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Sample records for active contour segmentation

  1. Vascular active contour for vessel tree segmentation.

    PubMed

    Shang, Yanfeng; Deklerck, Rudi; Nyssen, Edgard; Markova, Aneta; de Mey, Johan; Yang, Xin; Sun, Kun

    2011-04-01

    In this paper, a novel active contour model is proposed for vessel tree segmentation. First, we introduce a region competition-based active contour model exploiting the gaussian mixture model, which mainly segments thick vessels. Second, we define a vascular vector field to evolve the active contour along its center line into the thin and weak vessels. The vector field is derived from the eigenanalysis of the Hessian matrix of the image intensity in a multiscale framework. Finally, a dual curvature strategy, which uses a vesselness measure-dependent function selecting between a minimal principal curvature and a mean curvature criterion, is added to smoothen the surface of the vessel without changing its shape. The developed model is used to extract the liver and lung vessel tree as well as the coronary artery from high-resolution volumetric computed tomography images. Comparisons are made with several classical active contour models and manual extraction. The experiments show that our model is more accurate and robust than these classical models and is, therefore, more suited for automatic vessel tree extraction.

  2. Image Segmentation With Cage Active Contours.

    PubMed

    Garrido, Lluís; Guerrieri, Marité; Igual, Laura

    2015-12-01

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

  3. Automatic segmentation of leg bones by using active contours.

    PubMed

    Kim, Sunhee; Kim, Youngjun; Park, Sehyung; Lee, Deukhee

    2014-01-01

    In this paper, we present a new active contours model to segment human leg bones in computed tomography images that is based on a variable-weighted combination of local and global intensity. This model can split an object surrounded by both weak and strong boundaries, and also distinguish very adjacent objects with those boundaries. The ability of this model is required for segmentation in medical images, e.g., human leg bones, which are usually composed of highly inhomogeneous objects and where the distances among organs are very close. We developed an evolution equation of a level set function whose zero level set represents a contour. An initial contour is automatically obtained by applying a histogram based multiphase segmentation method. We experimented with computed tomography images from three patients, and demonstrate the efficiency of the proposed method in experimental results.

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

    SciTech Connect

    Weeratunga, S K; Kamath, C

    2003-10-29

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

  5. 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.

  6. Localized Patch-Based Fuzzy Active Contours for Image Segmentation

    PubMed Central

    Liu, Huaxiang; Zhang, Liting; Liu, Jun

    2016-01-01

    This paper presents a novel fuzzy region-based active contour model for image segmentation. By incorporating local patch-energy functional along each pixel of the evolving curve into the fuzziness of the energy, we construct a patch-based energy function without the regurgitation term. Its purpose is not only to make the active contour evolve very stably without the periodical initialization during the evolution but also to reduce the effect of noise. In particular, in order to reject local minimal of the energy functional, we utilize a direct method to calculate the energy alterations instead of solving the Euler-Lagrange equation of the underlying problem. Compared with other fuzzy active contour models, experimental results on synthetic and real images show the advantages of the proposed method in terms of computational efficiency and accuracy. PMID:28070210

  7. Active contour segmentation for hyperspectral oil spill remote sensing

    NASA Astrophysics Data System (ADS)

    Song, Mei-ping; Chang, Ming; An, Ju-bai; Huang, Jian; Lin, Bin

    2013-08-01

    Oil spills could occur in many conditions, which results in pollution of the natural resources, marine environment and economic health of the area. Whenever we need to identify oil spill, confirm the location or get the shape and acreage of oil spill, we have to get the edge information of oil slick images firstly. Hyperspectral remote sensing imaging is now widely used to detect oil spill. Active Contour Models (ACMs) is a widely used image segmentation method that utilizes the geometric information of objects within images. Region based models are less sensitive to noise and give good performance for images with weak edges or without edges. One of the popular Region based ACMs, active contours without edges Models, is implemented by Chan-Vese. The model has the property of global segmentation to segment all the objects within an image irrespective of the initial contour. In this paper, we propose an improved CV model, which can perform well in the oil spill hyper-spectral image segmentation. The energy function embeds spectral and spatial information, introduces the vector edge stopping function, and constructs a novel length term. Results of the improved model on airborne hyperspectral oil spill images show that it improves the ability of distinguishing between oil spills and sea water, as well as the capability of noise reduction.

  8. Segmentation of Coronal Holes Using Active Contours Without Edges

    NASA Astrophysics Data System (ADS)

    Boucheron, L. E.; Valluri, M.; McAteer, R. T. J.

    2016-10-01

    An application of active contours without edges is presented as an efficient and effective means of extracting and characterizing coronal holes. Coronal holes are regions of low-density plasma on the Sun with open magnetic field lines. The detection and characterization of these regions is important for testing theories of their formation and evolution, and also from a space weather perspective because they are the source of the fast solar wind. Coronal holes are detected in full-disk extreme ultraviolet (EUV) images of the corona obtained with the Solar Dynamics Observatory Atmospheric Imaging Assembly (SDO/AIA). The proposed method detects coronal boundaries without determining any fixed intensity value in the data. Instead, the active contour segmentation employs an energy-minimization in which coronal holes are assumed to have more homogeneous intensities than the surrounding active regions and quiet Sun. The segmented coronal holes tend to correspond to unipolar magnetic regions, are consistent with concurrent solar wind observations, and qualitatively match the coronal holes segmented by other methods. The means to identify a coronal hole without specifying a final intensity threshold may allow this algorithm to be more robust across multiple datasets, regardless of data type, resolution, and quality.

  9. Segmentation of volumetric tissue images using constrained active contour models.

    PubMed

    Adiga, P S Umesh

    2003-06-01

    In this article we describe an application of active contour model for the segmentation of 3D histo-pathological images. The 3D images of a thick tissue specimen are obtained as a stack of optical sections using confocal laser beam scanning microscope (CLSM). We have applied noise reduction and feature enhancement methods so that a smooth and slowly varying potential surface is obtained for proper convergence. To increase the capture range of the potential surface, we use a combination of distance potential and the diffused gradient potential as external forces. It has been shown that the region-based information obtained from low-level segmentation can be applied to reduce the adverse influence of the neighbouring nucleus having a strong boundary feature. We have also shown that, by increasing the axial resolution of the image stack, we can automatically propagate the optimum active contour of one image slice to its neighbouring image slices as an appropriate initial model. Results on images of prostate tissue section are presented.

  10. 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.

  11. Evaluating geodesic active contours in microcalcifications segmentation on mammograms.

    PubMed

    Duarte, Marcelo A; Alvarenga, Andre V; Azevedo, Carolina M; Calas, Maria Julia G; Infantosi, Antonio F C; Pereira, Wagner C A

    2015-12-01

    Breast cancer is the most commonly occurring type of cancer among women, and it is the major cause of female cancer-related deaths worldwide. Its incidence is increasing in developed as well as developing countries. Efficient strategies to reduce the high death rates due to breast cancer include early detection and tumor removal in the initial stages of the disease. Clinical and mammographic examinations are considered the best methods for detecting the early signs of breast cancer; however, these techniques are highly dependent on breast characteristics, equipment quality, and physician experience. Computer-aided diagnosis (CADx) systems have been developed to improve the accuracy of mammographic diagnosis; usually such systems may involve three steps: (i) segmentation; (ii) parameter extraction and selection of the segmented lesions and (iii) lesions classification. Literature considers the first step as the most important of them, as it has a direct impact on the lesions characteristics that will be used in the further steps. In this study, the original contribution is a microcalcification segmentation method based on the geodesic active contours (GAC) technique associated with anisotropic texture filtering as well as the radiologists' knowledge. Radiologists actively participate on the final step of the method, selecting the final segmentation that allows elaborating an adequate diagnosis hypothesis with the segmented microcalcifications presented in a region of interest (ROI). The proposed method was assessed by employing 1000 ROIs extracted from images of the Digital Database for Screening Mammography (DDSM). For the selected ROIs, the rate of adequately segmented microcalcifications to establish a diagnosis hypothesis was at least 86.9%, according to the radiologists. The quantitative test, based on the area overlap measure (AOM), yielded a mean of 0.52±0.20 for the segmented images, when all 2136 segmented microcalcifications were considered. Moreover, a

  12. Parametric kernel-driven active contours for image segmentation

    NASA Astrophysics Data System (ADS)

    Wu, Qiongzhi; Fang, Jiangxiong

    2012-10-01

    We investigated a parametric kernel-driven active contour (PKAC) model, which implicitly transfers kernel mapping and piecewise constant to modeling the image data via kernel function. The proposed model consists of curve evolution functional with three terms: global kernel-driven and local kernel-driven terms, which evaluate the deviation of the mapped image data within each region from the piecewise constant model, and a regularization term expressed as the length of the evolution curves. In the local kernel-driven term, the proposed model can effectively segment images with intensity inhomogeneity by incorporating the local image information. By balancing the weight between the global kernel-driven term and the local kernel-driven term, the proposed model can segment the images with either intensity homogeneity or intensity inhomogeneity. To ensure the smoothness of the level set function and reduce the computational cost, the distance regularizing term is applied to penalize the deviation of the level set function and eliminate the requirement of re-initialization. Compared with the local image fitting model and local binary fitting model, experimental results show the advantages of the proposed method in terms of computational efficiency and accuracy.

  13. Efficient hyperspectral image segmentation using geometric active contour formulation

    NASA Astrophysics Data System (ADS)

    Albalooshi, Fatema A.; Sidike, Paheding; Asari, Vijayan K.

    2014-10-01

    In this paper, we present a new formulation of geometric active contours that embeds the local hyperspectral image information for an accurate object region and boundary extraction. We exploit self-organizing map (SOM) unsupervised neural network to train our model. The segmentation process is achieved by the construction of a level set cost functional, in which, the dynamic variable is the best matching unit (BMU) coming from SOM map. In addition, we use Gaussian filtering to discipline the deviation of the level set functional from a signed distance function and this actually helps to get rid of the re-initialization step that is computationally expensive. By using the properties of the collective computational ability and energy convergence capability of the active control models (ACM) energy functional, our method optimizes the geometric ACM energy functional with lower computational time and smoother level set function. The proposed algorithm starts with feature extraction from raw hyperspectral images. In this step, the principal component analysis (PCA) transformation is employed, and this actually helps in reducing dimensionality and selecting best sets of the significant spectral bands. Then the modified geometric level set functional based ACM is applied on the optimal number of spectral bands determined by the PCA. By introducing local significant spectral band information, our proposed method is capable to force the level set functional to be close to a signed distance function, and therefore considerably remove the need of the expensive re-initialization procedure. To verify the effectiveness of the proposed technique, we use real-life hyperspectral images and test our algorithm in varying textural regions. This framework can be easily adapted to different applications for object segmentation in aerial hyperspectral imagery.

  14. A Vessel Active Contour Model for Vascular Segmentation

    PubMed Central

    Chen, Qingli; Wang, Wei; Peng, Yu; Wang, Qingjun; Wu, Zhongke; Zhou, Mingquan

    2014-01-01

    This paper proposes a vessel active contour model based on local intensity weighting and a vessel vector field. Firstly, the energy function we define is evaluated along the evolving curve instead of all image points, and the function value at each point on the curve is based on the interior and exterior weighted means in a local neighborhood of the point, which is good for dealing with the intensity inhomogeneity. Secondly, a vascular vector field derived from a vesselness measure is employed to guide the contour to evolve along the vessel central skeleton into thin and weak vessels. Thirdly, an automatic initialization method that makes the model converge rapidly is developed, and it avoids repeated trails in conventional local region active contour models. Finally, a speed-up strategy is implemented by labeling the steadily evolved points, and it avoids the repeated computation of these points in the subsequent iterations. Experiments using synthetic and real vessel images validate the proposed model. Comparisons with the localized active contour model, local binary fitting model, and vascular active contour model show that the proposed model is more accurate, efficient, and suitable for extraction of the vessel tree from different medical images. PMID:25101262

  15. Comparison of segmentation using fast marching and geodesic active contours methods for bone

    NASA Astrophysics Data System (ADS)

    Bilqis, A.; Widita, R.

    2016-03-01

    Image processing is important in diagnosing diseases or damages of human organs. One of the important stages of image processing is segmentation process. Segmentation is a separation process of the image into regions of certain similar characteristics. It is used to simplify the image to make an analysis easier. The case raised in this study is image segmentation of bones. Bone's image segmentation is a way to get bone dimensions, which is needed in order to make prosthesis that is used to treat broken or cracked bones. Segmentation methods chosen in this study are fast marching and geodesic active contours. This study uses ITK (Insight Segmentation and Registration Toolkit) software. The success of the segmentation was then determined by calculating its accuracy, sensitivity, and specificity. Based on the results, the Active Contours method has slightly higher accuracy and sensitivity values than the fast marching method. As for the value of specificity, fast marching has produced three image results that have higher specificity values compared to those of geodesic active contour's. The result also indicates that both methods have succeeded in performing bone's image segmentation. Overall, geodesic active contours method is quite better than fast marching in segmenting bone images.

  16. Efficient thermal image segmentation through integration of nonlinear enhancement with unsupervised active contour model

    NASA Astrophysics Data System (ADS)

    Albalooshi, Fatema A.; Krieger, Evan; Sidike, Paheding; Asari, Vijayan K.

    2015-03-01

    Thermal images are exploited in many areas of pattern recognition applications. Infrared thermal image segmentation can be used for object detection by extracting regions of abnormal temperatures. However, the lack of texture and color information, low signal-to-noise ratio, and blurring effect of thermal images make segmenting infrared heat patterns a challenging task. Furthermore, many segmentation methods that are used in visible imagery may not be suitable for segmenting thermal imagery mainly due to their dissimilar intensity distributions. Thus, a new method is proposed to improve the performance of image segmentation in thermal imagery. The proposed scheme efficiently utilizes nonlinear intensity enhancement technique and Unsupervised Active Contour Models (UACM). The nonlinear intensity enhancement improves visual quality by combining dynamic range compression and contrast enhancement, while the UACM incorporates active contour evolutional function and neural networks. The algorithm is tested on segmenting different objects in thermal images and it is observed that the nonlinear enhancement has significantly improved the segmentation performance.

  17. Adaptable active contour model with applications to infrared ship target segmentation

    NASA Astrophysics Data System (ADS)

    Fang, Lingling; Wang, Xianghai; Wan, Yu

    2016-07-01

    Active contour model is widely and popularly used in the field of image segmentation because of its superior theoretical properties and efficient numerical methods. An algorithm to segment a ship target in infrared (IR) images using Chan-Vese (C-V) active contour model is proposed here. The method effectively integrates both image regional and boundary information by an adaptable weight function. The method can segment IR ship images, which usually contain noises, blurry boundaries, and heterogeneous regions. In addition, compared with the state-of-the-art methods, experiment results demonstrate the performance and effectiveness of this method.

  18. Robust segmentation of moving objects in video based on spatiotemporal visual saliency and active contour model

    NASA Astrophysics Data System (ADS)

    Ramadan, Hiba; Tairi, Hamid

    2016-11-01

    This paper presents an algorithm for automatic segmentation of moving objects in video based on spatiotemporal visual saliency and an active contour model. Our algorithm exploits the visual saliency and motion information to build a spatiotemporal visual saliency map used to extract a moving region of interest. This region is used to automatically provide the seeds for the convex active contour (CAC) model to segment the moving object accurately. The experiments show a good performance of our algorithm for moving object segmentation in video without user interaction, especially on the SegTrack dataset.

  19. Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation

    PubMed Central

    Cruz-Aceves, I.; Avina-Cervantes, J. G.; Lopez-Hernandez, J. M.; Rostro-Gonzalez, H.; Garcia-Capulin, C. H.; Torres-Cisneros, M.; Guzman-Cabrera, R.

    2013-01-01

    This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation. PMID:23983809

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2015-01-01

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

  3. Coupling of radial-basis network and active contour model for multispectral brain MRI segmentation.

    PubMed

    Valdés-Cristerna, Raquel; Medina-Bañuelos, Verónica; Yáñez-Suárez, Oscar

    2004-03-01

    Magnetic resonance (MR) has been accepted as the reference image study in the clinical environment. The development of new sequences has allowed obtaining diverse images with high clinical importance and whose interpretation requires their joint analysis (multispectral MRI). Recent approaches to segment MRI point toward the definition of hybrid models, where the advantages of region and contour-based methods can be exploited to look for the integration or fusion of information, thus enhancing the performance of the individual approaches. Following this perspective, a hybrid model for multispectral brain MRI segmentation is presented. The model couples a segmenter, based on a radial basis network (RBFNNcc), and an active contour model, based on a cubic spline active contour (CSAC) interpolation. Both static and dynamic coupling of RBFNNcc and CSAC are proposed; the RBFNNcc stage provides an initial contour to the CSAC; the initial contour is optimally sampled with respect to its curvature variations; multispectral information and a restriction term are included into the CSAC energy equation. Segmentations were compared to a reference stack, indicating high-quality performance as measured by Tanimoto indexes of 0.74 +/- 0.08.

  4. Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates

    NASA Technical Reports Server (NTRS)

    Mikic, I.; Krucinski, S.; Thomas, J. D.

    1998-01-01

    This paper presents a method for segmentation and tracking of cardiac structures in ultrasound image sequences. The developed algorithm is based on the active contour framework. This approach requires initial placement of the contour close to the desired position in the image, usually an object outline. Best contour shape and position are then calculated, assuming that at this configuration a global energy function, associated with a contour, attains its minimum. Active contours can be used for tracking by selecting a solution from a previous frame as an initial position in a present frame. Such an approach, however, fails for large displacements of the object of interest. This paper presents a technique that incorporates the information on pixel velocities (optical flow) into the estimate of initial contour to enable tracking of fast-moving objects. The algorithm was tested on several ultrasound image sequences, each covering one complete cardiac cycle. The contour successfully tracked boundaries of mitral valve leaflets, aortic root and endocardial borders of the left ventricle. The algorithm-generated outlines were compared against manual tracings by expert physicians. The automated method resulted in contours that were within the boundaries of intraobserver variability.

  5. Interactive medical image segmentation using PDE control of active contours.

    PubMed

    Karasev, Peter; Kolesov, Ivan; Fritscher, Karl; Vela, Patricio; Mitchell, Phillip; Tannenbaum, Allen

    2013-11-01

    Segmentation of injured or unusual anatomic structures in medical imagery is a problem that has continued to elude fully automated solutions. In this paper, the goal of easy-to-use and consistent interactive segmentation is transformed into a control synthesis problem. A nominal level set partial differential equation (PDE) is assumed to be given; this open-loop system achieves correct segmentation under ideal conditions, but does not agree with a human expert's ideal boundary for real image data. Perturbing the state and dynamics of a level set PDE via the accumulated user input and an observer-like system leads to desirable closed-loop behavior. The input structure is designed such that a user can stabilize the boundary in some desired state without needing to understand any mathematical parameters. Effectiveness of the technique is illustrated with applications to the challenging segmentations of a patellar tendon in magnetic resonance and a shattered femur in computed tomography.

  6. Interactive Medical Image Segmentation using PDE Control of Active Contours

    PubMed Central

    Karasev, Peter; Kolesov, Ivan; Fritscher, Karl; Vela, Patricio; Mitchell, Phillip; Tannenbaum, Allen

    2014-01-01

    Segmentation of injured or unusual anatomic structures in medical imagery is a problem that has continued to elude fully automated solutions. In this paper, the goal of easy-to-use and consistent interactive segmentation is transformed into a control synthesis problem. A nominal level set PDE is assumed to be given; this open-loop system achieves correct segmentation under ideal conditions, but does not agree with a human expert's ideal boundary for real image data. Perturbing the state and dynamics of a level set PDE via the accumulated user input and an observer-like system leads to desirable closed-loop behavior. The input structure is designed such that a user can stabilize the boundary in some desired state without needing to understand any mathematical parameters. Effectiveness of the technique is illustrated with applications to the challenging segmentations of a patellar tendon in MR and a shattered femur in CT. PMID:23893712

  7. Soft-tissues Image Processing: Comparison of Traditional Segmentation Methods with 2D active Contour Methods

    NASA Astrophysics Data System (ADS)

    Mikulka, J.; Gescheidtova, E.; Bartusek, K.

    2012-01-01

    The paper deals with modern methods of image processing, especially image segmentation, classification and evaluation of parameters. It focuses primarily on processing medical images of soft tissues obtained by magnetic resonance tomography (MR). It is easy to describe edges of the sought objects using segmented images. The edges found can be useful for further processing of monitored object such as calculating the perimeter, surface and volume evaluation or even three-dimensional shape reconstruction. The proposed solutions can be used for the classification of healthy/unhealthy tissues in MR or other imaging. Application examples of the proposed segmentation methods are shown. Research in the area of image segmentation focuses on methods based on solving partial differential equations. This is a modern method for image processing, often called the active contour method. It is of great advantage in the segmentation of real images degraded by noise with fuzzy edges and transitions between objects. In the paper, results of the segmentation of medical images by the active contour method are compared with results of the segmentation by other existing methods. Experimental applications which demonstrate the very good properties of the active contour method are given.

  8. Memory based active contour algorithm using pixel-level classified images for colon crypt segmentation.

    PubMed

    Cohen, Assaf; Rivlin, Ehud; Shimshoni, Ilan; Sabo, Edmond

    2015-07-01

    In this paper, we introduce a novel method for detection and segmentation of crypts in colon biopsies. Most of the approaches proposed in the literature try to segment the crypts using only the biopsy image without understanding the meaning of each pixel. The proposed method differs in that we segment the crypts using an automatically generated pixel-level classification image of the original biopsy image and handle the artifacts due to the sectioning process and variance in color, shape and size of the crypts. The biopsy image pixels are classified to nuclei, immune system, lumen, cytoplasm, stroma and goblet cells. The crypts are then segmented using a novel active contour approach, where the external force is determined by the semantics of each pixel and the model of the crypt. The active contour is applied for every lumen candidate detected using the pixel-level classification. Finally, a false positive crypt elimination process is performed to remove segmentation errors. This is done by measuring their adherence to the crypt model using the pixel level classification results. The method was tested on 54 biopsy images containing 4944 healthy and 2236 cancerous crypts, resulting in 87% detection of the crypts with 9% of false positive segments (segments that do not represent a crypt). The segmentation accuracy of the true positive segments is 96%.

  9. Liver segmentation with new supervised method to create initial curve for active contour.

    PubMed

    Zareei, Abouzar; Karimi, Abbas

    2016-08-01

    The liver performs a critical task in the human body; therefore, detecting liver diseases and preparing a robust plan for treating them are both crucial. Liver diseases kill nearly 25,000 Americans every year. A variety of image segmentation methods are available to determine the liver's position and to detect possible liver tumors. Among these is the Active Contour Model (ACM), a framework which has proven very sensitive to initial contour delineation and control parameters. In the proposed method based on image energy, we attempted to obtain an initial segmentation close to the liver's boundary, and then implemented an ACM to improve the initial segmentation. The ACM used in this work incorporates gradient vector flow (GVF) and balloon energy in order to overcome ACM limitations, such as local minima entrapment and initial contour dependency. Additionally, in order to adjust active contour control parameters, we applied a genetic algorithm to produce a proper parameter set close to the optimal solution. The pre-processing method has a better ability to segment the liver tissue during a short time with respect to other mentioned methods in this paper. The proposed method was performed using Sliver CT image datasets. The results show high accuracy, precision, sensitivity, specificity and low overlap error, MSD and runtime with few ACM iterations.

  10. Active contour-based cell segmentation during freezing and its application in cryopreservation.

    PubMed

    Wu, Pengxiang; Yi, Jingru; Zhao, Gang; Huang, Zhangjin; Qiu, Bensheng; Gao, Dayong

    2015-01-01

    Water permeability of the plasma membrane plays an important role in making optimal cryopreservation protocols for different types of cells. To quantify water permeability effectively, automated cell volume segmentation during freezing is necessary. Unfortunately, there exists so far no efficient and accurate segmentation method to handle this kind of image processing task gracefully. The existence of extracellular ice and variable background present significant challenges for most traditional segmentation algorithms. In this paper, we propose a novel approach to reliably extract cells from the extracellular ice, which attaches to or surrounds cells. Our method operates on temporal image sequences and is composed of two steps. First, for each image from the sequence, a greedy search strategy is employed to track approximate locations of cells in motion. Second, we utilize a localized competitive active contour model to obtain the contour of each cell. Based on the first step's result, the initial contour for level set evolution can be determined appropriately, thus considerably easing the pain of initialization for an active contour model. Experimental results demonstrate that the proposed method is efficient and effective in segmenting cells during freezing.

  11. Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow

    PubMed Central

    Michailovich, Oleg; Rathi, Yogesh; Tannenbaum, Allen

    2013-01-01

    This paper addresses the problem of image segmentation by means of active contours, whose evolution is driven by the gradient flow derived from an energy functional that is based on the Bhattacharyya distance. In particular, given the values of a photometric variable (or of a set thereof), which is to be used for classifying the image pixels, the active contours are designed to converge to the shape that results in maximal discrepancy between the empirical distributions of the photometric variable inside and outside of the contours. The above discrepancy is measured by means of the Bhattacharyya distance that proves to be an extremely useful tool for solving the problem at hand. The proposed methodology can be viewed as a generalization of the segmentation methods, in which active contours maximize the difference between a finite number of empirical moments of the “inside” and “outside” distributions. Furthermore, it is shown that the proposed methodology is very versatile and flexible in the sense that it allows one to easily accommodate a diversity of the image features based on which the segmentation should be performed. As an additional contribution, a method for automatically adjusting the smoothness properties of the empirical distributions is proposed. Such a procedure is crucial in situations when the number of data samples (supporting a certain segmentation class) varies considerably in the course of the evolution of the active contour. In this case, the smoothness properties of the empirical distributions have to be properly adjusted to avoid either over- or underestimation artifacts. Finally, a number of relevant segmentation results are demonstrated and some further research directions are discussed. PMID:17990755

  12. 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

  13. A partition-based active contour model incorporating local information for image segmentation.

    PubMed

    Shi, Jiao; Wu, Jiaji; Paul, Anand; Jiao, Licheng; Gong, Maoguo

    2014-01-01

    Active contour models are always designed on the assumption that images are approximated by regions with piecewise-constant intensities. This assumption, however, cannot be satisfied when describing intensity inhomogeneous images which frequently occur in real world images and induced considerable difficulties in image segmentation. A milder assumption that the image is statistically homogeneous within different local regions may better suit real world images. By taking local image information into consideration, an enhanced active contour model is proposed to overcome difficulties caused by intensity inhomogeneity. In addition, according to curve evolution theory, only the region near contour boundaries is supposed to be evolved in each iteration. We try to detect the regions near contour boundaries adaptively for satisfying the requirement of curve evolution theory. In the proposed method, pixels within a selected region near contour boundaries have the opportunity to be updated in each iteration, which enables the contour to be evolved gradually. Experimental results on synthetic and real world images demonstrate the advantages of the proposed model when dealing with intensity inhomogeneity images.

  14. Color diffusion model for active contours - an application to skin lesion segmentation.

    PubMed

    Ivanovici, Mihai; Stoica, Diana

    2012-01-01

    Most of the existing diffusion models are defined for gray-scale images. We propose a diffusion model for color images to be used as external energy for active contours. Our diffusion model is based on the first-order moment of the correlation integral expressed using ΔE distances in the CIE Lab color space. We use a multi-scale approach for active contours, the diffusion being independently computed at various scales. We validate the model on synthetic images, including multi-fractal color textures, as well as medical images representing melanoma. We conclude that the proposed diffusion model is valid for use in skin lesion segmentation in color images using active contours.

  15. Contour Completion Without Region Segmentation.

    PubMed

    Ming, Yansheng; Li, Hongdong; He, Xuming

    2016-08-01

    Contour completion plays an important role in visual perception, where the goal is to group fragmented low-level edge elements into perceptually coherent and salient contours. Most existing methods for contour completion have focused on pixelwise detection accuracy. In contrast, fewer methods have addressed the global contour closure effect, despite psychological evidences for its importance. This paper proposes a purely contour-based higher order CRF model to achieve contour closure, through local connectedness approximation. This leads to a simplified problem structure, where our higher order inference problem can be transformed into an integer linear program and be solved efficiently. Compared with the methods based on the same bottom-up edge detector, our method achieves a superior contour grouping ability (measured by Rand index), a comparable precision-recall performance, and more visually pleasing results. Our results suggest that contour closure can be effectively achieved in contour domain, in contrast to a popular view that segmentation is essential for this purpose.

  16. A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours

    PubMed Central

    Gao, Yi; Kikinis, Ron; Bouix, Sylvain; Shenton, Martha; Tannenbaum, Allen

    2012-01-01

    Extracting anatomical and functional significant structures renders one of the important tasks for both the theoretical study of the medical image analysis, and the clinical and practical community. In the past, much work has been dedicated only to the algorithmic development. Nevertheless, for clinical end users, a well designed algorithm with an interactive software is necessary for an algorithm to be utilized in their daily work. Furthermore, the software would better be open sourced in order to be used and validated by not only the authors but also the entire community. Therefore, the contribution of the present work is twofolds: First, we propose a new robust statistics based conformal metric and the conformal area driven multiple active contour framework, to simultaneously extract multiple targets from MR and CT medical imagery in 3D. Second, an open source graphically interactive 3D segmentation tool based on the aforementioned contour evolution is implemented and is publicly available for end users on multiple platforms. In using this software for the segmentation task, the process is initiated by the user drawn strokes (seeds) in the target region in the image. Then, the local robust statistics are used to describe the object features, and such features are learned adaptively from the seeds under a non-parametric estimation scheme. Subsequently, several active contours evolve simultaneously with their interactions being motivated by the principles of action and reaction — This not only guarantees mutual exclusiveness among the contours, but also no longer relies upon the assumption that the multiple objects fill the entire image domain, which was tacitly or explicitly assumed in many previous works. In doing so, the contours interact and converge to equilibrium at the desired positions of the desired multiple objects. Furthermore, with the aim of not only validating the algorithm and the software, but also demonstrating how the tool is to be used, we

  17. Active contour segmentation using level set function with enhanced image from prior intensity.

    PubMed

    Kim, Sunhee; Kim, Youngjun; Lee, Deukhee; Park, Sehyung

    2015-01-01

    This paper presents a new active contour segmentation model using a level set function that can correctly capture both the strong and the weak boundaries of a target enclosed by bright and dark regions at the same time. We introduce an enhanced image obtained from prior information about the intensity of the target. The enhanced image emphasizes the regions where pixels have intensities close to the prior intensity. This enables a desirable segmentation of an image having a partially low contrast with the target surrounded by regions that are brighter or darker than the target. We define an edge indicator function on an original image, and local and regularization forces on an enhanced image. An edge indicator function and two forces are incorporated in order to identify the strong and weak boundaries, respectively. We established an evolution equation of contours in the level set formulation and experimented with several medical images to show the performance of the proposed method.

  18. Lung nodule segmentation and recognition using SVM classifier and active contour modeling: a complete intelligent system.

    PubMed

    Keshani, Mohsen; Azimifar, Zohreh; Tajeripour, Farshad; Boostani, Reza

    2013-05-01

    In this paper, a novel method for lung nodule detection, segmentation and recognition using computed tomography (CT) images is presented. Our contribution consists of several steps. First, the lung area is segmented by active contour modeling followed by some masking techniques to transfer non-isolated nodules into isolated ones. Then, nodules are detected by the support vector machine (SVM) classifier using efficient 2D stochastic and 3D anatomical features. Contours of detected nodules are then extracted by active contour modeling. In this step all solid and cavitary nodules are accurately segmented. Finally, lung tissues are classified into four classes: namely lung wall, parenchyma, bronchioles and nodules. This classification helps us to distinguish a nodule connected to the lung wall and/or bronchioles (attached nodule) from the one covered by parenchyma (solitary nodule). At the end, performance of our proposed method is examined and compared with other efficient methods through experiments using clinical CT images and two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. Solid, non-solid and cavitary nodules are detected with an overall detection rate of 89%; the number of false positive is 7.3/scan and the location of all detected nodules are recognized correctly.

  19. Fast Cell Segmentation Using Scalable Sparse Manifold Learning and Affine Transform-approximated Active Contour.

    PubMed

    Xing, Fuyong; Yang, Lin

    2015-10-01

    Efficient and effective cell segmentation of neuroendocrine tumor (NET) in whole slide scanned images is a difficult task due to a large number of cells. The weak or misleading cell boundaries also present significant challenges. In this paper, we propose a fast, high throughput cell segmentation algorithm by combining top-down shape models and bottom-up image appearance information. A scalable sparse manifold learning method is proposed to model multiple subpopulations of different cell shape priors. Followed by a shape clustering on the manifold, a novel affine transform-approximated active contour model is derived to deform contours without solving a large amount of computationally-expensive Euler-Lagrange equations, and thus dramatically reduces the computational time. To the best of our knowledge, this is the first report of a high throughput cell segmentation algorithm for whole slide scanned pathology specimens using manifold learning to accelerate active contour models. The proposed approach is tested using 12 NET images, and the comparative experiments with the state of the arts demonstrate its superior performance in terms of both efficiency and effectiveness.

  20. A robust region-based active contour model with point classification for ultrasound breast lesion segmentation

    NASA Astrophysics Data System (ADS)

    Liu, Zhihua; Zhang, Lidan; Ren, Haibing; Kim, Ji-Yeun

    2013-02-01

    Lesion segmentation is one of the key technologies for computer-aided diagnosis (CAD) system. In this paper, we propose a robust region-based active contour model (ACM) with point classification to segment high-variant breast lesion in ultrasound images. First, a local signed pressure force (LSPF) function is proposed to classify the contour points into two classes: local low contrast class and local high contrast class. Secondly, we build a sub-model for each class. For low contrast class, the sub-model is built by combining global energy with local energy model to find a global optimal solution. For high contrast class, the sub-model is just the local energy model for its good level set initialization. Our final energy model is built by adding the two sub-models. Finally, the model is minimized and evolves the level set contour to get the segmentation result. We compare our method with other state-of-art methods on a very large ultrasound database and the result shows that our method can achieve better performance.

  1. 3D Brain Segmentation Using Dual-Front Active Contours with Optional User Interaction

    PubMed Central

    Yezzi, Anthony; Cohen, Laurent D.

    2006-01-01

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

  2. An active contour framework based on the Hermite transform for shape segmentation of cardiac MR images

    NASA Astrophysics Data System (ADS)

    Barba-J, Leiner; Escalante-Ramírez, Boris

    2016-04-01

    Early detection of cardiac affections is fundamental to address a correct treatment that allows preserving the patient's life. Since heart disease is one of the main causes of death in most countries, analysis of cardiac images is of great value for cardiac assessment. Cardiac MR has become essential for heart evaluation. In this work we present a segmentation framework for shape analysis in cardiac magnetic resonance (MR) images. The method consists of an active contour model which is guided by the spectral coefficients obtained from the Hermite transform (HT) of the data. The HT is used as model to code image features of the analyzed images. Region and boundary based energies are coded using the zero and first order coefficients. An additional shape constraint based on an elliptical function is used for controlling the active contour deformations. The proposed framework is applied to the segmentation of the endocardial and epicardial boundaries of the left ventricle using MR images with short axis view. The segmentation is sequential for both regions: the endocardium is segmented followed by the epicardium. The algorithm is evaluated with several MR images at different phases of the cardiac cycle demonstrating the effectiveness of the proposed method. Several metrics are used for performance evaluation.

  3. Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field

    PubMed Central

    Tan, Yongqiang; Schwartz, Lawrence H.; Zhao, Binsheng

    2013-01-01

    Purpose: Lung lesions vary considerably in size, density, and shape, and can attach to surrounding anatomic structures such as chest wall or mediastinum. Automatic segmentation of the lesions poses a challenge. This work communicates a new three-dimensional algorithm for the segmentation of a wide variety of lesions, ranging from tumors found in patients with advanced lung cancer to small nodules detected in lung cancer screening programs. Methods: The authors’ algorithm uniquely combines the image processing techniques of marker-controlled watershed, geometric active contours as well as Markov random field (MRF). The user of the algorithm manually selects a region of interest encompassing the lesion on a single slice and then the watershed method generates an initial surface of the lesion in three dimensions, which is refined by the active geometric contours. MRF improves the segmentation of ground glass opacity portions of part-solid lesions. The algorithm was tested on an anthropomorphic thorax phantom dataset and two publicly accessible clinical lung datasets. These clinical studies included a same-day repeat CT (prewalk and postwalk scans were performed within 15 min) dataset containing 32 lung lesions with one radiologist's delineated contours, and the first release of the Lung Image Database Consortium (LIDC) dataset containing 23 lung nodules with 6 radiologists’ delineated contours. The phantom dataset contained 22 phantom nodules of known volumes that were inserted in a phantom thorax. Results: For the prewalk scans of the same-day repeat CT dataset and the LIDC dataset, the mean overlap ratios of lesion volumes generated by the computer algorithm and the radiologist(s) were 69% and 65%, respectively. For the two repeat CT scans, the intra-class correlation coefficient (ICC) was 0.998, indicating high reliability of the algorithm. The mean relative difference was −3% for the phantom dataset. Conclusions: The performance of this new segmentation

  4. Locally constrained active contour: a region-based level set for ovarian cancer metastasis segmentation

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

    Accurate segmentation of ovarian cancer metastases is clinically useful to evaluate tumor growth and determine follow-up treatment. We present a region-based level set algorithm with localization constraints to segment ovarian cancer metastases. Our approach is established on a representative region-based level set, Chan-Vese model, in which an active contour is driven by region competition. To reduce over-segmentation, we constrain the level set propagation within a narrow image band by embedding a dynamic localization function. The metastasis intensity prior is also estimated from image regions within the level set initialization. The localization function and intensity prior force the level set to stop at the desired metastasis boundaries. Our approach was validated on 19 ovarian cancer metastases with radiologist-labeled ground-truth on contrast-enhanced CT scans from 15 patients. The comparison between our algorithm and geodesic active contour indicated that the volume overlap was 75+/-10% vs. 56+/-6%, the Dice coefficient was 83+/-8% vs. 63+/-8%, and the average surface distance was 2.2+/-0.6mm vs. 4.4+/-0.9mm. Experimental results demonstrated that our algorithm outperformed traditional level set algorithms.

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

    NASA Astrophysics Data System (ADS)

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

    2012-03-01

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

  6. Brachial artery vasomotion and transducer pressure effect on measurements by active contour segmentation on ultrasound

    SciTech Connect

    Cary, Theodore W.; Sultan, Laith R.; Sehgal, Chandra M.; Reamer, Courtney B.; Mohler, Emile R.

    2014-02-15

    Purpose: To use feed-forward active contours (snakes) to track and measure brachial artery vasomotion on ultrasound images recorded in both transverse and longitudinal views; and to compare the algorithm's performance in each view. Methods: Longitudinal and transverse view ultrasound image sequences of 45 brachial arteries were segmented by feed-forward active contour (FFAC). The segmented regions were used to measure vasomotion artery diameter, cross-sectional area, and distention both as peak-to-peak diameter and as area. ECG waveforms were also simultaneously extracted frame-by-frame by thresholding a running finite-difference image between consecutive images. The arterial and ECG waveforms were compared as they traced each phase of the cardiac cycle. Results: FFAC successfully segmented arteries in longitudinal and transverse views in all 45 cases. The automated analysis took significantly less time than manual tracing, but produced superior, well-behaved arterial waveforms. Automated arterial measurements also had lower interobserver variability as measured by correlation, difference in mean values, and coefficient of variation. Although FFAC successfully segmented both the longitudinal and transverse images, transverse measurements were less variable. The cross-sectional area computed from the longitudinal images was 27% lower than the area measured from transverse images, possibly due to the compression of the artery along the image depth by transducer pressure. Conclusions: FFAC is a robust and sensitive vasomotion segmentation algorithm in both transverse and longitudinal views. Transverse imaging may offer advantages over longitudinal imaging: transverse measurements are more consistent, possibly because the method is less sensitive to variations in transducer pressure during imaging.

  7. Segmenting multiple overlapping objects via a hybrid active contour model incorporating shape priors: applications to digital pathology

    NASA Astrophysics Data System (ADS)

    Ali, Sahirzeeshan; Madabhushi, Anant

    2011-03-01

    Active contours and active shape models (ASM) have been widely employed in image segmentation. A major limitation of active contours, however, is in their (a) inability to resolve boundaries of intersecting objects and to (b) handle occlusion. Multiple overlapping objects are typically segmented out as a single object. On the other hand, ASMs are limited by point correspondence issues since object landmarks need to be identified across multiple objects for initial object alignment. ASMs are also are constrained in that they can usually only segment a single object in an image. In this paper, we present a novel synergistic boundary and region-based active contour model that incorporates shape priors in a level set formulation. We demonstrate an application of these synergistic active contour models using multiple level sets to segment nuclear and glandular structures on digitized histopathology images of breast and prostate biopsy specimens. Unlike previous related approaches, our model is able to resolve object overlap and separate occluded boundaries of multiple objects simultaneously. The energy functional of the active contour is comprised of three terms. The first term comprises the prior shape term, modeled on the object of interest, thereby constraining the deformation achievable by the active contour. The second term, a boundary based term detects object boundaries from image gradients. The third term drives the shape prior and the contour towards the object boundary based on region statistics. The results of qualitative and quantitative evaluation on 100 prostate and 14 breast cancer histology images for the task of detecting and segmenting nuclei, lymphocytes, and glands reveals that the model easily outperforms two state of the art segmentation schemes (Geodesic Active Contour (GAC) and Roussons shape based model) and resolves up to 92% of overlapping/occluded lymphocytes and nuclei on prostate and breast cancer histology images.

  8. Contour Completion without Region Segmentation.

    PubMed

    Ming, Yansheng; Li, Hongdong; He, Xuming

    2016-05-06

    Contour completion plays an important role in visual perception, where the goal is to group fragmented low-level edge elements into perceptually coherent and salient contours. Most existing methods for contour completion have focused on pixelwise detection accuracy. In contrast, fewer methods have addressed the global contour closure effect, despite of psychological evidences for its importance. This paper proposes a purely contour-based higher-order CRF model to achieve contour closure, through local connectedness approximation. This leads to a simplified problem structure, where our higher-order inference problem can be transformed into an integer linear program (ILP) and be solved efficiently. Compared with methods based on the same bottom-up edge detector, our method achieves a superior contour grouping ability (measured by Rand index), a comparable precision-recall performance, and more visually pleasing results. Our results suggest that contour closure can be effectively achieved in contour domain, in contrast to a popular view that segmentation is essential for this purpose.

  9. SOM-based nonlinear least squares twin SVM via active contours for noisy image segmentation

    NASA Astrophysics Data System (ADS)

    Xie, Xiaomin; Wang, Tingting

    2017-02-01

    In this paper, a nonlinear least square twin support vector machine (NLSTSVM) with the integration of active contour model (ACM) is proposed for noisy image segmentation. Efforts have been made to seek the kernel-generated surfaces instead of hyper-planes for the pixels belonging to the foreground and background, respectively, using the kernel trick to enhance the performance. The concurrent self organizing maps (SOMs) are applied to approximate the intensity distributions in a supervised way, so as to establish the original training sets for the NLSTSVM. Further, the two sets are updated by adding the global region average intensities at each iteration. Moreover, a local variable regional term rather than edge stop function is adopted in the energy function to ameliorate the noise robustness. Experiment results demonstrate that our model holds the higher segmentation accuracy and more noise robustness.

  10. A novel active contour model for unsupervised low-key image segmentation

    NASA Astrophysics Data System (ADS)

    Mei, Jiangyuan; Si, Yulin; Karimi, Hamid; Gao, Huijun

    2013-06-01

    Unsupervised image segmentation is greatly useful in many vision-based applications. In this paper, we aim at the unsupervised low-key image segmentation. In low-key images, dark tone dominates the background, and gray level distribution of the foreground is heterogeneous. They widely exist in the areas of space exploration, machine vision, medical imaging, etc. In our algorithm, a novel active contour model with the probability density function of gamma distribution is proposed. The flexible gamma distribution gives a better description for both of the foreground and background in low-key images. Besides, an unsupervised curve initialization method is designed, which helps to accelerate the convergence speed of curve evolution. The experimental results demonstrate the effectiveness of the proposed algorithm through comparison with the CV model. Also, one real-world application based on our approach is described in this paper.

  11. Synthetic aperture radar image segmentation based on edge-region active contour model

    NASA Astrophysics Data System (ADS)

    Zhang, Xin; Wen, Xianbin; Xu, Haixia; Meng, Qingxia

    2016-07-01

    An energy functional is proposed based on an edge-region active contour model for synthetic aperture radar (SAR) image segmentation. The proposed energy functional not only has a desirable property to process inhomogeneous regions in SAR images, but also shows satisfactory convergence speed. Our proposed energy functional consists of two main energy terms: an edge-region term and a regularization term. The edge-region term is derived from a Gamma model and gradient term model, which can process the speckle noises and drive the motion of the curves toward desired locations. The regularization term is not only able to maintain a desired shape of the evolution curves but also has a strong smoothing curve effect and avoid the occurrence of small, isolated regions in the final segmentation. Finally, the gradient descent flow method is introduced for minimizing our energy functional. A desirable feature of the proposed method is that it is not sensitive to the contour initialization. Compared with other methods, experimental results show that the proposed approach has promising edge detection results on the synthetic and real SAR images.

