. The quantitative results of previously published RASM segmentation method achieved mean dice score of (0.74,0.96), mean accuracy of (0.66,0.98), and mean relative error of (0.04, 0.38) with 95% CI. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance compared with a robust active shape model method. Conclusion: The atlas-based segmentation approach achieved relatively high accuracy with less variance compared to RASM in the sample dataset and the proposed method will be useful in image analysis applications for lung nodule or lung cancer diagnosis and radiotherapy assessment in thoracic

  8. Improving image segmentation by learning region affinities

    SciTech Connect

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

    2010-11-03

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

  9. Automatic segmentation and statistical shape modeling of the paranasal sinuses to estimate natural variations

    NASA Astrophysics Data System (ADS)

    Sinha, Ayushi; Leonard, Simon; Reiter, Austin; Ishii, Masaru; Taylor, Russell H.; Hager, Gregory D.

    2016-03-01

    We present an automatic segmentation and statistical shape modeling system for the paranasal sinuses which allows us to locate structures in and around the sinuses, as well as to observe the variability in these structures. This system involves deformably registering a given patient image to a manually segmented template image, and using the resulting deformation field to transfer labels from the template to the patient image. We use 3D snake splines to correct errors in this initial segmentation. Once we have several accurately segmented images, we build statistical shape models to observe the population mean and variance for each structure. These shape models are useful to us in several ways. Regular registration methods are insufficient to accurately register pre-operative computed tomography (CT) images with intra-operative endoscopy video of the sinuses. This is because of deformations that occur in structures containing erectile tissue. Our aim is to estimate these deformations using our shape models in order to improve video-CT registration, as well as to distinguish normal variations in anatomy from abnormal variations, and automatically detect and stage pathology. We can also compare the mean shapes and variances in different populations, such as different genders or ethnicities, in order to observe differences and similarities, as well as in different age groups in order to observe the developmental changes that occur in the sinuses.

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

    PubMed

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

    2013-10-21

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-10-01

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

  12. Unsupervised Retinal Vessel Segmentation Using Combined Filters

    PubMed Central

    Oliveira, Wendeson S.; Teixeira, Joyce Vitor; Ren, Tsang Ing; Cavalcanti, George D. C.; Sijbers, Jan

    2016-01-01

    Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels’ appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi’s filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods. PMID:26919587

  13. Unsupervised Retinal Vessel Segmentation Using Combined Filters.

    PubMed

    Oliveira, Wendeson S; Teixeira, Joyce Vitor; Ren, Tsang Ing; Cavalcanti, George D C; Sijbers, Jan

    2016-01-01

    Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels' appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi's filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods. PMID:26919587

  14. Hyperspectral image segmentation using active contours

    NASA Astrophysics Data System (ADS)

    Lee, Cheolha P.; Snyder, Wesley E.

    2004-08-01

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

  15. Automatic evaluation of uterine cervix segmentations

    NASA Astrophysics Data System (ADS)

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

    2007-03-01

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

  16. Nonlocal regularization for active appearance model: Application to medial temporal lobe segmentation.

    PubMed

    Hu, Shiyan; Coupé, Pierrick; Pruessner, Jens C; Collins, D Louis

    2014-02-01

    The human medial temporal lobe (MTL) is an important part of the limbic system, and its substructures play key roles in learning, memory, and neurodegeneration. The MTL includes the hippocampus (HC), amygdala (AG), parahippocampal cortex (PHC), entorhinal cortex, and perirhinal cortex--structures that are complex in shape and have low between-structure intensity contrast, making them difficult to segment manually in magnetic resonance images. This article presents a new segmentation method that combines active appearance modeling and patch-based local refinement to automatically segment specific substructures of the MTL including HC, AG, PHC, and entorhinal/perirhinal cortex from MRI data. Appearance modeling, relying on eigen-decomposition to analyze statistical variations in image intensity and shape information in study population, is used to capture global shape characteristics of each structure of interest with a generative model. Patch-based local refinement, using nonlocal means to compare the image local intensity properties, is applied to locally refine the segmentation results along the structure borders to improve structure delimitation. In this manner, nonlocal regularization and global shape constraints could allow more accurate segmentations of structures. Validation experiments against manually defined labels demonstrate that this new segmentation method is computationally efficient, robust, and accurate. In a leave-one-out validation on 54 normal young adults, the method yielded a mean Dice κ of 0.87 for the HC, 0.81 for the AG, 0.73 for the anterior parts of the parahippocampal gyrus (entorhinal and perirhinal cortex), and 0.73 for the posterior parahippocampal gyrus. PMID:22987811