  12. CT liver volumetry using geodesic active contour segmentation with a level-set algorithm

    NASA Astrophysics Data System (ADS)

    Suzuki, Kenji; Epstein, Mark L.; Kohlbrenner, Ryan; Obajuluwa, Ademola; Xu, Jianwu; Hori, Masatoshi; Baron, Richard

    2010-03-01

    Automatic liver segmentation on CT images is challenging because the liver often abuts other organs of a similar density. Our purpose was to develop an accurate automated liver segmentation scheme for measuring liver volumes. We developed an automated volumetry scheme for the liver in CT based on a 5 step schema. First, an anisotropic smoothing filter was applied to portal-venous phase CT images to remove noise while preserving the liver structure, followed by an edge enhancer to enhance the liver boundary. By using the boundary-enhanced image as a speed function, a fastmarching algorithm generated an initial surface that roughly estimated the liver shape. A geodesic-active-contour segmentation algorithm coupled with level-set contour-evolution refined the initial surface so as to more precisely fit the liver boundary. The liver volume was calculated based on the refined liver surface. Hepatic CT scans of eighteen prospective liver donors were obtained under a liver transplant protocol with a multi-detector CT system. Automated liver volumes obtained were compared with those manually traced by a radiologist, used as "gold standard." The mean liver volume obtained with our scheme was 1,520 cc, whereas the mean manual volume was 1,486 cc, with the mean absolute difference of 104 cc (7.0%). CT liver volumetrics based on an automated scheme agreed excellently with "goldstandard" manual volumetrics (intra-class correlation coefficient was 0.95) with no statistically significant difference (p(F<=f)=0.32), and required substantially less completion time. Our automated scheme provides an efficient and accurate way of measuring liver volumes.

  13. A validated active contour method driven by parabolic arc model for detection and segmentation of mitochondria.

    PubMed

    Tasel, Serdar F; Mumcuoglu, Erkan U; Hassanpour, Reza Z; Perkins, Guy

    2016-06-01

    Recent studies reveal that mitochondria take substantial responsibility in cellular functions that are closely related to aging diseases caused by degeneration of neurons. These studies emphasize that the membrane and crista morphology of a mitochondrion should receive attention in order to investigate the link between mitochondrial function and its physical structure. Electron microscope tomography (EMT) allows analysis of the inner structures of mitochondria by providing highly detailed visual data from large volumes. Computerized segmentation of mitochondria with minimum manual effort is essential to accelerate the study of mitochondrial structure/function relationships. In this work, we improved and extended our previous attempts to detect and segment mitochondria from transmission electron microcopy (TEM) images. A parabolic arc model was utilized to extract membrane structures. Then, curve energy based active contours were employed to obtain roughly outlined candidate mitochondrial regions. Finally, a validation process was applied to obtain the final segmentation data. 3D extension of the algorithm is also presented in this paper. Our method achieved an average F-score performance of 0.84. Average Dice Similarity Coefficient and boundary error were measured as 0.87 and 14nm respectively.

  14. Computerized Liver Volumetry on MRI by Using 3D Geodesic Active Contour Segmentation

    PubMed Central

    Huynh, Hieu Trung; Karademir, Ibrahim; Oto, Aytekin; Suzuki, Kenji

    2014-01-01

    OBJECTIVE Our purpose was to develop an accurate automated 3D liver segmentation scheme for measuring liver volumes on MRI. SUBJECTS AND METHODS Our scheme for MRI liver volumetry consisted of three main stages. First, the preprocessing stage was applied to T1-weighted MRI of the liver in the portal venous phase to reduce noise and produce the boundary-enhanced image. This boundary-enhanced image was used as a speed function for a 3D fast-marching algorithm to generate an initial surface that roughly approximated the shape of the liver. A 3D geodesic-active-contour segmentation algorithm refined the initial surface to precisely determine the liver boundaries. The liver volumes determined by our scheme were compared with those manually traced by a radiologist, used as the reference standard. RESULTS The two volumetric methods reached excellent agreement (intraclass correlation coefficient, 0.98) without statistical significance (p = 0.42). The average (± SD) accuracy was 99.4% ± 0.14%, and the average Dice overlap coefficient was 93.6% ± 1.7%. The mean processing time for our automated scheme was 1.03 ± 0.13 minutes, whereas that for manual volumetry was 24.0 ± 4.4 minutes (p < 0.001). CONCLUSION The MRI liver volumetry based on our automated scheme agreed excellently with reference-standard volumetry, and it required substantially less completion time. PMID:24370139

  15. Adaptive energy selective active contour with shape priors for nuclear segmentation and gleason grading of prostate cancer.

    PubMed

    Ali, Sahirzeeshan; Veltri, Robert; Epstein, Jonathan I; Christudass, Christhunesa; Madabhushi, Anant

    2011-01-01

    Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all 3 terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particularly relevant consideration when multiple objects have to be segmented on very large histopathological images. The AdACM was employed for the task of segmenting nuclei on 80 prostate cancer tissue microarray images. Morphological features extracted from these segmentations were found to able to discriminate different Gleason grade patterns with a classification accuracy of 84% via a Support Vector Machine classifier. On average the AdACM model provided 100% savings in computational times compared to a non-optimized hybrid AC model involving a shape prior.

  16. Segmenting breast cancerous regions in thermal images using fuzzy active contours.

    PubMed

    Ghayoumi Zadeh, Hossein; Haddadnia, Javad; Rahmani Seryasat, Omid; Mostafavi Isfahani, Sayed Mohammad

    2016-01-01

    Breast cancer is the main cause of death among young women in developing countries. The human body temperature carries critical medical information related to the overall body status. Abnormal rise in total and regional body temperature is a natural symptom in diagnosing many diseases. Thermal imaging (Thermography) utilizes infrared beams which are fast, non-invasive, and non-contact and the output created images by this technique are flexible and useful to monitor the temperature of the human body. In some clinical studies and biopsy tests, it is necessary for the clinician to know the extent of the cancerous area. In such cases, the thermal image is very useful. In the same line, to detect the cancerous tissue core, thermal imaging is beneficial. This paper presents a fully automated approach to detect the thermal edge and core of the cancerous area in thermography images. In order to evaluate the proposed method, 60 patients with an average age of 44/9 were chosen. These cases were suspected of breast tissue disease. These patients referred to Tehran Imam Khomeini Imaging Center. Clinical examinations such as ultrasound, biopsy, questionnaire, and eventually thermography were done precisely on these individuals. Finally, the proposed model is applied for segmenting the proved abnormal area in thermal images. The proposed model is based on a fuzzy active contour designed by fuzzy logic. The presented method can segment cancerous tissue areas from its borders in thermal images of the breast area. In order to evaluate the proposed algorithm, Hausdorff and mean distance between manual and automatic method were used. Estimation of distance was conducted to accurately separate the thermal core and edge. Hausdorff distance between the proposed and the manual method for thermal core and edge was 0.4719 ± 0.4389, 0.3171 ± 0.1056 mm respectively, and the average distance between the proposed and the manual method for core and thermal edge was 0.0845 ± 0.0619, 0.0710

  17. Segmenting breast cancerous regions in thermal images using fuzzy active contours

    PubMed Central

    Ghayoumi Zadeh, Hossein; Haddadnia, Javad; Rahmani Seryasat, Omid; Mostafavi Isfahani, Sayed Mohammad

    2016-01-01

    Breast cancer is the main cause of death among young women in developing countries. The human body temperature carries critical medical information related to the overall body status. Abnormal rise in total and regional body temperature is a natural symptom in diagnosing many diseases. Thermal imaging (Thermography) utilizes infrared beams which are fast, non-invasive, and non-contact and the output created images by this technique are flexible and useful to monitor the temperature of the human body. In some clinical studies and biopsy tests, it is necessary for the clinician to know the extent of the cancerous area. In such cases, the thermal image is very useful. In the same line, to detect the cancerous tissue core, thermal imaging is beneficial. This paper presents a fully automated approach to detect the thermal edge and core of the cancerous area in thermography images. In order to evaluate the proposed method, 60 patients with an average age of 44/9 were chosen. These cases were suspected of breast tissue disease. These patients referred to Tehran Imam Khomeini Imaging Center. Clinical examinations such as ultrasound, biopsy, questionnaire, and eventually thermography were done precisely on these individuals. Finally, the proposed model is applied for segmenting the proved abnormal area in thermal images. The proposed model is based on a fuzzy active contour designed by fuzzy logic. The presented method can segment cancerous tissue areas from its borders in thermal images of the breast area. In order to evaluate the proposed algorithm, Hausdorff and mean distance between manual and automatic method were used. Estimation of distance was conducted to accurately separate the thermal core and edge. Hausdorff distance between the proposed and the manual method for thermal core and edge was 0.4719 ± 0.4389, 0.3171 ± 0.1056 mm respectively, and the average distance between the proposed and the manual method for core and thermal edge was 0.0845 ± 0.0619, 0.0710

  18. Segmenting the thoracic, abdominal and pelvic musculature on CT scans combining atlas-based model and active contour model

    NASA Astrophysics Data System (ADS)

    Zhang, Weidong; Liu, Jiamin; Yao, Jianhua; Summers, Ronald M.

    2013-03-01

    Segmentation of the musculature is very important for accurate organ segmentation, analysis of body composition, and localization of tumors in the muscle. In research fields of computer assisted surgery and computer-aided diagnosis (CAD), muscle segmentation in CT images is a necessary pre-processing step. This task is particularly challenging due to the large variability in muscle structure and the overlap in intensity between muscle and internal organs. This problem has not been solved completely, especially for all of thoracic, abdominal and pelvic regions. We propose an automated system to segment the musculature on CT scans. The method combines an atlas-based model, an active contour model and prior segmentation of fat and bones. First, body contour, fat and bones are segmented using existing methods. Second, atlas-based models are pre-defined using anatomic knowledge at multiple key positions in the body to handle the large variability in muscle shape. Third, the atlas model is refined using active contour models (ACM) that are constrained using the pre-segmented bone and fat. Before refining using ACM, the initialized atlas model of next slice is updated using previous atlas. The muscle is segmented using threshold and smoothed in 3D volume space. Thoracic, abdominal and pelvic CT scans were used to evaluate our method, and five key position slices for each case were selected and manually labeled as the reference. Compared with the reference ground truth, the overlap ratio of true positives is 91.1%+/-3.5%, and that of false positives is 5.5%+/-4.2%.

  19. Detection of the intima and media layer thickness of ultrasound common carotid artery image using efficient active contour segmentation technique.

    PubMed

    Santhiyakumari, N; Rajendran, P; Madheswaran, M; Suresh, S

    2011-11-01

    An active contour segmentation technique for extracting the intima-media layer of the common carotid artery (CCA) ultrasound images employing semiautomatic region of interest identification and speckle reduction techniques is presented in this paper. An attempt has been made to test the ultrasound images of the carotid artery of different subjects with this contour segmentation based on improved dynamic programming method. It is found that the preprocessing of ultrasound images of the CCA with region identification and despeckleing followed by active contour segmentation algorithm can be successfully used in evaluating the intima-media thickness (IMT) of the normal and abnormal subjects. It is also estimated that the segmentation used in this paper results an intermethod error of 0.09 mm and a coefficient of variation of 18.9%, for the despeckled images. The magnitudes of the IMT values have been used to explore the rate of prediction of blockage existing in the cerebrovascular and cardiovascular pathologies and also hypertension and atherosclerosis.

  20. Segmentation of Uterus Using Laparoscopic Ultrasound by an Image-Based Active Contour Approach for Guiding Gynecological Diagnosis and Surgery.

    PubMed

    Gong, Xue-Hao; Lu, Jun; Liu, Jin; Deng, Ying-Yuan; Liu, Wei-Zong; Huang, Xian; Yang, Yong-Heng; Xu, Qin; Yu, Zhi-Ying

    2015-01-01

    In laparoscopic gynecologic surgery, ultrasound has been typically implemented to diagnose urological and gynecological conditions. We applied laparoscopic ultrasonography (using Esaote 7.5~10MHz laparoscopic transducer) on the retrospective analyses of 42 women subjects during laparoscopic extirpation and excision of gynecological tumors in our hospital from August 2011 to August 2013. The objective of our research is to develop robust segmentation technique for isolation and identification of the uterus from the ultrasound images, so as to assess, locate and guide in removing the lesions during laparoscopic operations. Our method enables segmentation of the uterus by the active contour algorithm. We evaluated 42 in-vivo laparoscopic images acquired from the 42 patients (age 39.1 ± 7.2 years old) and selected images pertaining to 4 cases of congenital uterine malformations and 2 cases of pelvic adhesions masses. These cases (n = 6) were used for our uterus segmentation experiments. Based on them, the active contour method was compared with the manual segmentation method by a medical expert using linear regression and the Bland-Altman analysis (used to measure the correlation and the agreement). Then, the Dice and Jaccard indices are computed for measuring the similarity of uterus segmented between computational and manual methods. Good correlation was achieved whereby 84%-92% results fall within the 95% confidence interval in the Student t-test) and we demonstrate that the proposed segmentation method of uterus using laparoscopic images is effective.

  1. An Active Contour Model for the Segmentation of Images with Intensity Inhomogeneities and Bias Field Estimation

    PubMed Central

    Huang, Chencheng; Zeng, Li

    2015-01-01

    Intensity inhomogeneity causes many difficulties in image segmentation and the understanding of magnetic resonance (MR) images. Bias correction is an important method for addressing the intensity inhomogeneity of MR images before quantitative analysis. In this paper, a modified model is developed for segmenting images with intensity inhomogeneity and estimating the bias field simultaneously. In the modified model, a clustering criterion energy function is defined by considering the difference between the measured image and estimated image in local region. By using this difference in local region, the modified method can obtain accurate segmentation results and an accurate estimation of the bias field. The energy function is incorporated into a level set formulation with a level set regularization term, and the energy minimization is conducted by a level set evolution process. The proposed model first appeared as a two-phase model and then extended to a multi-phase one. The experimental results demonstrate the advantages of our model in terms of accuracy and insensitivity to the location of the initial contours. In particular, our method has been applied to various synthetic and real images with desirable results. PMID:25837416

  2. Contour detection and hierarchical image segmentation.

    PubMed

    Arbeláez, Pablo; Maire, Michael; Fowlkes, Charless; Malik, Jitendra

    2011-05-01

    This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms. The automatically generated hierarchical segmentations can be interactively refined by user-specified annotations. Computation at multiple image resolutions provides a means of coupling our system to recognition applications.

  3. Segmentation of follicular regions on H&E slides using a matching filter and active contour model

    NASA Astrophysics Data System (ADS)

    Belkacem-Boussaid, Kamel; Prescott, Jeffrey; Lozanski, Gerard; Gurcan, Metin N.

    2010-03-01

    Follicular Lymphoma (FL) accounts for 20-25% of non-Hodgkin lymphomas in the United States. The first step in follicular lymphoma grading is the identification of follicles. The goal of this paper is to develop a technique to segment follicular regions in H&E stained images. The method is based on a robust active contour model, which is initialized by a seed point selected inside the follicle manually by the user. The novel aspect of this method is the introduction of a matched filter for the flattening of background in the L channel of the Lab color space. The performance of the algorithm was tested by comparing it against the manual segmentations of trained readers using the Zijbendos similarity index. The mean accuracy of the final segmentation compared to the manual ground truth was 0.71 with a standard deviation of 0.12.

  4. A new background distribution-based active contour model for three-dimensional lesion segmentation in breast DCE-MRI

    SciTech Connect

    Liu, Hui; Liu, Yiping; Qiu, Tianshuang; Zhao, Zuowei; Zhang, Lina

    2014-08-15

    Purpose: To develop and evaluate a computerized semiautomatic segmentation method for accurate extraction of three-dimensional lesions from dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) of the breast. Methods: The authors propose a new background distribution-based active contour model using level set (BDACMLS) to segment lesions in breast DCE-MRIs. The method starts with manual selection of a region of interest (ROI) that contains the entire lesion in a single slice where the lesion is enhanced. Then the lesion volume from the volume data of interest, which is captured automatically, is separated. The core idea of BDACMLS is a new signed pressure function which is based solely on the intensity distribution combined with pathophysiological basis. To compare the algorithm results, two experienced radiologists delineated all lesions jointly to obtain the ground truth. In addition, results generated by other different methods based on level set (LS) are also compared with the authors’ method. Finally, the performance of the proposed method is evaluated by several region-based metrics such as the overlap ratio. Results: Forty-two studies with 46 lesions that contain 29 benign and 17 malignant lesions are evaluated. The dataset includes various typical pathologies of the breast such as invasive ductal carcinoma, ductal carcinomain situ, scar carcinoma, phyllodes tumor, breast cysts, fibroadenoma, etc. The overlap ratio for BDACMLS with respect to manual segmentation is 79.55% ± 12.60% (mean ± s.d.). Conclusions: A new active contour model method has been developed and shown to successfully segment breast DCE-MRI three-dimensional lesions. The results from this model correspond more closely to manual segmentation, solve the weak-edge-passed problem, and improve the robustness in segmenting different lesions.

  5. Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images.

    PubMed

    Rebouças Filho, Pedro Pedrosa; Cortez, Paulo César; da Silva Barros, Antônio C; C Albuquerque, Victor Hugo; R S Tavares, João Manuel

    2017-01-01

    The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level-set algorithm based on coherent propagation and the semi-automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F-measure of 99.22%, revealing its superiority and competency to segment lungs in CT images.

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

    NASA Astrophysics Data System (ADS)

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

    2015-07-01

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

  7. Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.

    PubMed

    Way, Ted W; Hadjiiski, Lubomir M; Sahiner, Berkman; Chan, Heang-Ping; Cascade, Philip N; Kazerooni, Ella A; Bogot, Naama; Zhou, Chuan

    2006-07-01

    We are developing a computer-aided diagnosis (CAD) system to classify malignant and benign lung nodules found on CT scans. A fully automated system was designed to segment the nodule from its surrounding structured background in a local volume of interest (VOI) and to extract image features for classification. Image segmentation was performed with a three-dimensional (3D) active contour (AC) method. A data set of 96 lung nodules (44 malignant, 52 benign) from 58 patients was used in this study. The 3D AC model is based on two-dimensional AC with the addition of three new energy components to take advantage of 3D information: (1) 3D gradient, which guides the active contour to seek the object surface, (2) 3D curvature, which imposes a smoothness constraint in the z direction, and (3) mask energy, which penalizes contours that grow beyond the pleura or thoracic wall. The search for the best energy weights in the 3D AC model was guided by a simplex optimization method. Morphological and gray-level features were extracted from the segmented nodule. The rubber band straightening transform (RBST) was applied to the shell of voxels surrounding the nodule. Texture features based on run-length statistics were extracted from the RBST image. A linear discriminant analysis classifier with stepwise feature selection was designed using a second simplex optimization to select the most effective features. Leave-one-case-out resampling was used to train and test the CAD system. The system achieved a test area under the receiver operating characteristic curve (A(z)) of 0.83 +/- 0.04. Our preliminary results indicate that use of the 3D AC model and the 3D texture features surrounding the nodule is a promising approach to the segmentation and classification of lung nodules with CAD. The segmentation performance of the 3D AC model trained with our data set was evaluated with 23 nodules available in the Lung Image Database Consortium (LIDC). The lung nodule volumes segmented by the 3D

  8. Computer-aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours

    PubMed Central

    Way, Ted W.; Hadjiiski, Lubomir M.; Sahiner, Berkman; Chan, Heang-Ping; Cascade, Philip N.; Kazerooni, Ella A.; Bogot, Naama; Zhou, Chuan

    2009-01-01

    We are developing a computer-aided diagnosis (CAD) system to classify malignant and benign lung nodules found on CT scans. A fully automated system was designed to segment the nodule from its surrounding structured background in a local volume of interest (VOI) and to extract image features for classification. Image segmentation was performed with a three-dimensional (3D) active contour (AC) method. A data set of 96 lung nodules (44 malignant, 52 benign) from 58 patients was used in this study. The 3D AC model is based on two-dimensional AC with the addition of three new energy components to take advantage of 3D information: (1) 3D gradient, which guides the active contour to seek the object surface, (2) 3D curvature, which imposes a smoothness constraint in the z direction, and (3) mask energy, which penalizes contours that grow beyond the pleura or thoracic wall. The search for the best energy weights in the 3D AC model was guided by a simplex optimization method. Morphological and gray-level features were extracted from the segmented nodule. The rubber band straightening transform (RBST) was applied to the shell of voxels surrounding the nodule. Texture features based on run-length statistics were extracted from the RBST image. A linear discriminant analysis classifier with stepwise feature selection was designed using a second simplex optimization to select the most effective features. Leave-one-case-out resampling was used to train and test the CAD system. The system achieved a test area under the receiver operating characteristic curve (Az) of 0.83±0.04. Our preliminary results indicate that use of the 3D AC model and the 3D texture features surrounding the nodule is a promising approach to the segmentation and classification of lung nodules with CAD. The segmentation performance of the 3D AC model trained with our data set was evaluated with 23 nodules available in the Lung Image Database Consortium (LIDC). The lung nodule volumes segmented by the 3D AC

  9. Segmentation of solid subregion of high grade gliomas in MRI images based on active contour model (ACM)

    NASA Astrophysics Data System (ADS)

    Seow, P.; Win, M. T.; Wong, J. H. D.; Abdullah, N. A.; Ramli, N.

    2016-03-01

    Gliomas are tumours arising from the interstitial tissue of the brain which are heterogeneous, infiltrative and possess ill-defined borders. Tumour subregions (e.g. solid enhancing part, edema and necrosis) are often used for tumour characterisation. Tumour demarcation into substructures facilitates glioma staging and provides essential information. Manual segmentation had several drawbacks that include laborious, time consuming, subjected to intra and inter-rater variability and hindered by diversity in the appearance of tumour tissues. In this work, active contour model (ACM) was used to segment the solid enhancing subregion of the tumour. 2D brain image acquisition data using 3T MRI fast spoiled gradient echo sequence in post gadolinium of four histologically proven high-grade glioma patients were obtained. Preprocessing of the images which includes subtraction and skull stripping were performed and then followed by ACM segmentation. The results of the automatic segmentation method were compared against the manual delineation of the tumour by a trainee radiologist. Both results were further validated by an experienced neuroradiologist and a brief quantitative evaluations (pixel area and difference ratio) were performed. Preliminary results of the clinical data showed the potential of ACM model in the application of fast and large scale tumour segmentation in medical imaging.

  10. A Combined Random Forests and Active Contour Model Approach for Fully Automatic Segmentation of the Left Atrium in Volumetric MRI

    PubMed Central

    Luo, Gongning

    2017-01-01

    Segmentation of the left atrium (LA) from cardiac magnetic resonance imaging (MRI) datasets is of great importance for image guided atrial fibrillation ablation, LA fibrosis quantification, and cardiac biophysical modelling. However, automated LA segmentation from cardiac MRI is challenging due to limited image resolution, considerable variability in anatomical structures across subjects, and dynamic motion of the heart. In this work, we propose a combined random forests (RFs) and active contour model (ACM) approach for fully automatic segmentation of the LA from cardiac volumetric MRI. Specifically, we employ the RFs within an autocontext scheme to effectively integrate contextual and appearance information from multisource images together for LA shape inferring. The inferred shape is then incorporated into a volume-scalable ACM for further improving the segmentation accuracy. We validated the proposed method on the cardiac volumetric MRI datasets from the STACOM 2013 and HVSMR 2016 databases and showed that it outperforms other latest automated LA segmentation methods. Validation metrics, average Dice coefficient (DC) and average surface-to-surface distance (S2S), were computed as 0.9227 ± 0.0598 and 1.14 ± 1.205 mm, versus those of 0.6222–0.878 and 1.34–8.72 mm, obtained by other methods, respectively. PMID:28316992

  11. New region-scalable discriminant and fitting energy functional for driving geometric active contours in medical image segmentation.

    PubMed

    Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

    2014-01-01

    We propose a novel region-based geometric active contour model that uses region-scalable discriminant and fitting energy functional for handling the intensity inhomogeneity and weak boundary problems in medical image segmentation. The region-scalable discriminant and fitting energy functional is defined to capture the image intensity characteristics in local and global regions for driving the evolution of active contour. The discriminant term in the model aims at separating background and foreground in scalable regions while the fitting term tends to fit the intensity in these regions. This model is then transformed into a variational level set formulation with a level set regularization term for accurate computation. The new model utilizes intensity information in the local and global regions as much as possible; so it not only handles better intensity inhomogeneity, but also allows more robustness to noise and more flexible initialization in comparison to the original global region and regional-scalable based models. Experimental results for synthetic and real medical image segmentation show the advantages of the proposed method in terms of accuracy and robustness.

  12. Automated segmentation of the quadratus lumborum muscle from magnetic resonance images using a hybrid atlas based - geodesic active contour scheme.

    PubMed

    Jurcak, V; Fripp, J; Engstrom, C; Walker, D; Salvado, O; Ourselin, S; Crozier, S

    2008-01-01

    This study presents a novel method for the automatic segmentation of the quadratus lumborum (QL) muscle from axial magnetic resonance (MR) images using a hybrid scheme incorporating the use of non-rigid registration with probabilistic atlases (PAs) and geodesic active contours (GACs). The scheme was evaluated on an MR database of 7mm axial images of the lumbar spine from 20 subjects (fast bowlers and athletic controls). This scheme involved several steps, including (i) image pre-processing, (ii) generation of PAs for the QL, psoas (PS) and erector spinae+multifidus (ES+MT) muscles and (iii) segmentation, using 3D GACs initialized and constrained by the propagation of the PAs using non-rigid registration. Pre-processing of the images involved bias field correction based on local entropy minimization with a bicubic spline model and a reverse diffusion interpolation algorithm to increase the slice resolution to 0.98 x 0.98 x 1.75mm. The processed images were then registered (affine and non-rigid) and used to generate an average atlas. The PAs for the QL, PS and ES+MT were then generated by propagation of manual segmentations. These atlases were further analysed with specialised filtering to constrain the QL segmentation from adjacent non-muscle tissues (kidney, fat). This information was then used in 3D GACs to obtain the final segmentation of the QL. The automatic segmentation results were compared with the manual segmentations using the Dice similarity metric (DSC), with a median DSC for the right and left QL muscles of 0.78 (mean = 0.77, sd=0.07) and 0.75 (mean =0.74, sd=0.07), respectively.

  13. Computerized segmentation of liver in hepatic CT and MRI by means of level-set geodesic active contouring.

    PubMed

    Suzuki, Kenji; Huynh, Hieu Trung; Liu, Yipeng; Calabrese, Dominic; Zhou, Karen; Oto, Aytekin; Hori, Masatoshi

    2013-01-01

    Computerized liver volumetry has been studied, because the current "gold-standard" manual volumetry is subjective and very time-consuming. Liver volumetry is done in either CT or MRI. A number of researchers have developed computerized liver segmentation in CT, but there are fewer studies on ones for MRI. Our purpose in this study was to develop a general framework for liver segmentation in both CT and MRI. Our scheme consisted of 1) an anisotropic diffusion filter to reduce noise while preserving liver structures, 2) a scale-specific gradient magnitude filter to enhance liver boundaries, 3) a fast-marching algorithm to roughly determine liver boundaries, and 4) a geodesic-active-contour model coupled with a level-set algorithm to refine the initial boundaries. Our CT database contained hepatic CT scans of 18 liver donors obtained under a liver transplant protocol. Our MRI database contains 23 patients with 1.5T MRI scanners. To establish "gold-standard" liver volumes, radiologists manually traced the contour of the liver on each CT or MR slice. We compared our computer volumetry with "gold-standard" manual volumetry. Computer volumetry in CT and MRI reached excellent agreement with manual volumetry (intra-class correlation coefficient = 0.94 and 0.98, respectively). Average user time for computer volumetry in CT and MRI was 0.57 ± 0.06 and 1.0 ± 0.13 min. per case, respectively, whereas those for manual volumetry were 39.4 ± 5.5 and 24.0 ± 4.4 min. per case, respectively, with statistically significant difference (p < .05). Our computerized liver segmentation framework provides an efficient and accurate way of measuring liver volumes in both CT and MRI.

  14. Image Segmentation Using Parametric Contours With Free Endpoints.

    PubMed

    Benninghoff, Heike; Garcke, Harald

    2016-04-01

    In this paper, we introduce a novel approach for active contours with free endpoints. A scheme for image segmentation is presented based on a discrete version of the Mumford-Shah functional where the contours can be both closed and open curves. Additional to a flow of the curves in normal direction, evolution laws for the tangential flow of the endpoints are derived. Using a parametric approach to describe the evolving contours together with an edge-preserving denoising, we obtain a fast method for image segmentation and restoration. The analytical and numerical schemes are presented followed by numerical experiments with artificial test images and with a real medical image.

  15. Image Segmentation Using Parametric Contours With Free Endpoints

    NASA Astrophysics Data System (ADS)

    Benninghoff, Heike; Garcke, Harald

    2016-04-01

    In this paper, we introduce a novel approach for active contours with free endpoints. A scheme is presented for image segmentation and restoration based on a discrete version of the Mumford-Shah functional where the contours can be both closed and open curves. Additional to a flow of the curves in normal direction, evolution laws for the tangential flow of the endpoints are derived. Using a parametric approach to describe the evolving contours together with an edge-preserving denoising, we obtain a fast method for image segmentation and restoration. The analytical and numerical schemes are presented followed by numerical experiments with artificial test images and with a real medical image.

  16. Contour-Driven Atlas-Based Segmentation

    PubMed Central

    Wachinger, Christian; Fritscher, Karl; Sharp, Greg; Golland, Polina

    2016-01-01

    We propose new methods for automatic segmentation of images based on an atlas of manually labeled scans and contours in the image. First, we introduce a Bayesian framework for creating initial label maps from manually annotated training images. Within this framework, we model various registration- and patch-based segmentation techniques by changing the deformation field prior. Second, we perform contour-driven regression on the created label maps to refine the segmentation. Image contours and image parcellations give rise to non-stationary kernel functions that model the relationship between image locations. Setting the kernel to the covariance function in a Gaussian process establishes a distribution over label maps supported by image structures. Maximum a posteriori estimation of the distribution over label maps conditioned on the outcome of the atlas-based segmentation yields the refined segmentation. We evaluate the segmentation in two clinical applications: the segmentation of parotid glands in head and neck CT scans and the segmentation of the left atrium in cardiac MR angiography images. PMID:26068202

  17. pSnakes: a new radial active contour model and its application in the segmentation of the left ventricle from echocardiographic images.

    PubMed

    de Alexandria, Auzuir Ripardo; Cortez, Paulo César; Bessa, Jessyca Almeida; da Silva Félix, John Hebert; de Abreu, José Sebastião; de Albuquerque, Victor Hugo C

    2014-10-01

    Active contours are image segmentation methods that minimize the total energy of the contour to be segmented. Among the active contour methods, the radial methods have lower computational complexity and can be applied in real time. This work aims to present a new radial active contour technique, called pSnakes, using the 1D Hilbert transform as external energy. The pSnakes method is based on the fact that the beams in ultrasound equipment diverge from a single point of the probe, thus enabling the use of polar coordinates in the segmentation. The control points or nodes of the active contour are obtained in pairs and are called twin nodes. The internal energies as well as the external one, Hilbertian energy, are redefined. The results showed that pSnakes can be used in image segmentation of short-axis echocardiogram images and that they were effective in image segmentation of the left ventricle. The echo-cardiologist's golden standard showed that the pSnakes was the best method when compared with other methods. The main contributions of this work are the use of pSnakes and Hilbertian energy, as the external energy, in image segmentation. The Hilbertian energy is calculated by the 1D Hilbert transform. Compared with traditional methods, the pSnakes method is more suitable for ultrasound images because it is not affected by variations in image contrast, such as noise. The experimental results obtained by the left ventricle segmentation of echocardiographic images demonstrated the advantages of the proposed model. The results presented in this paper are justified due to an improved performance of the Hilbert energy in the presence of speckle noise.

  18. 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

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

    SciTech Connect

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

    2007-12-15

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

  20. Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging

    PubMed Central

    Agner, Shannon C.; Xu, Jun; Madabhushi, Anant

    2013-01-01

    Purpose: Segmentation of breast lesions on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is the first step in lesion diagnosis in a computer-aided diagnosis framework. Because manual segmentation of such lesions is both time consuming and highly susceptible to human error and issues of reproducibility, an automated lesion segmentation method is highly desirable. Traditional automated image segmentation methods such as boundary-based active contour (AC) models require a strong gradient at the lesion boundary. Even when region-based terms are introduced to an AC model, grayscale image intensities often do not allow for clear definition of foreground and background region statistics. Thus, there is a need to find alternative image representations that might provide (1) strong gradients at the margin of the object of interest (OOI); and (2) larger separation between intensity distributions and region statistics for the foreground and background, which are necessary to halt evolution of the AC model upon reaching the border of the OOI. Methods: In this paper, the authors introduce a spectral embedding (SE) based AC (SEAC) for lesion segmentation on breast DCE-MRI. SE, a nonlinear dimensionality reduction scheme, is applied to the DCE time series in a voxelwise fashion to reduce several time point images to a single parametric image where every voxel is characterized by the three dominant eigenvectors. This parametric eigenvector image (PrEIm) representation allows for better capture of image region statistics and stronger gradients for use with a hybrid AC model, which is driven by both boundary and region information. They compare SEAC to ACs that employ fuzzy c-means (FCM) and principal component analysis (PCA) as alternative image representations. Segmentation performance was evaluated by boundary and region metrics as well as comparing lesion classification using morphological features from SEAC, PCA+AC, and FCM+AC. Results: On a cohort of 50

  1. Segmentation of Opacified Thorax Vessels using Model-driven Active Contour.

    PubMed

    Sebbe, Raphael; Gosselin, Bernard; Coche, Emmanuel; Macq, Benoit

    2005-01-01

    We propose a novel method, guided slice marching to segment opacified vessels tree in 3D image sets (CT scans). It combines a front propagation technique, slice marching, and an anatomical model to guide the propagation for solving the particular case of touching vessels. The formulation of this method, which is based on interface evolution theory, enables easy integration of an a priori model of knowledge of vessels topology to handle the case of touching vessels, where image-based method systematically fails. The a priori knowledge is expressed as parametric curves that model vessels centerline. That information is injected in the fast marching method through the speed of propagation, setting it to zero at missing vessels boundaries. The model is intended to be reused across patients, and must therefore be registered with the image.

  2. Inner and outer coronary vessel wall segmentation from CCTA using an active contour model with machine learning-based 3D voxel context-aware image force

    NASA Astrophysics Data System (ADS)

    Sivalingam, Udhayaraj; Wels, Michael; Rempfler, Markus; Grosskopf, Stefan; Suehling, Michael; Menze, Bjoern H.

    2016-03-01

    In this paper, we present a fully automated approach to coronary vessel segmentation, which involves calcification or soft plaque delineation in addition to accurate lumen delineation, from 3D Cardiac Computed Tomography Angiography data. Adequately virtualizing the coronary lumen plays a crucial role for simulating blood ow by means of fluid dynamics while additionally identifying the outer vessel wall in the case of arteriosclerosis is a prerequisite for further plaque compartment analysis. Our method is a hybrid approach complementing Active Contour Model-based segmentation with an external image force that relies on a Random Forest Regression model generated off-line. The regression model provides a strong estimate of the distance to the true vessel surface for every surface candidate point taking into account 3D wavelet-encoded contextual image features, which are aligned with the current surface hypothesis. The associated external image force is integrated in the objective function of the active contour model, such that the overall segmentation approach benefits from the advantages associated with snakes and from the ones associated with machine learning-based regression alike. This yields an integrated approach achieving competitive results on a publicly available benchmark data collection (Rotterdam segmentation challenge).

  3. Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms

    SciTech Connect

    Suzuki, Kenji; Kohlbrenner, Ryan; Epstein, Mark L.; Obajuluwa, Ademola M.; Xu Jianwu; Hori, Masatoshi

    2010-05-15

    Purpose: Computerized liver extraction from hepatic CT images is challenging because the liver often abuts other organs of a similar density. The purpose of this study was to develop a computer-aided measurement of liver volumes in hepatic CT. Methods: The authors developed a computerized liver extraction scheme based on geodesic active contour segmentation coupled with level-set contour evolution. First, an anisotropic diffusion filter was applied to portal-venous-phase CT images for noise reduction while preserving the liver structure, followed by a scale-specific gradient magnitude filter to enhance the liver boundaries. Then, a nonlinear grayscale converter enhanced the contrast of the liver parenchyma. By using the liver-parenchyma-enhanced image as a speed function, a fast-marching level-set algorithm generated an initial contour that roughly estimated the liver shape. A geodesic active contour segmentation algorithm coupled with level-set contour evolution refined the initial contour to define the liver boundaries more precisely. The liver volume was then calculated using these refined boundaries. Hepatic CT scans of 15 prospective liver donors were obtained under a liver transplant protocol with a multidetector CT system. The liver volumes extracted by the computerized scheme were compared to those traced manually by a radiologist, used as ''gold standard.''Results: The mean liver volume obtained with our scheme was 1504 cc, whereas the mean gold standard manual volume was 1457 cc, resulting in a mean absolute difference of 105 cc (7.2%). The computer-estimated liver volumetrics agreed excellently with the gold-standard manual volumetrics (intraclass correlation coefficient was 0.95) with no statistically significant difference (F=0.77; p(F{<=}f)=0.32). The average accuracy, sensitivity, specificity, and percent volume error were 98.4%, 91.1%, 99.1%, and 7.2%, respectively. Computerized CT liver volumetry would require substantially less completion time

  4. Segmentation of the endocardial wall of the left atrium using local region-based active contours and statistical shape learning

    NASA Astrophysics Data System (ADS)

    Gao, Yi; Gholami, Behnood; MacLeod, Robert S.; Blauer, Joshua; Haddad, Wassim M.; Tannenbaum, Allen R.

    2010-03-01

    Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. One treatment, referred to as catheter ablation, targets specific parts of the left atrium for radio frequency ablation using an intracardiac catheter. Magnetic resonance imaging has been used for both pre- and and post-ablation assessment of the atrial wall. Magnetic resonance imaging can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this paper we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.

  5. Computer-assisted segmentation of videocapsule images using alpha-divergence-based active contour in the framework of intestinal pathologies detection.

    PubMed

    Meziou, L; Histace, A; Precioso, F; Romain, O; Dray, X; Granado, B; Matuszewski, B J

    2014-01-01

    Visualization of the entire length of the gastrointestinal tract through natural orifices is a challenge for endoscopists. Videoendoscopy is currently the "gold standard" technique for diagnosis of different pathologies of the intestinal tract. Wireless capsule endoscopy (WCE) has been developed in the 1990s as an alternative to videoendoscopy to allow direct examination of the gastrointestinal tract without any need for sedation. Nevertheless, the systematic postexamination by the specialist of the 50,000 (for the small bowel) to 150,000 images (for the colon) of a complete acquisition using WCE remains time-consuming and challenging due to the poor quality of WCE images. In this paper, a semiautomatic segmentation for analysis of WCE images is proposed. Based on active contour segmentation, the proposed method introduces alpha-divergences, a flexible statistical similarity measure that gives a real flexibility to different types of gastrointestinal pathologies. Results of segmentation using the proposed approach are shown on different types of real-case examinations, from (multi)polyp(s) segmentation, to radiation enteritis delineation.

  6. Decoupled active contour (DAC) for boundary detection.

    PubMed

    Mishra, Akshaya Kumar; Fieguth, Paul W; Clausi, David A

    2011-02-01

    The accurate detection of object boundaries via active contours is an ongoing research topic in computer vision. Most active contours converge toward some desired contour by minimizing a sum of internal (prior) and external (image measurement) energy terms. Such an approach is elegant, but suffers from a slow convergence rate and frequently misconverges in the presence of noise or complex contours. To address these limitations, a decoupled active contour (DAC) is developed which applies the two energy terms separately. Essentially, the DAC consists of a measurement update step, employing a Hidden Markov Model (HMM) and Viterbi search, and then a separate prior step, which modifies the updated curve based on the relative strengths of the measurement uncertainty and the nonstationary prior. By separating the measurement and prior steps, the algorithm is less likely to misconverge; furthermore, the use of a Viterbi optimizer allows the method to converge far more rapidly than energy-based iterative solvers. The results clearly demonstrate that the proposed approach is robust to noise, can capture regions of very high curvature, and exhibits limited dependence on contour initialization or parameter settings. Compared to five other published methods and across many image sets, the DAC is found to be faster with better or comparable segmentation accuracy.

  7. 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

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

    PubMed

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

    2017-04-01

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

  9. A Method for Extracting Suspected Parotid Lesions in CT Images using Feature-based Segmentation and Active Contours based on Stationary Wavelet Transform

    NASA Astrophysics Data System (ADS)

    Wu, T. Y.; Lin, S. F.

    2013-10-01

    Automatic suspected lesion extraction is an important application in computer-aided diagnosis (CAD). In this paper, we propose a method to automatically extract the suspected parotid regions for clinical evaluation in head and neck CT images. The suspected lesion tissues in low contrast tissue regions can be localized with feature-based segmentation (FBS) based on local texture features, and can be delineated with accuracy by modified active contour models (ACM). At first, stationary wavelet transform (SWT) is introduced. The derived wavelet coefficients are applied to derive the local features for FBS, and to generate enhanced energy maps for ACM computation. Geometric shape features (GSFs) are proposed to analyze each soft tissue region segmented by FBS; the regions with higher similarity GSFs with the lesions are extracted and the information is also applied as the initial conditions for fine delineation computation. Consequently, the suspected lesions can be automatically localized and accurately delineated for aiding clinical diagnosis. The performance of the proposed method is evaluated by comparing with the results outlined by clinical experts. The experiments on 20 pathological CT data sets show that the true-positive (TP) rate on recognizing parotid lesions is about 94%, and the dimension accuracy of delineation results can also approach over 93%.

  10. Combining prior day contours to improve automated prostate segmentation

    SciTech Connect

    Godley, Andrew; Sheplan Olsen, Lawrence J.; Stephans, Kevin; Zhao Anzi

    2013-02-15

    Purpose: To improve the accuracy of automatically segmented prostate, rectum, and bladder contours required for online adaptive therapy. The contouring accuracy on the current image guidance [image guided radiation therapy (IGRT)] scan is improved by combining contours from earlier IGRT scans via the simultaneous truth and performance level estimation (STAPLE) algorithm. Methods: Six IGRT prostate patients treated with daily kilo-voltage (kV) cone-beam CT (CBCT) had their original plan CT and nine CBCTs contoured by the same physician. Three types of automated contours were produced for analysis. (1) Plan: By deformably registering the plan CT to each CBCT and then using the resulting deformation field to morph the plan contours to match the CBCT anatomy. (2) Previous: The contour set drawn by the physician on the previous day CBCT is similarly deformed to match the current CBCT anatomy. (3) STAPLE: The contours drawn by the physician, on each prior CBCT and the plan CT, are deformed to match the CBCT anatomy to produce multiple contour sets. These sets are combined using the STAPLE algorithm into one optimal set. Results: Compared to plan and previous, STAPLE improved the average Dice's coefficient (DC) with the original physician drawn CBCT contours to a DC as follows: Bladder: 0.81 {+-} 0.13, 0.91 {+-} 0.06, and 0.92 {+-} 0.06; Prostate: 0.75 {+-} 0.08, 0.82 {+-} 0.05, and 0.84 {+-} 0.05; and Rectum: 0.79 {+-} 0.06, 0.81 {+-} 0.06, and 0.85 {+-} 0.04, respectively. The STAPLE results are within intraobserver consistency, determined by the physician blindly recontouring a subset of CBCTs. Comparing plans recalculated using the physician and STAPLE contours showed an average disagreement less than 1% for prostate D98 and mean dose, and 5% and 3% for bladder and rectum mean dose, respectively. One scan takes an average of 19 s to contour. Using five scans plus STAPLE takes less than 110 s on a 288 core graphics processor unit. Conclusions: Combining the plan and

  11. Contour tracking and probabilistic segmentation of tissue phase mapping MRI

    NASA Astrophysics Data System (ADS)

    Chitiboi, Teodora; Hennemuth, Anja; Schnell, Susanne; Chowdhary, Varun; Honarmand, Amir; Markl, Michael; Linsen, Lars; Hahn, Horst

    2016-03-01

    Many cardiovascular diseases manifest as an abnormal motion pattern of the heart muscle (myocardium). Local cardiac motion can be non-invasively quantified with magnetic resonance imaging (MRI), using methods such as tissue phase mapping (TPM), which directly measures the local myocardial velocities over time with high temporal and spatial resolution. The challenges for routine clinical use of TPM for the diagnosis and monitoring of cardiac function lie in providing a fast and accurate myocardium segmentation and a robust quantitative analysis of the velocity field. Both of these tasks are difficult to automate on routine clinical data because of the reduced contrast in the presence of noise. In this work, we propose to address these challenges with a segmentation approach that combines smooth, iterative contour displacement and probabilistic segmentation using particle tracing, based on the underlying velocity field. The proposed solution enabled the efficient and reproducible segmentation of TPM datasets from 27 patients and 14 volunteers, showing good potential for routine use in clinical studies. Our method allows for a more reliable quantitative analysis of local myocardial velocities, by giving a higher weight to velocity vectors corresponding to pixels more likely to belong to the myocardium. The accuracy of the contour propagation was evaluated on nine subjects, showing an average error smaller than the spatial resolution of the image data. Statistical analysis concluded that the difference between the segmented contours and the ground truths was not significantly higher than the variability between the manual ground truth segmentations.