  17. Segmentation of abdominal organs from CT using a multi-level, hierarchical neural network strategy.

    PubMed

    Selver, M Alper

    2014-03-01

    Precise measurements on abdominal organs are vital prior to the important clinical procedures. Such measurements require accurate segmentation of these organs, which is a very challenging task due to countless anatomical variations and technical difficulties. Although, several features with various classifiers have been designed to overcome these challenges, abdominal organ segmentation via classification is still an emerging field in order to reach desired precision. Recent studies on multiple feature-classifier combinations show that hierarchical systems outperform composite feature-single classifier models. In this study, how hierarchical formations can translate to improved accuracy, when large size feature spaces are involved, is explored for the problem of abdominal organ segmentation. As a result, a semi-automatic, slice-by-slice segmentation method is developed using a novel multi-level and hierarchical neural network (MHNN). MHNN is designed to collect complementary information about organs at each level of the hierarchy via different feature-classifier combinations. Moreover, each level of MHNN receives residual data from the previous level. The residual data is constructed to preserve zero false positive error until the last level of the hierarchy, where only most challenging samples remain. The algorithm mimics analysis behaviour of a radiologist by using the slice-by-slice iteration, which is supported with adjacent slice similarity features. This enables adaptive determination of system parameters and turns into the advantage of online training, which is done in parallel to the segmentation process. Proposed design can perform robust and accurate segmentation of abdominal organs as validated by using diverse data sets with various challenges. PMID:24480371

  18. Accurate positioning of long, flexible ARM's (Articulated Robotic Manipulator)

    NASA Technical Reports Server (NTRS)

    Malachowski, Michael J.

    1988-01-01

    An articulated robotic manipulator (ARM) system is being designed for space applications. Work being done on a concept utilizing an infinitely stiff laser beam for position reference is summarized. The laser beam is projected along the segments of the ARM, and the position is sensed by the beam rider modules (BRM) mounted on the distal ends of the segments. The BRM concept is the heart of the system. It utilizes a combination of lateral displacements and rotational and distance measurement sensors. These determine the relative position of the two ends of the segments with respect to each other in six degrees of freedom. The BRM measurement devices contain microprocessor controlled data acquisition and active positioning components. An indirect adaptive controller is used to accurately control the position of the ARM.

  19. A comprehensive segmentation analysis of crude oil market based on time irreversibility

    NASA Astrophysics Data System (ADS)

    Xia, Jianan; Shang, Pengjian; Lu, Dan; Yin, Yi

    2016-05-01

    In this paper, we perform a comprehensive entropic segmentation analysis of crude oil future prices from 1983 to 2014 which used the Jensen-Shannon divergence as the statistical distance between segments, and analyze the results from original series S and series begin at 1986 (marked as S∗) to find common segments which have same boundaries. Then we apply time irreversibility analysis of each segment to divide all segments into two groups according to their asymmetry degree. Based on the temporal distribution of the common segments and high asymmetry segments, we figure out that these two types of segments appear alternately and do not overlap basically in daily group, while the common portions are also high asymmetry segments in weekly group. In addition, the temporal distribution of the common segments is fairly close to the time of crises, wars or other events, because the hit from severe events to oil price makes these common segments quite different from their adjacent segments. The common segments can be confirmed in daily group series, or weekly group series due to the large divergence between common segments and their neighbors. While the identification of high asymmetry segments is helpful to know the segments which are not affected badly by the events and can recover to steady states automatically. Finally, we rearrange the segments by merging the connected common segments or high asymmetry segments into a segment, and conjoin the connected segments which are neither common nor high asymmetric.

  20. The cascaded moving k-means and fuzzy c-means clustering algorithms for unsupervised segmentation of malaria images

    NASA Astrophysics Data System (ADS)

    Abdul-Nasir, Aimi Salihah; Mashor, Mohd Yusoff; Halim, Nurul Hazwani Abd; Mohamed, Zeehaida

    2015-05-01

    Malaria is a life-threatening parasitic infectious disease that corresponds for nearly one million deaths each year. Due to the requirement of prompt and accurate diagnosis of malaria, the current study has proposed an unsupervised pixel segmentation based on clustering algorithm in order to obtain the fully segmented red blood cells (RBCs) infected with malaria parasites based on the thin blood smear images of P. vivax species. In order to obtain the segmented infected cell, the malaria images are first enhanced by using modified global contrast stretching technique. Then, an unsupervised segmentation technique based on clustering algorithm has been applied on the intensity component of malaria image in order to segment the infected cell from its blood cells background. In this study, cascaded moving k-means (MKM) and fuzzy c-means (FCM) clustering algorithms has been proposed for malaria slide image segmentation. After that, median filter algorithm has been applied to smooth the image as well as to remove any unwanted regions such as small background pixels from the image. Finally, seeded region growing area extraction algorithm has been applied in order to remove large unwanted regions that are still appeared on the image due to their size in which cannot be cleaned by using median filter. The effectiveness of the proposed cascaded MKM and FCM clustering algorithms has been analyzed qualitatively and quantitatively by comparing the proposed cascaded clustering algorithm with MKM and FCM clustering algorithms. Overall, the results indicate that segmentation using the proposed cascaded clustering algorithm has produced the best segmentation performances by achieving acceptable sensitivity as well as high specificity and accuracy values compared to the segmentation results provided by MKM and FCM algorithms.

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

    NASA Astrophysics Data System (ADS)

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

    2005-04-01

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

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

    PubMed

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

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

    2016-01-01

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

  5. Polyp Segmentation in NBI Colonoscopy

    NASA Astrophysics Data System (ADS)

    Gross, Sebastian; Kennel, Manuel; Stehle, Thomas; Wulff, Jonas; Tischendorf, Jens; Trautwein, Christian; Aach, Til

    Endoscopic screening of the colon (colonoscopy) is performed to prevent cancer and to support therapy. During intervention colon polyps are located, inspected and, if need be, removed by the investigator. We propose a segmentation algorithm as a part of an automatic polyp classification system for colonoscopic Narrow-Band images. Our approach includes multi-scale filtering for noise reduction, suppression of small blood vessels, and enhancement of major edges. Results of the subsequent edge detection are compared to a set of elliptic templates and evaluated. We validated our algorithm on our polyp database with images acquired during routine colonoscopic examinations. The presented results show the reliable segmentation performance of our method and its robustness to image variations.

  6. The LOFT ground segment

    NASA Astrophysics Data System (ADS)

    Bozzo, E.; Antonelli, A.; Argan, A.; Barret, D.; Binko, Pavel; Brandt, S.; Cavazzuti, E.; Courvoisier, T.; den Herder, J. W.; Feroci, M.; Ferrigno, C.; Giommi, P.; Götz, D.; Guy, L.; Hernanz, M.; in't Zand, J. J. M.; Klochkov, D.; Kuulkers, Erik; Motch, C.; Lumb, D.; Papitto, A.; Pittori, Carlotta; Rohlfs, R.; Santangelo, A.; Schmid, C.; Schwope, A. D.; Smith, P. J.; Webb, N. A.; Wilms, J.; Zane, S.

    2014-07-01

    LOFT, the Large Observatory For X-ray Timing, was one of the ESA M3 mission candidates that completed their assessment phase at the end of 2013. LOFT is equipped with two instruments, the Large Area Detector (LAD) and the Wide Field Monitor (WFM). The LAD performs pointed observations of several targets per orbit (~90 minutes), providing roughly ~80 GB of proprietary data per day (the proprietary period will be 12 months). The WFM continuously monitors about 1/3 of the sky at a time and provides data for about ~100 sources a day, resulting in a total of ~20 GB of additional telemetry. The LOFT Burst alert System additionally identifies on-board bright impulsive events (e.g., Gamma-ray Bursts, GRBs) and broadcasts the corresponding position and trigger time to the ground using a dedicated system of ~15 VHF receivers. All WFM data are planned to be made public immediately. In this contribution we summarize the planned organization of the LOFT ground segment (GS), as established in the mission Yellow Book1. We describe the expected GS contributions from ESA and the LOFT consortium. A review is provided of the planned LOFT data products and the details of the data flow, archiving and distribution. Despite LOFT was not selected for launch within the M3 call, its long assessment phase ( >2 years) led to a very solid mission design and an efficient planning of its ground operations.