  12. Radial-searching contour extraction method based on a modified active contour model for mammographic masses.

    PubMed

    Nakagawa, Toshiaki; Hara, Takeshi; Fujita, Hiroshi; Horita, Katsuhei; Iwase, Takuji; Endo, Tokiko

    2008-07-01

    In this study, we developed an automatic extraction scheme for the precise recognition of the contours of masses on digital mammograms in order to improve a computer-aided diagnosis (CAD) system. We propose a radial-searching contour extraction method based on a modified active contour model (ACM). In this technique, after determining the central point of a mass by searching for the direction of the density gradient, we arranged an initial contour at the central point, and the movement of a control point was limited to directions radiating from the central point. Moreover, it became possible to increase the extraction accuracy by sorting out the pixel used for processing and using two images-an edge-intensity image and a degree-of-separation image defined based on the pixel-value histogram-for calculation of the image forces used for constraints on deformation of the ACM. We investigated the accuracy of the automated extraction method by using 53 masses with several "difficult contours" on 53 digitized mammograms. The extraction results were compared quantitatively with the "correct segmentation" represented by an experienced physician's sketches. The numbers of cases in which the extracted region corresponded to the correct region with overlap ratios of more than 81 and 61% were 30 and 45, respectively. The initial results obtained with this technique show that it will be useful for the segmentation of masses in CAD schemes.

  13. Decoupled external forces in a predictor-corrector segmentation scheme for LV contours in Tagged MR images.

    PubMed

    Garcia-Barnes, Jaume; Andaluz, Albert; Carreras, Francesc; Gil, Debora

    2010-01-01

    Computation of functional regional scores requires proper identification of LV contours. On one hand, manual segmentation is robust, but it is time consuming and requires high expertise. On the other hand, the tag pattern in TMR sequences is a problem for automatic segmentation of LV boundaries. We propose a segmentation method based on a predictor-corrector (Active Contours - Shape Models) scheme. Special stress is put in the definition of the AC external forces. First, we introduce a semantic description of the LV that discriminates myocardial tissue by using texture and motion descriptors. Second, in order to ensure convergence regardless of the initial contour, the external energy is decoupled according to the orientation of the edges in the image potential. We have validated the model in terms of error in segmented contours and accuracy of regional clinical scores.

  14. Active segmentation of 3D axonal images.

    PubMed

    Muralidhar, Gautam S; Gopinath, Ajay; Bovik, Alan C; Ben-Yakar, Adela

    2012-01-01

    We present an active contour framework for segmenting neuronal axons on 3D confocal microscopy data. Our work is motivated by the need to conduct high throughput experiments involving microfluidic devices and femtosecond lasers to study the genetic mechanisms behind nerve regeneration and repair. While most of the applications for active contours have focused on segmenting closed regions in 2D medical and natural images, there haven't been many applications that have focused on segmenting open-ended curvilinear structures in 2D or higher dimensions. The active contour framework we present here ties together a well known 2D active contour model [5] along with the physics of projection imaging geometry to yield a segmented axon in 3D. Qualitative results illustrate the promise of our approach for segmenting neruonal axons on 3D confocal microscopy data.

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

    SciTech Connect

    Zhou, R; Yang, J; Pan, T; Milgrom, S; Pinnix, C; Shi, A; Yang, J; Liu, Y; Nguyen, Q; Gomez, D; Dabaja, B; Balter, P; Court, L; Liao, Z

    2015-06-15

    Purpose: To develop a set of atlases for automatic contouring of cardiac structures to determine heart radiation dose and the associated toxicity. Methods: Six thoracic cancer patients with both contrast and non-contrast CT images were acquired for this study. Eight radiation oncologists manually and independently delineated cardiac contours on the non-contrast CT by referring to the fused contrast CT and following the RTOG 1106 atlas contouring guideline. Fifteen regions of interest (ROIs) were delineated, including heart, four chambers, four coronary arteries, pulmonary artery and vein, inferior and superior vena cava, and ascending and descending aorta. Individual expert contours were fused using the simultaneous truth and performance level estimation (STAPLE) algorithm for each ROI and each patient. The fused contours became atlases for an in-house multi-atlas segmentation. Using leave-one-out test, we generated auto-segmented contours for each ROI and each patient. The auto-segmented contours were compared with the fused contours using the Dice similarity coefficient (DSC) and the mean surface distance (MSD). Results: Inter-observer variability was not obvious for heart, chambers, and aorta but was large for other structures that were not clearly distinguishable on CT image. The average DSC between individual expert contours and the fused contours were less than 50% for coronary arteries and pulmonary vein, and the average MSD were greater than 4.0 mm. The largest MSD of expert contours deviating from the fused contours was 2.5 cm. The mean DSC and MSD of auto-segmented contours were within one standard deviation of expert contouring variability except the right coronary artery. The coronary arteries, vena cava, and pulmonary vein had DSC<70% and MSD>3.0 mm. Conclusion: A set of cardiac atlases was created for cardiac automatic contouring, the accuracy of which was comparable to the variability in expert contouring. However, substantial modification may need

  16. Interactive segmentation of tongue contours in ultrasound video sequences using quality maps

    NASA Astrophysics Data System (ADS)

    Ghrenassia, Sarah; Ménard, Lucie; Laporte, Catherine

    2014-03-01

    Ultrasound (US) imaging is an effective and non invasive way of studying the tongue motions involved in normal and pathological speech, and the results of US studies are of interest for the development of new strategies in speech therapy. State-of-the-art tongue shape analysis techniques based on US images depend on semi-automated tongue segmentation and tracking techniques. Recent work has mostly focused on improving the accuracy of the tracking techniques themselves. However, occasional errors remain inevitable, regardless of the technique used, and the tongue tracking process must thus be supervised by a speech scientist who will correct these errors manually or semi-automatically. This paper proposes an interactive framework to facilitate this process. In this framework, the user is guided towards potentially problematic portions of the US image sequence by a segmentation quality map that is based on the normalized energy of an active contour model and automatically produced during tracking. When a problematic segmentation is identified, corrections to the segmented contour can be made on one image and propagated both forward and backward in the problematic subsequence, thereby improving the user experience. The interactive tools were tested in combination with two different tracking algorithms. Preliminary results illustrate the potential of the proposed framework, suggesting that the proposed framework generally improves user interaction time, with little change in segmentation repeatability.

  17. 3D dento-maxillary osteolytic lesion and active contour segmentation pilot study in CBCT: semi-automatic vs manual methods

    PubMed Central

    Kacem, A; Legoux, H; Le Tenier, M; Hamitouche, C; Arbab-Chirani, R

    2015-01-01

    Objectives: This study was designed to evaluate the reliability of a semi-automatic segmentation tool for dento-maxillary osteolytic image analysis compared with manually defined segmentation in CBCT scans. Methods: Five CBCT scans were selected from patients for whom periapical radiolucency images were available. All images were obtained using a ProMax® 3D Mid Planmeca (Planmeca Oy, Helsinki, Finland) and were acquired with 200-μm voxel size. Two clinicians performed the manual segmentations. Four operators applied three different semi-automatic procedures. The volumes of the lesions were measured. An analysis of dispersion was made for each procedure and each case. An ANOVA was used to evaluate the operator effect. Non-paired t-tests were used to compare semi-automatic procedures with the manual procedure. Statistical significance was set at α = 0.01. Results: The coefficients of variation for the manual procedure were 2.5–3.5% on average. There was no statistical difference between the two operators. The results of manual procedures can be used as a reference. For the semi-automatic procedures, the dispersion around the mean can be elevated depending on the operator and case. ANOVA revealed significant differences between the operators for the three techniques according to cases. Conclusions: Region-based segmentation was only comparable with the manual procedure for delineating a circumscribed osteolytic dento-maxillary lesion. The semi-automatic segmentations tested are interesting but are limited to complex surface structures. A methodology that combines the strengths of both methods could be of interest and should be tested. The improvement in the image analysis that is possible through the segmentation procedure and CBCT image quality could be of value. PMID:25996572

  18. Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming.

    PubMed

    Chiu, Stephanie J; Toth, Cynthia A; Bowes Rickman, Catherine; Izatt, Joseph A; Farsiu, Sina

    2012-05-01

    This paper presents a generalized framework for segmenting closed-contour anatomical and pathological features using graph theory and dynamic programming (GTDP). More specifically, the GTDP method previously developed for quantifying retinal and corneal layer thicknesses is extended to segment objects such as cells and cysts. The presented technique relies on a transform that maps closed-contour features in the Cartesian domain into lines in the quasi-polar domain. The features of interest are then segmented as layers via GTDP. Application of this method to segment closed-contour features in several ophthalmic image types is shown. Quantitative validation experiments for retinal pigmented epithelium cell segmentation in confocal fluorescence microscopy images attests to the accuracy of the presented technique.

  19. Human body contour data based activity recognition.

    PubMed

    Myagmarbayar, Nergui; Yuki, Yoshida; Imamoglu, Nevrez; Gonzalez, Jose; Otake, Mihoko; Yu, Wenwei

    2013-01-01

    This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients' motions, recognizing the patients' behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs). In our previous research, a full framework was established towards this research goal. In this research, we aimed at improving the human activity recognition by using contour data of the tracked human subject extracted from the depth images as the signal source, instead of the lower limb joint angle data used in the previous research, which are more likely to be affected by the motion of the robot and human subjects. Several geometric parameters, such as, the ratio of height to weight of the tracked human subject, and distance (pixels) between centroid points of upper and lower parts of human body, were calculated from the contour data, and used as the features for the activity recognition. A Hidden Markov Model (HMM) is employed to classify different human activities from the features. Experimental results showed that the human activity recognition could be achieved with a high correct rate.

  20. A shape constrained parametric active contour model for breast contour detection.

    PubMed

    Lee, Juhun; Muralidhar, Gautam S; Reece, Gregory P; Markey, Mia K

    2012-01-01

    Quantitative measures of breast morphology can help a breast cancer survivor to understand outcomes of reconstructive surgeries. One bottleneck of quantifying breast morphology is that there are only a few reliable automation algorithms for detecting the breast contour. This study proposes a novel approach for detecting the breast contour, which is based on a parametric active contour model. In addition to employing the traditional parametric active contour model, the proposed approach enforces a mathematical shape constraint based on the catenary curve, which has been previously shown to capture the overall shape of the breast contour reliably. The mathematical shape constraint regulates the evolution of the active contour and helps the contour evolve towards the breast, while minimizing the undesired effects of other structures such as, the nipple/areola and scars. The efficacy of the proposed approach was evaluated on anterior posterior photographs of women who underwent or were scheduled for breast reconstruction surgery including autologous tissue reconstruction. The proposed algorithm shows promising results for detecting the breast contour.

  1. Brain extraction using geodesic active contours

    NASA Astrophysics Data System (ADS)

    Huang, Albert; Abugharbieh, Rafeef; Tam, Roger; Traboulsee, Anthony

    2006-03-01

    Extracting the brain cortex from magnetic resonance imaging (MRI) head scans is an essential preprocessing step of which the accuracy greatly affects subsequent image analysis. The currently popular Brain Extraction Tool (BET) produces a brain mask which may be too smooth for practical use. This paper presents a novel brain extraction tool based on three-dimensional geodesic active contours, connected component analysis and mathematical morphology. Based on user-specified intensity and contrast levels, the proposed algorithm allows an active contour to evolve naturally and extract the brain cortex. Experiments on synthetic MRI data and scanned coronal and axial MRI image volumes indicate successful extraction of tight perimeters surrounding the brain cortex. Quantitative evaluations on both synthetic phantoms and manually labeled data resulted in better accuracy than BET in terms of true and false voxel assignment. Based on these results, we illustrate that our brain extraction tool is a robust and accurate approach for the challenging task of automatically extracting the brain cortex in MRI data.

  2. Automatic 4D Reconstruction of Patient-Specific Cardiac Mesh with 1-to-1 Vertex Correspondence from Segmented Contours Lines

    PubMed Central

    Lim, Chi Wan; Su, Yi; Yeo, Si Yong; Ng, Gillian Maria; Nguyen, Vinh Tan; Zhong, Liang; Tan, Ru San; Poh, Kian Keong; Chai, Ping

    2014-01-01

    We propose an automatic algorithm for the reconstruction of patient-specific cardiac mesh models with 1-to-1 vertex correspondence. In this framework, a series of 3D meshes depicting the endocardial surface of the heart at each time step is constructed, based on a set of border delineated magnetic resonance imaging (MRI) data of the whole cardiac cycle. The key contribution in this work involves a novel reconstruction technique to generate a 4D (i.e., spatial–temporal) model of the heart with 1-to-1 vertex mapping throughout the time frames. The reconstructed 3D model from the first time step is used as a base template model and then deformed to fit the segmented contours from the subsequent time steps. A method to determine a tree-based connectivity relationship is proposed to ensure robust mapping during mesh deformation. The novel feature is the ability to handle intra- and inter-frame 2D topology changes of the contours, which manifests as a series of merging and splitting of contours when the images are viewed either in a spatial or temporal sequence. Our algorithm has been tested on five acquisitions of cardiac MRI and can successfully reconstruct the full 4D heart model in around 30 minutes per subject. The generated 4D heart model conforms very well with the input segmented contours and the mesh element shape is of reasonably good quality. The work is important in the support of downstream computational simulation activities. PMID:24743555

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

    PubMed

    Guan, Tao; Zhou, Dongxiang; Liu, Yunhui

    2015-07-01

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

  4. Vesselness-guided Active Contour: A Coronary Vessel Extraction Method

    PubMed Central

    Dehkordi, Maryam Taghizadeh; Jalalat, Morteza; Sadri, Saeed; Doosthoseini, Alimohamad; Ahmadzadeh, Mohammad Reza; Amirfattahi, Rasoul

    2014-01-01

    Vessel extraction is a critical task in clinical practice. In this paper, we propose a new approach for vessel extraction using an active contour model by defining a novel vesselness-based term, based on accurate analysis of the vessel structure in the image. To achieve the novel term, a simple and fast directional filter bank is proposed, which does not employ down sampling and resampling used in earlier versions of directional filter banks. The proposed model not only preserves the performance of the existing models on images with intensity inhomogeneity, but also overcomes their inability both to segment low contrast vessels and to omit non-vessel structures. Experimental results for synthetic images and coronary X-ray angiograms show desirable performance of our model. PMID:24761379

  5. Feature-based active contour model and occluding object detection.

    PubMed

    Memar, Sara; Ksantini, Riadh; Boufama, Boubakeur

    2016-04-01

    This paper presents a method for image segmentation and object detection. The proposed strategy consists of two major stages. The first one corresponds to image segmentation, which is based on the active contour model (ACM) algorithm, using an automatic selection of the best candidate features among gradient, polarity, and depth, coupled with a combination of them by the kernel support vector machine (KSVM). Although existing techniques, such as the ones based on ACM, perform well in the single-object case and non-noisy environments, these techniques fail when the scene consists of multiple occluding objects, with possibly similar colors. Thus, the second stage corresponds to the identification of salient and occluded objects based on the fuzzy C-mean algorithm (FCM). In this stage, the depth is included as another clue that allows us to estimate the cluster number and to make the clustering process more robust. In particular, complex occlusions can be handled this way, and the objects can be properly segmented and identified. Experimental results on real images and on several standard datasets have shown the success and effectiveness of the proposed method.

  6. Multiplatform GPGPU implementation of the active contours without edges algorithm

    NASA Astrophysics Data System (ADS)

    Zavala-Romero, Olmo; Meyer-Baese, Anke; Meyer-Baese, Uwe

    2012-05-01

    An OpenCL implementation of the Active Contours Without Edges algorithm is presented. The proposed algorithm uses the General Purpose Computing on Graphics Processing Units (GPGPU) to accelerate the original model by parallelizing the two main steps of the segmentation process, the computation of the Signed Distance Function (SDF) and the evolution of the segmented curve. The proposed scheme for the computation of the SDF is based on the iterative construction of partial Voronoi diagrams of a reduced dimension and obtains the exact Euclidean distance in a time of order O(N/p), where N is the number of pixels and p the number of processors. With high resolution images the segmentation algorithm runs 10 times faster than its equivalent sequential implementation. This work is being done as an open source software that, being programmed in OpenCL, can be used in dierent platforms allowing a broad number of nal users and can be applied in dierent areas of computer vision, like medical imaging, tracking, robotics, etc. This work uses OpenGL to visualize the algorithm results in real time.

  7. CT urography: segmentation of urinary bladder using CLASS with local contour refinement

    NASA Astrophysics Data System (ADS)

    Cha, Kenny; Hadjiiski, Lubomir; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.; Zhou, Chuan

    2014-06-01

    We are developing a computerized system for bladder segmentation on CT urography (CTU), as a critical component for computer-aided detection of bladder cancer. The presence of regions filled with intravenous contrast and without contrast presents a challenge for bladder segmentation. Previously, we proposed a conjoint level set analysis and segmentation system (CLASS). In case the bladder is partially filled with contrast, CLASS segments the non-contrast (NC) region and the contrast-filled (C) region separately and automatically conjoins the NC and C region contours; however, inaccuracies in the NC and C region contours may cause the conjoint contour to exclude portions of the bladder. To alleviate this problem, we implemented a local contour refinement (LCR) method that exploits model-guided refinement (MGR) and energy-driven wavefront propagation (EDWP). MGR propagates the C region contours if the level set propagation in the C region stops prematurely due to substantial non-uniformity of the contrast. EDWP with regularized energies further propagates the conjoint contours to the correct bladder boundary. EDWP uses changes in energies, smoothness criteria of the contour, and previous slice contour to determine when to stop the propagation, following decision rules derived from training. A data set of 173 cases was collected for this study: 81 cases in the training set (42 lesions, 21 wall thickenings, 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, 13 normal bladders). For all cases, 3D hand segmented contours were obtained as reference standard and used for the evaluation of the computerized segmentation accuracy. For CLASS with LCR, the average volume intersection ratio, average volume error, absolute average volume error, average minimum distance and Jaccard index were 84.2 ± 11.4%, 8.2 ± 17.4%, 13.0 ± 14.1%, 3.5 ± 1.9 mm, 78.8 ± 11.6%, respectively, for the training set and 78.0 ± 14.7%, 16.4 ± 16.9%, 18.2 ± 15.0%, 3

  8. CT urography: segmentation of urinary bladder using CLASS with local contour refinement.

    PubMed

    Cha, Kenny; Hadjiiski, Lubomir; Chan, Heang-Ping; Caoili, Elaine M; Cohan, Richard H; Zhou, Chuan

    2014-06-07

    We are developing a computerized system for bladder segmentation on CT urography (CTU), as a critical component for computer-aided detection of bladder cancer. The presence of regions filled with intravenous contrast and without contrast presents a challenge for bladder segmentation. Previously, we proposed a conjoint level set analysis and segmentation system (CLASS). In case the bladder is partially filled with contrast, CLASS segments the non-contrast (NC) region and the contrast-filled (C) region separately and automatically conjoins the NC and C region contours; however, inaccuracies in the NC and C region contours may cause the conjoint contour to exclude portions of the bladder. To alleviate this problem, we implemented a local contour refinement (LCR) method that exploits model-guided refinement (MGR) and energy-driven wavefront propagation (EDWP). MGR propagates the C region contours if the level set propagation in the C region stops prematurely due to substantial non-uniformity of the contrast. EDWP with regularized energies further propagates the conjoint contours to the correct bladder boundary. EDWP uses changes in energies, smoothness criteria of the contour, and previous slice contour to determine when to stop the propagation, following decision rules derived from training. A data set of 173 cases was collected for this study: 81 cases in the training set (42 lesions, 21 wall thickenings, 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, 13 normal bladders). For all cases, 3D hand segmented contours were obtained as reference standard and used for the evaluation of the computerized segmentation accuracy. For CLASS with LCR, the average volume intersection ratio, average volume error, absolute average volume error, average minimum distance and Jaccard index were 84.2 ± 11.4%, 8.2 ± 17.4%, 13.0 ± 14.1%, 3.5 ± 1.9 mm, 78.8 ± 11.6%, respectively, for the training set and 78.0 ± 14.7%, 16.4 ± 16.9%, 18.2 ± 15.0%, 3

  9. Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis.

    PubMed

    Hoogi, Assaf; Subramaniam, Arjun; Veerapaneni, Rishi; Rubin, Daniel

    2016-11-11

    In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process. Finally, the adaptive window size surrounding each contour point is re-estimated by an iterative process that considers lesion size and spatial texture. We demonstrate the capabilities of our method on a dataset of 164 MRI and 112 CT images of liver lesions that includes low contrast and heterogeneous lesions as well as noisy images. To illustrate the strength of our method, we evaluated it against state of the art CNNbased and active contour techniques. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better than currently available methods. An average Dice improvement of 0.27 was found across the entire dataset over all comparisons. We also analyzed two challenging subsets of lesions and obtained a significant Dice improvement of ����.�������� with our method (p < 0.001, Wilcoxon).

  10. Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis.

    PubMed

    Hoogi, Assaf; Subramaniam, Arjun; Veerapaneni, Rishi; Rubin, Daniel

    2016-11-11

    In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process. Finally, the adaptive window size surrounding each contour point is re-estimated by an iterative process that considers lesion size and spatial texture. We demonstrate the capabilities of our method on a dataset of 164 MRI and 112 CT images of liver lesions that includes low contrast and heterogeneous lesions as well as noisy images. To illustrate the strength of our method, we evaluated it against state of the art CNNbased and active contour techniques. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better than currently available methods. An average Dice improvement of 0.27 was found across the entire dataset over all comparisons. We also analyzed two challenging subsets of lesions and obtained a significant Dice improvement of 0.24 with our method (p < 0.001, Wilcoxon).

  11. Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

    SciTech Connect

    Maduskar, Pragnya Hogeweg, Laurens; Sánchez, Clara I.; Ginneken, Bram van; Jong, Pim A. de; Peters-Bax, Liesbeth; Dawson, Rodney; Ayles, Helen

    2014-07-15

    Purpose: Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs. Methods: A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihood value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers:k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space. Results: The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure Ω was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap Ω of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were

  12. DCAN: Deep contour-aware networks for object instance segmentation from histology images.

    PubMed

    Chen, Hao; Qi, Xiaojuan; Yu, Lequan; Dou, Qi; Qin, Jing; Heng, Pheng-Ann

    2017-02-01

    In histopathological image analysis, the morphology of histological structures, such as glands and nuclei, has been routinely adopted by pathologists to assess the malignancy degree of adenocarcinomas. Accurate detection and segmentation of these objects of interest from histology images is an essential prerequisite to obtain reliable morphological statistics for quantitative diagnosis. While manual annotation is error-prone, time-consuming and operator-dependant, automated detection and segmentation of objects of interest from histology images can be very challenging due to the large appearance variation, existence of strong mimics, and serious degeneration of histological structures. In order to meet these challenges, we propose a novel deep contour-aware network (DCAN) under a unified multi-task learning framework for more accurate detection and segmentation. In the proposed network, multi-level contextual features are explored based on an end-to-end fully convolutional network (FCN) to deal with the large appearance variation. We further propose to employ an auxiliary supervision mechanism to overcome the problem of vanishing gradients when training such a deep network. More importantly, our network can not only output accurate probability maps of histological objects, but also depict clear contours simultaneously for separating clustered object instances, which further boosts the segmentation performance. Our method ranked the first in two histological object segmentation challenges, including 2015 MICCAI Gland Segmentation Challenge and 2015 MICCAI Nuclei Segmentation Challenge. Extensive experiments on these two challenging datasets demonstrate the superior performance of our method, surpassing all the other methods by a significant margin.

  13. Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours.

    PubMed

    Li, Dengwang; Liu, Li; Chen, Jinhu; Li, Hongsheng; Yin, Yong; Ibragimov, Bulat; Xing, Lei

    2017-01-07

    Atlas-based segmentation utilizes a library of previously delineated contours of similar cases to facilitate automatic segmentation. The problem, however, remains challenging because of limited information carried by the contours in the library. In this studying, we developed a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. This study presented a new concept of atlas based segmentation method. Instead of using the complete volume of the target organs, only information along the organ contours from the atlas images was used for guiding segmentation of the new image. In setting up an atlas-based library, we included not only the coordinates of contour points, but also the image features adjacent to the contour. In this work, 139 CT images with normal appearing livers collected for radiotherapy treatment planning were used to construct the library. The CT images within the library were first registered to each other using affine registration. The nonlinear narrow shell was generated alongside the object contours of registered images. Matching voxels were selected inside common narrow shell image features of a library case and a new case using a speed-up robust features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the new image by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy optimization within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by physicians. A novel atlas-based segmentation technique with inclusion of neighborhood image features through the introduction of a narrow-shell surrounding the target objects was established. Application of the technique to

  14. Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours

    NASA Astrophysics Data System (ADS)

    Li, Dengwang; Liu, Li; Chen, Jinhu; Li, Hongsheng; Yin, Yong; Ibragimov, Bulat; Xing, Lei

    2017-01-01

    Atlas-based segmentation utilizes a library of previously delineated contours of similar cases to facilitate automatic segmentation. The problem, however, remains challenging because of limited information carried by the contours in the library. In this studying, we developed a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. This study presented a new concept of atlas based segmentation method. Instead of using the complete volume of the target organs, only information along the organ contours from the atlas images was used for guiding segmentation of the new image. In setting up an atlas-based library, we included not only the coordinates of contour points, but also the image features adjacent to the contour. In this work, 139 CT images with normal appearing livers collected for radiotherapy treatment planning were used to construct the library. The CT images within the library were first registered to each other using affine registration. The nonlinear narrow shell was generated alongside the object contours of registered images. Matching voxels were selected inside common narrow shell image features of a library case and a new case using a speed-up robust features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the new image by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy optimization within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by physicians. A novel atlas-based segmentation technique with inclusion of neighborhood image features through the introduction of a narrow-shell surrounding the target objects was established. Application of the technique to

  15. An explicit shape-constrained MRF-based contour evolution method for 2-D medical image segmentation.

    PubMed

    Chittajallu, Deepak R; Paragios, Nikos; Kakadiaris, Ioannis A

    2014-01-01

    Image segmentation is, in general, an ill-posed problem and additional constraints need to be imposed in order to achieve the desired segmentation result. While segmenting organs in medical images, which is the topic of this paper, a significant amount of prior knowledge about the shape, appearance, and location of the organs is available that can be used to constrain the solution space of the segmentation problem. Among the various types of prior information, the incorporation of prior information about shape, in particular, is very challenging. In this paper, we present an explicit shape-constrained MAP-MRF-based contour evolution method for the segmentation of organs in 2-D medical images. Specifically, we represent the segmentation contour explicitly as a chain of control points. We then cast the segmentation problem as a contour evolution problem, wherein the evolution of the contour is performed by iteratively solving a MAP-MRF labeling problem. The evolution of the contour is governed by three types of prior information, namely: (i) appearance prior, (ii) boundary-edgeness prior, and (iii) shape prior, each of which is incorporated as clique potentials into the MAP-MRF problem. We use the master-slave dual decomposition framework to solve the MAP-MRF labeling problem in each iteration. In our experiments, we demonstrate the application of the proposed method to the challenging problem of heart segmentation in non-contrast computed tomography data.

  16. Identification of breast contour for nipple segmentation in breast magnetic resonance images

    SciTech Connect

    Gwo, Chih-Ying; Gwo, Allen; Wei, Chia-Hung; Huang, Pai Jung

    2014-02-15

    Purpose: The purpose of this study is to develop a method to simulate the breast contour and segment the nipple in breast magnetic resonance images. Methods: This study first identifies the chest wall and removes the chest part from the breast MR images. Subsequently, the cleavage and its motion artifacts are removed, distinguishing the separate breasts, where the edge points are sampled for curve fitting. Next, a region growing method is applied to find the potential nipple region. Finally, the potential nipple region above the simulated curve can be removed in order to retain the original smooth contour. Results: The simulation methods can achieve the least root mean square error (RMSE) for certain cases. The proposed YBnd and (Dmin+Dmax)/2 methods are significant due toP = 0.000. The breast contour curve detected by the two proposed methods is closer than that determined by the edge detection method. The (Dmin+Dmax)/2 method can achieve the lowest RMSE of 1.1029 on average, while the edge detection method results in the highest RMSE of 6.5655. This is only slighter better than the comparison methods, which implies that the performance of these methods depends upon the conditions of the cases themselves. Under this method, the maximal Dice coefficient is 0.881, and the centroid difference is 0.36 pixels. Conclusions: The contributions of this study are twofold. First, a method was proposed to identify and segment the nipple in breast MR images. Second, a curve-fitting method was used to simulate the breast contour, allowing the breast to retain its original smooth shape.

  17. Active Mask Segmentation of Fluorescence Microscope Images

    PubMed Central

    Srinivasa, Gowri; Fickus, Matthew C.; Guo, Yusong; Linstedt, Adam D.; Kovačević, Jelena

    2009-01-01

    We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the “contour” to that of “inside and outside”, or, masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well, and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively as well as quantitatively. PMID:19380268

  18. On the Relationship between Variational Level Set-Based and SOM-Based Active Contours

    PubMed Central

    Abdelsamea, Mohammed M.; Gnecco, Giorgio; Gaber, Mohamed Medhat; Elyan, Eyad

    2015-01-01

    Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses. PMID:25960736

  19. Structural Stereo Matching Of Laplacian-Of-Gaussian Contour Segments For 3D Perception

    NASA Astrophysics Data System (ADS)

    Boyer, K. L.; Sotak, G. E.

    1989-03-01

    We solve the stereo correspondence problem using Lapla-cian of Gaussian (LoG) zero-crossing contours as a source of primitives for structural stereopsis, as opposed to traditional point-based algorithms. For each image in the stereo pair, we apply the LoG operator, extract and link zero crossing points, filter and segment the contours into meaningful primitives, and compute a parametric structural description over the resulting primitive set. We then apply a variant of the inexact structural matching technique of Boyer and Kak Ill to recover the optimal interprimitive mapping (correspon-dence) function. Since an extended image feature conveys more information than a single point, its spatial and photometric behavior may be exploited to advantage; there are also fewer features to match, resulting in a smaller combinatorial problem. The structural approach allows greater use of spatial relational constraints, which allows us to eliminate (or reduce) the coarse-to-fine tracking of most point-based algorithms. Solving the correspondence problem at this level requires only an approximate probabilistic characterization of the image-to-image structural distortion, and does not require detailed knowledge of the epipolar geometry.

  20. Automated optic disk boundary detection by modified active contour model.

    PubMed

    Xu, Juan; Chutatape, Opas; Chew, Paul

    2007-03-01

    This paper presents a novel deformable-model-based algorithm for fully automated detection of optic disk boundary in fundus images. The proposed method improves and extends the original snake (deforming-only technique) in two aspects: clustering and smoothing update. The contour points are first self-separated into edge-point group or uncertain-point group by clustering after each deformation, and these contour points are then updated by different criteria based on different groups. The updating process combines both the local and global information of the contour to achieve the balance of contour stability and accuracy. The modifications make the proposed algorithm more accurate and robust to blood vessel occlusions, noises, ill-defined edges and fuzzy contour shapes. The comparative results show that the proposed method can estimate the disk boundaries of 100 test images closer to the groundtruth, as measured by mean distance to closest point (MDCP) <3 pixels, with the better success rate when compared to those obtained by gradient vector flow snake (GVF-snake) and modified active shape models (ASM).

  1. A Model for Diagnosing Breast Cancerous Tissue from Thermal Images Using Active Contour and Lyapunov Exponent

    PubMed Central

    GHAYOUMI ZADEH, Hossein; HADDADNIA, Javad; MONTAZERI, Alimohammad

    2016-01-01

    Background: The segmentation of cancerous areas in breast images is important for the early detection of disease. Thermal imaging has advantages, such as being non-invasive, non-radiation, passive, quick, painless, inexpensive, and non-contact. Imaging technique is the focus of this research. Methods: The proposed model in this paper is a combination of surf and corners that are very resistant. Obtained features are resistant to changes in rotation and revolution then with the help of active contours, this feature has been used for segmenting cancerous areas. Results: Comparing the obtained results from the proposed method and mammogram show that proposed method is Accurate and appropriate. Benign and malignance of segmented areas are detected by Lyapunov exponent. Values obtained include TP=91.31%, FN=8.69%, FP=7.26%. Conclusion: The proposed method can classify those abnormally segmented areas of the breast, to the Benign and malignant cancer. PMID:27398339

  2. SU-E-J-131: Augmenting Atlas-Based Segmentation by Incorporating Image Features Proximal to the Atlas Contours

    SciTech Connect

    Li, Dengwang; Liu, Li; Kapp, Daniel S.; Xing, Lei

    2015-06-15

    Purpose: For facilitating the current automatic segmentation, in this work we propose a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. Methods: In setting up an atlas-based library, we include not only the coordinates of contour points, but also the image features adjacent to the contour. 139 planning CT scans with normal appearing livers obtained during their radiotherapy treatment planning were used to construct the library. The CT images within the library were registered each other using affine registration. A nonlinear narrow shell with the regional thickness determined by the distance between two vertices alongside the contour. The narrow shell was automatically constructed both inside and outside of the liver contours. The common image features within narrow shell between a new case and a library case were first selected by a Speed-up Robust Features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the images of the new patient by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy function within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by a physician. Results: Application of the technique to 30 liver cases suggested that the technique was capable of reliably segment organs such as the liver with little human intervention. Compared with the manual segmentation results by a physician, the average and discrepancies of the volumetric overlap percentage (VOP) was found to be 92.43%+2.14%. Conclusion: Incorporation of image features into the library contours improves the currently available atlas-based auto-contouring techniques and provides a clinically

  3. A fast region-based active contour model for boundary detection of echocardiographic images.

    PubMed

    Saini, Kalpana; Dewal, M L; Rohit, Manojkumar

    2012-04-01

    This paper presents the boundary detection of atrium and ventricle in echocardiographic images. In case of mitral regurgitation, atrium and ventricle may get dilated. To examine this, doctors draw the boundary manually. Here the aim of this paper is to evolve the automatic boundary detection for carrying out segmentation of echocardiography images. Active contour method is selected for this purpose. There is an enhancement of Chan-Vese paper on active contours without edges. Our algorithm is based on Chan-Vese paper active contours without edges, but it is much faster than Chan-Vese model. Here we have developed a method by which it is possible to detect much faster the echocardiographic boundaries. The method is based on the region information of an image. The region-based force provides a global segmentation with variational flow robust to noise. Implementation is based on level set theory so it easy to deal with topological changes. In this paper, Newton-Raphson method is used which makes possible the fast boundary detection.

  4. Atlas-Based Segmentation Improves Consistency and Decreases Time Required for Contouring Postoperative Endometrial Cancer Nodal Volumes

    SciTech Connect

    Young, Amy V.; Wortham, Angela; Wernick, Iddo; Evans, Andrew; Ennis, Ronald D.

    2011-03-01

    Purpose: Accurate target delineation of the nodal volumes is essential for three-dimensional conformal and intensity-modulated radiotherapy planning for endometrial cancer adjuvant therapy. We hypothesized that atlas-based segmentation ('autocontouring') would lead to time savings and more consistent contours among physicians. Methods and Materials: A reference anatomy atlas was constructed using the data from 15 postoperative endometrial cancer patients by contouring the pelvic nodal clinical target volume on the simulation computed tomography scan according to the Radiation Therapy Oncology Group 0418 trial using commercially available software. On the simulation computed tomography scans from 10 additional endometrial cancer patients, the nodal clinical target volume autocontours were generated. Three radiation oncologists corrected the autocontours and delineated the manual nodal contours under timed conditions while unaware of the other contours. The time difference was determined, and the overlap of the contours was calculated using Dice's coefficient. Results: For all physicians, manual contouring of the pelvic nodal target volumes and editing the autocontours required a mean {+-} standard deviation of 32 {+-} 9 vs. 23 {+-} 7 minutes, respectively (p = .000001), a 26% time savings. For each physician, the time required to delineate the manual contours vs. correcting the autocontours was 30 {+-} 3 vs. 21 {+-} 5 min (p = .003), 39 {+-} 12 vs. 30 {+-} 5 min (p = .055), and 29 {+-} 5 vs. 20 {+-} 5 min (p = .0002). The mean overlap increased from manual contouring (0.77) to correcting the autocontours (0.79; p = .038). Conclusion: The results of our study have shown that autocontouring leads to increased consistency and time savings when contouring the nodal target volumes for adjuvant treatment of endometrial cancer, although the autocontours still required careful editing to ensure that the lymph nodes at risk of recurrence are properly included in the target

  5. Tracking Epithelial Cell Junctions in C. elegans Embryogenesis With Active Contours Guided by SIFT Flow

    PubMed Central

    Lee, Chen-Yu; Gonçalves, Monira; Chisholm, Andrew D.; Cosman, Pamela C.

    2015-01-01

    Quantitative analysis of cell shape in live samples is an important goal in developmental biology. Automated or semiautomated segmentation and tracking of cell nuclei has been successfully implemented in several biological systems. Segmentation and tracking of cell surfaces has been more challenging. Here, we present a new approach to tracking cell junctions in the developing epidermis of C. elegans embryos. Epithelial junctions as visualized with DLG-1::GFP form lines at the subapical circumference of differentiated epidermal cells and delineate changes in epidermal cell shape and position. We develop and compare two approaches for junction segmentation. For the first method (projection approach), 3-D cell boundaries are projected into 2D for segmentation using active contours with a nonintersecting force, and subsequently tracked using scale-invariant feature transform (SIFT) flow. The resulting 2-D tracked boundaries are then back-projected into 3-D space. The second method (volumetric approach) uses a 3-D extended version of active contours guided by SIFT flow in 3-D space. In both methods, cell junctions are manually located at the first time point and tracked in a fully automated way for the remainder of the video. Using these methods, we have generated the first quantitative description of ventral epidermal cell movements and shape changes during epidermal enclosure. PMID:24771564

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

    NASA Astrophysics Data System (ADS)

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

    2009-02-01

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

  7. Automatic exudate detection using active contour model and regionwise classification.

    PubMed

    Harangi, B; Lazar, I; Hajdu, A

    2012-01-01

    Diabetic retinopathy is one the most common cause of blindness in the world. Exudates are among the early signs of this disease, so its proper detection is a very important task to prevent consequent effects. In this paper, we propose a novel approach for exudate detection. First, we identify possible regions containing exudates using grayscale morphology. Then, we apply an active contour based method to minimize the Chan-Vese energy to extract accurate borders of the candidates. To remove those false candidates that have sufficient strong borders to pass the active contour method we use a regionwise classifier. Hence, we extract several shape features for each candidate and let a boosted Naïve Bayes classifier eliminate the false candidates. We considered the publicly available DiaretDB1 color fundus image set for testing, where the proposed method outperformed several state-of-the-art exudate detectors.

  8. Flux Tensor Constrained Geodesic Active Contours with Sensor Fusion for Persistent Object Tracking.

    PubMed

    Bunyak, Filiz; Palaniappan, Kannappan; Nath, Sumit Kumar; Seetharaman, Gunasekaran

    2007-08-01

    This paper makes new contributions in motion detection, object segmentation and trajectory estimation to create a successful object tracking system. A new efficient motion detection algorithm referred to as the flux tensor is used to detect moving objects in infrared video without requiring background modeling or contour extraction. The flux tensor-based motion detector when applied to infrared video is more accurate than thresholding "hot-spots", and is insensitive to shadows as well as illumination changes in the visible channel. In real world monitoring tasks fusing scene information from multiple sensors and sources is a useful core mechanism to deal with complex scenes, lighting conditions and environmental variables. The object segmentation algorithm uses level set-based geodesic active contour evolution that incorporates the fusion of visible color and infrared edge informations in a novel manner. Touching or overlapping objects are further refined during the segmentation process using an appropriate shape-based model. Multiple object tracking using correspondence graphs is extended to handle groups of objects and occlusion events by Kalman filter-based cluster trajectory analysis and watershed segmentation. The proposed object tracking algorithm was successfully tested on several difficult outdoor multispectral videos from stationary sensors and is not confounded by shadows or illumination variations.

  9. An Active Contour Model Based on Adaptive Threshold for Extraction of Cerebral Vascular Structures

    PubMed Central

    Wang, Jiaxin; Zhao, Shifeng; Liu, Zifeng; Duan, Fuqing; Pan, Yutong

    2016-01-01

    Cerebral vessel segmentation is essential and helpful for the clinical diagnosis and the related research. However, automatic segmentation of brain vessels remains challenging because of the variable vessel shape and high complex of vessel geometry. This study proposes a new active contour model (ACM) implemented by the level-set method for segmenting vessels from TOF-MRA data. The energy function of the new model, combining both region intensity and boundary information, is composed of two region terms, one boundary term and one penalty term. The global threshold representing the lower gray boundary of the target object by maximum intensity projection (MIP) is defined in the first-region term, and it is used to guide the segmentation of the thick vessels. In the second term, a dynamic intensity threshold is employed to extract the tiny vessels. The boundary term is used to drive the contours to evolve towards the boundaries with high gradients. The penalty term is used to avoid reinitialization of the level-set function. Experimental results on 10 clinical brain data sets demonstrate that our method is not only able to achieve better Dice Similarity Coefficient than the global threshold based method and localized hybrid level-set method but also able to extract whole cerebral vessel trees, including the thin vessels. PMID:27597878

  10. An Active Contour Model Based on Adaptive Threshold for Extraction of Cerebral Vascular Structures.

    PubMed

    Wang, Jiaxin; Zhao, Shifeng; Liu, Zifeng; Tian, Yun; Duan, Fuqing; Pan, Yutong

    2016-01-01

    Cerebral vessel segmentation is essential and helpful for the clinical diagnosis and the related research. However, automatic segmentation of brain vessels remains challenging because of the variable vessel shape and high complex of vessel geometry. This study proposes a new active contour model (ACM) implemented by the level-set method for segmenting vessels from TOF-MRA data. The energy function of the new model, combining both region intensity and boundary information, is composed of two region terms, one boundary term and one penalty term. The global threshold representing the lower gray boundary of the target object by maximum intensity projection (MIP) is defined in the first-region term, and it is used to guide the segmentation of the thick vessels. In the second term, a dynamic intensity threshold is employed to extract the tiny vessels. The boundary term is used to drive the contours to evolve towards the boundaries with high gradients. The penalty term is used to avoid reinitialization of the level-set function. Experimental results on 10 clinical brain data sets demonstrate that our method is not only able to achieve better Dice Similarity Coefficient than the global threshold based method and localized hybrid level-set method but also able to extract whole cerebral vessel trees, including the thin vessels.

  11. Flux Tensor Constrained Geodesic Active Contours with Sensor Fusion for Persistent Object Tracking

    PubMed Central

    Bunyak, Filiz; Palaniappan, Kannappan; Nath, Sumit Kumar; Seetharaman, Gunasekaran

    2007-01-01

    This paper makes new contributions in motion detection, object segmentation and trajectory estimation to create a successful object tracking system. A new efficient motion detection algorithm referred to as the flux tensor is used to detect moving objects in infrared video without requiring background modeling or contour extraction. The flux tensor-based motion detector when applied to infrared video is more accurate than thresholding ”hot-spots”, and is insensitive to shadows as well as illumination changes in the visible channel. In real world monitoring tasks fusing scene information from multiple sensors and sources is a useful core mechanism to deal with complex scenes, lighting conditions and environmental variables. The object segmentation algorithm uses level set-based geodesic active contour evolution that incorporates the fusion of visible color and infrared edge informations in a novel manner. Touching or overlapping objects are further refined during the segmentation process using an appropriate shape-based model. Multiple object tracking using correspondence graphs is extended to handle groups of objects and occlusion events by Kalman filter-based cluster trajectory analysis and watershed segmentation. The proposed object tracking algorithm was successfully tested on several difficult outdoor multispectral videos from stationary sensors and is not confounded by shadows or illumination variations. PMID:19096530

  12. A circumscribing active contour model for delineation of nuclei and membranes of megakaryocytes in bone marrow trephine biopsy images

    NASA Astrophysics Data System (ADS)

    Song, Tzu-Hsi; Sanchez, Victor; EIDaly, Hesham; Rajpoot, Nasir M.

    2015-03-01

    The assessment of megakaryocytes (MKs) in bone marrow trephine images is an important step in the classification of different subtypes of myeloproliferative neoplasms (MPNs). In general, bone marrow trephine images include several types of cells mixed together, which make it quite difficult to visually identify MKs. In order to aid hematopathologists in the identification and study of MKs, we develop an image processing framework with supervised machine learning approaches and a novel circumscribing active contour model to identify potential MKs and then to accurately delineate the corresponding nucleus and membrane. Specifically, a number of color and texture features are used in a nave Bayesian classifier and an Adaboost classifier to locate the regions with a high probability of depicting MKs. A region-based active contour is used on the candidate MKs to accurately delineate the boundaries of nucleus and membrane. The proposed circumscribing active contour model employs external forces not only based on pixel intensities, but also on the probabilities of depicting MKs as computed by the classifiers. Experimental results suggest that the machine learning approach can detect potential MKs with an accuracy of more than 75%. When our circumscribing active contour model is employed on the candidate MKs, the nucleus and membrane boundaries are segmented with an accuracy of more than 80% as measured by the Dice similarity coefficient. Compared to traditional region-based active contours, the use of additional external forces based on the probability of depicting MKs improves segmentation performance and computational time by an average 5%.