  7. A Novel Active Contour Model for MRI Brain Segmentation used in Radiotherapy Treatment Planning

    PubMed Central

    Mostaar, Ahmad; Houshyari, Mohammad; Badieyan, Saeedeh

    2016-01-01

    Introduction Brain image segmentation is one of the most important clinical tools used in radiology and radiotherapy. But accurate segmentation is a very difficult task because these images mostly contain noise, inhomogeneities, and sometimes aberrations. The purpose of this study was to introduce a novel, locally statistical active contour model (ACM) for magnetic resonance image segmentation in the presence of intense inhomogeneity with the ability to determine the position of contour and energy diagram. Methods A Gaussian distribution model with different means and variances was used for inhomogeneity, and a moving window was used to map the original image into another domain in which the intensity distributions of inhomogeneous objects were still Gaussian but were better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field by the original signal within the window. Then, a statistical energy function is defined for each local region. Also, to evaluate the performance of our method, experiments were conducted on MR images of the brain for segment tumors or normal tissue as visualization and energy functions. Results In the proposed method, we were able to determine the size and position of the initial contour and to count iterations to have a better segmentation. The energy function for 20 to 430 iterations was calculated. The energy function was reduced by about 5 and 7% after 70 and 430 iterations, respectively. These results showed that, with increasing iterations, the energy function decreased, but it decreased faster during the early iterations, after which it decreased slowly. Also, this method enables us to stop the segmentation based on the threshold that we define for the energy equation. Conclusion An active contour model based on the energy function is a useful tool for medical image segmentation. The proposed method combined the information about neighboring pixels that

  8. Segmented-field radiography in scoliosis

    SciTech Connect

    Daniel, W.W.; Barnes, G.T.; Nasca, R.J.; Annegan, D.C.

    1985-02-01

    A method of scoliosis imaging using segmented fields is presented. The method is advantageous for patients requiring serial radiographic monitoring, as it results in markedly reduced radiation doses to critical organs, particularly the breast. Absorbed dose to the breast was measured to be 8.8 mrad (88 ..mu..Gy) for a full-field examination and 0.051 mrad (5.1 ..mu..Gy) for the segmented-field study. The segmented-field technique also results in improved image quality. Experience with 53 studies in 23 patients is reported.

  9. Rediscovering market segmentation.

    PubMed

    Yankelovich, Daniel; Meer, David

    2006-02-01

    In 1964, Daniel Yankelovich introduced in the pages of HBR the concept of nondemographic segmentation, by which he meant the classification of consumers according to criteria other than age, residence, income, and such. The predictive power of marketing studies based on demographics was no longer strong enough to serve as a basis for marketing strategy, he argued. Buying patterns had become far better guides to consumers' future purchases. In addition, properly constructed nondemographic segmentations could help companies determine which products to develop, which distribution channels to sell them in, how much to charge for them, and how to advertise them. But more than 40 years later, nondemographic segmentation has become just as unenlightening as demographic segmentation had been. Today, the technique is used almost exclusively to fulfill the needs of advertising, which it serves mainly by populating commercials with characters that viewers can identify with. It is true that psychographic types like "High-Tech Harry" and "Joe Six-Pack" may capture some truth about real people's lifestyles, attitudes, self-image, and aspirations. But they are no better than demographics at predicting purchase behavior. Thus they give corporate decision makers very little idea of how to keep customers or capture new ones. Now, Daniel Yankelovich returns to these pages, with consultant David Meer, to argue the case for a broad view of nondemographic segmentation. They describe the elements of a smart segmentation strategy, explaining how segmentations meant to strengthen brand identity differ from those capable of telling a company which markets it should enter and what goods to make. And they introduce their "gravity of decision spectrum", a tool that focuses on the form of consumer behavior that should be of the greatest interest to marketers--the importance that consumers place on a product or product category. PMID:16485810

  10. Fault rupture segmentation

    NASA Astrophysics Data System (ADS)

    Cleveland, Kenneth Michael

    A critical foundation to earthquake study and hazard assessment is the understanding of controls on fault rupture, including segmentation. Key challenges to understanding fault rupture segmentation include, but are not limited to: What determines if a fault segment will rupture in a single great event or multiple moderate events? How is slip along a fault partitioned between seismic and seismic components? How does the seismicity of a fault segment evolve over time? How representative are past events for assessing future seismic hazards? In order to address the difficult questions regarding fault rupture segmentation, new methods must be developed that utilize the information available. Much of the research presented in this study focuses on the development of new methods for attacking the challenges of understanding fault rupture segmentation. Not only do these methods exploit a broader band of information within the waveform than has traditionally been used, but they also lend themselves to the inclusion of even more seismic phases providing deeper understandings. Additionally, these methods are designed to be fast and efficient with large datasets, allowing them to utilize the enormous volume of data available. Key findings from this body of work include demonstration that focus on fundamental earthquake properties on regional scales can provide general understanding of fault rupture segmentation. We present a more modern, waveform-based method that locates events using cross-correlation of the Rayleigh waves. Additionally, cross-correlation values can also be used to calculate precise earthquake magnitudes. Finally, insight regarding earthquake rupture directivity can be easily and quickly exploited using cross-correlation of surface waves.

  11. On the evaluation of segmentation editing tools

    PubMed Central

    Heckel, Frank; Moltz, Jan H.; Meine, Hans; Geisler, Benjamin; Kießling, Andreas; D’Anastasi, Melvin; dos Santos, Daniel Pinto; Theruvath, Ashok Joseph; Hahn, Horst K.

    2014-01-01

    Abstract. Efficient segmentation editing tools are important components in the segmentation process, as no automatic methods exist that always generate sufficient results. Evaluating segmentation editing algorithms is challenging, because their quality depends on the user’s subjective impression. So far, no established methods for an objective, comprehensive evaluation of such tools exist and, particularly, intermediate segmentation results are not taken into account. We discuss the evaluation of editing algorithms in the context of tumor segmentation in computed tomography. We propose a rating scheme to qualitatively measure the accuracy and efficiency of editing tools in user studies. In order to objectively summarize the overall quality, we propose two scores based on the subjective rating and the quantified segmentation quality over time. Finally, a simulation-based evaluation approach is discussed, which allows a more reproducible evaluation without the need for human input. This automated evaluation complements user studies, allowing a more convincing evaluation, particularly during development, where frequent user studies are not possible. The proposed methods have been used to evaluate two dedicated editing algorithms on 131 representative tumor segmentations. We show how the comparison of editing algorithms benefits from the proposed methods. Our results also show the correlation of the suggested quality score with the qualitative ratings. PMID:26158063

  12. Segmented polyether-ester copolymers

    SciTech Connect

    Souffie, R.D.

    1982-08-01

    This article touches on the chemistry of manufacture and structure of thermoplastic elastomers. The physical properties and environmental resistance characteristics of these copolymers are related to their molecular makeup. Results indicate that segmented polyether esters, because of their basic chemical structure, are resistant to a wide range of oils, solvents and chemicals. They are also highly elastic, resilient polymers which can be both cost and performance effective when used in a number of industrial applications.

  13. Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique

    SciTech Connect

    Wang Jiahui; Engelmann, Roger; Li Qiang

    2007-12-15

    Accurate segmentation of pulmonary nodules in computed tomography (CT) is an important and difficult task for computer-aided diagnosis of lung cancer. Therefore, the authors developed a novel automated method for accurate segmentation of nodules in three-dimensional (3D) CT. First, a volume of interest (VOI) was determined at the location of a nodule. To simplify nodule segmentation, the 3D VOI was transformed into a two-dimensional (2D) image by use of a key 'spiral-scanning' technique, in which a number of radial lines originating from the center of the VOI spirally scanned the VOI from the 'north pole' to the 'south pole'. The voxels scanned by the radial lines provided a transformed 2D image. Because the surface of a nodule in the 3D image became a curve in the transformed 2D image, the spiral-scanning technique considerably simplified the segmentation method and enabled reliable segmentation results to be obtained. A dynamic programming technique was employed to delineate the 'optimal' outline of a nodule in the 2D image, which corresponded to the surface of the nodule in the 3D image. The optimal outline was then transformed back into 3D image space to provide the surface of the nodule. An overlap between nodule regions provided by computer and by the radiologists was employed as a performance metric for evaluating the segmentation method. The database included two Lung Imaging Database Consortium (LIDC) data sets that contained 23 and 86 CT scans, respectively, with 23 and 73 nodules that were 3 mm or larger in diameter. For the two data sets, six and four radiologists manually delineated the outlines of the nodules as reference standards in a performance evaluation for nodule segmentation. The segmentation method was trained on the first and was tested on the second LIDC data sets. The mean overlap values were 66% and 64% for the nodules in the first and second LIDC data sets, respectively, which represented a higher performance level than those of two

  14. Iterative Vessel Segmentation of Fundus Images.

    PubMed

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

    2015-07-01

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

  15. How to accurately bypass damage

    PubMed Central

    Broyde, Suse; Patel, Dinshaw J.