  13. Automatic segmentation of mitochondria in EM data using pairwise affinity factorization and graph-based contour searching.

    PubMed

    Ghita, Ovidiu; Dietlmeier, Julia; Whelan, Paul F

    2014-10-01

    In this paper, we investigate the segmentation of closed contours in subcellular data using a framework that primarily combines the pairwise affinity grouping principles with a graph partitioning contour searching approach. One salient problem that precluded the application of these methods to large scale segmentation problems is the onerous computational complexity required to generate comprehensive representations that include all pairwise relationships between all pixels in the input data. To compensate for this problem, a practical solution is to reduce the complexity of the input data by applying an over-segmentation technique prior to the application of the computationally demanding strands of the segmentation process. This approach opens the opportunity to build specific shape and intensity models that can be successfully employed to extract the salient structures in the input image which are further processed to identify the cycles in an undirected graph. The proposed framework has been applied to the segmentation of mitochondria membranes in electron microscopy data which are characterized by low contrast and low signal-to-noise ratio. The algorithm has been quantitatively evaluated using two datasets where the segmentation results have been compared with the corresponding manual annotations. The performance of the proposed algorithm has been measured using standard metrics, such as precision and recall, and the experimental results indicate a high level of segmentation accuracy.

  14. Semi-automated identification of white blood cell using active contour technique

    NASA Astrophysics Data System (ADS)

    Marzuki, Nurhanis Izzati Binti Che; Mahmood, Nasrul Humaimi Bin; Razak, Mohd Azhar Bin Abdul

    2015-05-01

    Manual and automated diagnosis can be used to identify the morphology of blood cells. However, the manual diagnosis of the blood cells is time consuming and need hematologist and pathologist experts in order to diagnose diseases. Recently, the automated diagnosis which is require image processing technique are often been used in this area. This paper focuses on image processing technique to do segmentation on the nucleus of white blood cells (WBC). To identify the nucleus region, there are several image processing techniques applied besides the active contour method. The results obtained show that the detection on the edge of the nucleus is almost same as the original image of the nucleus.

  15. Iterative weighted average diffusion as a novel external force in the active contour model

    NASA Astrophysics Data System (ADS)

    Mirov, Ilya S.; Nakhmani, Arie

    2016-03-01

    The active contour model has good performance in boundary extraction for medical images; particularly, Gradient Vector Flow (GVF) active contour model shows good performance at concavity convergence and insensitivity to initialization, yet it is susceptible to edge leaking, deep and narrow concavities, and has some issues handling noisy images. This paper proposes a novel external force, called Iterative Weighted Average Diffusion (IWAD), which used in tandem with parametric active contours, provides superior performance in images with high values of concavity. The image gradient is first turned into an edge image, smoothed, and modified with enhanced corner detection, then the IWAD algorithm diffuses the force at a given pixel based on its 3x3 pixel neighborhood. A forgetting factor, φ, is employed to ensure that forces being spread away from the boundary of the image will attenuate. The experimental results show better behavior in high curvature regions, faster convergence, and less edge leaking than GVF when both are compared to expert manual segmentation of the images.

  16. Shoreline Mapping with Integrated HSI-DEM using Active Contour Method

    NASA Astrophysics Data System (ADS)

    Sukcharoenpong, Anuchit

    Shoreline mapping has been a critical task for federal/state agencies and coastal communities. It supports important applications such as nautical charting, coastal zone management, and legal boundary determination. Current attempts to incorporate data from hyperspectral imagery to increase the efficiency and efficacy of shoreline mapping have been limited due to the complexity in processing its data as well as its inferior spatial resolution when compared to multispectral imagery or to sensors such as LiDAR. As advancements in remote-sensing technologies increase sensor capabilities, the ability to exploit the spectral formation carried in hyperspectral images becomes more imperative. This work employs a new approach to extracting shorelines from AVIRIS hyperspectral images by combination with a LiDAR-based DEM using a multiphase active contour segmentation technique. Several techniques, such as study of object spectra and knowledge-based segmentation for initial contour generation, have been employed in order to achieve a sub-pixel level of accuracy and maintain low computational expenses. Introducing a DEM into hyperspectral image segmentation proves to be a useful tool to eliminate misclassifications and improve shoreline positional accuracy. Experimental results show that mapping shorelines from hyperspectral imagery and a DEM can be a promising approach as many further applications can be developed to exploit the rich information found in hyperspectral imagery.

  17. Automated segmentation of optic disc region on retinal fundus photographs: Comparison of contour modeling and pixel classification methods.

    PubMed

    Muramatsu, Chisako; Nakagawa, Toshiaki; Sawada, Akira; Hatanaka, Yuji; Hara, Takeshi; Yamamoto, Tetsuya; Fujita, Hiroshi

    2011-01-01

    The automatic determination of the optic disc area in retinal fundus images can be useful for calculation of the cup-to-disc (CD) ratio in the glaucoma screening. We compared three different methods that employed active contour model (ACM), fuzzy c-mean (FCM) clustering, and artificial neural network (ANN) for the segmentation of the optic disc regions. The results of these methods were evaluated using new databases that included the images captured by different camera systems. The average measures of overlap between the disc regions determined by an ophthalmologist and by using the ACM (0.88 and 0.87 for two test datasets) and ANN (0.88 and 0.89) methods were slightly higher than that by using FCM (0.86 and 0.86) method. These results on the unknown datasets were comparable with those of the resubstitution test; this indicates the generalizability of these methods. The differences in the vertical diameters, which are often used for CD ratio calculation, determined by the proposed methods and based on the ophthalmologist's outlines were even smaller than those in the case of the measure of overlap. The proposed methods can be useful for automatic determination of CD ratios.

  18. QUANTITATIVE CELL MOTILITY FOR IN VITRO WOUND HEALING USING LEVEL SET-BASED ACTIVE CONTOUR TRACKING.

    PubMed

    Bunyak, Filiz; Palaniappan, Kannappan; Nath, Sumit K; Baskin, Tobias I; Dong, Gang

    2006-04-06

    Quantifying the behavior of cells individually, and in clusters as part of a population, under a range of experimental conditions, is a challenging computational task with many biological applications. We propose a versatile algorithm for segmentation and tracking of multiple motile epithelial cells during wound healing using time-lapse video. The segmentation part of the proposed method relies on a level set-based active contour algorithm that robustly handles a large number of cells. The tracking part relies on a detection-based multiple-object tracking method with delayed decision enabled by multi-hypothesis testing. The combined method is robust to complex cell behavior including division and apoptosis, and to imaging artifacts such as illumination changes.

  19. Active contour-based visual tracking by integrating colors, shapes, and motions.

    PubMed

    Hu, Weiming; Zhou, Xue; Li, Wei; Luo, Wenhan; Zhang, Xiaoqin; Maybank, Stephen

    2013-05-01

    In this paper, we present a framework for active contour-based visual tracking using level sets. The main components of our framework include contour-based tracking initialization, color-based contour evolution, adaptive shape-based contour evolution for non-periodic motions, dynamic shape-based contour evolution for periodic motions, and the handling of abrupt motions. For the initialization of contour-based tracking, we develop an optical flow-based algorithm for automatically initializing contours at the first frame. For the color-based contour evolution, Markov random field theory is used to measure correlations between values of neighboring pixels for posterior probability estimation. For adaptive shape-based contour evolution, the global shape information and the local color information are combined to hierarchically evolve the contour, and a flexible shape updating model is constructed. For the dynamic shape-based contour evolution, a shape mode transition matrix is learnt to characterize the temporal correlations of object shapes. For the handling of abrupt motions, particle swarm optimization is adopted to capture the global motion which is applied to the contour in the current frame to produce an initial contour in the next frame.

  20. A Method for Lung Boundary Correction Using Split Bregman Method and Geometric Active Contour Model

    PubMed Central

    Zhang, Jianxun; Liang, Rui

    2015-01-01

    In order to get the extracted lung region from CT images more accurately, a model that contains lung region extraction and edge boundary correction is proposed. Firstly, a new edge detection function is presented with the help of the classic structure tensor theory. Secondly, the initial lung mask is automatically extracted by an improved active contour model which combines the global intensity information, local intensity information, the new edge information, and an adaptive weight. It is worth noting that the objective function of the improved model is converted to a convex model, which makes the proposed model get the global minimum. Then, the central airway was excluded according to the spatial context messages and the position relationship between every segmented region and the rib. Thirdly, a mesh and the fractal theory are used to detect the boundary that surrounds the juxtapleural nodule. Finally, the geometric active contour model is employed to correct the detected boundary and reinclude juxtapleural nodules. We also evaluated the performance of the proposed segmentation and correction model by comparing with their popular counterparts. Efficient computing capability and robustness property prove that our model can correct the lung boundary reliably and reproducibly. PMID:26089976

  1. Automatic Approach for Lung Segmentation with Juxta-Pleural Nodules from Thoracic CT Based on Contour Tracing and Correction.

    PubMed

    Wang, Jinke; Guo, Haoyan

    2016-01-01

    This paper presents a fully automatic framework for lung segmentation, in which juxta-pleural nodule problem is brought into strong focus. The proposed scheme consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest skin boundary is extracted through image aligning, morphology operation, and connective region analysis. Secondly, diagonal-based border tracing is implemented for lung contour segmentation, with maximum cost path algorithm used for separating the left and right lungs. Finally, by arc-based border smoothing and concave-based border correction, the refined pulmonary parenchyma is obtained. The proposed scheme is evaluated on 45 volumes of chest scans, with volume difference (VD) 11.15 ± 69.63 cm(3), volume overlap error (VOE) 3.5057 ± 1.3719%, average surface distance (ASD) 0.7917 ± 0.2741 mm, root mean square distance (RMSD) 1.6957 ± 0.6568 mm, maximum symmetric absolute surface distance (MSD) 21.3430 ± 8.1743 mm, and average time-cost 2 seconds per image. The preliminary results on accuracy and complexity prove that our scheme is a promising tool for lung segmentation with juxta-pleural nodules.

  2. Automatic Approach for Lung Segmentation with Juxta-Pleural Nodules from Thoracic CT Based on Contour Tracing and Correction

    PubMed Central

    2016-01-01

    This paper presents a fully automatic framework for lung segmentation, in which juxta-pleural nodule problem is brought into strong focus. The proposed scheme consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest skin boundary is extracted through image aligning, morphology operation, and connective region analysis. Secondly, diagonal-based border tracing is implemented for lung contour segmentation, with maximum cost path algorithm used for separating the left and right lungs. Finally, by arc-based border smoothing and concave-based border correction, the refined pulmonary parenchyma is obtained. The proposed scheme is evaluated on 45 volumes of chest scans, with volume difference (VD) 11.15 ± 69.63 cm3, volume overlap error (VOE) 3.5057 ± 1.3719%, average surface distance (ASD) 0.7917 ± 0.2741 mm, root mean square distance (RMSD) 1.6957 ± 0.6568 mm, maximum symmetric absolute surface distance (MSD) 21.3430 ± 8.1743 mm, and average time-cost 2 seconds per image. The preliminary results on accuracy and complexity prove that our scheme is a promising tool for lung segmentation with juxta-pleural nodules. PMID:27974907

  3. Fast Virtual Stenting with Active Contour Models in Intracranical Aneurysm

    PubMed Central

    Zhong, Jingru; Long, Yunling; Yan, Huagang; Meng, Qianqian; Zhao, Jing; Zhang, Ying; Yang, Xinjian; Li, Haiyun

    2016-01-01

    Intracranial stents are becoming increasingly a useful option in the treatment of intracranial aneurysms (IAs). Image simulation of the releasing stent configuration together with computational fluid dynamics (CFD) simulation prior to intervention will help surgeons optimize intervention scheme. This paper proposed a fast virtual stenting of IAs based on active contour model (ACM) which was able to virtually release stents within any patient-specific shaped vessel and aneurysm models built on real medical image data. In this method, an initial stent mesh was generated along the centerline of the parent artery without the need for registration between the stent contour and the vessel. Additionally, the diameter of the initial stent volumetric mesh was set to the maximum inscribed sphere diameter of the parent artery to improve the stenting accuracy and save computational cost. At last, a novel criterion for terminating virtual stent expanding that was based on the collision detection of the axis aligned bounding boxes was applied, making the stent expansion free of edge effect. The experiment results of the virtual stenting and the corresponding CFD simulations exhibited the efficacy and accuracy of the ACM based method, which are valuable to intervention scheme selection and therapy plan confirmation. PMID:26876026

  4. Detection of Pulmonary Nodules in CT Images Based on Fuzzy Integrated Active Contour Model and Hybrid Parametric Mixture Model

    PubMed Central

    Li, Bin; Chen, Kan; Tian, Lianfang; Yeboah, Yao; Ou, Shanxing

    2013-01-01

    The segmentation and detection of various types of nodules in a Computer-aided detection (CAD) system present various challenges, especially when (1) the nodule is connected to a vessel and they have very similar intensities; (2) the nodule with ground-glass opacity (GGO) characteristic possesses typical weak edges and intensity inhomogeneity, and hence it is difficult to define the boundaries. Traditional segmentation methods may cause problems of boundary leakage and “weak” local minima. This paper deals with the above mentioned problems. An improved detection method which combines a fuzzy integrated active contour model (FIACM)-based segmentation method, a segmentation refinement method based on Parametric Mixture Model (PMM) of juxta-vascular nodules, and a knowledge-based C-SVM (Cost-sensitive Support Vector Machines) classifier, is proposed for detecting various types of pulmonary nodules in computerized tomography (CT) images. Our approach has several novel aspects: (1) In the proposed FIACM model, edge and local region information is incorporated. The fuzzy energy is used as the motivation power for the evolution of the active contour. (2) A hybrid PMM Model of juxta-vascular nodules combining appearance and geometric information is constructed for segmentation refinement of juxta-vascular nodules. Experimental results of detection for pulmonary nodules show desirable performances of the proposed method. PMID:23690876

  5. Detection of pulmonary nodules in CT images based on fuzzy integrated active contour model and hybrid parametric mixture model.

    PubMed

    Li, Bin; Chen, Kan; Tian, Lianfang; Yeboah, Yao; Ou, Shanxing

    2013-01-01

    The segmentation and detection of various types of nodules in a Computer-aided detection (CAD) system present various challenges, especially when (1) the nodule is connected to a vessel and they have very similar intensities; (2) the nodule with ground-glass opacity (GGO) characteristic possesses typical weak edges and intensity inhomogeneity, and hence it is difficult to define the boundaries. Traditional segmentation methods may cause problems of boundary leakage and "weak" local minima. This paper deals with the above mentioned problems. An improved detection method which combines a fuzzy integrated active contour model (FIACM)-based segmentation method, a segmentation refinement method based on Parametric Mixture Model (PMM) of juxta-vascular nodules, and a knowledge-based C-SVM (Cost-sensitive Support Vector Machines) classifier, is proposed for detecting various types of pulmonary nodules in computerized tomography (CT) images. Our approach has several novel aspects: (1) In the proposed FIACM model, edge and local region information is incorporated. The fuzzy energy is used as the motivation power for the evolution of the active contour. (2) A hybrid PMM Model of juxta-vascular nodules combining appearance and geometric information is constructed for segmentation refinement of juxta-vascular nodules. Experimental results of detection for pulmonary nodules show desirable performances of the proposed method.

  6. An active contour method for bone cement reconstruction from C-arm x-ray images.

    PubMed

    Lucas, Blake C; Otake, Yoshito; Armand, Mehran; Taylor, Russell H

    2012-04-01

    A novel algorithm is presented to segment and reconstruct injected bone cement from a sparse set of X-ray images acquired at arbitrary poses. The sparse X-ray multi-view active contour (SxMAC-pronounced "smack") can 1) reconstruct objects for which the background partially occludes the object in X-ray images, 2) use X-ray images acquired on a noncircular trajectory, and 3) incorporate prior computed tomography (CT) information. The algorithm's inputs are preprocessed X-ray images, their associated pose information, and prior CT, if available. The algorithm initiates automated reconstruction using visual hull computation from a sparse number of X-ray images. It then improves the accuracy of the reconstruction by optimizing a geodesic active contour. Experiments with mathematical phantoms demonstrate improvements over a conventional silhouette based approach, and a cadaver experiment demonstrates SxMAC's ability to reconstruct high contrast bone cement that has been injected into a femur and achieve sub-millimeter accuracy with four images.

  7. Active Lip Contour Using Hue Characteristics Energy Model for A Lip Reading System

    NASA Astrophysics Data System (ADS)

    Ogoshi, Yasuhiro; Ide, Hisato; Araki, Chikahiro; Kimura, Haruhiko

    Active contour model (SNAKES) is very used as one of the powerful technique in a contour extraction that utilizes principle of energy-minimizing. Performing extraction of lip contour with the lip image that has strong edges or noises on the lips and oral cavity is an important problem. This paper proposes a new energy model of SNAKES based on hue characteristics of lip images.

  8. 3D actin network centerline extraction with multiple active contours.

    PubMed

    Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

    2014-02-01

    Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and actin cables. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we propose a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D Total Internal Reflection Fluorescence Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy. Quantitative evaluation of the method using synthetic images shows that for images with SNR above 5.0, the average vertex error measured by the distance between our result and ground truth is 1 voxel, and the average Hausdorff distance is below 10 voxels.

  9. Structural stereo matching of Laplacian-of-Gaussian contour segments for 3D perception

    NASA Technical Reports Server (NTRS)

    Boyer, K. L.; Sotak, G. E., Jr.

    1989-01-01

    The stereo correspondence problem is solved using Laplacian-of-Gaussian zero-crossing contours as a source of primitives for structural stereopsis, as opposed to traditional point-based algorithms. Up to 74 percent matching of candidate zero crossing points are being achieved on 240 x 246 images at small scales and large ranges of disparity, without coarse-to-fine tracking and without precise knowledge of the epipolar geometry. This approach should prove particularly useful in recovering the epipolar geometry automatically for stereo pairs for which it is unavailable a priori. Such situations occur in the extraction of terrain models from stereo aerial photographs.

  10. Rapid Activation of Motor Responses by Illusory Contours

    ERIC Educational Resources Information Center

    Seydell-Greenwald, Anna; Schmidt, Thomas

    2012-01-01

    Whereas physiological studies indicate that illusory contours (ICs) are signaled in early visual areas at short latencies, behavioral studies are divided as to whether IC processing can proceed in a fast, automatic, bottom-up manner or whether it requires extensive top-down intracortical feedback or even awareness and cognition. Here, we employ a…

  11. Tubular Enhanced Geodesic Active Contours for Continuum Robot Detection using 3D Ultrasound.

    PubMed

    Ren, Hongliang; Dupont, Pierre E

    2012-01-01

    Three dimensional ultrasound is a promising imaging modality for minimally invasive robotic surgery. As the robots are typically metallic, they interact strongly with the sound waves in ways that are not modeled by the ultrasound system's signal processing algorithms. Consequently, they produce substantial imaging artifacts that can make image guidance difficult, even for experienced surgeons. This paper introduces a new approach for detecting curved continuum robots in 3D ultrasound images. The proposed approach combines geodesic active contours with a speed function that is based on enhancing the "tubularity" of the continuum robot. In particular, it takes advantage of the known robot diameter along its length. It also takes advantage of the fact that the robot surface facing the ultrasound probe provides the most accurate image. This method, termed Tubular Enhanced Geodesic Active Contours (TEGAC), is demonstrated through ex vivo intracardiac experiments to offer superior performance compared to conventional active contours.

  12. Method for non-referential defect characterization using fractal encoding and active contours

    DOEpatents

    Gleason, Shaun S.; Sari-Sarraf, Hamed

    2007-05-15

    A method for identification of anomalous structures, such as defects, includes the steps of providing a digital image and applying fractal encoding to identify a location of at least one anomalous portion of the image. The method does not require a reference image to identify the location of the anomalous portion. The method can further include the step of initializing an active contour based on the location information obtained from the fractal encoding step and deforming an active contour to enhance the boundary delineation of the anomalous portion.

  13. Reconstruction of surfaces from planar contours through contour interpolation

    NASA Astrophysics Data System (ADS)

    Sunderland, Kyle; Woo, Boyeong; Pinter, Csaba; Fichtinger, Gabor

    2015-03-01

    Segmented structures such as targets or organs at risk are typically stored as 2D contours contained on evenly spaced cross sectional images (slices). Contour interpolation algorithms are implemented in radiation oncology treatment planning software to turn 2D contours into a 3D surface, however the results differ between algorithms, causing discrepancies in analysis. Our goal was to create an accurate and consistent contour interpolation algorithm that can handle issues such as keyhole contours, rapid changes, and branching. This was primarily motivated by radiation therapy research using the open source SlicerRT extension for the 3D Slicer platform. The implemented algorithm triangulates the mesh by minimizing the length of edges spanning the contours with dynamic programming. The first step in the algorithm is removing keyholes from contours. Correspondence is then found between contour layers and branching patterns are determined. The final step is triangulating the contours and sealing the external contours. The algorithm was tested on contours segmented on computed tomography (CT) images. Some cases such as inner contours, rapid changes in contour size, and branching were handled well by the algorithm when encountered individually. There were some special cases in which the simultaneous occurrence of several of these problems in the same location could cause the algorithm to produce suboptimal mesh. An open source contour interpolation algorithm was implemented in SlicerRT for reconstructing surfaces from planar contours. The implemented algorithm was able to generate qualitatively good 3D mesh from the set of 2D contours for most tested structures.

  14. An active contour-based atlas registration model applied to automatic subthalamic nucleus targeting on MRI: method and validation.

    PubMed

    Duay, Valérie; Bresson, Xavier; Castro, Javier Sanchez; Pollo, Claudio; Cuadra, Meritxell Bach; Thiran, Jean-Philippe

    2008-01-01

    This paper presents a new non parametric atlas registration framework, derived from the optical flow model and the active contour theory, applied to automatic subthalamic nucleus (STN) targeting in deep brain stimulation (DBS) surgery. In a previous work, we demonstrated that the STN position can be predicted based on the position of surrounding visible structures, namely the lateral and third ventricles. A STN targeting process can thus be obtained by registering these structures of interest between a brain atlas and the patient image. Here we aim to improve the results of the state of the art targeting methods and at the same time to reduce the computational time. Our simultaneous segmentation and registration model shows mean STN localization errors statistically similar to the most performing registration algorithms tested so far and to the targeting expert's variability. Moreover, the computational time of our registration method is much lower, which is a worthwhile improvement from a clinical point of view.

  15. Inter-element orientation and distance influence the duration of persistent contour integration.

    PubMed

    Strother, Lars; Alferov, Danila

    2014-01-01

    Contour integration is a fundamental form of perceptual organization. We introduce a new method of studying the mechanisms responsible for contour integration. This method capitalizes on the perceptual persistence of contours under conditions of impending camouflage. Observers viewed arrays of randomly arranged line segments upon which circular contours comprised of similar line segments were superimposed via abrupt onset. Crucially, these contours remained visible for up to a few seconds following onset, but eventually disappeared due to the camouflaging effects of surrounding background line segments. Our main finding was that the duration of contour visibility depended on the distance and degree of co-alignment between adjacent contour segments such that relatively dense smooth contours persisted longest. The stimulus-related effects reported here parallel similar results from contour detection studies, and complement previous reported top-down influences on contour persistence (Strother et al., 2011). We propose that persistent contour visibility reflects the sustained activity of recurrent processing loops within and between visual cortical areas involved in contour integration and other important stages of visual object recognition.

  16. Phase-based probabilistic active contour for nerve detection in ultrasound images for regional anesthesia.

    PubMed

    Hafiane, Adel; Vieyres, Pierre; Delbos, Alain

    2014-09-01

    Ultrasound guided regional anesthesia (UGRA) is steadily growing in popularity, owing to advances in ultrasound imaging technology and the advantages that this technique presents for safety and efficiency. The aim of this work is to assist anaesthetists during the UGRA procedure by automatically detecting the nerve blocks in the ultrasound images. The main disadvantage of ultrasound images is the poor quality of the images, which are also affected by the speckle noise. Moreover, the nerve structure is not salient amid the other tissues, which makes its detection a challenging problem. In this paper we propose a new method to tackle the problem of nerve zone detection in ultrasound images. The method consists in a combination of three approaches: probabilistic, edge phase information and active contours. The gradient vector flow (GVF) is adopted as an edge-based active contour. The phase analysis of the monogenic signal is used to provide reliable edges for the GVF. Then, a learned probabilistic model reduces the false positives and increases the likelihood energy term of the target region. It yields a new external force field that attracts the active contour toward the desired region of interest. The proposed scheme has been applied to sciatic nerve regions. The qualitative and quantitative evaluations show a high accuracy and a significant improvement in performance.

  17. Highway extraction from high resolution aerial photography using a geometric active contour model

    NASA Astrophysics Data System (ADS)

    Niu, Xutong

    Highway extraction and vehicle detection are two of the most important steps in traffic-flow analysis from multi-frame aerial photographs. The traditional method of deriving traffic flow trajectories relies on manual vehicle counting from a sequence of aerial photographs, which is tedious and time-consuming. This research presents a new framework for semi-automatic highway extraction. The basis of the new framework is an improved geometric active contour (GAC) model. This novel model seeks to minimize an objective function that transforms a problem of propagation of regular curves into an optimization problem. The implementation of curve propagation is based on level set theory. By using an implicit representation of a two-dimensional curve, a level set approach can be used to deal with topological changes naturally, and the output is unaffected by different initial positions of the curve. However, the original GAC model, on which the new model is based, only incorporates boundary information into the curve propagation process. An error-producing phenomenon called leakage is inevitable wherever there is an uncertain weak edge. In this research, region-based information is added as a constraint into the original GAC model, thereby, giving this proposed method the ability of integrating both boundary and region-based information during the curve propagation. Adding the region-based constraint eliminates the leakage problem. This dissertation applies the proposed augmented GAC model to the problem of highway extraction from high-resolution aerial photography. First, an optimized stopping criterion is designed and used in the implementation of the GAC model. It effectively saves processing time and computations. Second, a seed point propagation framework is designed and implemented. This framework incorporates highway extraction, tracking, and linking into one procedure. A seed point is usually placed at an end node of highway segments close to the boundary of the

  18. Intrinsic Bayesian Active Contours for Extraction of Object Boundaries in Images

    PubMed Central

    Srivastava, Anuj

    2010-01-01

    We present a framework for incorporating prior information about high-probability shapes in the process of contour extraction and object recognition in images. Here one studies shapes as elements of an infinite-dimensional, non-linear quotient space, and statistics of shapes are defined and computed intrinsically using differential geometry of this shape space. Prior models on shapes are constructed using probability distributions on tangent bundles of shape spaces. Similar to the past work on active contours, where curves are driven by vector fields based on image gradients and roughness penalties, we incorporate the prior shape knowledge in the form of vector fields on curves. Through experimental results, we demonstrate the use of prior shape models in the estimation of object boundaries, and their success in handling partial obscuration and missing data. Furthermore, we describe the use of this framework in shape-based object recognition or classification. PMID:21076692

  19. Dissociable neural correlates of contour completion and contour representation in illusory contour perception.

    PubMed

    Wu, Xiang; He, Sheng; Bushara, Khalaf; Zeng, Feiyan; Liu, Ying; Zhang, Daren

    2012-10-01

    Object recognition occurs even when environmental information is incomplete. Illusory contours (ICs), in which a contour is perceived though the contour edges are incomplete, have been extensively studied as an example of such a visual completion phenomenon. Despite the neural activity in response to ICs in visual cortical areas from low (V1 and V2) to high (LOC: the lateral occipital cortex) levels, the details of the neural processing underlying IC perception are largely not clarified. For example, how do the visual areas function in IC perception and how do they interact to archive the coherent contour perception? IC perception involves the process of completing the local discrete contour edges (contour completion) and the process of representing the global completed contour information (contour representation). Here, functional magnetic resonance imaging was used to dissociate contour completion and contour representation by varying each in opposite directions. The results show that the neural activity was stronger to stimuli with more contour completion than to stimuli with more contour representation in V1 and V2, which was the reverse of that in the LOC. When inspecting the neural activity change across the visual pathway, the activation remained high for the stimuli with more contour completion and increased for the stimuli with more contour representation. These results suggest distinct neural correlates of contour completion and contour representation, and the possible collaboration between the two processes during IC perception, indicating a neural connection between the discrete retinal input and the coherent visual percept.

  20. Automated tumour boundary delineation on 18F-FDG PET images using active contour coupled with shifted-optimal thresholding method

    NASA Astrophysics Data System (ADS)

    Khamwan, Kitiwat; Krisanachinda, Anchali; Pluempitiwiriyawej, Charnchai

    2012-10-01

    This study presents an automatic method to trace the boundary of the tumour in positron emission tomography (PET) images. It has been discovered that Otsu's threshold value is biased when the within-class variances between the object and the background are significantly different. To solve the problem, a double-stage threshold search that minimizes the energy between the first Otsu's threshold and the maximum intensity value is introduced. Such shifted-optimal thresholding is embedded into a region-based active contour so that both algorithms are performed consecutively. The efficiency of the method is validated using six sphere inserts (0.52-26.53 cc volume) of the IEC/2001 torso phantom. Both spheres and phantom were filled with 18F solution with four source-to-background ratio (SBR) measurements of PET images. The results illustrate that the tumour volumes segmented by combined algorithm are of higher accuracy than the traditional active contour. The method had been clinically implemented in ten oesophageal cancer patients. The results are evaluated and compared with the manual tracing by an experienced radiation oncologist. The advantage of the algorithm is the reduced erroneous delineation that improves the precision and accuracy of PET tumour contouring. Moreover, the combined method is robust, independent of the SBR threshold-volume curves, and it does not require prior lesion size measurement.

  1. Space Adaptation of Active Mirror Segment Concepts

    NASA Technical Reports Server (NTRS)

    Ames, Gregory H.

    1999-01-01

    This report summarizes the results of a three year effort by Blue Line Engineering Co. to advance the state of segmented mirror systems in several separate but related areas. The initial set of tasks were designed to address the issues of system level architecture, digital processing system, cluster level support structures, and advanced mirror fabrication concepts. Later in the project new tasks were added to provide support to the existing segmented mirror testbed at Marshall Space Flight Center (MSFC) in the form of upgrades to the 36 subaperture wavefront sensor. Still later, tasks were added to build and install a new system processor based on the results of the new system architecture. The project was successful in achieving a number of important results. These include the following most notable accomplishments: 1) The creation of a new modular digital processing system that is extremely capable and may be applied to a wide range of segmented mirror systems as well as many classes of Multiple Input Multiple Output (MIMO) control systems such as active structures or industrial automation. 2) A new graphical user interface was created for operation of segmented mirror systems. 3) The development of a high bit rate serial data loop that permits bi-directional flow of data to and from as many as 39 segments daisy-chained to form a single cluster of segments. 4) Upgrade of the 36 subaperture Hartmann type Wave Front Sensor (WFS) of the Phased Array Mirror, Extendible Large Aperture (PAMELA) testbed at MSFC resulting in a 40 to 5OX improvement in SNR which in turn enabled NASA personnel to achieve many significant strides in improved closed-loop system operation in 1998. 5) A new system level processor was built and delivered to MSFC for use with the PAMELA testbed. This new system featured a new graphical user interface to replace the obsolete and non-supported menu system originally delivered with the PAMELA system. The hardware featured Blue Line's new stackable

  2. Perceiving Object Shape from Specular Highlight Deformation, Boundary Contour Deformation, and Active Haptic Manipulation

    PubMed Central

    Cheeseman, Jacob R.; Thomason, Kelsey E.; Ronning, Cecilia; Behari, Kriti; Kleinman, Kayla; Calloway, Autum B.; Lamirande, Davora

    2016-01-01

    It is well known that motion facilitates the visual perception of solid object shape, particularly when surface texture or other identifiable features (e.g., corners) are present. Conventional models of structure-from-motion require the presence of texture or identifiable object features in order to recover 3-D structure. Is the facilitation in 3-D shape perception similar in magnitude when surface texture is absent? On any given trial in the current experiments, participants were presented with a single randomly-selected solid object (bell pepper or randomly-shaped “glaven”) for 12 seconds and were required to indicate which of 12 (for bell peppers) or 8 (for glavens) simultaneously visible objects possessed the same shape. The initial single object’s shape was defined either by boundary contours alone (i.e., presented as a silhouette), specular highlights alone, specular highlights combined with boundary contours, or texture. In addition, there was a haptic condition: in this condition, the participants haptically explored with both hands (but could not see) the initial single object for 12 seconds; they then performed the same shape-matching task used in the visual conditions. For both the visual and haptic conditions, motion (rotation in depth or active object manipulation) was present in half of the trials and was not present for the remaining trials. The effect of motion was quantitatively similar for all of the visual and haptic conditions–e.g., the participants’ performance in Experiment 1 was 93.5 percent higher in the motion or active haptic manipulation conditions (when compared to the static conditions). The current results demonstrate that deforming specular highlights or boundary contours facilitate 3-D shape perception as much as the motion of objects that possess texture. The current results also indicate that the improvement with motion that occurs for haptics is similar in magnitude to that which occurs for vision. PMID:26863531

  3. Infrared active polarimetric imaging system controlled by image segmentation algorithms: application to decamouflage

    NASA Astrophysics Data System (ADS)

    Vannier, Nicolas; Goudail, François; Plassart, Corentin; Boffety, Matthieu; Feneyrou, Patrick; Leviandier, Luc; Galland, Frédéric; Bertaux, Nicolas

    2016-05-01

    We describe an active polarimetric imager with laser illumination at 1.5 µm that can generate any illumination and analysis polarization state on the Poincar sphere. Thanks to its full polarization agility and to image analysis of the scene with an ultrafast active-contour based segmentation algorithm, it can perform adaptive polarimetric contrast optimization. We demonstrate the capacity of this imager to detect manufactured objects in different types of environments for such applications as decamouflage and hazardous object detection. We compare two imaging modes having different number of polarimetric degrees of freedom and underline the characteristics that a polarimetric imager aimed at this type of applications should possess.

  4. A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model

    SciTech Connect

    Bellotti, R.; De Carlo, F.; Gargano, G.; Tangaro, S.; Cascio, D.; Catanzariti, E.; Cerello, P.; Cheran, S. C.; Delogu, P.; De Mitri, I.; Fulcheri, C.; Grosso, D.; Retico, A.; Squarcia, S.; Tommasi, E.; Golosio, Bruno

    2007-12-15

    A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency.

  5. Contour complexity and contour detection.

    PubMed

    Wilder, John; Feldman, Jacob; Singh, Manish

    2015-01-01

    Itis well-known that "smooth" chains of oriented elements-contours-are more easily detected amid background noise than more undulating (i.e., "less smooth") chains. Here, we develop a Bayesian framework for contour detection and show that it predicts that contour detection performance should decrease with the contour's complexity, quantified as the description length (DL; i.e., the negative logarithm of probability integrated along the contour). We tested this prediction in two experiments in which subjects were asked to detect simple open contours amid pixel noise. In Experiment 1, we demonstrate a consistent decline in performance with increasingly complex contours, as predicted by the Bayesian model. In Experiment 2, we confirmed that this effect is due to integrated complexity along the contour, and does not seem to depend on local stretches of linear structure. The results corroborate the probabilistic model of contours, and show how contour detection can be understood as a special case of a more general process-the identification of organized patterns in the environment.

  6. Adaptive tracking of weld joints using active contour model in arc-welding processes

    NASA Astrophysics Data System (ADS)

    Kim, Jaeseon; Koh, Kyoungchul; Cho, Hyungsuck

    2001-02-01

    12 This paper presents a vision processing scheme to automatic weld joint tracking in robotic arc welding process. Particular attention is concentrated on its robustness against various optical disturbances, such as arc glares and weld spatters radiating from the melted weld pool. Underlying the developed vision processing is a kind of model-based pattern searching, which is necessarily accompanied by two separate stages of modeling and tracking. In the modeling stage, a syntactic approach is adopted to identify unknown weld joint structure. The joint profile identified in the modeling stage is used as a starting point for successive tracking of variations in the geometry of weld joint during welding, which is automatically achieved by an active contour model technology following feature- based template matching. The performance of the developed scheme is investigated through a series of practical welding experiments.

  7. Automated detection of lung tumors in PET/CT images using active contour filter

    NASA Astrophysics Data System (ADS)

    Teramoto, Atsushi; Adachi, Hayato; Tsujimoto, Masakazu; Fujita, Hiroshi; Takahashi, Katsuaki; Yamamuro, Osamu; Tamaki, Tsuneo; Nishio, Masami; Kobayashi, Toshiki

    2015-03-01

    In a previous study, we developed a hybrid tumor detection method that used both computed tomography (CT) and positron emission tomography (PET) images. However, similar to existing computer-aided detection (CAD) schemes, it was difficult to detect low-contrast lesions that touch to the normal organs such as the chest wall or blood vessels in the lung. In the current study, we proposed a novel lung tumor detection method that uses active contour filters to detect the nodules deemed "difficult" in previous CAD schemes. The proposed scheme detects lung tumors using both CT and PET images. As for the detection in CT images, the massive region was first enhanced using an active contour filter (ACF), which is a type of contrast enhancement filter that has a deformable kernel shape. The kernel shape involves closed curves that are connected by several nodes that move iteratively in order to enclose the massive region. The final output of ACF is the difference between the maximum pixel value on the deformable kernel, and pixel value on the center of the filter kernel. Subsequently, the PET images were binarized to detect the regions of increased uptake. The results were integrated, followed by the false positive reduction using 21 characteristic features and three support vector machines. In the experiment, we evaluated the proposed method using 100 PET/CT images. More than half of nodules missed using previous methods were accurately detected. The results indicate that our method may be useful for the detection of lung tumors using PET/CT images.

  8. Robust contour tracking in ultrasound tongue image sequences.

    PubMed

    Xu, Kele; Yang, Yin; Stone, Maureen; Jaumard-Hakoun, Aurore; Leboullenger, Clémence; Dreyfus, Gérard; Roussel, Pierre; Denby, Bruce

    2016-01-01

    A new contour-tracking algorithm is presented for ultrasound tongue image sequences, which can follow the motion of tongue contours over long durations with good robustness. To cope with missing segments caused by noise, or by the tongue midsagittal surface being parallel to the direction of ultrasound wave propagation, active contours with a contour-similarity constraint are introduced, which can be used to provide 'prior' shape information. Also, in order to address accumulation of tracking errors over long sequences, we present an automatic re-initialization technique, based on the complex wavelet image similarity index. Experiments on synthetic data and on real 60 frame per second (fps) data from different subjects demonstrate that the proposed method gives good contour tracking for ultrasound image sequences even over durations of minutes, which can be useful in applications such as speech recognition where very long sequences must be analyzed in their entirety.

  9. Sunspots and Coronal Bright Points Tracking using a Hybrid Algorithm of PSO and Active Contour Model

    NASA Astrophysics Data System (ADS)

    Dorotovic, I.; Shahamatnia, E.; Lorenc, M.; Rybansky, M.; Ribeiro, R. A.; Fonseca, J. M.

    2014-02-01

    In the last decades there has been a steady increase of high-resolution data, from ground-based and space-borne solar instruments, and also of solar data volume. These huge image archives require efficient automatic image processing software tools capable of detecting and tracking various features in the solar atmosphere. Results of application of such tools are essential for studies of solar activity evolution, climate change understanding and space weather prediction. The follow up of interplanetary and near-Earth phenomena requires, among others, automatic tracking algorithms that can determine where a feature is located, on successive images taken along the period of observation. Full-disc solar images, obtained both with the ground-based solar telescopes and the instruments onboard the satellites, provide essential observational material for solar physicists and space weather researchers for better understanding the Sun, studying the evolution of various features in the solar atmosphere, and also investigating solar differential rotation by tracking such features along time. Here we demonstrate and discuss the suitability of applying a hybrid Particle Swarm Optimization (PSO) algorithm and Active Contour model for tracking and determining the differential rotation of sunspots and coronal bright points (CBPs) on a set of selected solar images. The results obtained confirm that the proposed approach constitutes a promising tool for investigating the evolution of solar activity and also for automating tracking features on massive solar image archives.

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

    SciTech Connect

    Yu, J; Gong, Y; Bar-Ad, V; Giaddui, T; Galvin, J; Xiao, Y; Hu, C; Gore, E; Wheatley, M; Witt, J; Robinson, C; Bradley, J; Kong, F

    2015-06-15

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

  11. Early processing in human LOC is highly responsive to illusory contours but not to salient regions

    PubMed Central

    Shpaner, Marina; Murray, Micah M.; Foxe, John J.

    2011-01-01

    Human electrophysiological studies support a model whereby sensitivity to so-called illusory contour stimuli is first seen within the lateral occipital complex. A challenge to this model posits that the lateral occipital complex is a general site for crude region-based segmentation, based on findings of equivalent hemodynamic activations in the lateral occipital complex to illusory contour and so-called salient region stimuli, a stimulus class that lacks the classic bounding contours of illusory contours. Using high-density electrical mapping of visual evoked potentials, we show that early lateral occipital cortex activity is substantially stronger to illusory contour than to salient region stimuli, while later lateral occipital complex activity is stronger to salient region than to illusory contour stimuli. Our results suggest that equivalent hemodynamic activity to illusory contour and salient region stimuli likely reflects temporally integrated responses, a result of the poor temporal resolution of hemodynamic imaging. The temporal precision of visual evoked potentials is critical for establishing viable models of completion processes and visual scene analysis. We propose that crude spatial segmentation analyses, which are insensitive to illusory contours, occur first within dorsal visual regions, not lateral occipital complex, and that initial illusory contour sensitivity is a function of the lateral occipital complex. PMID:19895562

  12. Fully automatic contour detection in intravascular ultrasound imaging

    NASA Astrophysics Data System (ADS)

    Brusseau, Elisabeth F.; de Korte, Chris L.; Mastik, Fritz; Schaar, Johannes; van der Steen, Anton F.

    2004-04-01

    Segmentation of deformable structures remains a challenging task in ultrasound imaging especially in low signal-to-noise ratio applications. In this paper a fully automatic method, dedicated to the luminal contour segmentation in intracoronary ultrasound imaging is introduced. The method is based on an active contour with a priori properties that evolves according to the statistics of the ultrasound texture brightness, determined as being mainly Rayleigh distributed. However, contrary to classical snake-based algorithms, the presented technique neither requires from the user the pre-selection of a region of interest tight around the boundary, nor parameter tuning. This fully automatic character is achieved by an initial contour that is not set, but estimated and thus adapted to each image. Its estimation combines two statistical criteria extracted from the a posteriori probability, function of the contour position. These criteria are the location of the function maximum (or maximum a posteriori estimator) and the first zero-crossing of the function derivative. Then starting form the initial contour, a region of interest is automatically selected and the process iterated until the contour evolution can be ignored. In vivo coronary images from 15 patients, acquired with a 20 MHz central frequency Jomed Invision ultrasound scanner were segmented with the developed method. Automatic contours were compared to those manually drawn by two physician in terms of mean absolute difference. Results demonstrate that the error between automatic contours and the average of manual ones (0.099+/-0.032mm) and the inter-expert error (0.097+/-0.027mm) are similar and of small amplitude.

  13. A robust active contour edge detection algorithm based on local Gaussian statistical model for oil slick remote sensing image

    NASA Astrophysics Data System (ADS)

    Jing, Yu; Wang, Yaxuan; Liu, Jianxin; Liu, Zhaoxia

    2015-08-01

    Edge detection is a crucial method for the location and quantity estimation of oil slick when oil spills on the sea. In this paper, we present a robust active contour edge detection algorithm for oil spill remote sensing images. In the proposed algorithm, we define a local Gaussian data fitting energy term with spatially varying means and variances, and this data fitting energy term is introduced into a global minimization active contour (GMAC) framework. The energy function minimization is achieved fast by a dual formulation of the weighted total variation norm. The proposed algorithm avoids the existence of local minima, does not require the definition of initial contour, and is robust to weak boundaries, high noise and severe intensity inhomogeneity exiting in oil slick remote sensing images. Furthermore, the edge detection of oil slick and the correction of intensity inhomogeneity are simultaneously achieved via the proposed algorithm. The experiment results have shown that a superior performance of proposed algorithm over state-of-the-art edge detection algorithms. In addition, the proposed algorithm can also deal with the special images with the object and background of the same intensity means but different variances.

  14. Segmentation of knee cartilage by using a hierarchical active shape model based on multi-resolution transforms in magnetic resonance images

    NASA Astrophysics Data System (ADS)

    León, Madeleine; Escalante-Ramirez, Boris

    2013-11-01

    Knee osteoarthritis (OA) is characterized by the morphological degeneration of cartilage. Efficient segmentation of cartilage is important for cartilage damage diagnosis and to support therapeutic responses. We present a method for knee cartilage segmentation in magnetic resonance images (MRI). Our method incorporates the Hermite Transform to obtain a hierarchical decomposition of contours which describe knee cartilage shapes. Then, we compute a statistical model of the contour of interest from a set of training images. Thereby, our Hierarchical Active Shape Model (HASM) captures a large range of shape variability even from a small group of training samples, improving segmentation accuracy. The method was trained with a training set of 16- MRI of knee and tested with leave-one-out method.

  15. Active hexagonally segmented mirror to investigate new optical phasing technologies for segmented telescopes.

    PubMed

    Gonté, Frédéric; Dupuy, Christophe; Luong, Bruno; Frank, Christoph; Brast, Roland; Sedghi, Baback

    2009-11-10

    The primary mirror of the future European Extremely Large Telescope will be equipped with 984 hexagonal segments. The alignment of the segments in piston, tip, and tilt within a few nanometers requires an optical phasing sensor. A test bench has been designed to study four different optical phasing sensor technologies. The core element of the test bench is an active segmented mirror composed of 61 flat hexagonal segments with a size of 17 mm side to side. Each of them can be controlled in piston, tip, and tilt by three piezoactuators with a precision better than 1 nm. The context of this development, the requirements, the design, and the integration of this system are explained. The first results on the final precision obtained in closed-loop control are also presented.

  16. Contour Mapping

    NASA Technical Reports Server (NTRS)

    1995-01-01

    In the early 1990s, the Ohio State University Center for Mapping, a NASA Center for the Commercial Development of Space (CCDS), developed a system for mobile mapping called the GPSVan. While driving, the users can map an area from the sophisticated mapping van equipped with satellite signal receivers, video cameras and computer systems for collecting and storing mapping data. George J. Igel and Company and the Ohio State University Center for Mapping advanced the technology for use in determining the contours of a construction site. The new system reduces the time required for mapping and staking, and can monitor the amount of soil moved.

  17. THE ORIGIN OF SEGMENTATION MOTOR ACTIVITY IN THE INTESTINE

    PubMed Central

    Huizinga, Jan D.; Chen, Ji-Hong; Zhu, Yong Fang; Pawelka, Andrew; McGinn, Ryan J.; Bardakjian, Berj L.; Parsons, Sean P.; Kunze, Wolfgang A.; Wu, Richard You; Bercik, Premysl; Khoshdel, Amir; Chen, Sifeng; Yin, Sheng; Zhang, Qian; Yu, Yuanjie; Gao, Qingmin; Li, Kongling; Hu, Xinghai; Zarate, Natalia; Collins, Phillip; Pistilli, Marc; Ma, Junling; Zhang, Ruixue; Chen, David

    2016-01-01

    The segmentation motor activity of the gut that facilitates absorption of nutrients, was first described in the late 19th century but the fundamental mechanisms underlying it remain poorly understood. The dominant theory suggests alternate excitation and inhibition from the enteric nervous system. Here we demonstrate that typical segmentation can occur after total nerve blockade. The segmentation motor pattern emerges when the amplitude of the dominant pacemaker, the slow wave generated by ICC associated with the myenteric plexus (ICC-MP), is modulated by the phase of induced lower frequency rhythmic transient depolarizations, generated by ICC associated with the deep muscular plexus (ICC-DMP), resulting in a waxing and waning of the amplitude of the slow wave and a rhythmic checkered pattern of segmentation motor activity. Phase amplitude modulation of the slow waves points to an underlying system of coupled nonlinear oscillators originating in ICC. PMID:24561718

  18. The origin of segmentation motor activity in the intestine.

    PubMed

    Huizinga, Jan D; Chen, Ji-Hong; Zhu, Yong Fang; Pawelka, Andrew; McGinn, Ryan J; Bardakjian, Berj L; Parsons, Sean P; Kunze, Wolfgang A; Wu, Richard You; Bercik, Premysl; Khoshdel, Amir; Chen, Sifeng; Yin, Sheng; Zhang, Qian; Yu, Yuanjie; Gao, Qingmin; Li, Kongling; Hu, Xinghai; Zarate, Natalia; Collins, Phillip; Pistilli, Marc; Ma, Junling; Zhang, Ruixue; Chen, David

    2014-01-01

    The segmentation motor activity of the gut that facilitates absorption of nutrients was first described in the late 19th century, but the fundamental mechanisms underlying it remain poorly understood. The dominant theory suggests alternate excitation and inhibition from the enteric nervous system. Here we demonstrate that typical segmentation can occur after total nerve blockade. The segmentation motor pattern emerges when the amplitude of the dominant pacemaker, the slow wave generated by interstitial cells of Cajal associated with the myenteric plexus (ICC-MP), is modulated by the phase of induced lower frequency rhythmic transient depolarizations, generated by ICC associated with the deep muscular plexus (ICC-DMP), resulting in a waxing and waning of the amplitude of the slow wave and a rhythmic checkered pattern of segmentation motor activity. Phase-amplitude modulation of the slow waves points to an underlying system of coupled nonlinear oscillators originating in the networks of ICC.

  19. Application and histology-driven refinement of active contour models to functional region and nerve delineation: towards a digital brainstem atlas

    NASA Astrophysics Data System (ADS)

    Patel, Nirmal; Sultana, Sharmin; Rashid, Tanweer; Krusienski, Dean; Audette, Michel A.

    2015-03-01

    This paper presents a methodology for the digital formatting of a printed atlas of the brainstem and the delineation of cranial nerves from this digital atlas. It also describes on-going work on the 3D resampling and refinement of the 2D functional regions and nerve contours. In MRI-based anatomical modeling for neurosurgery planning and simulation, the complexity of the functional anatomy entails a digital atlas approach, rather than less descriptive voxel or surface-based approaches. However, there is an insufficiency of descriptive digital atlases, in particular of the brainstem. Our approach proceeds from a series of numbered, contour-based sketches coinciding with slices of the brainstem featuring both closed and open contours. The closed contours coincide with functionally relevant regions, whereby our objective is to fill in each corresponding label, which is analogous to painting numbered regions in a paint-by-numbers kit. Any open contour typically coincides with a cranial nerve. This 2D phase is needed in order to produce densely labeled regions that can be stacked to produce 3D regions, as well as identifying the embedded paths and outer attachment points of cranial nerves. Cranial nerves are modeled using an explicit contour based technique called 1-Simplex. The relevance of cranial nerves modeling of this project is two-fold: i) this atlas will fill a void left by the brain segmentation communities, as no suitable digital atlas of the brainstem exists, and ii) this atlas is necessary to make explicit the attachment points of major nerves (except I and II) having a cranial origin. Keywords: digital atlas, contour models, surface models

  20. Real-time segmentation by Active Geometric Functions.

    PubMed

    Duan, Qi; Angelini, Elsa D; Laine, Andrew F

    2010-06-01

    Recent advances in 4D imaging and real-time imaging provide image data with clinically important cardiac dynamic information at high spatial or temporal resolution. However, the enormous amount of information contained in these data has also raised a challenge for traditional image analysis algorithms in terms of efficiency. In this paper, a novel deformable model framework, Active Geometric Functions (AGF), is introduced to tackle the real-time segmentation problem. As an implicit framework paralleling to level-set, AGF has mathematical advantages in efficiency and computational complexity as well as several flexible feature similar to level-set framework. AGF is demonstrated in two cardiac applications: endocardial segmentation in 4D ultrasound and myocardial segmentation in MRI with super high temporal resolution. In both applications, AGF can perform real-time segmentation in several milliseconds per frame, which was less than the acquisition time per frame. Segmentation results are compared to manual tracing with comparable performance with inter-observer variability. The ability of such real-time segmentation will not only facilitate the diagnoses and workflow, but also enables novel applications such as interventional guidance and interactive image acquisition with online segmentation.

  1. Algorithm for Constructing Contour Plots

    NASA Technical Reports Server (NTRS)

    Johnson, W.; Silva, F.

    1984-01-01

    General computer algorithm developed for construction of contour plots. algorithm accepts as input data values at set of points irregularly distributed over plane. Algorithm based on interpolation scheme: points in plane connected by straight-line segments to form set of triangles. Program written in FORTRAN IV.

  2. Three-Dimensional Contour Maps

    ERIC Educational Resources Information Center

    Lee, Edward

    2005-01-01

    In summary, this highly conceptual activity helps middle school students understand that the lines on the contour map represent intersections of the surface of the landform with regularly spaced horizontal planes. Building the landform and relating its features to the contour map offer many opportunities for visualization, all grounded in concrete…

  3. Balloon energy based on parametric active contour and directional Walsh-Hadamard transform and its application in tracking of texture object in texture background

    NASA Astrophysics Data System (ADS)

    Tahvilian, Homa; Moallem, Payman; Monadjemi, Amirhassan

    2012-12-01

    One of the popular approaches in object boundary detecting and tracking is active contour models (ACM). This article presents a new balloon energy in parametric active contour for tracking a texture object in texture background. In this proposed method, by adding the balloon energy to the energy function of the parametric ACM, a precise detection and tracking of texture target in texture background has been elaborated. In this method, texture feature of contour and object points have been calculated using directional Walsh-Hadamard transform, which is a modified version of the Walsh-Hadamard. Then, by comparing the texture feature of contour points with texture feature of the target object, movement direction of the balloon has been determined, whereupon contour curves are expanded or shrunk in order to adapt to the target boundaries. The tracking process is iterated to the last frames. The comparison between our method and the active contour method based on the moment demonstrates that our method is more effective in tracking object boundary edges used for video streams with a changing background. Consequently, the tracking precision of our method is higher; in addition, it converges more rapidly due to it slower complexity.

  4. Magnetic Resonance Imaging-Based Target Volume Delineation in Radiation Therapy Treatment Planning for Brain Tumors Using Localized Region-Based Active Contour

    SciTech Connect

    Aslian, Hossein; Sadeghi, Mahdi; Mahdavi, Seied Rabie; Babapour Mofrad, Farshid; Astarakee, Mahdi; Khaledi, Navid; Fadavi, Pedram

    2013-09-01

    Purpose: To evaluate the clinical application of a robust semiautomatic image segmentation method to determine the brain target volumes in radiation therapy treatment planning. Methods and Materials: A local robust region-based algorithm was used on MRI brain images to study the clinical target volume (CTV) of several patients. First, 3 oncologists delineated CTVs of 10 patients manually, and the process time for each patient was calculated. The averages of the oncologists’ contours were evaluated and considered as reference contours. Then, to determine the CTV through the semiautomatic method, a fourth oncologist who was blind to all manual contours selected 4-8 points around the edema and defined the initial contour. The time to obtain the final contour was calculated again for each patient. Manual and semiautomatic segmentation were compared using 3 different metric criteria: Dice coefficient, Hausdorff distance, and mean absolute distance. A comparison also was performed between volumes obtained from semiautomatic and manual methods. Results: Manual delineation processing time of tumors for each patient was dependent on its size and complexity and had a mean (±SD) of 12.33 ± 2.47 minutes, whereas it was 3.254 ± 1.7507 minutes for the semiautomatic method. Means of Dice coefficient, Hausdorff distance, and mean absolute distance between manual contours were 0.84 ± 0.02, 2.05 ± 0.66 cm, and 0.78 ± 0.15 cm, and they were 0.82 ± 0.03, 1.91 ± 0.65 cm, and 0.7 ± 0.22 cm between manual and semiautomatic contours, respectively. Moreover, the mean volume ratio (=semiautomatic/manual) calculated for all samples was 0.87. Conclusions: Given the deformability of this method, the results showed reasonable accuracy and similarity to the results of manual contouring by the oncologists. This study shows that the localized region-based algorithms can have great ability in determining the CTV and can be appropriate alternatives for manual approaches in brain cancer.

  5. Active Region Segmentation Based on Stokes Asymmetries

    NASA Astrophysics Data System (ADS)

    Choi, Jieun; Harker-Lundberg, B.

    2011-01-01

    During the Stokes inversion process, we would ideally use a distinct model for each structure in an active region which addresses the differences in the physical conditions of these regions. While the Milne-Eddington model of the atmosphere---a frequently-used ideal model that assumes all local thermodynamic equilibrium (LTE) conditions are satisfied---is a sufficient approximation for the description of the solar photosphere, we almost always observe deviations from this model. It is thus of interest to devise a method to systematically and accurately identify the active regions based on their spectra, such that we could use a more sophisticated model catered to each structure in an active region during the actual Stokes inversion process. We present a classification scheme for different active region structures using Stokes asymmetries and line core depths as discriminators. The data used for this investigation were obtained from the Synoptic Optical Long-term Investigations of the Sun (SOLIS) facility using the Vector Spectromagnetograph (VSM), observed in a 3 A bandpass around Fe I 6302.5 A, from March 27, 2008 to March 29, 2008. This work is carried out through the National Solar Observatory Research Experiences for Undergraduate (REU) site program, which is co-funded by the Department of Defense in partnership with the National Science Foundation REU Program. The National Solar Observatory is operated by the Association of Universities for Research in Astronomy, Inc. (AURA) under cooperative agreement with the National Science Foundation.

  6. Activity recognition using Video Event Segmentation with Text (VEST)

    NASA Astrophysics Data System (ADS)

    Holloway, Hillary; Jones, Eric K.; Kaluzniacki, Andrew; Blasch, Erik; Tierno, Jorge

    2014-06-01

    Multi-Intelligence (multi-INT) data includes video, text, and signals that require analysis by operators. Analysis methods include information fusion approaches such as filtering, correlation, and association. In this paper, we discuss the Video Event Segmentation with Text (VEST) method, which provides event boundaries of an activity to compile related message and video clips for future interest. VEST infers meaningful activities by clustering multiple streams of time-sequenced multi-INT intelligence data and derived fusion products. We discuss exemplar results that segment raw full-motion video (FMV) data by using extracted commentary message timestamps, FMV metadata, and user-defined queries.

  7. Abdomen and spinal cord segmentation with augmented active shape models.

    PubMed

    Xu, Zhoubing; Conrad, Benjamin N; Baucom, Rebeccah B; Smith, Seth A; Poulose, Benjamin K; Landman, Bennett A

    2016-07-01

    Active shape models (ASMs) have been widely used for extracting human anatomies in medical images given their capability for shape regularization of topology preservation. However, sensitivity to model initialization and local correspondence search often undermines their performances, especially around highly variable contexts in computed-tomography (CT) and magnetic resonance (MR) images. In this study, we propose an augmented ASM (AASM) by integrating the multiatlas label fusion (MALF) and level set (LS) techniques into the traditional ASM framework. Using AASM, landmark updates are optimized globally via a region-based LS evolution applied on the probability map generated from MALF. This augmentation effectively extends the searching range of correspondent landmarks while reducing sensitivity to the image contexts and improves the segmentation robustness. We propose the AASM framework as a two-dimensional segmentation technique targeting structures with one axis of regularity. We apply AASM approach to abdomen CT and spinal cord (SC) MR segmentation challenges. On 20 CT scans, the AASM segmentation of the whole abdominal wall enables the subcutaneous/visceral fat measurement, with high correlation to the measurement derived from manual segmentation. On 28 3T MR scans, AASM yields better performances than other state-of-the-art approaches in segmenting white/gray matter in SC.

  8. 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…

  9. Pre-cancer risk assessment in habitual smokers from DIC images of oral exfoliative cells using active contour and SVM analysis.

    PubMed

    Dey, Susmita; Sarkar, Ripon; Chatterjee, Kabita; Datta, Pallab; Barui, Ananya; Maity, Santi P

    2017-02-09

    Habitual smokers are known to be at higher risk for developing oral cancer, which is increasing at an alarming rate globally. Conventionally, oral cancer is associated with high mortality rates, although recent reports show the improved survival outcomes by early diagnosis of disease. An effective prediction system which will enable to identify the probability of cancer development amongst the habitual smokers, is thus expected to benefit sizable number of populations. Present work describes a non-invasive, integrated method for early detection of cellular abnormalities based on analysis of different cyto-morphological features of exfoliative oral epithelial cells. Differential interference contrast (DIC) microscopy provides a potential optical tool as this mode provides a pseudo three dimensional (3-D) image with detailed morphological and textural features obtained from noninvasive, label free epithelial cells. For segmentation of DIC images, gradient vector flow snake model active contour process has been adopted. To evaluate cellular abnormalities amongst habitual smokers, the selected morphological and textural features of epithelial cells are compared with the non-smoker (-ve control group) group and clinically diagnosed pre-cancer patients (+ve control group) using support vector machine (SVM) classifier. Accuracy of the developed SVM based classification has been found to be 86% with 80% sensitivity and 89% specificity in classifying the features from the volunteers having smoking habit.

  10. Reprogramming the Chemodiversity of Terpenoid Cyclization by Remolding the Active Site Contour of epi-Isozizaene Synthase

    PubMed Central

    2015-01-01

    The class I terpenoid cyclase epi-isozizaene synthase (EIZS) utilizes the universal achiral isoprenoid substrate, farnesyl diphosphate, to generate epi-isozizaene as the predominant sesquiterpene cyclization product and at least five minor sesquiterpene products, making EIZS an ideal platform for the exploration of fidelity and promiscuity in a terpenoid cyclization reaction. The hydrophobic active site contour of EIZS serves as a template that enforces a single substrate conformation, and chaperones subsequently formed carbocation intermediates through a well-defined mechanistic sequence. Here, we have used the crystal structure of EIZS as a guide to systematically remold the hydrophobic active site contour in a library of 26 site-specific mutants. Remolded cyclization templates reprogram the reaction cascade not only by reproportioning products generated by the wild-type enzyme but also by generating completely new products of diverse structure. Specifically, we have tripled the overall number of characterized products generated by EIZS. Moreover, we have converted EIZS into six different sesquiterpene synthases: F96A EIZS is an (E)-β-farnesene synthase, F96W EIZS is a zizaene synthase, F95H EIZS is a β-curcumene synthase, F95M EIZS is a β-acoradiene synthase, F198L EIZS is a β-cedrene synthase, and F96V EIZS and W203F EIZS are (Z)-γ-bisabolene synthases. Active site aromatic residues appear to be hot spots for reprogramming the cyclization cascade by manipulating the stability and conformation of critical carbocation intermediates. A majority of mutant enzymes exhibit only relatively modest 2–100-fold losses of catalytic activity, suggesting that residues responsible for triggering substrate ionization readily tolerate mutations deeper in the active site cavity. PMID:24517311

  11. Active Contours for Multispectral Images With Non-Homogeneous Sub-Regions

    DTIC Science & Technology

    2005-09-16

    green , and blue. Hyperspectral images, used in remote sensing, are other examples of multispectral im- ages. A set of images, measured by physically...method 1, (b) method 2, (c) proposed method The green solid line of all three graphs presents the same statistics measured within class 2 in the...segmentation result. As the green solid line exists closer to the blue solid line, it presents better result. In figure 8.7(a), the dotted green line with

  12. Optimality of human contour integration.

    PubMed

    Ernst, Udo A; Mandon, Sunita; Schinkel-Bielefeld, Nadja; Neitzel, Simon D; Kreiter, Andreas K; Pawelzik, Klaus R

    2012-01-01

    For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects. We performed experiments where observers detected contours of curvilinearly aligned edge configurations embedded into randomly oriented distractors. The key feature of our framework is to use a generative process for creating the contours, for which it is possible to derive a class of ideal detection models. This allowed us to compare human detection for contours with different statistical properties to the corresponding ideal detection models for the same stimuli. We then subjected the detection models to realistic constraints and required them to reproduce human decisions for every stimulus as well as possible. By independently varying the four model parameters, we identify a single detection model which quantitatively captures all correlations of human decision behaviour for more than 2000 stimuli from 42 contour ensembles with greatly varying statistical properties. This model reveals specific interactions between edges closely matching independent findings from physiology and psychophysics. These interactions imply a statistics of contours for which edge stimuli are indeed optimally integrated by the visual system, with the objective of inferring the presence of contours in cluttered scenes. The recurrent algorithm of our model makes testable predictions about the temporal dynamics of neuronal populations engaged in contour integration, and it suggests a strong directionality of the underlying functional anatomy.

  13. Segmental origins of cardiac sympathetic nerve activity in rats.

    PubMed

    Pracejus, Natasha H; Farmer, David G S; McAllen, Robin M

    2015-01-01

    The segmental origins of cardiac sympathetic nerve activity (CSNA) were investigated in 8 urethane-anesthetized, artificially ventilated rats. The left upper thoracic sympathetic chain was exposed retropleurally after removing the heads of the second to fourth ribs. The preganglionic inputs to the chain from segments T1-T3 and the trunk distal to T3 were marked for later sectioning. CSNA was recorded conventionally, amplified, rectified and smoothed. Its mean level was quantified before and after each preganglionic input was cut, usually in rostro-caudal sequence. The level after all inputs were cut (i.e. noise and residual ECG pickup) was subtracted from previous measurements. The signal decrement from cutting each preganglionic input was then calculated as a percentage. CSNA in all rats depended on preganglionic drive from two or more segments, which were not always contiguous. Over the population, most preganglionic drive came from T3 and below, while the least came from T1. But there was striking inter-individual variation, such that the strongest drive to CSNA in any one rat could come from T1, T2, T3, or below T3. These findings provide new functional data on the segmental origins of CSNA in rats.

  14. Midbrain volume segmentation using active shape models and LBPs

    NASA Astrophysics Data System (ADS)

    Olveres, Jimena; Nava, Rodrigo; Escalante-Ramírez, Boris; Cristóbal, Gabriel; García-Moreno, Carla María.

    2013-09-01

    In recent years, the use of Magnetic Resonance Imaging (MRI) to detect different brain structures such as midbrain, white matter, gray matter, corpus callosum, and cerebellum has increased. This fact together with the evidence that midbrain is associated with Parkinson's disease has led researchers to consider midbrain segmentation as an important issue. Nowadays, Active Shape Models (ASM) are widely used in literature for organ segmentation where the shape is an important discriminant feature. Nevertheless, this approach is based on the assumption that objects of interest are usually located on strong edges. Such a limitation may lead to a final shape far from the actual shape model. This paper proposes a novel method based on the combined use of ASM and Local Binary Patterns for segmenting midbrain. Furthermore, we analyzed several LBP methods and evaluated their performance. The joint-model considers both global and local statistics to improve final adjustments. The results showed that our proposal performs substantially better than the ASM algorithm and provides better segmentation measurements.

  15. Joint contrast optimization and object segmentation in active polarimetric images.

    PubMed

    Anna, Guillaume; Bertaux, Nicolas; Galland, Frédéric; Goudail, François; Dolfi, Daniel

    2012-08-15

    We present a method for automatic target detection based on the iterative interplay between an active polarimetric imager with adaptive capabilities and a snake-based image segmentation algorithm. It successfully addresses the difficult situations where the target and the background differ only by their polarimetric properties. This method illustrates the benefits of integrating digital processing algorithms at the heart of the image acquisition process rather than using them only for postprocessing.

  16. [Body-contouring surgery].

    PubMed

    Pitanguy, Ivo

    2003-01-01

    Concepts of beauty have been continuously evolving throughout the history of mankind. The voluptuous figures that were idealized by artists in the past have been substituted by slimmer forms. Medical advances in this century have permitted safe and efficient surgical correction of contour deformities. Until recently, these alterations were mostly hidden under heavy clothing or were reluctantly accepted. Current fashion trends generally promote body-revealing attire. The media frequently encourages the importance of fitness and good health linking these qualities with youthfulness and beauty. The subliminal as well as overt message is that these are necessary and desirable requirements for social acceptance and professional success. On the other hand, current sedentary lifestyle and dietary excesses, associated with factors such as genetic determination, pregnancy and the aging process, contribute to alterations of body contour that result in the loss of the individual's body image. This creates a strong psychological motivation for surgical correction. Localized fat deposits and skin flaccidity are sometimes resistant to the most sincere efforts in weight loss and sport activities. This ever-increasing request for contour surgery has been favorably met by safe and effective anesthesiology as well as efficient surgical techniques, resulting in a high degree of patient satisfaction. It is essential that today's aesthetic surgeon understand the motivations of patients who present with body contour deformities. A request for surgical treatment should be seen as a legitimate desire to achieve a physical form that approximates the individual with his or her ideal self-image. Additionally, the surgeon must always consider the possible benefit of including the participation of a multidisciplinary team approach. Depending on each case, this team should include consultants in endocrinology, dermatology, oculoplastics, pediatrics and other appropriate specialties.

  17. Top-down control in contour grouping.

    PubMed

    Volberg, Gregor; Wutz, Andreas; Greenlee, Mark W

    2013-01-01

    Human observers tend to group oriented line segments into full contours if they follow the Gestalt rule of 'good continuation'. It is commonly assumed that contour grouping emerges automatically in early visual cortex. In contrast, recent work in animal models suggests that contour grouping requires learning and thus involves top-down control from higher brain structures. Here we explore mechanisms of top-down control in perceptual grouping by investigating synchronicity within EEG oscillations. Human participants saw two micro-Gabor arrays in a random order, with the task to indicate whether the first (S1) or the second stimulus (S2) contained a contour of collinearly aligned elements. Contour compared to non-contour S1 produced a larger posterior post-stimulus beta power (15-21 Hz). Contour S2 was associated with a pre-stimulus decrease in posterior alpha power (11-12 Hz) and in fronto-posterior theta (4-5 Hz) phase couplings, but not with a post-stimulus increase in beta power. The results indicate that subjects used prior knowledge from S1 processing for S2 contour grouping. Expanding previous work on theta oscillations, we propose that long-range theta synchrony shapes neural responses to perceptual groupings regulating lateral inhibition in early visual cortex.

  18. Outer contour extraction of skull from CT scan images

    NASA Astrophysics Data System (ADS)

    Ulinuha, M. A.; Yuniarno, E. M.; Nugroho, S. M. S.; Hariadi, M.

    2017-03-01

    Extraction of the outer contour of the skull is an important step in craniofacial reconstruction. The outer contour is required for surface reconstruction of the skull. In this paper, we propose a method to extract the outer contour of the skull. The extraction process consists of four stages: defining the region of interest, segmentation of the bone, noise removal and extraction of the outer contour based on scanning from the four sides of the image. The proposed method successfully extracts the outermost contour of the skull and avoids redundant data.

  19. Image segmentation on adaptive sub-region smoothing

    NASA Astrophysics Data System (ADS)

    Gao, Junruo; Liu, Xin; He, Kun

    2017-01-01

    To improve the performance of the active contour segmentation on real images, a new segmentation method is proposed. In this model, we construct a function about Gaussian variance according to sub-regions intensity. Further, to avoid the curve vanishing, we design the convergence condition based on the confidence level of segmentation sub-regions. Experimental results show that the proposed method is less sensitive to noise and can suppress inhomogeneous intensity regions efficiently.

  20. Contour integration with corners.

    PubMed

    Persike, Malte; Meinhardt, Günter

    2016-10-01

    Contour integration refers to the ability of the visual system to bind disjoint local elements into coherent global shapes. In cluttered images containing randomly oriented elements a contour becomes salient when its elements are coaligned with a smooth global trajectory, as described by the Gestalt law of good continuation. Abrupt changes of curvature strongly diminish contour salience. Here we show that by inserting local corner elements at points of angular discontinuity, a jagged contour becomes as salient as a straight one. We report results from detection experiments for contours with and without corner elements which indicate their psychophysical equivalence. This presents a challenge to the notion that contour integration mostly relies on local interactions between neurons tuned to single orientations, and suggests that a site where single orientations and more complex local features are combined constitutes the early basis of contour and 2D shape processing.

  1. Pulse-coupled neural networks for contour and motion matchings.

    PubMed

    Yu, Bo; Zhang, Liming

    2004-09-01

    Two neural networks based on temporal coding are proposed in this paper to perform contour and motion matchings. Both of the proposed networks are three-dimensional (3-D) pulse-coupled neural networks (PCNNs). They are composed of simplified Eckhorn neurons and mimic the structure of the primary visual cortex. The PCNN for contour matching can segment from the background the object with a particular contour, which has been stored as prior knowledge and controls the network activity in the form of spike series; The PCNN for motion matching not only detects the motion in the visual field, but also extracts the object moving in an arbitrarily specified direction. The basic idea of these two models is to encode information into the timing of spikes and later to decode this information through coincidence detectors and synapse delays to realize the knowledge-controlled object matchings. The simulation results demonstrate that the temporal coding and the decoding mechanisms are powerful enough to perform the contour and motion matchings.

  2. Shape from equal thickness contours

    SciTech Connect

    Cong, G.; Parvin, B.

    1998-05-10

    A unique imaging modality based on Equal Thickness Contours (ETC) has introduced a new opportunity for 3D shape reconstruction from multiple views. We present a computational framework for representing each view of an object in terms of its object thickness, and then integrating these representations into a 3D surface by algebraic reconstruction. The object thickness is inferred by grouping curve segments that correspond to points of second derivative maxima. At each step of the process, we use some form of regularization to ensure closeness to the original features, as well as neighborhood continuity. We apply our approach to images of a sub-micron crystal structure obtained through a holographic process.

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  4. Guided energy-minimizing model for segmentation of vector fields

    NASA Astrophysics Data System (ADS)

    Binias, Bartosz

    2016-06-01

    Active contours or snakes, are a group of image segmentation methods based on the idea of energy-minimizng curves. In this paper classical snake model with added Balloon Force is modified, granting it the capability of performing object segmentation task on data with unlimited number of channels. Thanks to introduction of novel component, named the Guiding Energy, into the classical active contour energy functional, the method is now capable of focusing on the objects which posses a specified features provided to the model.

  5. Uterus segmentation in dynamic MRI using LBP texture descriptors

    NASA Astrophysics Data System (ADS)

    Namias, R.; Bellemare, M.-E.; Rahim, M.; Pirró, N.

    2014-03-01

    Pelvic floor disorders cover pathologies of which physiopathology is not well understood. However cases get prevalent with an ageing population. Within the context of a project aiming at modelization of the dynamics of pelvic organs, we have developed an efficient segmentation process. It aims at alleviating the radiologist with a tedious one by one image analysis. From a first contour delineating the uterus-vagina set, the organ border is tracked along a dynamic mri sequence. The process combines movement prediction, local intensity and texture analysis and active contour geometry control. Movement prediction allows a contour intitialization for next image in the sequence. Intensity analysis provides image-based local contour detection enhanced by local binary pattern (lbp) texture descriptors. Geometry control prohibits self intersections and smoothes the contour. Results show the efficiency of the method with images produced in clinical routine.

  6. Strain gauge ambiguity sensor for segmented mirror active optical system

    NASA Technical Reports Server (NTRS)

    Wyman, C. L.; Howe, T. L. (Inventor)

    1974-01-01

    A system is described to measure alignment between interfacing edges of mirror segments positioned to form a segmented mirror surface. It serves as a gauge having a bending beam with four piezoresistive elements coupled across the interfaces of the edges of adjacent mirror segments. The bending beam has a first position corresponding to alignment of the edges of adjacent mirror segments, and it is bendable from the first position in a direction and to a degree dependent upon the relative misalignment between the edges of adjacent mirror segments to correspondingly vary the resistance of the strain guage. A source of power and an amplifier are connected in circuit with the strain gauge whereby the output of the amplifier varies according to the misalignment of the edges of adjacent mirror segments.

  7. Distributed Contour Trees

    SciTech Connect

    Morozov, Dmitriy; Weber, Gunther H.

    2014-03-31

    Topological techniques provide robust tools for data analysis. They are used, for example, for feature extraction, for data de-noising, and for comparison of data sets. This chapter concerns contour trees, a topological descriptor that records the connectivity of the isosurfaces of scalar functions. These trees are fundamental to analysis and visualization of physical phenomena modeled by real-valued measurements. We study the parallel analysis of contour trees. After describing a particular representation of a contour tree, called local{global representation, we illustrate how di erent problems that rely on contour trees can be solved in parallel with minimal communication.

  8. Evidence for segmental mobility in the active site of pepsin

    SciTech Connect

    Pohl, J.; Strop, P.; Senn, H.; Foundling, S.; Kostka, V.

    1986-05-01

    The low hydrolytic activity (k/sub cat/ < 0.001 s/sup -1/) of chicken pepsin (CP) towards tri- and tetrapeptides is enhanced at least 100 times by modification of its single sulfhydryl group of Cys-115, with little effect on K/sub m/-values. Modification thus simulates the effect of secondary substrate binding on pepsin catalysis. The rate of Cys-115 modification is substantially decreased in the presence of some competitive inhibitors, suggesting its active site location. Experiments with CP alkylated at Cys-115 with Acrylodan as a fluorescent probe or with N-iodoacetyl-(4-fluoro)-aniline as a /sup 19/F-nmr probe suggest conformation change around Cys-115 to occur on substrate or substrate analog binding. The difference /sup 1/H-nmr spectra (500 MHz) of unmodified free and inhibitor-complexed CP reveal chemical shifts almost exclusively in the aromatic region. The effects of Cu/sup + +/ on /sup 19/F- and /sup 1/H-nmr spectra have been studied. Examination of a computer graphics model of CP based on E. parasitica pepsin-inhibitor complex X-ray coordinates suggests that Cys-115 is located near the S/sub 3//S/sub 5/ binding site. The results are interpreted in favor of segmental mobility of this region important for pepsin substrate binding and catalysis.

  9. Segmentation of polycystic kidneys from MR images

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

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

    PubMed

    Zhou, Wu; Xie, Yaoqin

    2013-01-01

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

  11. A Typology of Middle School Girls: Audience Segmentation Related to Physical Activity

    PubMed Central

    Staten, Lisa K.; Birnbaum, Amanda S.; Jobe, Jared B.; Elder, John P.

    2008-01-01

    The Trial of Activity for Adolescent Girls (TAAG) combines social ecological and social marketing approaches to promote girls’ participation in physical activity programs implemented at 18 middle schools throughout the United States. Key to the TAAG approach is targeting materials to a variety of audience segments. TAAG segments are individuals who share one or more common characteristic that is expected to correlate with physical activity. Thirteen focus groups with seventh and eighth grade girls were conducted to identify and characterize segments. Potential messages and channels of communication were discussed for each segment. Based on participant responses, six primary segments were identified: athletic, preppy, quiet, rebel, smart, and tough. The focus group information was used to develop targeted promotional tools to appeal to a diversity of girls. Using audience segmentation for targeting persuasive communication is potentially useful for intervention programs but may be sensitive; therefore, ethical issues must be critically examined. PMID:16397160

  12. A typology of middle school girls: audience segmentation related to physical activity.

    PubMed

    Staten, Lisa K; Birnbaum, Amanda S; Jobe, Jared B; Elder, John P

    2006-02-01

    The Trial of Activity for Adolescent Girls (TAAG) combines social ecological and social marketing approaches to promote girls' participation in physical activity programs implemented at 18 middle schools throughout the United States. Key to the TAAG approach is targeting materials to a variety of audience segments. TAAG segments are individuals who share one or more common characteristic that is expected to correlate with physical activity. Thirteen focus groups with seventh and eighth grade girls were conducted to identify and characterize segments. Potential messages and channels of communication were discussed for each segment. Based on participant responses, six primary segments were identified: athletic, preppy, quiet, rebel, smart, and tough. The focus group information was used to develop targeted promotional tools to appeal to a diversity of girls. Using audience segmentation for targeting persuasive communication is potentially useful for intervention programs but may be sensitive; therefore, ethical issues must be critically examined.

  13. Fast marching over the 2D Gabor magnitude domain for tongue body segmentation

    NASA Astrophysics Data System (ADS)

    Cui, Zhenchao; Zhang, Hongzhi; Zhang, David; Li, Naimin; Zuo, Wangmeng

    2013-12-01

    Tongue body segmentation is a prerequisite to tongue image analysis and has recently received considerable attention. The existing tongue body segmentation methods usually involve two key steps: edge detection and active contour model (ACM)-based segmentation. However, conventional edge detectors cannot faithfully detect the contour of the tongue body, and the initialization of ACM suffers from the edge discontinuity problem. To address these issues, we proposed a novel tongue body segmentation method, GaborFM, which initializes ACM by performing fast marching over the two-dimensional (2D) Gabor magnitude domain of the tongue images. For the enhancement of the contour of the tongue body, we used the 2D Gabor magnitude-based detector. To cope with the edge discontinuity problem, the fast marching method was utilized to connect the discontinuous contour segments, resulting in a closed and continuous tongue body contour for subsequent ACM-based segmentation. Qualitative and quantitative results showed that GaborFM is superior to the other methods for tongue body segmentation.

  14. A Typology of Middle School Girls: Audience Segmentation Related to Physical Activity

    ERIC Educational Resources Information Center

    Staten, Lisa K.; Birnbaum, Amanda S.; Jobe, Jared B.; Elder, John P.

    2006-01-01

    The Trial of Activity for Adolescent Girls (TAAG) combines social ecological and social marketing approaches to promote girls' participation in physical activity programs implemented at 18 middle schools throughout the United States. Key to the TAAG approach is targeting materials to a variety of audience segments. TAAG segments are individuals…

  15. Variable contour securing system

    NASA Technical Reports Server (NTRS)

    Zebus, P. P.; Packer, P. N.; Haynie, C. C. (Inventor)

    1978-01-01

    A variable contour securing system has a retaining structure for a member whose surface contains a variable contour. The retaining mechanism includes a spaced array of adjustable spindles mounted on a housing. Each spindle has a base member support cup at one end. A vacuum source is applied to the cups for seating the member adjacent to the cups. A locking mechanism sets the spindles in a predetermined position once the member has been secured to the spindle support cups.

  16. GENERALIZED DIGITAL CONTOURING PROGRAM

    NASA Technical Reports Server (NTRS)

    Jones, R. L.

    1994-01-01

    This is a digital computer contouring program developed by combining desirable characteristics from several existing contouring programs. It can easily be adapted to many different research requirements. The overlaid structure of the program permits desired modifications to be made with ease. The contouring program performs both the task of generating a depth matrix from either randomly or regularly spaced surface heights and the task of contouring the data. Each element of the depth matrix is computed as a weighted mean of heights predicted at an element by planes tangent to the surface at neighboring control points. Each contour line is determined by its intercepts with the sides of geometrical figures formed by connecting the various elements of the depth matrix with straight lines. Although contour charts are usually thought of as being two-dimensional pictorial representations of topographic formations of land masses, they can also be useful in portraying data which are obtained during the course of research in various scientific disciplines and which would ordinarily be tabulated. Any set of data which can be referenced to a two-dimensional coordinate system can be graphically represented by this program. This program is written in FORTRAN IV and ASSEMBLER for batch execution and has been implemented on the CDC 6000 Series. This program was developed in 1971.

  17. A geometric deformable model for echocardiographic image segmentation

    NASA Technical Reports Server (NTRS)

    Hang, X.; Greenberg, N. L.; Thomas, J. D.

    2002-01-01

    Gradient vector flow (GVF), an elegant external force for parametric deformable models, can capture object boundaries from both sides. A new geometric deformable model is proposed that combines GVF and the geodesic active contour model. The level set method is used as the numerical method of this model. The model is applied for echocardiographic image segmentation.

  18. Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography

    PubMed Central

    Shim, Hackjoon; Jeon, Byunghwan; Jang, Yeonggul; Hong, Youngtaek; Jung, Sunghee; Ha, Seongmin; Chang, Hyuk-Jae

    2016-01-01

    We propose a Bayesian tracking and segmentation method of coronary arteries on coronary computed tomographic angiography (CCTA). The geometry of coronary arteries including lumen boundary is estimated in Maximum A Posteriori (MAP) framework. Three consecutive sphere based filtering is combined with a stochastic process that is based on the similarity of the consecutive local neighborhood voxels and the geometric constraint of a vessel. It is also founded on the prior knowledge that an artery can be seen locally disconnected and consist of branches which may be seemingly disconnected due to plaque build up. For such problem, an active search method is proposed to find branches and seemingly disconnected but actually connected vessel segments. Several new measures have been developed for branch detection, disconnection check and planar vesselness measure. Using public domain Rotterdam CT dataset, the accuracy of extracted centerline is demonstrated and automatic reconstruction of coronary artery mesh is shown. PMID:27536939

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

    PubMed

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

    2012-01-01

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

  20. Aerial targets detection using improved ULPCNN combined with contour tracking

    NASA Astrophysics Data System (ADS)

    Peng, Zhenming; Jiang, Biao; Wang, Hongbing

    2008-03-01

    This paper presents a novel method for automatically segmenting and detecting targets in complex environment using the improved unit linking pulse coupled neural networks (ULPCNN) combining with contour tracking. On the one hand, the typical ULPCNN model is improved including linear modulate, linear attenuation of dynamic threshold and the attenuation parameter matrix Δ , which is more suitable for segmenting and detecting the target under complex environment. On the other hand, we determine the iteration times and obtain the optimal segmentation result using contour tracking based on maximum line contour point. In order to verify the efficiency, various simulations were conducted for different images acquired from real scenes. Experimental results show, as compared to the conventional approaches, the proposed method can overcome the drawbacks of PCNN and obtain the good results for segmenting and detecting targets against complex background.

  1. Robustness of shape descriptors to incomplete contour representations.

    PubMed

    Ghosh, Anarta; Petkov, Nicolai

    2005-11-01

    With inspiration from psychophysical researches of the human visual system, we propose a novel aspect and a method for performance evaluation of contour-based shape recognition algorithms regarding their robustness to incompleteness of contours. We use complete contour representations of objects as a reference (training) set. Incomplete contour representations of the same objects are used as a test set. The performance of an algorithm is reported using the recognition rate as a function of the percentage of contour retained. We call this evaluation procedure the ICR test. We consider three types of contour incompleteness, viz. segment-wise contour deletion, occlusion, and random pixel depletion. As an illustration, the robustness of two shape recognition algorithms to contour incompleteness is evaluated. These algorithms use a shape context and a distance multiset as local shape descriptors. Qualitatively, both algorithms mimic human visual perception in the sense that recognition performance monotonously increases with the degree of completeness and that they perform best in the case of random depletion and worst in the case of occluded contours. The distance multiset method performs better than the shape context method in this test framework.

  2. Mechanism for activation of the EGF receptor catalytic domain by the juxtamembrane segment

    PubMed Central

    Jura, Natalia; Endres, Nicholas F.; Engel, Kate; Deindl, Sebastian; Das, Rahul; Lamers, Meindert H.; Wemmer, David E.; Zhang, Xuewu; Kuriyan, John

    2009-01-01

    Signaling by the epidermal growth factor receptor requires an allosteric interaction between the kinase domains of two receptors, whereby one activates the other. We show that the intracellular juxtamembrane segment of the receptor, known to potentiate kinase activity, is able to dimerize the kinase domains. The C-terminal half of the juxtamembrane segment latches the activated kinase domain to the activator, and the N-terminal half of this segment further potentiates dimerization, most likely by forming an antiparallel helical dimer that engages the transmembrane helices of the activated receptor. Our data are consistent with a mechanism in which the extracellular domains block the intrinsic ability of the transmembrane and cytoplasmic domains to dimerize and activate, with ligand binding releasing this block. The formation of the activating juxtamembrane latch is prevented by the C-terminal tails in a new structure of an inactive kinase domain dimer, suggesting how alternative dimers can prevent ligand-independent activation. PMID:19563760

  3. Contour detection based on brightness and contour completion

    NASA Astrophysics Data System (ADS)

    Zou, Lamei; Wan, Min; Jin, Liujia; Gao, Yahong; Yang, Weidong

    2015-12-01

    The further research of visual processing mechanism provides a new idea for contour detection. On the primary visual cortex, the non-classical receptive field of the neurons has the orientation selectivity exerts suppression effect on the response of classical receptive field, which influences edge or line perception. Based on the suppression property of non-classical receptive field and contour completion, this paper proposed a contour detection method based on brightness and contour completion. The experiment shows that the proposed method can not only effectively eliminate clutter information, but also connect the broken contour points by taking advantage of contour completion.

  4. Multiquadric Spline-Based Interactive Segmentation of Vascular Networks

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-08-01

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

  6. A simple shape prior model for iris image segmentation

    NASA Astrophysics Data System (ADS)

    Bishop, Daniel A.; Yezzi, Anthony, Jr.

    2011-06-01

    In order to make biometric systems faster and more user-friendly, lower-quality images must be accepted. A major hurdle in this task is accurate segmentation of the boundaries of the iris in these images. Quite commonly, circle-fitting is used to approximate the boundaries of the inner (pupil) and outer (limbic) boundaries of the iris, but this assumption does not hold for off-axis or otherwise non-circular boundaries. In this paper we present a novel, foundational method for elliptical segmentation of off-axis iris images. This method uses active contours with constrained flow to achieve a simplified form of shape prior active contours. This is done by calculating a region-based contour evolution and projecting it upon a properly chosen set of vectors to confine it to a class of shapes. In this case, that class of shapes is ellipses. This serves to regularize the contour, simplifying the curve evolution and preventing the development of irregularities that present challenges in iris segmentation. The proposed method is tested using images from the UBIRIS v.1 and CASIA-IrisV3 image data sets, with both near-ideal and off-axis images. Additional testing has been performed using the WVU Off Axis/Angle Iris Dataset, Release 1. By avoiding many of the assumptions commonly used in iris segmentation methods, the proposed method is able to accurately fit elliptical boundaries to off-axis images.

  7. Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays.

    PubMed

    Ali, Sahirzeeshan; Veltri, Robert; Epstein, Jonathan I; Christudass, Christhunesa; Madabhushi, Anant

    2015-04-01

    Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all three terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particularly relevant consideration when multiple objects have to be segmented on very large histopathological images. The AdACM was employed for the task of segmenting nuclei on 80 prostate cancer tissue microarray images from 40 patient studies. Nuclear shape based, architectural and textural features extracted from these segmentations were extracted and found to able to discriminate different Gleason grade patterns with a classification accuracy of 86% via a quadratic discriminant analysis (QDA) classifier. On average the AdACM model provided 60% savings in computational times compared to a non-optimized hybrid active contour model involving a shape prior.