    2016-01-01

    Ultraviolet radiation can cause cancer through DNA damage — specifically, by linking adjacent thymine bases. Crystal structures show how the enzyme DNA polymerase η accurately bypasses such lesions, offering protection. PMID:20577203

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

    NASA Astrophysics Data System (ADS)

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

    2011-03-01

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

  17. Dynamic thermal characteristics of heat pipe via segmented thermal resistance model for electric vehicle battery cooling

    NASA Astrophysics Data System (ADS)

    Liu, Feifei; Lan, Fengchong; Chen, Jiqing

    2016-07-01

    Heat pipe cooling for battery thermal management systems (BTMSs) in electric vehicles (EVs) is growing due to its advantages of high cooling efficiency, compact structure and flexible geometry. Considering the transient conduction, phase change and uncertain thermal conditions in a heat pipe, it is challenging to obtain the dynamic thermal characteristics accurately in such complex heat and mass transfer process. In this paper, a "segmented" thermal resistance model of a heat pipe is proposed based on thermal circuit method. The equivalent conductivities of different segments, viz. the evaporator and condenser of pipe, are used to determine their own thermal parameters and conditions integrated into the thermal model of battery for a complete three-dimensional (3D) computational fluid dynamics (CFD) simulation. The proposed "segmented" model shows more precise than the "non-segmented" model by the comparison of simulated and experimental temperature distribution and variation of an ultra-thin micro heat pipe (UMHP) battery pack, and has less calculation error to obtain dynamic thermal behavior for exact thermal design, management and control of heat pipe BTMSs. Using the "segmented" model, the cooling effect of the UMHP pack with different natural/forced convection and arrangements is predicted, and the results correspond well to the tests.

  18. A unified variational segmentation framework with a level-set based sparse composite shape prior

    NASA Astrophysics Data System (ADS)

    Liu, Wenyang; Ruan, Dan

    2015-03-01

    Image segmentation plays an essential role in many medical applications. Low SNR conditions and various artifacts makes its automation challenging. To achieve robust and accurate segmentation results, a good approach is to introduce proper shape priors. In this study, we present a unified variational segmentation framework that regularizes the target shape with a level-set based sparse composite prior. When the variational problem is solved with a block minimization/decent scheme, the regularizing impact of the sparse composite prior can be observed to adjust to the most recent shape estimate, and may be interpreted as a ‘dynamic’ shape prior, yet without compromising convergence thanks to the unified energy framework. The proposed method was applied to segment corpus callosum from 2D MR images and liver from 3D CT volumes. Its performance was evaluated using Dice Similarity Coefficient and Hausdorff distance, and compared with two benchmark level-set based segmentation methods. The proposed method has achieved statistically significant higher accuracy in both experiments and avoided faulty inclusion/exclusion of surrounding structures with similar intensities, as opposed to the benchmark methods.

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

    NASA Astrophysics Data System (ADS)

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

    2011-10-01

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

  20. A Unified Variational Segmentation Framework with a Level-set based Sparse Composite Shape Prior

    PubMed Central

    Liu, Wenyang; Ruan, Dan

    2015-01-01

    Image segmentation plays an essential role in many medical applications. Low SNR conditions and various artifacts makes its automation challenging. To achieve robust and accurate segmentation results, a good approach is to introduce proper shape priors. In this study, we present a unified variational segmentation framework that regularizes the target shape with a level-set based sparse composite prior. When the variational problem is solved with a block minimization/decent scheme, the regularizing impact of the sparse composite prior can be observed to adjust to the most recent shape estimate, and may be interpreted as a “dynamic” shape prior, yet without compromising convergence thanks to the unified energy framework. The proposed method was applied to segment corpus callosum from 2D MR images and liver from 3D CT volumes. Its performance was evaluated using Dice Similarity Coefficient and Hausdorff distance, and compared with two benchmark level-set based segmentation methods. The proposed method has achieved statistically significant higher accuracy in both experiments and avoided faulty inclusion/exclusion of surrounding structures with similar intensities, as opposed to the benchmark methods. PMID:25668234

  1. 3D automatic liver segmentation using feature-constrained Mahalanobis distance in CT images.

    PubMed

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

    2016-08-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  3. Segmentation and quantitative evaluation of brain MRI data with a multiphase 3D implicit deformable model

    NASA Astrophysics Data System (ADS)

    Angelini, Elsa D.; Song, Ting; Mensh, Brett D.; Laine, Andrew

    2004-05-01

    Segmentation of three-dimensional anatomical brain images into tissue classes has applications in both clinical and research settings. This paper presents the implementation and quantitative ev