  8. Robust contour decomposition using a constant curvature criterion

    NASA Technical Reports Server (NTRS)

    Wuescher, Daniel M.; Boyer, Kim L.

    1991-01-01

    The problem of decomposing an extended boundary or contour into simple primitives is addressed with particular emphasis on Laplacian-of-Gaussian (LoG) zero-crossing contours. A technique is introduced for partitioning such contours into constant curvature segments. A nonlinear `blip' filter matched to the impairment signature of the curvature computation process, an overlapped voting scheme, and a sequential contiguous segment extraction mechanism are used. This technique is insensitive to reasonable changes in algorithm parameters and robust to noise and minor viewpoint-induced distortions in the contour shape, such as those encountered between stereo image pairs. The results vary smoothly with the data, and local perturbations induce only local changes in the result. Robustness and insensitivity are experimentally verified.

  9. The Development of Contour Interpolation: Evidence from Subjective Contours

    ERIC Educational Resources Information Center

    Hadad, Bat-Sheva; Maurer, Daphne; Lewis, Terri L.

    2010-01-01

    Adults are skilled at perceiving subjective contours in regions without any local image information (e.g., [Ginsburg, 1975] and [Kanizsa, 1976]). Here we examined the development of this skill and the effect thereon of the support ratio (i.e., the ratio of the physically specified contours to the total contour length). Children (6-, 9-, and…

  10. Reconstruction of a 3D stereotactic brain atlas and its contour-to-contour elastic deformation

    NASA Astrophysics Data System (ADS)

    Kimura, Masahiko; Otsuki, Taisuke

    1993-06-01

    We describe a refined method for estimating the 3-D geometry of cerebral structures of a patient's brain from magnetic resonance (MR) images by adapting a 3-D atlas to the images. The 3-D atlas represents the figures of anatomical subdivisions of deep cerebral structures as series of contours reconstructed from a stereotactic printed atlas. The method correlates corresponding points and curve segments that are recognizable in both the atlas and the image, by elastically deforming the atlas two-dimensionally, while maintaining the point-to-point and contour-to-contour correspondence, until equilibrium is reached. We have used the method experimentally for a patient with Parkinson's disease, and successfully estimated the substructures of the thalamus to be treated.

  11. Diagnostic accuracy of ovarian cyst segmentation in B-mode ultrasound images

    NASA Astrophysics Data System (ADS)

    Bibicu, Dorin; Moraru, Luminita; Stratulat (Visan), Mirela

    2013-11-01

    Cystic and polycystic ovary syndrome is an endocrine disorder affecting women in the fertile age. The Moore Neighbor Contour, Watershed Method, Active Contour Models, and a recent method based on Active Contour Model with Selective Binary and Gaussian Filtering Regularized Level Set (ACM&SBGFRLS) techniques were used in this paper to detect the border of the ovarian cyst from echography images. In order to analyze the efficiency of the segmentation an original computer aided software application developed in MATLAB was proposed. The results of the segmentation were compared and evaluated against the reference contour manually delineated by a sonography specialist. Both the accuracy and time complexity of the segmentation tasks are investigated. The Fréchet distance (FD) as a similarity measure between two curves and the area error rate (AER) parameter as the difference between the segmented areas are used as estimators of the segmentation accuracy. In this study, the most efficient methods for the segmentation of the ovarian were analyzed cyst. The research was carried out on a set of 34 ultrasound images of the ovarian cyst.

  12. 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.

  13. 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.

  14. Comparing demographic, health status and psychosocial strategies of audience segmentation to promote physical activity.

    PubMed

    Boslaugh, Sarah E; Kreuter, Matthew W; Nicholson, Robert A; Naleid, Kimberly

    2005-08-01

    The goal of audience segmentation is to identify population subgroups that are homogeneous with respect to certain variables associated with a given outcome or behavior. When such groups are identified and understood, targeted intervention strategies can be developed to address their unique characteristics and needs. This study compares the results of audience segmentation for physical activity that is based on either demographic, health status or psychosocial variables alone, or a combination of all three types of variables. Participants were 1090 African-American and White adults from two public health centers in St Louis, MO. Using a classification-tree algorithm to form homogeneous groups, analyses showed that more segments with greater variability in physical activity were created using psychosocial versus health status or demographic variables and that a combination of the three outperformed any individual set of variables. Simple segmentation strategies such as those relying on demographic variables alone provided little improvement over no segmentation at all. Audience segmentation appears to yield more homogeneous subgroups when psychosocial and health status factors are combined with demographic variables.

  15. Accelerometry-Derived Physical Activity of First through Third Grade Children during the Segmented School Day

    ERIC Educational Resources Information Center

    Weaver, R. Glenn; Crimarco, Anthony; Brusseau, Timothy A.; Webster, Collin A.; Burns, Ryan D.; Hannon, James C.

    2016-01-01

    Background: Schools should provide children 30 minutes/day of moderate-to-vigorous-physical-activity (MVPA). Determining school day segments that contribute to children's MVPA can inform school-based activity promotion. The purpose of this paper was to identify the proportion of children accumulating 30 minutes/day of school-based MVPA, and to…

  16. The period of the somite segmentation clock is sensitive to Notch activity

    PubMed Central

    Kim, Woong; Matsui, Takaaki; Yamao, Masataka; Ishibashi, Makoto; Tamada, Kota; Takumi, Toru; Kohno, Kenji; Oba, Shigeyuki; Ishii, Shin; Sakumura, Yuichi; Bessho, Yasumasa

    2011-01-01

    The number of vertebrae is defined strictly for a given species and depends on the number of somites, which are the earliest metameric structures that form in development. Somites are formed by sequential segmentation. The periodicity of somite segmentation is orchestrated by the synchronous oscillation of gene expression in the presomitic mesoderm (PSM), termed the “somite segmentation clock,” in which Notch signaling plays a crucial role. Here we show that the clock period is sensitive to Notch activity, which is fine-tuned by its feedback regulator, Notch-regulated ankyrin repeat protein (Nrarp), and that Nrarp is essential for forming the proper number and morphology of axial skeleton components. Null-mutant mice for Nrarp have fewer vertebrae and have defective morphologies. Notch activity is enhanced in the PSM of the Nrarp−/– embryo, where the ∼2-h segmentation period is extended by 5 min, thereby forming fewer somites and their resultant vertebrae. Reduced Notch activity partially rescues the Nrarp−/– phenotype in the number of somites, but not in morphology. Therefore we propose that the period of the somite segmentation clock is sensitive to Notch activity and that Nrarp plays essential roles in the morphology of vertebrae and ribs. PMID:21795391

  17. An Investigation of the Effects of Different Types of Activities during Pauses in a Segmented Instructional Animation

    ERIC Educational Resources Information Center

    Cheon, Jongpil; Chung, Sungwon; Crooks, Steven M.; Song, Jaeki; Kim, Jeakyeong

    2014-01-01

    Since the complex and transient information in instructional animations requires more cognitive resources, the segmenting principle has been proposed to reduce cognitive overload by providing smaller chunks with pauses between segments. This study examined the effects of different types of activities during pauses in a segmented animation. Four…

  18. Population responses to contour integration: early encoding of discrete elements and late perceptual grouping.

    PubMed

    Gilad, Ariel; Meirovithz, Elhanan; Slovin, Hamutal

    2013-04-24

    The neuronal mechanisms underlying perceptual grouping of discrete, similarly oriented elements are not well understood. To investigate this, we measured neural population responses using voltage-sensitive dye imaging in V1 of monkeys trained on a contour-detection task. By mapping the contour and background elements onto V1, we could study their neural processing. Population response early in time showed activation patches corresponding to the contour/background individual elements. However, late increased activity in the contour elements, along with suppressed activity in the background elements, enabled us to visualize in single trials a salient continuous contour "popping out" from a suppressed background. This modulated activity in the contour and in background extended beyond the cortical representation of individual contour or background elements. Finally, the late modulation was correlated with behavioral performance of contour saliency and the monkeys' perceptual report. Thus, opposing responses in the contour and background may underlie perceptual grouping in V1.

  19. A proposal of Texture Features for interactive CTA Segmentation by Active Learning.

    PubMed

    Maiora, J; Papakostas, G A; Kaburlasos, V G; Grana, M

    2014-01-01

    Our objective is to create an interactive image segmentation system of the abdominal area for quick volumetric segmentation of the aorta requiring minimal intervention of the human operator. The aforementioned goal is to be achieved by an Active Learning image segmentation system over enhanced image texture features, obtained from the standard Gray Level Co-occurrence Matrix (GLCM) and the Local Binary Patterns (LBP). The process iterates the following steps: first, image segmentation is produced by a Random Forest (RF) classifier trained on a set of image texture features for labeled voxels. The human operator is presented with the most uncertain unlabeled voxels to select some of them for inclusion in the training set, retraining the RF classifier. The approach will be applied to the segmentation of the thrombus in Computed Tomography Angiography (CTA) data of Abdominal Aortic Aneurysm (AAA) patients. A priori knowledge on the expected shape of the target structures is used to filter out undesired detections. On going preliminary experiments on datasets containing diverse number of CT slices (between 216 and 560), each one consisting a real human contrast-enhanced sample of the abdominal area, are underway. The segmentation results obtained with simple image features were promising and highlight the capacity of the used texture features to describe the local variation of the AAA thrombus and thus to provide useful information to the classifier.

  20. Evaluation of Semi-automatic Segmentation Methods for Persistent Ground Glass Nodules on Thin-Section CT Scans

    PubMed Central

    Kim, Young Jae; Lee, Seung Hyun; Park, Chang Min

    2016-01-01

    Objectives This work was a comparative study that aimed to find a proper method for accurately segmenting persistent ground glass nodules (GGN) in thin-section computed tomography (CT) images after detecting them. Methods To do this, we first applied five types of semi-automatic segmentation methods (i.e., level-set-based active contour model, localized region-based active contour model, seeded region growing, K-means clustering, and fuzzy C-means clustering) to preprocessed GGN images, respectively. Then, to measure the similarities, we calculated the Dice coefficient of the segmented area using each semiautomatic method with the result of the manually segmented area by two radiologists. Results Comparison experiments were performed using 40 persistent GGNs. In our experiment, the mean Dice coefficient for each semiautomatic segmentation tool with manually segmented area was 0.808 for the level-set-based active contour model, 0.8001 for the localized region-based active contour model, 0.629 for seeded region growing, 0.7953 for K-means clustering, and 0.7999 for fuzzy C-means clustering, respectively. Conclusions The level-set-based active contour model algorithm showed the best performance, which was most similar to the result of manual segmentation by two radiologists. From the differentiation between the normal parenchyma and the nodule, it was also the most efficient. Effective segmentation methods will be essential for the development of computer-aided diagnosis systems for more accurate early diagnosis and prognosis of lung cancer in thin-section CT images. PMID:27895963

  1. Framework of a Contour Based Depth Map Coding Method

    NASA Astrophysics Data System (ADS)

    Wang, Minghui; He, Xun; Jin, Xin; Goto, Satoshi

    Stereo-view and multi-view video formats are heavily investigated topics given their vast application potential. Depth Image Based Rendering (DIBR) system has been developed to improve Multiview Video Coding (MVC). Depth image is introduced to synthesize virtual views on the decoder side in this system. Depth image is a piecewise image, which is filled with sharp contours and smooth interior. Contours in a depth image show more importance than interior in view synthesis process. In order to improve the quality of the synthesized views and reduce the bitrate of depth image, a contour based coding strategy is proposed. First, depth image is divided into layers by different depth value intervals. Then regions, which are defined as the basic coding unit in this work, are segmented from each layer. The region is further divided into the contour and the interior. Two different procedures are employed to code contours and interiors respectively. A vector-based strategy is applied to code the contour lines. Straight lines in contours cost few of bits since they are regarded as vectors. Pixels, which are out of straight lines, are coded one by one. Depth values in the interior of a region are modeled by a linear or nonlinear formula. Coefficients in the formula are retrieved by regression. This process is called interior painting. Unlike conventional block based coding method, the residue between original frame and reconstructed frame (by contour rebuilt and interior painting) is not sent to decoder. In this proposal, contour is coded in a lossless way whereas interior is coded in a lossy way. Experimental results show that the proposed Contour Based Depth map Coding (CBDC) achieves a better performance than JMVC (reference software of MVC) in the high quality scenarios.

  2. Accurate vessel segmentation with constrained B-snake.

    PubMed

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

    2015-08-01

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

  3. Neurotensin Changes Propulsive Activity into a Segmental Motor Pattern in the Rat Colon

    PubMed Central

    Li, Hongfei; Chen, Ji-Hong; Yang, Zixian; Huang, Min; Yu, Yuanjie; Tan, Shiyun; Luo, Hesheng; Huizinga, Jan D

    2016-01-01

    Background/Aims Neurotensin is a gut-brain peptide with both inhibitory and excitatory actions on the colonic musculature; our objective was to understand the implications of this for motor patterns occurring in the intact colon of the rat. Methods The effects of neurotensin with concentrations ranging from 0.1–100 nM were studied in the intact rat colon in vitro, by investigating spatio-temporal maps created from video recordings of colonic motility before and after neurotensin. Results Low concentration of neurotensin (0.1–1 nM) inhibited propagating long distance contractions and rhythmic propagating motor complexes; in its place a slow propagating rhythmic segmental motor pattern developed. The neurotensin receptor 1 antagonist SR-48692 prevented the development of the segmental motor pattern. Higher concentrations of neurotensin (10 nM and 100 nM) were capable of restoring long distance contraction activity and inhibiting the segmental activity. The slow propagating segmental contraction showed a rhythmic contraction—relaxation cycle at the slow wave frequency originating from the interstitial cells of Cajal associated with the myenteric plexus pacemaker. High concentrations given without prior additions of low concentrations did not evoke the segmental motor pattern. These actions occurred when neurotensin was given in the bath solution or intraluminally. The segmental motor pattern evoked by neurotensin was inhibited by the neural conduction blocker lidocaine. Conclusions Neurotensin (0.1–1 nM) inhibits the dominant propulsive motor patterns of the colon and a distinct motor pattern of rhythmic slow propagating segmental contractions develops. This motor pattern has the hallmarks of haustral boundary contractions. PMID:26882114

  4. The role of eye movements in a contour detection task.

    PubMed

    Van Humbeeck, Nathalie; Schmitt, Nadine; Hermens, Frouke; Wagemans, Johan; Ernst, Udo A

    2013-12-04

    Vision combines local feature integration with active viewing processes, such as eye movements, to perceive complex visual scenes. However, it is still unclear how these processes interact and support each other. Here, we investigated how the dynamics of saccadic eye movements interact with contour integration, focusing on situations in which contours are difficult to find or even absent. We recorded observers' eye movements while they searched for a contour embedded in a background of randomly oriented elements. Task difficulty was manipulated by varying the contour's path angle. An association field model of contour integration was employed to predict potential saccade targets by identifying stimulus locations with high contour salience. We found that the number and duration of fixations increased with the increasing path angle of the contour. In addition, fixation duration increased over the course of a trial, and the time course of saccade amplitude depended on the percept of observers. Model fitting revealed that saccades fully compensate for the reduced saliency of peripheral contour targets. Importantly, our model predicted fixation locations to a considerable degree, indicating that observers fixated collinear elements. These results show that contour integration actively guides eye movements and determines their spatial and temporal parameters.

  5. A MultiScale Particle Filter Framework for Contour Detection.

    PubMed

    Widynski, Nicolas; Mignotte, Max

    2014-10-01

    We investigate the contour detection task in complex natural images. We propose a novel contour detection algorithm which jointly tracks at two scales small pieces of edges called edgelets. This multiscale edgelet structure naturally embeds semi-local information and is the basic element of the proposed recursive Bayesian modeling. Prior and transition distributions are learned offline using a shape database. Likelihood functions are learned online, thus are adaptive to an image, and integrate color and gradient information via local, textural, oriented, and profile gradient-based features. The underlying model is estimated using a sequential Monte Carlo approach, and the final soft contour detection map is retrieved from the approximated trajectory distribution. We also propose to extend the model to the interactive cut-out task. Experiments conducted on the Berkeley Segmentation data sets show that the proposed MultiScale Particle Filter Contour Detector method performs well compared to competing state-of-the-art methods.

  6. Robot Hand Would Adapt To Contours

    NASA Technical Reports Server (NTRS)

    Collins, Earl R., Jr.

    1990-01-01

    Conceptual device uses hydraulic pressure to activate fingers. Projections on opposing fingers of proposed robot hand automatically conform to contours of object on contact. Pistons connected to common reservoir provide gentle, firm grip. Fingers communicate with each other via hydraulic pressure, without elaborate control system. Pistons move in and out, and tips slope to match contour of object. Their action tends to center object on finger. Hand used to grasp objects of various shapes and sizes. Conforming process passive; pressure of object on one or several pad elements forces other pad elements to touch it. Would not use elaborate mechanisms involving motors, cams, and cables.

  7. Automated segmentation of retinal blood vessels in spectral domain optical coherence tomography scans.

    PubMed

    Pilch, Matthäus; Wenner, Yaroslava; Strohmayr, Elisabeth; Preising, Markus; Friedburg, Christoph; Meyer Zu Bexten, Erdmuthe; Lorenz, Birgit; Stieger, Knut

    2012-07-01

    The correct segmentation of blood vessels in optical coherence tomography (OCT) images may be an important requirement for the analysis of intra-retinal layer thickness in human retinal diseases. We developed a shape model based procedure for the automatic segmentation of retinal blood vessels in spectral domain (SD)-OCT scans acquired with the Spectralis OCT system. The segmentation procedure is based on a statistical shape model that has been created through manual segmentation of vessels in a training phase. The actual segmentation procedure is performed after the approximate vessel position has been defined by a shadowgraph that assigns the lateral vessel positions. The active shape model method is subsequently used to segment blood vessel contours in axial direction. The automated segmentation results were validated against the manual segmentation of the same vessels by three expert readers. Manual and automated segmentations of 168 blood vessels from 34 B-scans were analyzed with respect to the deviations in the mean Euclidean distance and surface area. The mean Euclidean distance between the automatically and manually segmented contours (on average 4.0 pixels respectively 20 µm against all three experts) was within the range of the manually marked contours among the three readers (approximately 3.8 pixels respectively 18 µm for all experts). The area deviations between the automated and manual segmentation also lie within the range of the area deviations among the 3 clinical experts. Intra reader variability for the experts was between 0.9 and 0.94. We conclude that the automated segmentation approach is able to segment blood vessels with comparable accuracy as expert readers and will provide a useful tool in vessel analysis of whole C-scans, and in particular in multicenter trials.

  8. Simultaneous Segmentation of Prostatic Zones Using Active Appearance Models With Multiple Coupled Levelsets.

    PubMed

    Toth, Robert; Ribault, Justin; Gentile, John; Sperling, Dan; Madabhushi, Anant

    2013-09-01

    In this work we present an improvement to the popular Active Appearance Model (AAM) algorithm, that we call the Multiple-Levelset AAM (MLA). The MLA can simultaneously segment multiple objects, and makes use of multiple levelsets, rather than anatomical landmarks, to define the shapes. AAMs traditionally define the shape of each object using a set of anatomical landmarks. However, landmarks can be difficult to identify, and AAMs traditionally only allow for segmentation of a single object of interest. The MLA, which is a landmark independent AAM, allows for levelsets of multiple objects to be determined and allows for them to be coupled with image intensities. This gives the MLA the flexibility to simulataneously segmentation multiple objects of interest in a new image. In this work we apply the MLA to segment the prostate capsule, the prostate peripheral zone (PZ), and the prostate central gland (CG), from a set of 40 endorectal, T2-weighted MRI images. The MLA system we employ in this work leverages a hierarchical segmentation framework, so constructed as to exploit domain specific attributes, by utilizing a given prostate segmentation to help drive the segmentations of the CG and PZ, which are embedded within the prostate. Our coupled MLA scheme yielded mean Dice accuracy values of .81, .79 and .68 for the prostate, CG, and PZ, respectively using a leave-one-out cross validation scheme over 40 patient studies. When only considering the midgland of the prostate, the mean DSC values were .89, .84, and .76 for the prostate, CG, and PZ respectively.

  9. Simultaneous Segmentation of Prostatic Zones Using Active Appearance Models With Multiple Coupled Levelsets

    PubMed Central

    Toth, Robert; Ribault, Justin; Gentile, John; Sperling, Dan; Madabhushi, Anant

    2013-01-01

    In this work we present an improvement to the popular Active Appearance Model (AAM) algorithm, that we call the Multiple-Levelset AAM (MLA). The MLA can simultaneously segment multiple objects, and makes use of multiple levelsets, rather than anatomical landmarks, to define the shapes. AAMs traditionally define the shape of each object using a set of anatomical landmarks. However, landmarks can be difficult to identify, and AAMs traditionally only allow for segmentation of a single object of interest. The MLA, which is a landmark independent AAM, allows for levelsets of multiple objects to be determined and allows for them to be coupled with image intensities. This gives the MLA the flexibility to simulataneously segmentation multiple objects of interest in a new image. In this work we apply the MLA to segment the prostate capsule, the prostate peripheral zone (PZ), and the prostate central gland (CG), from a set of 40 endorectal, T2-weighted MRI images. The MLA system we employ in this work leverages a hierarchical segmentation framework, so constructed as to exploit domain specific attributes, by utilizing a given prostate segmentation to help drive the segmentations of the CG and PZ, which are embedded within the prostate. Our coupled MLA scheme yielded mean Dice accuracy values of .81, .79 and .68 for the prostate, CG, and PZ, respectively using a leave-one-out cross validation scheme over 40 patient studies. When only considering the midgland of the prostate, the mean DSC values were .89, .84, and .76 for the prostate, CG, and PZ respectively. PMID:23997571

  10. Differential segmental strain during active lengthening in a large biarticular thigh muscle during running

    PubMed Central

    Carr, Jennifer A.; Ellerby, David J.; Marsh, Richard L.

    2011-01-01

    SUMMARY The iliotibialis lateralis pars postacetabularis (ILPO) is the largest muscle in the hindlimb of the guinea fowl and is thought to play an important role during the stance phase of running, both absorbing and producing work. Using sonomicrometry and electromyography, we examined whether the ILPO experiences differential strain between proximal, central and distal portions of the posterior fascicles. When the ILPO is being lengthened while active, the distal portion was found to lengthen significantly more than either the proximal or central portions of the muscle. Our data support the hypothesis that the distal segment lengthened farther and faster because it began activity at shorter sarcomere lengths on the ascending limb of the length–tension curve. Probably because of the self-stabilizing effects of operating on the ascending limb of the length–tension curve, all segments reached the end of lengthening and started shortening at the same sarcomere length. During shortening, this similarity in sarcomere length among the segments was maintained, as predicted from force–velocity effects, and shortening strain was similar in all segments. The differential active strain during active lengthening is thus ultimately determined by differences in strain during the passive portion of the cycle. The sarcomere lengths of all segments of the fascicles were similar at the end of active shortening, but after the passive portion of the cycle the distal segment was shorter. Differential strain in the segments during the passive portion of the cycle may be caused by differential joint excursions at the knee and hip acting on the ends of the muscle and being transmitted differentially by the passive visco-elastic properties of the muscle. Alternatively, the differential passive strain could be due to the action of active or passive muscles in the thigh that transmit force to the IPLO in shear. Based on basic sarcomere dynamics we predict that differential strain is

  11. A whole body atlas for segmentation and delineation of organs for radiation therapy planning

    NASA Astrophysics Data System (ADS)

    Qatarneh, S. M.; Crafoord, J.; Kramer, E. L.; Maguire, G. Q.; Brahme, A.; Noz, M. E.; Hyödynmaa, S.

    2001-09-01

    A semi-automatic procedure for delineation of organs to be used as the basis of a whole body atlas database for radiation therapy planning was developed. The Visible Human Male Computed Tomography (CT)-data set was used as a "standard man" reference. The organ of interest was outlined manually and then transformed by a polynomial warping algorithm onto a clinical patient CT. This provided an initial contour, which was then adjusted and refined by the semi-automatic active contour model to find the final organ outline. The liver was used as a test organ for evaluating the performance of the procedure. Liver outlines obtained by the segmentation algorithm on six patients were compared to those manually drawn by a radiologist. The combination of warping and semi-automatic active contour model generally provided satisfactory segmentation results, but the procedure has to be extended to three dimensions.

  12. Assessment of the relationship between lesion segmentation accuracy and computer-aided diagnosis scheme performance

    NASA Astrophysics Data System (ADS)

    Zheng, Bin; Pu, Jiantao; Park, Sang Cheol; Zuley, Margarita; Gur, David

    2008-03-01

    In this study we randomly select 250 malignant and 250 benign mass regions as a training dataset. The boundary contours of these regions were manually identified and marked. Twelve image features were computed for each region. An artificial neural network (ANN) was trained as a classifier. To select a specific testing dataset, we applied a topographic multi-layer region growth algorithm to detect boundary contours of 1,903 mass regions in an initial pool of testing regions. All processed regions are sorted based on a size difference ratio between manual and automated segmentation. We selected a testing dataset involving 250 malignant and 250 benign mass regions with larger size difference ratios. Using the area under ROC curve (A Z value) as performance index we investigated the relationship between the accuracy of mass segmentation and the performance of a computer-aided diagnosis (CAD) scheme. CAD performance degrades as the size difference ratio increases. Then, we developed and tested a hybrid region growth algorithm that combined the topographic region growth with an active contour approach. In this hybrid algorithm, the boundary contour detected by the topographic region growth is used as the initial contour of the active contour algorithm. The algorithm iteratively searches for the optimal region boundaries. A CAD likelihood score of the growth region being a true-positive mass is computed in each iteration. The region growth is automatically terminated once the first maximum CAD score is reached. This hybrid region growth algorithm reduces the size difference ratios between two areas segmented automatically and manually to less than +/-15% for all testing regions and the testing A Z value increases to from 0.63 to 0.90. The results indicate that CAD performance heavily depends on the accuracy of mass segmentation. In order to achieve robust CAD performance, reducing lesion segmentation error is important.

  13. Contour matching by epipolar geometry

    NASA Astrophysics Data System (ADS)

    Hu, Mao-Lin; Zhang, Damin; Wei, Sui

    2003-09-01

    Matching features computed in images is an important process in multiview image analysis. When the motion between two images is large, the matching problem becomes very difficult. In this paper, we propose a contour matching algorithm based on geometric constraints. With the assumption that the contours are obtained from images taken from a moving camera with static scenes, we apply the epipolar constraint between two sets of contours and compute the corresponding points on the contours. From the initial epipolar constraints obtained from comer point matching, candidate contours are selected according to the epipolar geometry, the linear relation among tangent vectors of the contour. In order to reduce the possibility of false matches, the curvature of the contour of match points on a contour is also used as a selection method. The initial epipolar constraint is refined from the matched sets of contours. The algorithm can be applied to a pair or two pairs of images. All of the processes are fully automatic and successfully implemented and tested with various synthetic images.

  14. Knee cartilage segmentation using active shape models and local binary patterns

    NASA Astrophysics Data System (ADS)

    González, Germán.; Escalante-Ramírez, Boris

    2014-05-01

    Segmentation of knee cartilage has been useful for opportune diagnosis and treatment of osteoarthritis (OA). This paper presents a semiautomatic segmentation technique based on Active Shape Models (ASM) combined with Local Binary Patterns (LBP) and its approaches to describe the surrounding texture of femoral cartilage. The proposed technique is tested on a 16-image database of different patients and it is validated through Leave- One-Out method. We compare different segmentation techniques: ASM-LBP, ASM-medianLBP, and ASM proposed by Cootes. The ASM-LBP approaches are tested with different ratios to decide which of them describes the cartilage texture better. The results show that ASM-medianLBP has better performance than ASM-LBP and ASM. Furthermore, we add a routine which improves the robustness versus two principal problems: oversegmentation and initialization.

  15. Method for contour extraction for object representation

    DOEpatents

    Skourikhine, Alexei N.; Prasad, Lakshman

    2005-08-30

    Contours are extracted for representing a pixelated object in a background pixel field. An object pixel is located that is the start of a new contour for the object and identifying that pixel as the first pixel of the new contour. A first contour point is then located on the mid-point of a transition edge of the first pixel. A tracing direction from the first contour point is determined for tracing the new contour. Contour points on mid-points of pixel transition edges are sequentially located along the tracing direction until the first contour point is again encountered to complete tracing the new contour. The new contour is then added to a list of extracted contours that represent the object. The contour extraction process associates regions and contours by labeling all the contours belonging to the same object with the same label.

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

    PubMed Central

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

    2014-01-01

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

  17. Intersubunit capture of regulatory segments is a component of cooperative CaMKII activation.

    PubMed

    Chao, Luke H; Pellicena, Patricia; Deindl, Sebastian; Barclay, Lauren A; Schulman, Howard; Kuriyan, John

    2010-03-01

    The dodecameric holoenzyme of calcium-calmodulin-dependent protein kinase II (CaMKII) responds to high-frequency Ca(2+) pulses to become Ca(2+) independent. A simple coincidence-detector model for Ca(2+)-frequency dependency assumes noncooperative activation of kinase domains. We show that activation of CaMKII by Ca(2+)-calmodulin is cooperative, with a Hill coefficient of approximately 3.0, implying sequential kinase-domain activation beyond dimeric units. We present data for a model in which cooperative activation includes the intersubunit 'capture' of regulatory segments. Such a capture interaction is seen in a crystal structure that shows extensive contacts between the regulatory segment of one kinase and the catalytic domain of another. These interactions are mimicked by a natural inhibitor of CaMKII. Our results show that a simple coincidence-detection model cannot be operative and point to the importance of kinetic dissection of the frequency-response mechanism in future experiments.

  18. The W. M. Keck Telescope segmented primary mirror active control system

    SciTech Connect

    Jared, R.C.; Arthur, A.A.; Andreae, S.; Biocca, A.; Cohen, R.W.; Fuertes, J.M.; Franck, J.; Gabor, G.; Llacer, J.; Mast, T.; Meng, J.; Merrick, T.; Minor, R.; Nelson, J.; Orayani, M.; Salz, P.; Schaefer, B.; Witebsky, C.

    1989-07-01

    The ten meter diameter primary mirror of the W. M. Keck Telescope is a mosaic of thirty-six hexagonal mirrors. An active control system stabilizes the primary mirror. The active control system uses 168 measurements of the relative positions of adjacent mirror segments and 3 measurements of the primary mirror position in the telescope structure to control the 108 degrees of freedom needed to stabilize the figure and position of the primary mirror. The components of the active control system are relative position sensors, electronics, computers, actuators that position the mirrors, and software. The software algorithms control the primary mirror, perform star image stacking, emulate the segments, store and fit calibration data, and locate hardware defects. We give an overview of the active control system, its functional requirements and test measurements. 12 refs.

  19. Persistence of seed-based activity following segmentation of a microRNA guide strand.

    PubMed

    Chorn, Guillaume; Zhao, Lihong; Sachs, Alan B; Flanagan, W Michael; Lim, Lee P

    2010-12-01

    microRNAs are ∼ 22 nucleotide regulatory RNAs that are processed into duplexes from hairpin structures and incorporated into Argonaute proteins. Here, we show that a nick in the middle of the guide strand of an miRNA sequence allows for seed-based targeting characteristic of miRNA activity. Insertion of an inverted abasic, a dye, or a small gap between the two segments still permits target knockdown. While activity from the seed region of the segmented miRNA is apparent, activity from the 3' half of the guide strand is impaired, suggesting that an intact guide backbone is required for contribution from the 3' half. miRNA activity was also observed following nicking of a miRNA precursor. These results illustrate a structural flexibility in miRNA duplexes and may have applications in the design of miRNA mimetics.

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

    PubMed Central

    2013-01-01

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

  1. Precision contour gage

    DOEpatents

    Bieg, Lothar F.

    1990-12-11

    An apparatus for gaging the contour of a machined part includes a rotary slide assembly, a kinematic mount to move the apparatus into and out of position for measuring the part while the part is still on the machining apparatus, a linear probe assembly with a suspension arm and a probe assembly including as probe tip for providing a measure of linear displacement of the tip on the surface of the part, a means for changing relative positions between the part and the probe tip, and a means for recording data points representing linear positions of the probe tip at prescribed rotation intervals in the position changes between the part and the probe tip.

  2. Precision contour gage

    DOEpatents

    Bieg, L.F.

    1990-12-11

    An apparatus for gaging the contour of a machined part includes a rotary slide assembly, a kinematic mount to move the apparatus into and out of position for measuring the part while the part is still on the machining apparatus, a linear probe assembly with a suspension arm and a probe assembly including as probe tip for providing a measure of linear displacement of the tip on the surface of the part, a means for changing relative positions between the part and the probe tip, and a means for recording data points representing linear positions of the probe tip at prescribed rotation intervals in the position changes between the part and the probe tip. 5 figs.

  3. Roads Centre-Axis Extraction in Airborne SAR Images: AN Approach Based on Active Contour Model with the Use of Semi-Automatic Seeding

    NASA Astrophysics Data System (ADS)

    Lotte, R. G.; Sant'Anna, S. J. S.; Almeida, C. M.

    2013-05-01

    Research works dealing with computational methods for roads extraction have considerably increased in the latest two decades. This procedure is usually performed on optical or microwave sensors (radar) imagery. Radar images offer advantages when compared to optical ones, for they allow the acquisition of scenes regardless of atmospheric and illumination conditions, besides the possibility of surveying regions where the terrain is hidden by the vegetation canopy, among others. The cartographic mapping based on these images is often manually accomplished, requiring considerable time and effort from the human interpreter. Maps for detecting new roads or updating the existing roads network are among the most important cartographic products to date. There are currently many studies involving the extraction of roads by means of automatic or semi-automatic approaches. Each of them presents different solutions for different problems, making this task a scientific issue still open. One of the preliminary steps for roads extraction can be the seeding of points belonging to roads, what can be done using different methods with diverse levels of automation. The identified seed points are interpolated to form the initial road network, and are hence used as an input for an extraction method properly speaking. The present work introduces an innovative hybrid method for the extraction of roads centre-axis in a synthetic aperture radar (SAR) airborne image. Initially, candidate points are fully automatically seeded using Self-Organizing Maps (SOM), followed by a pruning process based on specific metrics. The centre-axis are then detected by an open-curve active contour model (snakes). The obtained results were evaluated as to their quality with respect to completeness, correctness and redundancy.

  4. Image segmentation on adaptive edge-preserving smoothing

    NASA Astrophysics Data System (ADS)

    He, Kun; Wang, Dan; Zheng, Xiuqing

    2016-09-01

    Nowadays, typical active contour models are widely applied in image segmentation. However, they perform badly on real images with inhomogeneous subregions. In order to overcome the drawback, this paper proposes an edge-preserving smoothing image segmentation algorithm. At first, this paper analyzes the edge-preserving smoothing conditions for image segmentation and constructs an edge-preserving smoothing model inspired by total variation. The proposed model has the ability to smooth inhomogeneous subregions and preserve edges. Then, a kind of clustering algorithm, which reasonably trades off edge-preserving and subregion-smoothing according to the local information, is employed to learn the edge-preserving parameter adaptively. At last, according to the confidence level of segmentation subregions, this paper constructs a smoothing convergence condition to avoid oversmoothing. Experiments indicate that the proposed algorithm has superior performance in precision, recall, and F-measure compared with other segmentation algorithms, and it is insensitive to noise and inhomogeneous-regions.

  5. Segmentation of risk structures for otologic surgery using the Probabilistic Active Shape Model (PASM)

    NASA Astrophysics Data System (ADS)

    Becker, Meike; Kirschner, Matthias; Sakas, Georgios

    2014-03-01

    Our research project investigates a multi-port approach for minimally-invasive otologic surgery. For planning such a surgery, an accurate segmentation of the risk structures is crucial. However, the segmentation of these risk structures is a challenging task: The anatomical structures are very small and some have a complex shape, low contrast and vary both in shape and appearance. Therefore, prior knowledge is needed which is why we apply model-based approaches. In the present work, we use the Probabilistic Active Shape Model (PASM), which is a more flexible and specific variant of the Active Shape Model (ASM), to segment the following risk structures: cochlea, semicircular canals, facial nerve, chorda tympani, ossicles, internal auditory canal, external auditory canal and internal carotid artery. For the evaluation we trained and tested the algorithm on 42 computed tomography data sets using leave-one-out tests. Visual assessment of the results shows in general a good agreement of manual and algorithmic segmentations. Further, we achieve a good Average Symmetric Surface Distance while the maximum error is comparatively large due to low contrast at start and end points. Last, we compare the PASM to the standard ASM and show that the PASM leads to a higher accuracy.

  6. 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.

  7. Sensory Information and Subjective Contour

    ERIC Educational Resources Information Center

    Brussell, Edward M.; And Others

    1977-01-01

    The possibility that subjective contours are an artifact of brightness contrast was explored. Concludes that subjective contour and brightness contrast are distinct perceptual phenomena but share a dependency on the processing of edge information transmitted through the achromatic channels of the visual system. (Editor/RK)

  8. Contour integration across spatial frequency.

    PubMed

    Persike, Malte; Olzak, Lynn A; Meinhardt, Günter

    2009-12-01

    Association field models of contour integration suggest that local band-pass elements are spatially grouped to global contours within limited bands of spatial frequency (Field, Hayes, & Hess, 1993). While results for local orientation and spacing variation render support for AF models, effects of spatial frequency (SF) have rarely been addressed. To explore whether contour integration occurs across SF, we studied human contour detection in Gabor random fields with SF jitter along the contour, and in the embedding field. Results show no impairment of contour detection when the contour elements are 1.25 octaves apart. Even with a SF separation of 2.25 octaves there is only moderate impairment. Because SF tuning functions measured for contextual interactions of neighbored single band-pass elements indicate much smaller bandwidths (Polat & Sagi, 1993), the results imply that contour integration cannot rest solely on local locking among neighbored orientation and SF tuned mechanisms. Robustness across spatial frequency, and across color and depth, as found recently, indicates that local orientation based grouping integrates across other basic features. This suggests an origin in not too distal brain regions.

  9. Contour Integration across Spatial Frequency

    ERIC Educational Resources Information Center

    Persike, Malte; Olzak, Lynn A.; Meinhardt, Gunter

    2009-01-01

    Association field models of contour integration suggest that local band-pass elements are spatially grouped to global contours within limited bands of spatial frequency (Field, Hayes, & Hess, 1993). While results for local orientation and spacing variation render support for AF models, effects of spatial frequency (SF) have rarely been addressed.…

  10. EVENT SEGMENTATION

    PubMed Central

    Zacks, Jeffrey M.; Swallow, Khena M.

    2012-01-01

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

  11. A new distribution metric for image segmentation

    NASA Astrophysics Data System (ADS)

    Sandhu, Romeil; Georgiou, Tryphon; Tannenbaum, Allen

    2008-03-01

    In this paper, we present a new distribution metric for image segmentation that arises as a result in prediction theory. Forming a natural geodesic, our metric quantifies "distance" for two density functionals as the standard deviation of the difference between logarithms of those distributions. Using level set methods, we incorporate an energy model based on the metric into the Geometric Active Contour framework. Moreover, we briefly provide a theoretical comparison between the popular Fisher Information metric, from which the Bhattacharyya distance originates, with the newly proposed similarity metric. In doing so, we demonstrate that segmentation results are directly impacted by the type of metric used. Specifically, we qualitatively compare the Bhattacharyya distance and our algorithm on the Kaposi Sarcoma, a pathology that infects the skin. We also demonstrate the algorithm on several challenging medical images, which further ensure the viability of the metric in the context of image segmentation.

  12. Image analysis techniques for automated IVUS contour detection.

    PubMed

    Papadogiorgaki, Maria; Mezaris, Vasileios; Chatzizisis, Yiannis S; Giannoglou, George D; Kompatsiaris, Ioannis

    2008-09-01

    Intravascular ultrasound (IVUS) constitutes a valuable technique for the diagnosis of coronary atherosclerosis. The detection of lumen and media-adventitia borders in IVUS images represents a necessary step towards the reliable quantitative assessment of atherosclerosis. In this work, a fully automated technique for the detection of lumen and media-adventitia borders in IVUS images is presented. This comprises two different steps for contour initialization: one for each corresponding contour of interest and a procedure for the refinement of the detected contours. Intensity information, as well as the result of texture analysis, generated by means of a multilevel discrete wavelet frames decomposition, are used in two different techniques for contour initialization. For subsequently producing smooth contours, three techniques based on low-pass filtering and radial basis functions are introduced. The different combinations of the proposed methods are experimentally evaluated in large datasets of IVUS images derived from human coronary arteries. It is demonstrated that our proposed segmentation approaches can quickly and reliably perform automated segmentation of IVUS images.

  13. Abnormal contextual modulation of visual contour detection in patients with schizophrenia.

    PubMed

    Schallmo, Michael-Paul; Sponheim, Scott R; Olman, Cheryl A

    2013-01-01

    Schizophrenia patients demonstrate perceptual deficits consistent with broad dysfunction in visual context processing. These include poor integration of segments forming visual contours, and reduced visual contrast effects (e.g. weaker orientation-dependent surround suppression, ODSS). Background image context can influence contour perception, as stimuli near the contour affect detection accuracy. Because of ODSS, this contextual modulation depends on the relative orientation between the contour and flanking elements, with parallel flankers impairing contour perception. However in schizophrenia, the impact of abnormal ODSS during contour perception is not clear. It is also unknown whether deficient contour perception marks genetic liability for schizophrenia, or is strictly associated with clinical expression of this disorder. We examined contour detection in 25 adults with schizophrenia, 13 unaffected first-degree biological relatives of schizophrenia patients, and 28 healthy controls. Subjects performed a psychophysics experiment designed to quantify the effect of flanker orientation during contour detection. Overall, patients with schizophrenia showed poorer contour detection performance than relatives or controls. Parallel flankers suppressed and orthogonal flankers enhanced contour detection performance for all groups, but parallel suppression was relatively weaker for schizophrenia patients than healthy controls. Relatives of patients showed equivalent performance with controls. Computational modeling suggested that abnormal contextual modulation in schizophrenia may be explained by suppression that is more broadly tuned for orientation. Abnormal flanker suppression in schizophrenia is consistent with weaker ODSS and/or broader orientation tuning. This work provides the first evidence that such perceptual abnormalities may not be associated with a genetic liability for schizophrenia.

  14. Local adaptive approach toward segmentation of microscopic images of activated sludge flocs

    NASA Astrophysics Data System (ADS)

    Khan, Muhammad Burhan; Nisar, Humaira; Ng, Choon Aun; Lo, Po Kim; Yap, Vooi Voon

    2015-11-01

    Activated sludge process is a widely used method to treat domestic and industrial effluents. The conditions of activated sludge wastewater treatment plant (AS-WWTP) are related to the morphological properties of flocs (microbial aggregates) and filaments, and are required to be monitored for normal operation of the plant. Image processing and analysis is a potential time-efficient monitoring tool for AS-WWTPs. Local adaptive segmentation algorithms are proposed for bright-field microscopic images of activated sludge flocs. Two basic modules are suggested for Otsu thresholding-based local adaptive algorithms with irregular illumination compensation. The performance of the algorithms has been compared with state-of-the-art local adaptive algorithms of Sauvola, Bradley, Feng, and c-mean. The comparisons are done using a number of region- and nonregion-based metrics at different microscopic magnifications and quantification of flocs. The performance metrics show that the proposed algorithms performed better and, in some cases, were comparable to the state-of the-art algorithms. The performance metrics were also assessed subjectively for their suitability for segmentations of activated sludge images. The region-based metrics such as false negative ratio, sensitivity, and negative predictive value gave inconsistent results as compared to other segmentation assessment metrics.

  15. A GENERAL ALGORITHM FOR THE CONSTRUCTION OF CONTOUR PLOTS

    NASA Technical Reports Server (NTRS)

    Johnson, W.

    1994-01-01

    The graphical presentation of experimentally or theoretically generated data sets frequently involves the construction of contour plots. A general computer algorithm has been developed for the construction of contour plots. The algorithm provides for efficient and accurate contouring with a modular approach which allows flexibility in modifying the algorithm for special applications. The algorithm accepts as input data values at a set of points irregularly distributed over a plane. The algorithm is based on an interpolation scheme in which the points in the plane are connected by straight line segments to form a set of triangles. In general, the data is smoothed using a least-squares-error fit of the data to a bivariate polynomial. To construct the contours, interpolation along the edges of the triangles is performed, using the bivariable polynomial if data smoothing was performed. Once the contour points have been located, the contour may be drawn. This program is written in FORTRAN IV for batch execution and has been implemented on an IBM 360 series computer with a central memory requirement of approximately 100K of 8-bit bytes. This computer algorithm was developed in 1981.

  16. Active edge control in the precessions polishing process for manufacturing large mirror segments

    NASA Astrophysics Data System (ADS)

    Li, Hongyu; Zhang, Wei; Walker, David; Yu, Gouyo

    2014-09-01

    The segmentation of the primary mirror is the only promising solution for building the next generation of ground telescopes. However, manufacturing segmented mirrors presents its own challenges. The edge mis-figure impacts directly on the telescope's scientific output. The `Edge effect' significantly dominates the polishing precision. Therefore, the edge control is regarded as one of the most difficult technical issues in the segment production that needs to be addressed urgently. This paper reports an active edge control technique for the mirror segments fabrication using the Precession's polishing technique. The strategy in this technique requires that the large spot be selected on the bulk area for fast polishing, and the small spot is used for edge figuring. This can be performed by tool lift and optimizing the dell time to compensate for non-uniform material removal at the edge zone. This requires accurate and stable edge tool influence functions. To obtain the full tool influence function at the edge, we have demonstrated in previous work a novel hybrid-measurement method which uses both simultaneous phase interferometry and profilometry. In this paper, the edge effect under `Bonnet tool' polishing is investigated. The pressure distribution is analyzed by means of finite element analysis (FEA). According to the `Preston' equation, the shape of the edge tool influence functions is predicted. With this help, the multiple process parameters at the edge zone are optimized. This is demonstrated on a 200mm crosscorners hexagonal part with a result of PV less than 200nm for entire surface.

  17. Contour Error Map Algorithm

    NASA Technical Reports Server (NTRS)

    Merceret, Francis; Lane, John; Immer, Christopher; Case, Jonathan; Manobianco, John

    2005-01-01

    The contour error map (CEM) algorithm and the software that implements the algorithm are means of quantifying correlations between sets of time-varying data that are binarized and registered on spatial grids. The present version of the software is intended for use in evaluating numerical weather forecasts against observational sea-breeze data. In cases in which observational data come from off-grid stations, it is necessary to preprocess the observational data to transform them into gridded data. First, the wind direction is gridded and binarized so that D(i,j;n) is the input to CEM based on forecast data and d(i,j;n) is the input to CEM based on gridded observational data. Here, i and j are spatial indices representing 1.25-km intervals along the west-to-east and south-to-north directions, respectively; and n is a time index representing 5-minute intervals. A binary value of D or d = 0 corresponds to an offshore wind, whereas a value of D or d = 1 corresponds to an onshore wind. CEM includes two notable subalgorithms: One identifies and verifies sea-breeze boundaries; the other, which can be invoked optionally, performs an image-erosion function for the purpose of attempting to eliminate river-breeze contributions in the wind fields.

  18. Recognizing the authenticity of emotional expressions: F0 contour matters when you need to know

    PubMed Central

    Drolet, Matthis; Schubotz, Ricarda I.; Fischer, Julia

    2014-01-01

    Authenticity of vocal emotion expression affects emotion recognition and brain activity in the so-called Theory of Mind (ToM) network, which is implied in the ability to explain and predict behavior by attributing mental states to other individuals. Exploiting the variability of the fundamental frequency (F0 contour), which varies more (higher contour) in play-acted expressions than authentic ones, we examined whether contour biases explicit categorization toward a particular authenticity or emotion category. Moreover, we tested whether contour modulates blood-oxygen-level dependent (BOLD) response in the ToM network and explored the role of task as a top-down modulator. The effects of contour on BOLD signal were analyzed by contrasting high and low contour stimuli within two previous fMRI studies that implemented emotion and authenticity rating tasks. Participants preferentially categorized higher contour stimuli as play-acted and lower contour stimuli as sad. Higher contour was found to up-regulate activation task-independently in the primary auditory cortex. Stimulus contour and task were found to interact in a network including medial prefrontal cortex, with an increase in BOLD signal for low-contour stimuli during explicit perception of authenticity and an increase for high-contour stimuli during explicit perception of emotion. Contour-induced BOLD effects appear to be purely stimulus-driven in early auditory and intonation perception, while being strongly task-dependent in regions involved in higher cognition. PMID:24701202

  19. Recognizing the authenticity of emotional expressions: F0 contour matters when you need to know.

    PubMed

    Drolet, Matthis; Schubotz, Ricarda I; Fischer, Julia

    2014-01-01

    Authenticity of vocal emotion expression affects emotion recognition and brain activity in the so-called Theory of Mind (ToM) network, which is implied in the ability to explain and predict behavior by attributing mental states to other individuals. Exploiting the variability of the fundamental frequency (F0 contour), which varies more (higher contour) in play-acted expressions than authentic ones, we examined whether contour biases explicit categorization toward a particular authenticity or emotion category. Moreover, we tested whether contour modulates blood-oxygen-level dependent (BOLD) response in the ToM network and explored the role of task as a top-down modulator. The effects of contour on BOLD signal were analyzed by contrasting high and low contour stimuli within two previous fMRI studies that implemented emotion and authenticity rating tasks. Participants preferentially categorized higher contour stimuli as play-acted and lower contour stimuli as sad. Higher contour was found to up-regulate activation task-independently in the primary auditory cortex. Stimulus contour and task were found to interact in a network including medial prefrontal cortex, with an increase in BOLD signal for low-contour stimuli during explicit perception of authenticity and an increase for high-contour stimuli during explicit perception of emotion. Contour-induced BOLD effects appear to be purely stimulus-driven in early auditory and intonation perception, while being strongly task-dependent in regions involved in higher cognition.

  20. Brain networks supporting perceptual grouping and contour selection.

    PubMed

    Volberg, Gregor; Greenlee, Mark W

    2014-01-01

    The human visual system groups local elements into global objects seemingly without effort. Using a contour integration task and EEG source level analyses, we tested the hypothesis that perceptual grouping requires a top-down selection, rather than a passive pooling, of neural information that codes local elements in the visual image. The participants were presented visual displays with or without a hidden contour. Two tasks were performed: a central luminance-change detection task and a peripheral contour detection task. Only in the contour-detection task could we find differential brain activity between contour and non-contour conditions, within a distributed brain network including parietal, lateral occipital and primary visual areas. Contour processing was associated with an inflow of information from lateral occipital into primary visual regions, as revealed from the slope of phase differences between source level oscillations within these areas. The findings suggest that contour integration results from a selection of neural information from lower visual areas, and that this selection is driven by the lateral occipital cortex.

  1. Left Ventricular Myocardial Segmentation in 3-D Ultrasound Recordings: Effect of Different Endocardial and Epicardial Coupling Strategies.

    PubMed

    Pedrosa, Joao; Barbosa, Daniel; Heyde, Brecht; Schnell, Frederic; Rosner, Assami; Claus, Piet; D'hooge, Jan

    2017-03-01

    Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Though development of automatic endocardial segmentation methods has received much attention, the same cannot be said about epicardial segmentation, in spite of the importance of full myocardial segmentation. In this paper, different ways of coupling the endocardial and epicardial segmentations are contrasted and compared with uncoupled segmentation. For this purpose, the B-spline explicit active surfaces framework was used; 27 3-D echocardiographic images were used to validate the different coupling strategies, which were compared with manual contouring of the endocardial and epicardial borders performed by an expert. It is shown that an independent segmentation of the endocardium followed by an epicardial segmentation coupled to the endocardium is the most advantageous. In this way, a framework for fully automatic 3-D myocardial segmentation is proposed using a novel coupling strategy.

  2. Medical image segmentation by combining graph cuts and oriented active appearance models.

    PubMed

    Chen, Xinjian; Udupa, Jayaram K; Bagci, Ulas; Zhuge, Ying; Yao, Jianhua

    2012-04-01

    In this paper, we propose a novel method based on a strategic combination of the active appearance model (AAM), live wire (LW), and graph cuts (GCs) for abdominal 3-D organ segmentation. The proposed method consists of three main parts: model building, object recognition, and delineation. In the model building part, we construct the AAM and train the LW cost function and GC parameters. In the recognition part, a novel algorithm is proposed for improving the conventional AAM matching method, which effectively combines the AAM and LW methods, resulting in the oriented AAM (OAAM). A multiobject strategy is utilized to help in object initialization. We employ a pseudo-3-D initialization strategy and segment the organs slice by slice via a multiobject OAAM method. For the object delineation part, a 3-D shape-constrained GC method is proposed. The object shape generated from the initialization step is integrated into the GC cost computation, and an iterative GC-OAAM method is used for object delineation. The proposed method was tested in segmenting the liver, kidneys, and spleen on a clinical CT data set and also on the MICCAI 2007 Grand Challenge liver data set. The results show the following: 1) The overall segmentation accuracy of true positive volume fraction TPVF > 94.3% and false positive volume fraction can be achieved; 2) the initialization performance can be improved by combining the AAM and LW; 3) the multiobject strategy greatly facilitates initialization; 4) compared with the traditional 3-D AAM method, the pseudo-3-D OAAM method achieves comparable performance while running 12 times faster; and 5) the performance of the proposed method is comparable to state-of-the-art liver segmentation algorithm. The executable version of the 3-D shape-constrained GC method with a user interface can be downloaded from http://xinjianchen.wordpress.com/research/.

  3. Segmentation of the common carotid artery with active shape models from 3D ultrasound images

    NASA Astrophysics Data System (ADS)

    Yang, Xin; Jin, Jiaoying; He, Wanji; Yuchi, Ming; Ding, Mingyue

    2012-03-01

    Carotid atherosclerosis is a major cause of stroke, a leading cause of death and disability. In this paper, we develop and evaluate a new segmentation method for outlining both lumen and adventitia (inner and outer walls) of common carotid artery (CCA) from three-dimensional ultrasound (3D US) images for carotid atherosclerosis diagnosis and evaluation. The data set consists of sixty-eight, 17× 2× 2, 3D US volume data acquired from the left and right carotid arteries of seventeen patients (eight treated with 80mg atorvastain and nine with placebo), who had carotid stenosis of 60% or more, at baseline and after three months of treatment. We investigate the use of Active Shape Models (ASMs) to segment CCA inner and outer walls after statin therapy. The proposed method was evaluated with respect to expert manually outlined boundaries as a surrogate for ground truth. For the lumen and adventitia segmentations, respectively, the algorithm yielded Dice Similarity Coefficient (DSC) of 93.6%+/- 2.6%, 91.8%+/- 3.5%, mean absolute distances (MAD) of 0.28+/- 0.17mm and 0.34 +/- 0.19mm, maximum absolute distances (MAXD) of 0.87 +/- 0.37mm and 0.74 +/- 0.49mm. The proposed algorithm took 4.4 +/- 0.6min to segment a single 3D US images, compared to 11.7+/-1.2min for manual segmentation. Therefore, the method would promote the translation of carotid 3D US to clinical care for the fast, safety and economical monitoring of the atherosclerotic disease progression and regression during therapy.

  4. Segmentation of common carotid artery with active appearance models from ultrasound images

    NASA Astrophysics Data System (ADS)

    Yang, Xin; He, Wanji; Fenster, Aaron; Yuchi, Ming; Ding, Mingyue

    2013-02-01

    Carotid atherosclerosis is a major cause of stroke, a leading cause of death and disability. In this paper, a new segmentation method is proposed and evaluated for outlining the common carotid artery (CCA) from transverse view images, which were sliced from three-dimensional ultrasound (3D US) of 1mm inter-slice distance (ISD), to support the monitoring and assessment of carotid atherosclerosis. The data set consists of forty-eight 3D US images acquired from both left and right carotid arteries of twelve patients in two time points who had carotid stenosis of 60% or more at the baseline. The 3D US data were collected at baseline and three-month follow-up, where seven treated with 80mg atorvastatin and five with placebo. The baseline manual boundaries were used for Active Appearance Models (AAM) training; while the treatment data for segmentation testing and evaluation. The segmentation results were compared with experts manually outlined boundaries, as a surrogate for ground truth, for further evaluation. For the adventitia and lumen segmentations, the algorithm yielded Dice Coefficients (DC) of 92.06%+/-2.73% and 89.67%+/-3.66%, mean absolute distances (MAD) of 0.28+/-0.18 mm and 0.22+/-0.16 mm, maximum absolute distances (MAXD) of 0.71+/-0.28 mm and 0.59+/-0.21 mm, respectively. The segmentation results were also evaluated via Pratt's figure of merit (FOM) with the value of 0.61+/-0.06 and 0.66+/-0.05, which provides a quantitative measure for judging the similarity. Experimental results indicate that the proposed method can promote the carotid 3D US usage for a fast, safe and economical monitoring of the atherosclerotic disease progression and regression during therapy.

  5. Active-source seismic imaging below Lake Malawi (Nyasa) from the SEGMeNT project

    NASA Astrophysics Data System (ADS)

    Shillington, D. J.; Scholz, C. A.; Gaherty, J. B.; Accardo, N. J.; McCartney, T.; Chindandali, P. R. N.; Kamihanda, G.; Trinhammer, P.; Wood, D. A.; Khalfan, M.; Ebinger, C. J.; Nyblade, A.; Mbogoni, G. J.; Mruma, A. H.; Salima, J.; Ferdinand-Wambura, R.

    2015-12-01

    Little is known about the controls on the initiation and development of magmatism and segmentation in young rift systems. The northern Lake Malawi (Nyasa) rift in the East African Rift System is an early stage rift exhibiting pronounced tectonic segmentation, which is defined in the upper crust by ~100-km-long border faults. Very little volcanism is associated with rifting; the only surface expression of magmatism occurs in an accommodation zone between segments to the north of the lake in the Rungwe Volcanic Province. The SEGMeNT (Study of Extension and maGmatism in Malawi aNd Tanzania) project is a multidisciplinary, multinational study that is acquiring a suite of geophysical, geological and geochemical data to characterize deformation and magmatism in the crust and mantle lithosphere along 2-3 segments of this rift. As a part of the SEGMeNT project, we acquired seismic reflection and refraction data in Lake Malawi (Nyasa) in March-April 2015. Over 2000 km of seismic reflection data were acquired with a 500 to 2580 cu in air gun array from GEUS/Aarhus and a 500- to 1500-m-long seismic streamer from Syracuse University over a grid of lines across and along the northern and central basins. Air gun shots from MCS profiles and 1000 km of additional shooting with large shot intervals were also recorded on 27 short-period and 6 broadband lake bottom seismometers from Scripps Oceanographic Institute as a part of the Ocean Bottom Seismic Instrument Pool (OBSIP) as well as the 55-station onshore seismic array. The OBS were deployed along one long strike line and two dip lines. We will present preliminary data and results from seismic reflection and refraction data acquired in the lake and their implications for crustal deformation within and between rift segments. Seismic reflection data image structures up to ~5-6 km below the lake bottom, including syntectonic sediments, intrabasinal faults and other complex horsts. Some intrabasinal faults in both the northern and

  6. 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.

  7. In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation

    PubMed Central

    Xia, Chunlei; Wang, Longtan; Chung, Bu-Keun; Lee, Jang-Myung

    2015-01-01

    In this paper, we present a challenging task of 3D segmentation of individual plant leaves from occlusions in the complicated natural scene. Depth data of plant leaves is introduced to improve the robustness of plant leaf segmentation. The low cost RGB-D camera is utilized to capture depth and color image in fields. Mean shift clustering is applied to segment plant leaves in depth image. Plant leaves are extracted from the natural background by examining vegetation of the candidate segments produced by mean shift. Subsequently, individual leaves are segmented from occlusions by active contour models. Automatic initialization of the active contour models is implemented by calculating the center of divergence from the gradient vector field of depth image. The proposed segmentation scheme is tested through experiments under greenhouse conditions. The overall segmentation rate is 87.97% while segmentation rates for single and occluded leaves are 92.10% and 86.67%, respectively. Approximately half of the experimental results show segmentation rates of individual leaves higher than 90%. Nevertheless, the proposed method is able to segment individual leaves from heavy occlusions. PMID:26295395

  8. Sequential activation of a segmented ground pad reduces skin heating during radiofrequency tumor ablation: optimization via computational models.

    PubMed

    Schutt, David J; Haemmerich, Dieter

    2008-07-01

    Radiofrequency (RF) ablation has become an accepted treatment modality for unresectable tumors. The need for larger ablation zones has resulted in increased RF generator power. Skin burns due to ground pad heating are increasingly limiting further increases in generator power, and thus, ablation zone size. We investigated a method for reducing ground pad heating in which a commercial ground pad is segmented into multiple ground electrodes, with sequential activation of ground electrode subsets. We created finite-element method computer models of a commercial ground pad (14 x 23 cm) and compared normal operation of a standard pad to sequential activation of a segmented pad (two to five separate ground electrode segments). A constant current of 1 A was applied for 12 min in all simulations. Time periods during sequential activation simulations were adjusted to keep the leading edge temperatures at each ground electrode equal. The maximum temperature using standard activation of the commercial pad was 41.7 degrees C. For sequential activation of a segmented pad, the maximum temperature ranged from 39.3 degrees C (five segments) to 40.9 degrees C (two segments). Sequential activation of a segmented ground pad resulted in lower tissue temperatures. This method may reduce the incidence of ground pad burns and enable the use of higher power generators during RF tumor ablation.

  9. Sequential Activation of a Segmented Ground Pad Reduces Skin Heating During Radiofrequency Tumor Ablation: Optimization via Computational Models

    PubMed Central

    Schutt, David J.; Haemmerich, Dieter

    2009-01-01

    Radiofrequency (RF) ablation has become an accepted treatment modality for unresectable tumors. The need for larger ablation zones has resulted in increased RF generator power. Skin burns due to ground pad heating are increasingly limiting further increases in generator power, and thus, ablation zone size. We investigated a method for reducing ground pad heating in which a commercial ground pad is segmented into multiple ground electrodes, with sequential activation of ground electrode subsets. We created finite-element method computer models of a commercial ground pad (14 × 23 cm) and compared normal operation of a standard pad to sequential activation of a segmented pad (two to five separate ground electrode segments). A constant current of 1 A was applied for 12 min in all simulations. Time periods during sequential activation simulations were adjusted to keep the leading edge temperatures at each ground electrode equal. The maximum temperature using standard activation of the commercial pad was 41.7 °C. For sequential activation of a segmented pad, the maximum temperature ranged from 39.3 °C (five segments) to 40.9 °C (two segments). Sequential activation of a segmented ground pad resulted in lower tissue temperatures. This method may reduce the incidence of ground pad burns and enable the use of higher power generators during RF tumor ablation. PMID:18595807

  10. Integrated structures/controls optimization of a smart composite plate with segmented active constrained layer damping

    NASA Astrophysics Data System (ADS)

    Beri, Rajan; Chattopadhyay, Aditi; Nam, Changho

    2000-06-01

    A rigorous multi-objective optimization procedure, is developed to address the integrated structures/control design of composite plates with surface bonded segmented active constrained layer (ACL) damping treatment. The Kresselmeier- Steinhauser function approach is used to formulate this multidisciplinary problem. The goal is to control vibration without incorporating a weight penalty. Objective functions and constraints include damping ratios, structural weight and natural frequencies. Design variables include the ply stacking sequence, dimensions and placement of segmented ACL. The optimal designs show improved plate vibratory characteristics and reduced structural weight. The results of the multi- objective optimization problem are compared to those of a single objective optimization with vibration control as the objective. Results establish the necessity for developing the integrated structures/controls optimization procedure.

  11. Segmentation of blurry object by learning from examples

    NASA Astrophysics Data System (ADS)

    Yuan, Xiaohui

    2010-03-01

    Object with blurry boundary is a very common problem across image modalities and applications in medical field. Examples include skin lesion segmentation, tumor delineation in mammogram, tongue tracing in MR images, etc. To address blurry boundary problem, region-based active contour methods have been developed which utilize global image feature to address the problem of fuzzy edge. Image feature, such as texture, intensity histograms, or structure tensors, have also been studied for region-based models. On the other hand, trained domain experts have been much more effective in performing such tasks than computer algorithms that are based on a set of carefully selected, sophisticated image features. In this paper, we present a novel method that employs a learning strategy to guide active contour algorithm for delineating blurry objects in the imagery. Our method consists of two steps. First, using gold-standard examples, we derive statistical descriptions of the object boundary. Second, in the segmentation process, the statistical description is reinforced to achieve desired delineation. Experiments were conducted using both synthetic images and the skin lesion images. Our synthetic images were created with 2D Gaussian function, which closely resembles objects with blurry boundary. The robustness of our method with respect to the initialization is evaluated. Using different initial curves, similar results were achieved consistently. In experiments with skin lesion images, the outcome matches the contour in reference image, which are prepared by human experts. In summary, our experiments using both synthetic images and skin lesion images demonstrated great segmentation accuracy and robustness.

  12. Non-contact contour gage

    DOEpatents

    Bieg, Lothar F.

    1990-12-18

    A fluid probe for measuring the surface contour of a machined part is provided whereby the machined part can remain on the machining apparatus during surface contour measurement. A measuring nozzle in a measuring probe directs a measuring fluid flow onto the surface. The measuring nozzle is on the probe situated midway between two guide nozzles that direct guide fluid flows onto the surface. When the guide fluid flows interact with the surface, they cause the measuring flow and measuring probe to be oriented perpendicular to the surface. The measuring probe includes a pressure chamber whose pressure is monitored. As the measuring fluid flow encounters changes in surface contour, pressure changes occur in the pressure chamber. The surface contour is represented as data corresponding to pressure changes in the pressure chamber as the surface is scanned.

  13. WCPP-THE WOLF PLOTTING AND CONTOURING PACKAGE

    NASA Technical Reports Server (NTRS)

    Masaki, G. T.

    1994-01-01

    The WOLF Contouring and Plotting Package provides the user with a complete general purpose plotting and contouring capability. This package is a complete system for producing line printer, SC4020, Gerber, Calcomp, and SD4060 plots. The package has been designed to be highly flexible and easy to use. Any plot from a quick simple plot (which requires only one call to the package) to highly sophisticated plots (including motion picture plots) can be easily generated with only a basic knowledge of FORTRAN and the plot commands. Anyone designing a software system that requires plotted output will find that this package offers many advantages over the standard hardware support packages available. The WCPP package is divided into a plot segment and a contour segment. The plot segment can produce output for any combination of line printer, SC4020, Gerber, Calcomp, and SD4060 plots. The line printer plots allow the user to have plots available immediately after a job is run at a low cost. Although the resolution of line printer plots is low, the quick results allows the user to judge if a high resolution plot of a particular run is desirable. The SC4020 and SD4060 provide high speed high resolution cathode ray plots with film and hard copy output available. The Gerber and Calcomp plotters provide very high quality (of publishable quality) plots of good resolution. Being bed or drum type plotters, the Gerber and Calcomp plotters are usually slow and not suited for large volume plotting. All output for any or all of the plotters can be produced simultaneously. The types of plots supported are: linear, semi-log, log-log, polar, tabular data using the FORTRAN WRITE statement, 3-D perspective linear, and affine transformations. The labeling facility provides for horizontal labels, vertical labels, diagonal labels, vector characters of a requested size (special character fonts are easily implemented), and rotated letters. The gridding routines label the grid lines according to

  14. Active surface model improvement by energy function optimization for 3D segmentation.

    PubMed

    Azimifar, Zohreh; Mohaddesi, Mahsa

    2015-04-01

    This paper proposes an optimized and efficient active surface model by improving the energy functions, searching method, neighborhood definition and resampling criterion. Extracting an accurate surface of the desired object from a number of 3D images using active surface and deformable models plays an important role in computer vision especially medical image processing. Different powerful segmentation algorithms have been suggested to address the limitations associated with the model initialization, poor convergence to surface concavities and slow convergence rate. This paper proposes a method to improve one of the strongest and recent segmentation algorithms, namely the Decoupled Active Surface (DAS) method. We consider a gradient of wavelet edge extracted image and local phase coherence as external energy to extract more information from images and we use curvature integral as internal energy to focus on high curvature region extraction. Similarly, we use resampling of points and a line search for point selection to improve the accuracy of the algorithm. We further employ an estimation of the desired object as an initialization for the active surface model. A number of tests and experiments have been done and the results show the improvements with regards to the extracted surface accuracy and computational time of the presented algorithm compared with the best and recent active surface models.

  15. Winding number constrained contour detection.

    PubMed

    Ming, Yansheng; Li, Hongdong; He, Xuming

    2015-01-01

    Salient contour detection can benefit from the integration of both contour cues and region cues. However, this task is difficult due to different nature of region representations and contour representations. To solve this problem, this paper proposes an energy minimization framework based on winding number constraints. In this framework, both region cues, such as color/texture homogeneity, and contour cues, such as local contrast and continuity, are represented in a joint objective function, which has both region and contour labels. The key problem is how to design constraints that ensure the topological consistency of the two kinds of labels. Our technique is based on the topological concept of winding number. Using a fast method for winding number computation, a small number of linear constraints are derived to ensure label consistency. Our method is instantiated by ratio-based energy functions. By successfully integrating both region and contour cues, our method shows advantages over competitive methods. Our method is extended to incorporate user interaction, which leads to further improvements.

  16. Upregulation and activation of eosinophil integrins in blood and airway after segmental lung antigen challenge1

    PubMed Central

    Johansson, Mats W.; Kelly, Elizabeth A. B.; Busse, William W.; Jarjour, Nizar N.; Mosher, Deane F.

    2008-01-01

    We hypothesized that there are clinically relevant differences in eosinophil integrin expression and activation in patients with asthma. To evaluate this, surface densities and activation states of integrins on eosinophils in blood and bronchoalveolar lavage (BAL) of 19 asthmatic subjects were studied before and 48 h after segmental Ag challenge. At 48 h, there was increased expression of αD and the N29 epitope of activated β1 integrins on blood eosinophils and of αM, β2, and the mAb24 epitope of activated β2 integrins on airway eosinophils. Changes correlated with the late-phase fall in forced expiratory volume in 1 s (FEV1) after whole-lung inhalation of the Ag that was subsequently used in segmental challenge and were greater in subjects defined as dual responders. Increased surface densities of αM and β2 and activation of β2 on airway eosinophils correlated with the concentration of IL-5 in BAL fluid. Activation of β1 and β2 on airway eosinophils correlated with eosinophil percentage in BAL. Thus, eosinophils respond to an allergic stimulus by activation of integrins in a sequence that likely promotes eosinophilic inflammation of the airway. Before challenge, β1 and β2 integrins of circulating eosinophils are in low-activation conformations, and αDβ2 surface expression is low. After Ag challenge, circulating eosinophils adopt a phenotype with activated β1 integrins and upregulated αDβ2, changes that are predicted to facilitate eosinophil arrest on VCAM-1 in bronchial vessels. Finally, eosinophils present in IL-5-rich airway fluid have a hyperadhesive phenotype associated with increased surface expression of αMβ2 and activation of β2 integrins. PMID:18490765

  17. A framework of vertebra segmentation using the active shape model-based approach.

    PubMed

    Benjelloun, Mohammed; Mahmoudi, Saïd; Lecron, Fabian

    2011-01-01

    We propose a medical image segmentation approach based on the Active Shape Model theory. We apply this method for cervical vertebra detection. The main advantage of this approach is the application of a statistical model created after a training stage. Thus, the knowledge and interaction of the domain expert intervene in this approach. Our application allows the use of two different models, that is, a global one (with several vertebrae) and a local one (with a single vertebra). Two modes of segmentation are also proposed: manual and semiautomatic. For the manual mode, only two points are selected by the user on a given image. The first point needs to be close to the lower anterior corner of the last vertebra and the second near the upper anterior corner of the first vertebra. These two points are required to initialize the segmentation process. We propose to use the Harris corner detector combined with three successive filters to carry out the semiautomatic process. The results obtained on a large set of X-ray images are very promising.

  18. An effective segmentation method of ultrasonic thyroid nodules

    NASA Astrophysics Data System (ADS)

    Du, Wenpeng; Sang, Nong

    2015-12-01

    Segmentation of ultrasound image is an important port of ultrasound medical computer-aided system. However, due to the speckle noise, intensity heterogeneity, and low contrast, the ultrasonic segmentation is much difficult. In this paper, we introduce an effective method which integrates edge phase information and an effective active contour model to make the segmentation better. First, we use the speckle reducing anisotropic diffusion method to suppress the noise in ultrasound images. Then, we utilize the local phase information from monogenic signal to form a novel edge indicator and we use the indicator to replace the traditional intensity-based speed stopping term in distance regularized level set evolution. Another contribution of this paper is that we extend the proposed method to the field of ultrasonic thyroid nodules segmentation, qualitative and quantitative comparative results demonstrate the outperformance of our approach.

  19. Basic features of low-temperature plasma formation in the course of composite coating synthesis at the active faces of complex contoured hard tools

    NASA Astrophysics Data System (ADS)

    Brzhozovsky, B. M.; Zimnyakov, D. A.; Zinina, E. P.; Martynov, V. V.; Pleshakova, E. S.; Yuvchenko, S. A.

    2016-04-01

    Basic features of combined-discharge low-temperature plasma formation around the surfaces of complex-contoured metal units are considered. It is shown that it makes the possibilities for synthesis of hardened high-durable coatings of hard tools appropriate for material processing in extreme load-temperature conditions. Experimental study of the coating formation was carried out in combination with the analysis of emission spectra of a low-temperature plasma cloud. Some practical examples of the coating applications are presented.

  20. Diffusion tensor driven contour closing for cell microinjection targeting.

    PubMed

    Becattini, Gabriele; Mattos, Leonardo S; Caldwell, Darwin G

    2010-01-01

    This article introduces a novel approach to robust automatic detection of unstained living cells in bright-field (BF) microscope images with the goal of producing a target list for an automated microinjection system. The overall image analysis process is described and includes: preprocessing, ridge enhancement, image segmentation, shape analysis and injection point definition. The developed algorithm implements a new version of anisotropic contour completion (ACC) based on the partial differential equation (PDE) for heat diffusion which improves the cell segmentation process by elongating the edges only along their tangent direction. The developed ACC algorithm is equivalent to a dilation of the binary edge image with a continuous elliptic structural element that takes into account local orientation of the contours preventing extension towards normal direction. Experiments carried out on real images of 10 to 50 microm CHO-K1 adherent cells show a remarkable reliability in the algorithm along with up to 85% success for cell detection and injection point definition.

  1. Entropy reduction via simplified image contourization

    NASA Technical Reports Server (NTRS)

    Turner, Martin J.

    1993-01-01

    The process of contourization is presented which converts a raster image into a set of plateaux or contours. These contours can be grouped into a hierarchical structure, defining total spatial inclusion, called a contour tree. A contour coder has been developed which fully describes these contours in a compact and efficient manner and is the basis for an image compression method. Simplification of the contour tree has been undertaken by merging contour tree nodes thus lowering the contour tree's entropy. This can be exploited by the contour coder to increase the image compression ratio. By applying general and simple rules derived from physiological experiments on the human vision system, lossy image compression can be achieved which minimizes noticeable artifacts in the simplified image.

  2. Interaction of antithrombin III with bovine aortic segments. Role of heparin in binding and enhanced anticoagulant activity

    SciTech Connect

    Stern, D.; Nawroth, P.; Marcum, J.; Handley, D.; Kisiel, W.; Rosenberg, R.; Stern, K.

    1985-01-01

    Bovine antithrombin III (AT III) interaction with the luminal surface of bovine aortic segments with a continuous layer of endothelium was examined. Incubation of /sup 125/I-AT III with vessel segments, previously washed free of endogenous AT III, demonstrated specific, time-dependent binding to the protease inhibitor to the endothelium. Half-maximal binding was observed at an added AT III concentration of 14 nM. Binding of /sup 125/I-AT III to the vessel wall was reversible (50% dissociated in 4 min), and addition of either heparin or Factor Xa accelerated displacement of /sup 125/I-AT III from the vessel segment. Dissociation of /sup 125/I-AT III from the vessel segment in the presence of factor Xa coincided with the formation of a Factor Xa-/sup 125/I-AT III complex. Inactivation of Factor IXa and Factor Xa by AT III was facilitated in the presence of vessel segments. Pretreatment of vessel segments with highly purified Flavobacterium heparinase precluded the vessel-dependent augmentation of AT III anticoagulant activity as well as specific binding of /sup 125/I-AT III to the vessel endothelium. In contrast, pretreatment of the vessel segments with chrondroitinases (ABC or AC) had no detectable effect on /sup 125/I-AT III binding or on AT III anticoagulant activity. AT III binding to vessel segments was competitively inhibited by increasing concentration of platelet factor 4. Binding of the protease inhibitor to vessel segments was inhibited by chemical modification of AT III lysyl or tryptophan residues. These AT III derivatives retained progressive inhibitory activity. These data suggest that heparin-like molecules are present on the aortic vessel wall and mediate binding of AT III to the vessel surface, as well as enhancing the anticoagulant activity of AT III at these sites.

  3. Optimization of object region and boundary extraction by energy minimization for activity recognition

    NASA Astrophysics Data System (ADS)

    Albalooshi, Fatema A.; Asari, Vijayan K.

    2013-05-01

    Automatic video segmentation for human activity recognition has played an important role in several computer vision applications. Active contour model (ACM) has been used extensively for unsupervised adaptive segmentation and automatic object region and boundary extraction in video sequences. This paper presents optimizing Active Contour Model using recurrent architecture for automatic object region and boundary extraction in human activity video sequences. Taking advantage of the collective computational ability and energy convergence capability of the recurrent architecture, energy function of Active Contour Model is optimized with lower computational time. The system starts with initializing recurrent architecture state based on the initial boundary points and ends up with final contour which represent actual boundary points of human body region. The initial contour of the Active Contour Model is computed using background subtraction based on Gaussian Mixture Model (GMM) such that background model is built dynamically and regularly updated to overcome different challenges including illumination changes, camera oscillations, and changes in background geometry. The recurrent nature is useful for dealing with optimization problems due to its dynamic nature, thus, ensuring convergence of the system. The proposed boundary detection and region extraction can be used for real time processing. This method results in an effective segmentation that is less sensitive to noise and complex environments. Experiments on different databases of human activity show that our method is effective and can be used for real-time video segmentation.

  4. Sonority contours in word recognition

    NASA Astrophysics Data System (ADS)

    McLennan, Sean

    2003-04-01

    Contrary to the Generativist distinction between competence and performance which asserts that speech or perception errors are due to random, nonlinguistic factors, it seems likely that errors are principled and possibly governed by some of the same constraints as language. A preliminary investigation of errors modeled after the child's ``Chain Whisper'' game (a degraded stimulus task) suggests that a significant number of recognition errors can be characterized as an improvement in syllable sonority contour towards the linguistically least-marked, voiceless-stop-plus-vowel syllable. An independent study of sonority contours showed that approximately half of the English lexicon can be uniquely identified by their contour alone. Additionally, ``sororities'' (groups of words that share a single sonority contour), surprisingly, show no correlation to familiarity or frequency in either size or membership. Together these results imply that sonority contours may be an important factor in word recognition and in defining word ``neighborhoods.'' Moreover, they suggest that linguistic markedness constraints may be more prevalent in performance-related phenomena than previously accepted.

  5. Global contour processing in amblyopia

    PubMed Central

    Levi, Dennis M.; Yu, Cong; Kuai, Shu-Guang; Rislove, Elizabeth

    2007-01-01

    The purpose of the experiments described here was to investigate global image processing using methods that require global processing while eliminating or compensating for low level abnormalities: visibility, shape perception and positional uncertainty. In order to accomplish this we used a closed figure made up of Gabor patches either in noise or on a blank field. The stimuli were circular or elliptical contours, formed by N equally spaced Gabor patches. We performed two separate experiments: In one experiment we fixed N and varied the aspect ratio using a staircase to determine the threshold aspect ratio; in the second experiment we held the aspect ratio constant (at twice the threshold aspect ratio) and varied N in order to measure the threshold number of elements required to judge the shape. Our results confirm and extend previous studies showing that humans with naturally occurring amblyopia show deficits in contour processing. Our results show that the deficits depend strongly on spatial scale (target size and spatial frequency). The deficit in global contour processing is substantially greater in noise (where contour-linking is required) than on a blank field. The magnitude of the deficits is modest when low-level deficits (reduced visibility, increased positional uncertainty, and abnormal shape perception) are minimized, and does not seem to depend much on acuity, crowding or stereoacuity. The residual deficits reported here cannot be simply ascribed to reduced visibility or increased positional uncertainty, and we therefore conclude that these are genuine deficits in global contour segregation and integration. PMID:17223155

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

    PubMed

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

    2016-02-01

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

  7. Influence of bacterial toxins on the GTPase activity of transducin from bovine retinal rod outer segments

    SciTech Connect

    Rybin, V.O.; Gureeva, A.A.

    1986-05-10

    The action of cholera toxin, capable of ADP-ribosylation of the activator N/sub s/ protein, and pertussis toxin, capable of ADP-ribosylation of the inhibitor N/sub i/ protein of the adenylate cyclase complex, on transducin, the GTP-binding protein of the rod outer segments of the retina, was investigated. It was shown that under the action of pertussis and cholera toxins, the GTPase activity of transducin is inhibited. Pertussin toxin inhibits the GTPase of native retinal rod outer segments by 30-40%, while GTPase of homogeneous transducin produces a 70-80% inhibition. The action of toxins on transducin depends on the presence and nature of the guanylic nucleotide with which incubation is performed. On the basis of the data obtained it is suggested that pertussis toxin interacts with pretransducin and with the transducin-GDP complex, while cholera toxin ADP-ribosylates the transducin-GTP complex and does not act on transducin lacking GTP.

  8. Magmatic activities on the Southwest Indian Ridge between 35°E and 40°E, the closest segment to the Marion hotspot

    NASA Astrophysics Data System (ADS)

    Sato, Taichi; Okino, Kyoko; Sato, Hiroshi; Mizuno, Mariko; Hanyu, Tomoko; Seama, Nobukazu

    2013-12-01

    We conducted geophysical surveys, including bathymetry, gravity, and magnetism, within a first-order segment of the Southwest Indian Ridge (SWIR) between the Prince Edward and Eric Simpson fracture zones (FZs) (latitude 35°-40°E, segment PE), in the vicinity of the Marion hotspot. Segment PE includes four orthogonally spreading second-order segments (PE-1, PE-2, PE-3, and PE-4) and a long, oblique axial valley (NTD-1). Segments PE-1, PE-2, and PE-4 are magmatic, whereas segment PE-3 and NTD-1 are characterized by low magmatic activity. Segment PE-3 is a nascent segment and NTD-1 contains three tiny magmatic sections. Each low-magmatic interval along the axis of segment PE lies between two magmatic segments. This segmentation pattern is similar to the SWIR between the Gallieni and Melville FZs; therefore, a strong melt-focusing process can be expected. Different characteristics of second-order magmatic segments suggest that the magmatic activity in each segment varies among each other as well as that of the other segments of SWIR. Continuous seafloor morphology and isochrons over off-axis areas of segment PE-1 and NTD-1 suggest that PE-1 shortened after the C2An chron. The V-shaped bathymetric structure between segment PE-1 and NTD-1 suggests that the melt supply center has migrated westward. This westward melt migration would have reduced magmatic activity at NTD-1 after C2An. Ridge obliquity may also have reduced magmatic activity. Geophysical characteristics of second-order segments suggest that magmatic activity of segment PE is mainly controlled by a strong melt-focusing process and a comparatively low contribution of melt supply from Marion hotspot.

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

    NASA Astrophysics Data System (ADS)

    Ali, Rehan; Gunduz-Demir, Cigdem; Szilágyi, Tünde; Durkee, Ben; Graves, Edward E.

    2013-11-01

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

  10. Saliency-aware food image segmentation for personal dietary assessment using a wearable computer

    PubMed Central

    Chen, Hsin-Chen; Jia, Wenyan; Sun, Xin; Li, Zhaoxin; Li, Yuecheng; Fernstrom, John D.; Burke, Lora E.; Baranowski, Thomas; Sun, Mingui

    2015-01-01

    Image-based dietary assessment has recently received much attention in the community of obesity research. In this assessment, foods in digital pictures are specified, and their portion sizes (volumes) are estimated. Although manual processing is currently the most utilized method, image processing holds much promise since it may eventually lead to automatic dietary assessment. In this paper we study the problem of segmenting food objects from images. This segmentation is difficult because of various food types, shapes and colors, different decorating patterns on food containers, and occlusions of food and non-food objects. We propose a novel method based on a saliency-aware active contour model (ACM) for automatic food segmentation from images acquired by a wearable camera. An integrated saliency estimation approach based on food location priors and visual attention features is designed to produce a salient map of possible food regions in the input image. Next, a geometric contour primitive is generated and fitted to the salient map by means of multi-resolution optimization with respect to a set of affine and elastic transformation parameters. The food regions are then extracted after contour fitting. Our experiments using 60 food images showed that the proposed method achieved significantly higher accuracy in food segmentation when compared to conventional segmentation methods. PMID:26257473

  11. Saliency-aware food image segmentation for personal dietary assessment using a wearable computer.

    PubMed

    Chen, Hsin-Chen; Jia, Wenyan; Sun, Xin; Li, Zhaoxin; Li, Yuecheng; Fernstrom, John D; Burke, Lora E; Baranowski, Thomas; Sun, Mingui

    2015-02-01

    Image-based dietary assessment has recently received much attention in the community of obesity research. In this assessment, foods in digital pictures are specified, and their portion sizes (volumes) are estimated. Although manual processing is currently the most utilized method, image processing holds much promise since it may eventually lead to automatic dietary assessment. In this paper we study the problem of segmenting food objects from images. This segmentation is difficult because of various food types, shapes and colors, different decorating patterns on food containers, and occlusions of food and non-food objects. We propose a novel method based on a saliency-aware active contour model (ACM) for automatic food segmentation from images acquired by a wearable camera. An integrated saliency estimation approach based on food location priors and visual attention features is designed to produce a salient map of possible food regions in the input image. Next, a geometric contour primitive is generated and fitted to the salient map by means of multi-resolution optimization with respect to a set of affine and elastic transformation parameters. The food regions are then extracted after contour fitting. Our experiments using 60 food images showed that the proposed method achieved significantly higher accuracy in food segmentation when compared to conventional segmentation methods.

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

    NASA Astrophysics Data System (ADS)

    Liu, Jianfei; Dubra, Alfredo; Tam, Johnny

    2016-03-01

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

  13. Saliency-aware food image segmentation for personal dietary assessment using a wearable computer

    NASA Astrophysics Data System (ADS)

    Chen, Hsin-Chen; Jia, Wenyan; Sun, Xin; Li, Zhaoxin; Li, Yuecheng; Fernstrom, John D.; Burke, Lora E.; Baranowski, Thomas; Sun, Mingui

    2015-02-01

    Image-based dietary assessment has recently received much attention in the community of obesity research. In this assessment, foods in digital pictures are specified, and their portion sizes (volumes) are estimated. Although manual processing is currently the most utilized method, image processing holds much promise since it may eventually lead to automatic dietary assessment. In this paper we study the problem of segmenting food objects from images. This segmentation is difficult because of various food types, shapes and colors, different decorating patterns on food containers, and occlusions of food and non-food objects. We propose a novel method based on a saliency-aware active contour model (ACM) for automatic food segmentation from images acquired by a wearable camera. An integrated saliency estimation approach based on food location priors and visual attention features is designed to produce a salient map of possible food regions in the input image. Next, a geometric contour primitive is generated and fitted to the salient map by means of multi-resolution optimization with respect to a set of affine and elastic transformation parameters. The food regions are then extracted after contour fitting. Our experiments using 60 food images showed that the proposed method achieved significantly higher accuracy in food segmentation when compared to conventional segmentation methods.

  14. Three-dimensional active net for volume extraction

    NASA Astrophysics Data System (ADS)

    Takanashi, Ikuko; Muraki, Shigeru; Doi, Akio; Kaufman, Arie E.

    1998-05-01

    3D Active Net, which is a 3D extension of Snakes, is an energy-minimizing surface model which can extract a volume of interest from 3D volume data. It is deformable and evolves in 3D space to be attracted to salient features, according to its internal and image energy. The net can be fitted to the contour of a target object by defining the image energy suitable for the contour property. We present testing results of the extraction of a muscle from the Visible Human Data by two methods: manual segmentation and the application of 3D Active Net. We apply principal component analysis, which utilizes the color information of the 3D volume data to emphasize an ill-defined contour of the muscle, and then apply 3D Active Net. We recognize that the extracted object has a smooth and natural contour in contrast with a comparable manual segmentation, proving an advantage of our approach.

  15. Body Image and Body Contouring Procedures.

    PubMed

    Sarwer, David B; Polonsky, Heather M

    2016-10-01

    Dissatisfaction with physical appearance and body image is a common psychological phenomena in Western society. Body image dissatisfaction is frequently reported by those who have excess body weight, but also is seen in those of normal body weight. For both groups of individuals, this dissatisfaction impacts self-esteem and quality of life. Furthermore, it is believed to be the motivational catalyst to a range of appearance-enhancing behaviors, including weight loss efforts and physical activity. Body image dissatisfaction is also believed to play a role in the decision to seek the wide range of body contouring procedures offered by aesthetic physicians. Individuals who seek these procedures typically report increased body image dissatisfaction, focus on the feature they wish to alter with treatment, and often experience improvement in body image following treatment. At the same time, extreme body image dissatisfaction is a symptom of a number of recognized psychiatric disorders. These include anorexia nervosa, bulimia nervosa, and body dysmorphic disorder (BDD), all of which can contraindicate aesthetic treatment. This special topic review paper provides an overview of the relationship between body image dissatisfaction and aesthetic procedures designed to improve body contouring. The review specifically focuses on the relationship of body image and body weight, as well as the presentation of body image psychopathology that would contraindicate aesthetic surgery. The overall goal of the paper is to highlight the clinical implications of the existing research and provide suggestions for future research on the psychological aspects of body contouring procedures.

  16. 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

  17. Model-based segmentation of hand radiographs

    NASA Astrophysics Data System (ADS)

    Weiler, Frank; Vogelsang, Frank

    1998-06-01

    An important procedure in pediatrics is to determine the skeletal maturity of a patient from radiographs of the hand. There is great interest in the automation of this tedious and time-consuming task. We present a new method for the segmentation of the bones of the hand, which allows the assessment of the skeletal maturity with an appropriate database of reference bones, similar to the atlas based methods. The proposed algorithm uses an extended active contour model for the segmentation of the hand bones, which incorporates a-priori knowledge of shape and topology of the bones in an additional energy term. This `scene knowledge' is integrated in a complex hierarchical image model, that is used for the image analysis task.

  18. Topological Cacti: Visualizing Contour-based Statistics

    SciTech Connect

    Weber, Gunther H.; Bremer, Peer-Timo; Pascucci, Valerio

    2011-05-26

    Contours, the connected components of level sets, play an important role in understanding the global structure of a scalar field. In particular their nestingbehavior and topology-often represented in form of a contour tree-have been used extensively for visualization and analysis. However, traditional contour trees onlyencode structural properties like number of contours or the nesting of contours, but little quantitative information such as volume or other statistics. Here we use thesegmentation implied by a contour tree to compute a large number of per-contour (interval) based statistics of both the function defining the contour tree as well asother co-located functions. We introduce a new visual metaphor for contour trees, called topological cacti, that extends the traditional toporrery display of acontour tree to display additional quantitative information as width of the cactus trunk and length of its spikes. We apply the new technique to scalar fields ofvarying dimension and different measures to demonstrate the effectiveness of the approach.

  19. Incremental integration of global contours through interplay between visual cortical areas.

    PubMed

    Chen, Minggui; Yan, Yin; Gong, Xiajing; Gilbert, Charles D; Liang, Hualou; Li, Wu

    2014-05-07

    The traditional view on visual processing emphasizes a hierarchy: local line segments are first linked into global contours, which in turn are assembled into more complex forms. Distinct from this bottom-up viewpoint, here we provide evidence for a theoretical framework whereby objects and their parts are processed almost concurrently in a bidirectional cortico-cortical loop. By simultaneous recordings from V1 and V4 in awake monkeys, we found that information about global contours in a cluttered background emerged initially in V4, started ∼40 ms later in V1, and continued to develop in parallel in both areas. Detailed analysis of neuronal response properties implicated contour integration to emerge from both bottom-up and reentrant processes. Our results point to an incremental integration mechanism: feedforward assembling accompanied by feedback disambiguating to define and enhance the global contours and to suppress background noise. The consequence is a parallel accumulation of contour information over multiple cortical areas.

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

    SciTech Connect

    Ren, X; Gao, H; Sharp, G

    2015-06-15

    Purpose: The delineation of targets and organs-at-risk is a critical step during image-guided radiation therapy, for which manual contouring is the gold standard. However, it is often time-consuming and may suffer from intra- and inter-rater variability. The purpose of this work is to investigate the automated segmentation. Methods: The automatic segmentation here is based on mutual information (MI), with the atlas from Public Domain Database for Computational Anatomy (PDDCA) with manually drawn contours.Using dice coefficient (DC) as the quantitative measure of segmentation accuracy, we perform leave-one-out cross-validations for all PDDCA images sequentially, during which other images are registered to each chosen image and DC is computed between registered contour and ground truth. Meanwhile, six strategies, including MI, are selected to measure the image similarity, with MI to be the best. Then given a target image to be segmented and an atlas, automatic segmentation consists of: (a) the affine registration step for image positioning; (b) the active demons registration method to register the atlas to the target image; (c) the computation of MI values between the deformed atlas and the target image; (d) the weighted image fusion of three deformed atlas images with highest MI values to form the segmented contour. Results: MI was found to be the best among six studied strategies in the sense that it had the highest positive correlation between similarity measure (e.g., MI values) and DC. For automated segmentation, the weighted image fusion of three deformed atlas images with highest MI values provided the highest DC among four proposed strategies. Conclusion: MI has the highest correlation with DC, and therefore is an appropriate choice for post-registration atlas selection in atlas-based segmentation. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)

  1. Human Mesenchymal Stem Cell Behavior on Segmented Polyurethanes Prepared with Biologically Active Chain Extenders

    PubMed Central

    Kavanaugh, Taylor E.; Clark, Amy Y.; Chan-Chan, Lerma H.; Ramírez-Saldaña, Maricela; Vargas-Coronado, Rossana F.; Cervantes-Uc, José M.; Hernández-Sánchez, Fernando; García, Andrés J.; Cauich-Rodríguez, Juan V.

    2016-01-01

    The development of elastomeric, bioresorbable and biocompatible segmented polyurethanes (SPUs) for use in tissue-engineering applications has attracted considerable interest because of the existing need of mechanically tunable scaffolds for regeneration of different tissues, but the incorporation of osteoinductive molecules into SPUs has been limited. In this study, segmented polyurethanes were synthesized from poly (ε-caprolactone)diol, 4,4’-methylene bis(cyclohexyl isocyanate) (HMDI) using biologically active compounds such as ascorbic acid, L-glutamine, β-glycerol phosphate, and dexamethasone as chain extenders. Fourier Transform Infrared Spectroscopy (FTIR) revealed the formation of both urethanes and urea linkages while Differential Scanning Calorimetry, Dynamic Mechanical Analysis, X-ray Diffraction and mechanical testing showed that these polyurethanes were semi-crystalline polymers exhibiting high deformations. Cytocompatibility studies showed that only SPUs containing β-glycerol phosphate supported human mesenchymal stem cell (hMSC) adhesion, growth, and osteogenic differentiation, rendering them potentially suitable for bone tissue regeneration, whereas other SPUs failed to support either cell growth or osteogenic differentiation, or both. This study demonstrates that modification of SPUs with osteogenic compounds can lead to new cytocompatible polymers for regenerative medicine applications. PMID:26704555

  2. Gage for 3-d contours

    NASA Technical Reports Server (NTRS)

    Haynie, C. C.

    1980-01-01

    Simple gage, used with template, can help inspectors determine whether three-dimensional curved surface has correct contour. Gage was developed as aid in explosive forming of Space Shuttle emergency-escape hatch. For even greater accuracy, wedge can be made of metal and calibrated by indexing machine.

  3. MULTISCALE DISCRETIZATION OF SHAPE CONTOURS

    SciTech Connect

    Prasad, L.; Rao, R.

    2000-09-01

    We present an efficient multi-scale scheme to adaptively approximate the continuous (or densely sampled) contour of a planar shape at varying resolutions. The notion of shape is intimately related to the notion of contour, and the efficient representation of the contour of a shape is vital to a computational understanding of the shape. Any polygonal approximation of a planar smooth curve is equivalent to a piecewise constant approximation of the parameterized X and Y coordinate functions of a discrete point set obtained by densely sampling the curve. Using the Haar wavelet transform for the piecewise approximation yields a hierarchical scheme in which the size of the approximating point set is traded off against the morphological accuracy of the approximation. Our algorithm compresses the representation of the initial shape contour to a sparse sequence of points in the plane defining the vertices of the shape's polygonal approximation. Furthermore, it is possible to control the overall resolution of the approximation by a single, scale-independent parameter.

  4. Measuring and Plotting Surface-Contour Deviations

    NASA Technical Reports Server (NTRS)

    Aragon, Lino A.; Shuck, Thomas; Crockett, Leroy K.

    1987-01-01

    Hand-held device measures deviation of contour of surface from desired contour and provides output to x-y plotter. Carriage on device rolled along track representing desired contour, while spring-loaded stylus on device deflects perpendicularly to track to follow surface. Operator moves carriage of contour-measuring device on beamlike track. Stylus on carriage traces contour of surface above it. Carriage of measuring device holds transducer measuring cross-track displacement of surface from desired contour, and multiple-turn potentiometer measuring position along track.

  5. Efficient graph-cut tattoo segmentation

    NASA Astrophysics Data System (ADS)

    Kim, Joonsoo; Parra, Albert; Li, He; Delp, Edward J.

    2015-03-01

    Law enforcement is interested in exploiting tattoos as an information source to identify, track and prevent gang-related crimes. Many tattoo image retrieval systems have been described. In a retrieval system tattoo segmentation is an important step for retrieval accuracy since segmentation removes background information in a tattoo image. Existing segmentation methods do not extract the tattoo very well when the background includes textures and color similar to skin tones. In this paper we describe a tattoo segmentation approach by determining skin pixels in regions near the tattoo. In these regions graph-cut segmentation using a skin color model and a visual saliency map is used to find skin pixels. After segmentation we determine which set of skin pixels are connected with each other that form a closed contour including a tattoo. The regions surrounded by the closed contours are considered tattoo regions. Our method segments tattoos well when the background includes textures and color similar to skin.

  6. Welding deviation detection algorithm based on extremum of molten pool image contour

    NASA Astrophysics Data System (ADS)

    Zou, Yong; Jiang, Lipei; Li, Yunhua; Xue, Long; Huang, Junfen; Huang, Jiqiang

    2016-01-01

    The welding deviation detection is the basis of robotic tracking welding, but the on-line real-time measurement of welding deviation is still not well solved by the existing methods. There is plenty of information in the gas metal arc welding(GMAW) molten pool images that is very important for the control of welding seam tracking. The physical meaning for the curvature extremum of molten pool contour is revealed by researching the molten pool images, that is, the deviation information points of welding wire center and the molten tip center are the maxima and the local maxima of the contour curvature, and the horizontal welding deviation is the position difference of these two extremum points. A new method of weld deviation detection is presented, including the process of preprocessing molten pool images, extracting and segmenting the contours, obtaining the contour extremum points, and calculating the welding deviation, etc. Extracting the contours is the premise, segmenting the contour lines is the foundation, and obtaining the contour extremum points is the key. The contour images can be extracted with the method of discrete dyadic wavelet transform, which is divided into two sub contours including welding wire and molten tip separately. The curvature value of each point of the two sub contour lines is calculated based on the approximate curvature formula of multi-points for plane curve, and the two points of the curvature extremum are the characteristics needed for the welding deviation calculation. The results of the tests and analyses show that the maximum error of the obtained on-line welding deviation is 2 pixels(0.16 mm), and the algorithm is stable enough to meet the requirements of the pipeline in real-time control at a speed of less than 500 mm/min. The method can be applied to the on-line automatic welding deviation detection.

  7. Positron emission tomography detects tissue metabolic activity in myocardial segments with persistent thallium perfusion defects

    SciTech Connect

    Brunken, R.; Schwaiger, M.; Grover-McKay, M.; Phelps, M.E.; Tillisch, J.; Schelbert, H.R.

    1987-09-01

    Positron emission tomography with /sup 13/N-ammonia and /sup 18/F-2-deoxyglucose was used to assess myocardial perfusion and glucose utilization in 51 myocardial segments with a stress thallium defect in 12 patients. Myocardial infarction was defined by a concordant reduction in segmental perfusion and glucose utilization, and myocardial ischemia was identified by preservation of glucose utilization in segments with rest hypoperfusion. Of the 51 segments studied, 36 had a fixed thallium defect, 11 had a partially reversible defect and 4 had a completely reversible defect. Only 15 (42%) of the 36 segments with a fixed defect and 4 (36%) of the 11 segments with a partially reversible defect exhibited myocardial infarction on study with positron tomography. In contrast, residual myocardial glucose utilization was identified in the majority of segments with a fixed (58%) or a partially reversible (64%) thallium defect. All of the segments with a completely reversible defect appeared normal on positron tomography. Apparent improvement in the thallium defect on delayed images did not distinguish segments with ischemia from infarction. Thus, positron emission tomography reveals evidence of persistent tissue metabolism in the majority of segments with a fixed or partially resolving stress thallium defect, implying that markers of perfusion alone may underestimate the extent of viable tissue in hypoperfused myocardial segments.

  8. Dynamic synchronization of ongoing neuronal activity across spinal segments regulates sensory information flow

    PubMed Central

    Contreras-Hernández, E; Chávez, D; Rudomin, P

    2015-01-01

    Previous studies on the correlation between spontaneous cord dorsum potentials recorded in the lumbar spinal segments of anaesthetized cats suggested the operation of a population of dorsal horn neurones that modulates, in a differential manner, transmission along pathways mediating Ib non-reciprocal postsynaptic inhibition and pathways mediating primary afferent depolarization and presynaptic inhibition. In order to gain further insight into the possible neuronal mechanisms that underlie this process, we have measured changes in the correlation between the spontaneous activity of individual dorsal horn neurones and the cord dorsum potentials associated with intermittent activation of these inhibitory pathways. We found that high levels of neuronal synchronization within the dorsal horn are associated with states of incremented activity along the pathways mediating presynaptic inhibition relative to pathways mediating Ib postsynaptic inhibition. It is suggested that ongoing changes in the patterns of functional connectivity within a distributed ensemble of dorsal horn neurones play a relevant role in the state-dependent modulation of impulse transmission along inhibitory pathways, among them those involved in the central control of sensory information. This feature would allow the same neuronal network to be involved in different functional tasks. Key points We have examined, in the spinal cord of the anaesthetized cat, the relationship between ongoing correlated fluctuations of dorsal horn neuronal activity and state-dependent activation of inhibitory reflex pathways. We found that high levels of synchronization between the spontaneous activity of dorsal horn neurones occur in association with the preferential activation of spinal pathways leading to primary afferent depolarization and presynaptic inhibition relative to activation of pathways mediating Ib postsynaptic inhibition. It is suggested that changes in synchronization of ongoing activity within a

  9. Active Optics for a Segmented Primary Mirror on a Deep-Space Optical Receiver Antenna (DSORA)

    NASA Technical Reports Server (NTRS)

    Clymer, B. D.

    1990-01-01

    This article investigates the active optical control of segments in the primary mirror to correct for wavefront errors in the Deep-Space Optical Receiver Antenna (DSORA). Although an exact assessment of improvement in signal blur radius cannot be made until a more detailed preliminary structural design is completed, analytical tools are identified for a time when such designs become available. A brief survey of appropriate sensing approaches is given. Since the choice of control algorithm and architecture depends on the particular sensing system used, typical control systems, estimated complexities, and the type of equipment required are discussed. Once specific sensor and actuator systems are chosen, the overall control system can be optimized using methods identified in the literature.

  10. Multi-object segmentation framework using deformable models for medical imaging analysis.

    PubMed

    Namías, Rafael; D'Amato, Juan Pablo; Del Fresno, Mariana; Vénere, Marcelo; Pirró, Nicola; Bellemare, Marc-Emmanuel

    2016-08-01

    Segmenting structures of interest in medical images is an important step in different tasks such as visualization, quantitative analysis, simulation, and image-guided surgery, among several other clinical applications. Numerous segmentation methods have been developed in the past three decades for extraction of anatomical or functional structures on medical imaging. Deformable models, which include the active contour models or snakes, are among the most popular methods for image segmentation combining several desirable features such as inherent connectivity and smoothness. Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms, there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual techniques. This paper presents a novel open-source framework called deformable model array (DMA) for the segmentation of multiple and complex structures of interest in different imaging modalities. While most active contour algorithms can extract one region at a time, DMA allows integrating several deformable models to deal with multiple segmentation scenarios. Moreover, it is possible to consider any existing explicit deformable model formulation and even to incorporate new active contour methods, allowing to select a suitable combination in different conditions. The framework also introduces a control module that coordinates the cooperative evolution of the snakes and is able to solve interaction issues toward the segmentation goal. Thus, DMA can implement complex object and multi-object segmentations in both 2D and 3D using the contextual information derived from the model interaction. These are important features for several medical image analysis tasks in which different but related objects need to be simultaneously extracted. Experimental results on both computed tomography and magnetic resonance imaging show that the proposed

  11. Lessons learned with the Active Phasing Experiment: comparison of four optical phasing sensors on a segmented Very Large Telescope

    NASA Astrophysics Data System (ADS)

    Gonte, F.; Surdej, I.

    The adaptive optics capabilities are strongly limited by the quality of the phasing of the primary mirror of the extremely large telescope. Up to date, the Keck telescopes are the only segmented telescope phased with a quality enabling the application of adaptive optics. The Active Phasing Experiment has been installed at the Namyth focus of the Very Large Telescope Melipal during the last 6 months. Its purpose is to understand and compare different technological concepts for an optical phasing sensor dedicated to the European Extremely Large Telescope. The pupil of the telescope is segmented in 61 hexagonal segments by projecting it on an Active Segmented Mirror. The ASM is controlled by a dual wavenlength interferometer made by Fogale Nanotech with a nanometric precision. The segmented pupil is distributed in parallel to four optical phasing sensors. They are a pyramid sensor, a curvature sensor, a phase filtering sensor and a ShackHartmann sensor. They have been developed respectively by Istituto Nazionale di Astrofisica in Florenze, Instituto Astrofisica Canarias in Tenerife, Laboratoire d'Astrophysique de Marseille and ESO. The global behaviour of the optical phasing sensors will be described and preliminary results of the Active Phasing Experiments obtained on sky will be explained. The extrapolation of the results to the EELT and the potential consequences for the adaptive optics will be given. The Active Phasing Experiment has been financed by the European Union and the European Southern Observatory via the Sixth European Union Framework Program for Research and Technological Development under the contract number 011863.

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

    PubMed

    Yu, Jinhua; Tan, Jinglu

    2009-12-01

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

  13. Design and evaluation of a new automated method for the segmentation and characterization of masses on ultrasound images

    NASA Astrophysics Data System (ADS)

    Cui, Jing; Sahiner, Berkman; Chan, Heang-Ping; Nees, Alexis; Paramagul, Chintana; Hadjiiski, Lubomir M.; Zhou, Chuan; Shi, Jiazheng

    2008-03-01

    Segmentation of masses is the first step in most computer-aided diagnosis (CAD) systems for characterization of breast masses as malignant or benign. In this study, we designed an automated method for segmentation of masses on ultrasound (US) images. The method automatically estimated an initial contour based on a manually-identified point approximately at the mass center. A two-stage active contour (AC) method iteratively refined the initial contour and performed self-examination and correction on the segmentation result. To evaluate our method, we compared it with manual segmentation by an experienced radiologists (R1) on a data set of 226 US images containing biopsy-proven masses from 121 patients (44 malignant and 77 benign). Four performance measures were used to evaluate the segmentation accuracy; two measures were related to the overlap between the computer and radiologist segmentation, and two were related to the area difference between the two segmentation results. To compare the difference between the segmentation results by the computer and R1 to inter-observer variation, a second radiologist (R2) also manually segmented all masses. The two overlap measures between the segmentation results by the computer and R1 were 0.87+ 0.16 and 0.73+ 0.17 respectively, indicating a high agreement. However, the segmentation results between two radiologists were more consistent. To evaluate the effect of the segmentation method on classification accuracy, three feature spaces were formed by extracting texture, width-to-height, and posterior shadowing features using the computer segmentation, R1's manual segmentation, and R2's manual segmentation. A linear discriminant analysis classifier using stepwise feature selection was tested and trained by a leave-one-case-out method to characterize the masses as malignant or benign. For case-based classification, the area A z under the test receiver operating characteristic (ROC) curve was 0.90+/-0.03, 0.87+/-0.03 and 0

  14. S100B is a potential disease activity marker in non-segmental vitiligo.

    PubMed

    Speeckaert, Reinhart; Voet, Sofie; Hoste, Esther; van Geel, Nanja

    2017-02-14

    Vitiligo is a chronic skin condition characterized by progressive depigmentation of the skin. S100B is a damage-associated molecular pattern (DAMP) protein expressed in melanocytes, which has been proposed as a marker of melanocyte cytotoxicity. While the use of S100B as a biomarker in melanoma is well established, its association with vitiligo activity has not yet been investigated. Here, we show that S100B serum levels were significantly increased in patients with active non-segmental vitiligo and strongly correlated with the affected body surface area. Prospective follow-up showed a predictive value of serum S100B levels on disease progression. In vitro experiments using repeated freeze-thaw procedures demonstrated an intracellular upregulation of S100B in normal and vitiligo melanocytes prior to an extensive release in the environment. This phenomenon may explain the increased S100B serum values in the active phase of vitiligo. In a monobenzone-induced vitiligo mouse model we could show the potential of S100B inhibition as a therapeutic strategy in vitiligo. In conclusion, this report demonstrates the possible use of S100B as a biomarker for disease activity in vitiligo. Our data suggest that this DAMP protein could play a substantial role in the pathogenesis of vitiligo and may be a potential new target for treatment.

  15. Adjusting the Contour of Reflector Panels

    NASA Technical Reports Server (NTRS)

    Palmer, W. B.; Giebler, M. M.

    1984-01-01

    Postfabrication adjustment of contour of panels for reflector, such as parabolic reflector for radio antennas, possible with simple mechanism consisting of threaded stud, two nuts, and flexure. Contours adjusted manually.

  16. Contoured Surface Eddy Current Inspection System

    DOEpatents

    Batzinger, Thomas James; Fulton, James Paul; Rose, Curtis Wayne; Perocchi, Lee Cranford

    2003-04-08

    Eddy current inspection of a contoured surface of a workpiece is performed by forming a backing piece of flexible, resiliently yieldable material with a contoured exterior surface conforming in shape to the workpiece contoured surface. The backing piece is preferably cast in place so as to conform to the workpiece contoured surface. A flexible eddy current array probe is attached to the contoured exterior surface of the backing piece such that the probe faces the contoured surface of the workpiece to be inspected when the backing piece is disposed adjacent to the workpiece. The backing piece is then expanded volumetrically by inserting at least one shim into a slot in the backing piece to provide sufficient contact pressure between the probe and the workpiece contoured surface to enable the inspection of the workpiece contoured surface to be performed.

  17. Automated identification of the lung contours in positron emission tomography

    NASA Astrophysics Data System (ADS)

    Nery, F.; Silvestre Silva, J.; Ferreira, N. C.; Caramelo, F. J.; Faustino, R.

    2013-03-01

    Positron Emission Tomography (PET) is a nuclear medicine imaging technique that permits to analyze, in three dimensions, the physiological processes in vivo. One of the areas where PET has demonstrated its advantages is in the staging of lung cancer, where it offers better sensitivity and specificity than other techniques such as CT. On the other hand, accurate segmentation, an important procedure for Computer Aided Diagnostics (CAD) and automated image analysis, is a challenging task given the low spatial resolution and the high noise that are intrinsic characteristics of PET images. This work presents an algorithm for the segmentation of lungs in PET images, to be used in CAD and group analysis in a large patient database. The lung boundaries are automatically extracted from a PET volume through the application of a marker-driven watershed segmentation procedure which is robust to the noise. In order to test the effectiveness of the proposed method, we compared the segmentation results in several slices using our approach with the results obtained from manual delineation. The manual delineation was performed by nuclear medicine physicians that used a software routine that we developed specifically for this task. To quantify the similarity between the contours obtained from the two methods, we used figures of merit based on region and also on contour definitions. Results show that the performance of the algorithm was similar to the performance of human physicians. Additionally, we found that the algorithm-physician agreement is similar (statistically significant) to the inter-physician agreement.

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

    PubMed Central

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

    2016-01-01

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

  19. Applying manifold learning to plotting approximate contour trees.

    PubMed

    Takahashi, Shigeo; Fujishiro, Issei; Okada, Masato

    2009-01-01

    A contour tree is a powerful tool for delineating the topological evolution of isosurfaces of a single-valued function, and thus has been frequently used as a means of extracting features from volumes and their time-varying behaviors. Several sophisticated algorithms have been proposed for constructing contour trees while they often complicate the software implementation especially for higher-dimensional cases such as time-varying volumes. This paper presents a simple yet effective approach to plotting in 3D space, approximate contour trees from a set of scattered samples embedded in the high-dimensional space. Our main idea is to take advantage of manifold learning so that we can elongate the distribution of high-dimensional data samples to embed it into a low-dimensional space while respecting its local proximity of sample points. The contribution of this paper lies in the introduction of new distance metrics to manifold learning, which allows us to reformulate existing algorithms as a variant of currently available dimensionality reduction scheme. Efficient reduction of data sizes together with segmentation capability is also developed to equip our approach with a coarse-to-fine analysis even for large-scale datasets. Examples are provided to demonstrate that our proposed scheme can successfully traverse the features of volumes and their temporal behaviors through the constructed contour trees.

  20. Dilated contour extraction and component labeling algorithm for object vector representation

    NASA Astrophysics Data System (ADS)

    Skourikhine, Alexei N.

    2005-08-01

    Object boundary extraction from binary images is important for many applications, e.g., image vectorization, automatic interpretation of images containing segmentation results, printed and handwritten documents and drawings, maps, and AutoCAD drawings. Efficient and reliable contour extraction is also important for pattern recognition due to its impact on shape-based object characterization and recognition. The presented contour tracing and component labeling algorithm produces dilated (sub-pixel) contours associated with corresponding regions. The algorithm has the following features: (1) it always produces non-intersecting, non-degenerate contours, including the case of one-pixel wide objects; (2) it associates the outer and inner (i.e., around hole) contours with the corresponding regions during the process of contour tracing in a single pass over the image; (3) it maintains desired connectivity of object regions as specified by 8-neighbor or 4-neighbor connectivity of adjacent pixels; (4) it avoids degenerate regions in both background and foreground; (5) it allows an easy augmentation that will provide information about the containment relations among regions; (6) it has a time complexity that is dominantly linear in the number of contour points. This early component labeling (contour-region association) enables subsequent efficient object-based processing of the image information.

  1. Dynamic synchronization of ongoing neuronal activity across spinal segments regulates sensory information flow.

    PubMed

    Contreras-Hernández, E; Chávez, D; Rudomin, P

    2015-05-15

    Previous studies on the correlation between spontaneous cord dorsum potentials recorded in the lumbar spinal segments of anaesthetized cats suggested the operation of a population of dorsal horn neurones that modulates, in a differential manner, transmission along pathways mediating Ib non-reciprocal postsynaptic inhibition and pathways mediating primary afferent depolarization and presynaptic inhibition. In order to gain further insight into the possible neuronal mechanisms that underlie this process, we have measured changes in the correlation between the spontaneous activity of individual dorsal horn neurones and the cord dorsum potentials associated with intermittent activation of these inhibitory pathways. We found that high levels of neuronal synchronization within the dorsal horn are associated with states of incremented activity along the pathways mediating presynaptic inhibition relative to pathways mediating Ib postsynaptic inhibition. It is suggested that ongoing changes in the patterns of functional connectivity within a distributed ensemble of dorsal horn neurones play a relevant role in the state-dependent modulation of impulse transmission along inhibitory pathways, among them those involved in the central control of sensory information. This feature would allow the same neuronal network to be involved in different functional tasks.

  2. Risk avoidance versus risk reduction: a framework and segmentation profile for understanding adolescent sexual activity.

    PubMed

    Hopkins, Christopher D; Tanner, John F; Raymond, Mary Anne

    2004-01-01

    The teen birthrate in the United States is twice that of other industrialized nations. Adolescents in the U.S. are among high-risk groups for HIV/AIDS and other sexually transmitted diseases. As a result, the Department of Health and Human Services changed its policy on the promotion of abstinence to teenagers from a focus on a risk reduction strategy to a focus on a risk avoidance strategy. In order to create more effective risk avoidance as well as risk reduction campaigns, this study proposes a framework to illustrate the distinction that teens make between spontaneous sexual activity and planned sexual activity, as well as those teens that make a commitment to abstinence versus abstinence by default. Furthermore, this study classifies teens into three behavior segments (abstemious, promiscuous and monogamous) and then assesses specific differences that exist within these groups relative to their attitudes and perceptions concerning abstinence, sexual activity, contraception, fear and norms. This change in focus from a risk reduction to a risk avoidance strategy has important implications for social marketing, public policy and marketing theory.

  3. Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach

    PubMed Central

    Beichel, Reinhard R.

    2014-01-01

    Model-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process but suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach for initializing an active shape model (ASM) and apply it to 3D lung segmentation in CT scans. Our method constructs an atlas consisting of a set of representative lung features and an average lung shape. The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features. Our evaluation on a diverse set of 190 images showed an average dice coefficient of 0.746 ± 0.068 for initialization and 0.974 ± 0.017 for subsequent segmentation, based on an independent reference standard. The mean absolute surface distance error was 0.948 ± 1.537 mm. The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches. The proposed initialization method can be generalized to other applications employing ASM-based segmentation. PMID:25400660

  4. The Poggendorff illusion driven by real and illusory contour: Behavioral and neural mechanisms.

    PubMed

    Shen, Lu; Zhang, Ming; Chen, Qi

    2016-05-01

    The Poggendorff illusion refers to the phenomenon that the human brain misperceives a diagonal line as being apparently misaligned once the diagonal line is interrupted by two parallel edges, and the size of illusion is negatively correlated with the angle of interception of the oblique, i.e. the sharper the oblique angle, the larger the illusion. This optical illusion can be produced by both real and illusory contour. In this fMRI study, by parametrically varying the oblique angle, we investigated the shared and specific neural mechanisms underlying the Poggendorff illusion induced by real and illusory contour. At the behavioral level, not only the real but also the illusory contours were capable of inducing significant Poggendorff illusion. The size of illusion induced by the real contour, however, was larger than that induced by the illusory contour. At the neural level, real and illusory contours commonly activated more dorsal visual areas, and the real contours specifically activated more ventral visual areas. More importantly, examinations on the parametric modulation effects of the size of illusion revealed the specific neural mechanisms underlying the Poggendorff illusion induced by the real and the illusory contours, respectively. Left precentral gyrus and right middle occipital cortex were specifically involved in the Poggendorff illusion induced by the real contour. On the other hand, bilateral intraparietal sulcus (IPS) and right lateral occipital complex (LOC) were specifically involved in the Poggendorff illusion induced by the illusory contour. Functional implications of the above findings were further discussed.

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

    PubMed

    Pan, Yongsheng; Zhou, Tao; Xia, Yong

    2015-01-01

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

  6. Contouring variations and the role of atlas in non-small-cell lung cancer radiotherapy: analysis of a multi-institutional pre-clinical trial planning study

    PubMed Central

    Cui, Yunfeng; Chen, Wenzhou; Kong, Feng-Ming (Spring); Olsen, Lindsey A.; Beatty, Ronald E.; Maxim, Peter G.; Ritter, Timothy; Sohn, Jason W.; Higgins, Jane; Galvin, James M.; Xiao, Ying

    2014-01-01

    Purpose To quantify variations in target and normal structure contouring and evaluate dosimetric impact of these variations in non-small-cell lung cancer (NSCLC) cases. To study whether providing an atlas can reduce potential variation. Methods and Materials Three NSCLC cases were distributed sequentially to multiple institutions for contouring and radiotherapy planning. No segmentation atlas was provided for the first two cases (Case1 and Case2). Contours were collected from submitted plans and consensus contour sets were generated. The volume variation among institution contours and the deviation of them from consensus contours were analyzed. The dose-volume histograms (DVH) for individual institution plans were re-calculated using consensus contours to quantify the dosimetric changes. An atlas containing targets and critical structures was constructed and was made available when the third case (Case3) was distributed for planning. The contouring variability in the submitted plans of Case3 was compared with that in first two cases. Results Planning Target Volume (PTV) showed large variation among institutions. The PTV coverage in institutions’ plans decreased dramatically when re-evaluated using the consensus PTV contour. The PTV contouring consistency did not show improvement with atlas use in Case3. For normal structures, lung contours presented very good agreement, while the brachial plexus showed the largest variation. The consistency of esophagus and heart contouring improved significantly (t-test, p<0.05) in Case3. Major factors contributing to the contouring variation were identified through a survey questionnaire. Conclusions The amount of contouring variations in NSCLC cases was presented. Its impact on dosimetric parameters can be significant. The segmentation atlas improved the contour agreement for esophagus and heart, but not for the PTV in this study. Quality assurance of contouring is essential for a successful multi-institutional clinical trial

  7. 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.

  8. 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.

  9. Dynamic Programming Using Polar Variance for Image Segmentation.

    PubMed

    Rosado-Toro, Jose A; Altbach, Maria I; Rodriguez, Jeffrey J

    2016-10-06

    When using polar dynamic programming (PDP) for image segmentation, the object size is one of the main features used. This is because if size is left unconstrained the final segmentation may include high-gradient regions that are not associated with the object. In this paper, we propose a new feature, polar variance, which allows the algorithm to segment objects of different sizes without the need for training data. The polar variance is the variance in a polar region between a user-selected origin and a pixel we want to analyze. We also incorporate a new technique that allows PDP to segment complex shapes by finding low-gradient regions and growing them. The experimental analysis consisted on comparing our technique with different active contour segmentation techniques on a series of tests. The tests consisted on robustness to additive Gaussian noise, segmentation accuracy with different grayscale images and finally robustness to algorithm-specific parameters. Experimental results show that our technique performs favorably when compared to other segmentation techniques.

  10. Prostate segmentation in MRI using fused T2-weighted and elastography images

    NASA Astrophysics Data System (ADS)

    Nir, Guy; Sahebjavaher, Ramin S.; Baghani, Ali; Sinkus, Ralph; Salcudean, Septimiu E.

    2014-03-01

    Segmentation of the prostate in medical imaging is a challenging and important task for surgical planning and delivery of prostate cancer treatment. Automatic prostate segmentation can improve speed, reproducibility and consistency of the process. In this work, we propose a method for automatic segmentation of the prostate in magnetic resonance elastography (MRE) images. The method utilizes the complementary property of the elastogram and the corresponding T2-weighted image, which are obtained from the phase and magnitude components of the imaging signal, respectively. It follows a variational approach to propagate an active contour model based on the combination of region statistics in the elastogram and the edge map of the T2-weighted image. The method is fast and does not require prior shape information. The proposed algorithm is tested on 35 clinical image pairs from five MRE data sets, and is evaluated in comparison with manual contouring. The mean absolute distance between the automatic and manual contours is 1.8mm, with a maximum distance of 5.6mm. The relative area error is 7.6%, and the duration of the segmentation process is 2s per slice.

  11. Livewire based single still image segmentation

    NASA Astrophysics Data System (ADS)

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

    2011-11-01

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

  12. What is in a contour map? A region-based logical formalization of contour semantics

    USGS Publications Warehouse

    Usery, E. Lynn; Hahmann, Torsten

    2015-01-01

    This paper analyses and formalizes contour semantics in a first-order logic ontology that forms the basis for enabling computational common sense reasoning about contour information. The elicited contour semantics comprises four key concepts – contour regions, contour lines, contour values, and contour sets – and their subclasses and associated relations, which are grounded in an existing qualitative spatial ontology. All concepts and relations are illustrated and motivated by physical-geographic features identifiable on topographic contour maps. The encoding of the semantics of contour concepts in first-order logic and a derived conceptual model as basis for an OWL ontology lay the foundation for fully automated, semantically-aware qualitative and quantitative reasoning about contours.

  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. Active Gaze Control Improves Optic Flow-Based Segmentation and Steering

    PubMed Central

    Raudies, Florian; Mingolla, Ennio; Neumann, Heiko

    2012-01-01

    An observer traversing an environment actively relocates gaze to fixate objects. Evidence suggests that gaze is frequently directed toward the center of an object considered as target but more likely toward the edges of an object that appears as an obstacle. We suggest that this difference in gaze might be motivated by specific patterns of optic flow that are generated by either fixating the center or edge of an object. To support our suggestion we derive an analytical model that shows: Tangentially fixating the outer surface of an obstacle leads to strong flow discontinuities that can be used for flow-based segmentation. Fixation of the target center while gaze and heading are locked without head-, body-, or eye-rotations gives rise to a symmetric expansion flow with its center at the point being approached, which facilitates steering toward a target. We conclude that gaze control incorporates ecological constraints to improve the robustness of steering and collision avoidance by actively generating flows appropriate to solve the task. PMID:22719889

  15. Spinal segment-specific transcutaneous stimulation differentially shapes activation pattern among motor pools in humans

    PubMed Central

    Atkinson, Darryn A.; Dy, Christine J.; Gurley, Katelyn M.; Smith, Valerie L.; Angeli, Claudia; Harkema, Susan J.; Edgerton, V. Reggie; Gerasimenko, Yury P.

    2015-01-01

    Transcutaneous and epidural electrical spinal cord stimulation techniques are becoming more valuable as electrophysiological and clinical tools. Recently, we observed selective activation of proximal and distal motor pools during epidural spinal stimulation. In the present study, we hypothesized that the characteristics of recruitment curves obtained from leg muscles will reflect a relative preferential activation of proximal and distal motor pools based on their arrangement along the lumbosacral enlargement. The purpose was to describe the electrophysiological responses to transcutaneous stimulation in leg muscles innervated by motoneurons from different segmental levels. Stimulation delivered along the rostrocaudal axis of the lumbosacral enlargement in the supine position resulted in a selective topographical recruitment of proximal and distal leg muscles, as described by threshold intensity, slope of the recruitment curves, and plateau point intensity and magnitude. Relatively selective recruitment of proximal and distal motor pools can be titrated by optimizing the site and intensity level of stimulation to excite a given combination of motor pools. The slope of the recruitment of particular muscles allows characterization of the properties of afferents projecting to specific motoneuron pools, as well as to the type and size of the motoneurons. The location and intensity of transcutaneous spinal electrical stimulation are critical to target particular neural structures across different motor pools in investigation of specific neuromodulatory effects. Finally, the asymmetry in bilateral evoked potentials is inevitable and can be attributed to both anatomical and functional peculiarities of individual muscles or muscle groups. PMID:25814642

  16. Liver Ultrasound Image Segmentation Using Region-Difference Filters.

    PubMed

    Jain, Nishant; Kumar, Vinod

    2016-12-26

    In this paper, region-difference filters for the segmentation of liver ultrasound (US) images are proposed. Region-difference filters evaluate maximum difference of the average of two regions of the window around the center pixel. Implementing the filters on the whole image gives region-difference image. This image is then converted into binary image and morphologically operated for segmenting the desired lesion from the ultrasound image. The proposed method is compared with the maximum a posteriori-Markov random field (MAP-MRF), Chan-Vese active contour method (CV-ACM), and active contour region-scalable fitting energy (RSFE) methods. MATLAB code available online for the RSFE method is used for comparison whereas MAP-MRF and CV-ACM methods are coded in MATLAB by authors. Since no comparison is available on common database for the performance of the three methods, therefore, performance comparison of the three methods and proposed method was done on liver US images obtained from PGIMER, Chandigarh, India and from online resource. A radiologist blindly analyzed segmentation results of the 4 methods implemented on 56 images and had selected the segmentation result obtained from the proposed method as best for 46 test US images. For the remaining 10 US images, the proposed method performance was very near to the other three segmentation methods. The proposed segmentation method obtained the overall accuracy of 99.32% in comparison to the overall accuracy of 85.9, 98.71, and 68.21% obtained by MAP-MRF, CV-ACM, and RSFE methods, respectively. Computational time taken by the proposed method is 5.05 s compared to the time of 26.44, 24.82, and 28.36 s taken by MAP-MRF, CV-ACM, and RSFE methods, respectively.

  17. Generation algorithm of craniofacial structure contour in cephalometric images

    NASA Astrophysics Data System (ADS)

    Mondal, Tanmoy; Jain, Ashish; Sardana, H. K.

    2010-02-01

    Anatomical structure tracing on cephalograms is a significant way to obtain cephalometric analysis. Computerized cephalometric analysis involves both manual and automatic approaches. The manual approach is limited in accuracy and repeatability. In this paper we have attempted to develop and test a novel method for automatic localization of craniofacial structure based on the detected edges on the region of interest. According to the grey scale feature at the different region of the cephalometric images, an algorithm for obtaining tissue contour is put forward. Using edge detection with specific threshold an improved bidirectional contour tracing approach is proposed by an interactive selection of the starting edge pixels, the tracking process searches repetitively for an edge pixel at the neighborhood of previously searched edge pixel to segment images, and then craniofacial structures are obtained. The effectiveness of the algorithm is demonstrated by the preliminary experimental results obtained with the proposed method.

  18. Grouping by proximity in haptic contour detection.

    PubMed

    Overvliet, Krista E; Krampe, Ralf Th; Wagemans, Johan

    2013-01-01

    We investigated the applicability of the Gestalt principle of perceptual grouping by proximity in the haptic modality. To do so, we investigated the influence of element proximity on haptic contour detection. In the course of four sessions ten participants performed a haptic contour detection task in which they freely explored a haptic random dot display that contained a contour in 50% of the trials. A contour was defined by a higher density of elements (raised dots), relative to the background surface. Proximity of the contour elements as well as the average proximity of background elements was systematically varied. We hypothesized that if proximity of contour elements influences haptic contour detection, detection will be more likely when contour elements are in closer proximity. This should be irrespective of the ratio with the proximity of the background elements. Results showed indeed that the closer the contour elements were, the higher the detection rates. Moreover, this was the case independent of the contour/background ratio. We conclude that the Gestalt law of proximity applies to haptic contour detection.

  19. Grouping by Proximity in Haptic Contour Detection

    PubMed Central

    Overvliet, Krista E.; Krampe, Ralf Th.; Wagemans, Johan

    2013-01-01

    We investigated the applicability of the Gestalt principle of perceptual grouping by proximity in the haptic modality. To do so, we investigated the influence of element proximity on haptic contour detection. In the course of four sessions ten participants performed a haptic contour detection task in which they freely explored a haptic random dot display that contained a contour in 50% of the trials. A contour was defined by a higher density of elements (raised dots), relative to the background surface. Proximity of the contour elements as well as the average proximity of background elements was systematically varied. We hypothesized that if proximity of contour elements influences haptic contour detection, detection will be more likely when contour elements are in closer proximity. This should be irrespective of the ratio with the proximity of the background elements. Results showed indeed that the closer the contour elements were, the higher the detection rates. Moreover, this was the case independent of the contour/background ratio. We conclude that the Gestalt law of proximity applies to haptic contour detection. PMID:23762364

  20. SU-E-J-108: Solving the Chinese Postman Problem for Effective Contour Deformation

    SciTech Connect

    Yang, J; Zhang, L; Balter, P; Court, L; Zhang, Y; Dong, L

    2015-06-15

    Purpose: To develop a practical approach for accurate contour deformation when deformable image registration (DIR) is used for atlas-based segmentation or contour propagation in image-guided radiotherapy. Methods: A contour deformation approach was developed on the basis of 3D mesh operations. The 2D contours represented by a series of points in each slice were first converted to a 3D triangular mesh, which was deformed by the deformation vectors resulting from DIR. A set of parallel 2D planes then cut through the deformed 3D mesh, generating unordered points and line segments, which should be reorganized into a set of 2D contour points. It was realized that the reorganization problem was equivalent to solving the Chinese Postman Problem (CPP) by traversing a graph built from the unordered points with the least cost. Alternatively, deformation could be applied to a binary mask converted from the original contours. The deformed binary mask was then converted back into contours at the CT slice locations. We performed a qualitative comparison to validate the mesh-based approach against the image-based approach. Results: The DIR could considerably change the 3D mesh, making complicated 2D contour representations after deformation. CPP was able to effectively reorganize the points in 2D planes no matter how complicated the 2D contours were. The mesh-based approach did not require a post-processing of the contour, thus accurately showing the actual deformation in DIR. The mesh-based approach could keep some fine details and resulted in smoother contours than the image-based approach did, especially for the lung structure. Image-based approach appeared to over-process contours and suffered from image resolution limits. The mesh-based approach was integrated into in-house DIR software for use in routine clinic and research. Conclusion: We developed a practical approach for accurate contour deformation. The efficiency of this approach was demonstrated in both clinic and

  1. Comparison of children's free-living physical activity derived from wrist and hip raw accelerations during the segmented week.

    PubMed

    Noonan, Robert J; Boddy, Lynne M; Kim, Youngwon; Knowles, Zoe R; Fairclough, Stuart J

    2016-11-14

    This study assessed children's physical activity (PA) levels derived from wrist-worn GENEActiv and hip-worn ActiGraph GT3X+ accelerometers and examined the comparability of PA levels between the two devices throughout the segmented week. One hundred and twenty-nine 9-10-year-old children (79 girls) wore a GENEActiv (GAwrist) and ActiGraph GT3X+ (AGhip) accelerometer on the left wrist and right hip, respectively, for 7 days. Mean minutes of light PA (LPA) and moderate-to-vigorous PA (MVPA) per weekday (whole-day, before-school, school and after-school) and weekend day (whole-day, morning and afternoon-evening) segments were calculated, and expressed as percentage of segment time. Repeated measures analysis of variance examined differences in LPA and MVPA between GAwrist and AGhip for each time segment. Bland-Altman plots assessed between-device agreement for LPA and MVPA for whole weekday and whole weekend day segments. Correlations between GAwrist and AGhip were weak for LPA (r = 0.18-0.28), but strong for MVPA (r = 0.80-0.86). LPA and MVPA levels during all weekday and weekend day segments were significantly higher for GAwrist than AGhip (p < 0.001). The largest inter-device percent difference of 26% was observed in LPA during the school day segment. Our data suggest that correction factors are needed to improve raw PA level comparability between GAwrist and AGhip.

  2. Level set based vertebra segmentation for the evaluation of Ankylosing Spondylitis

    NASA Astrophysics Data System (ADS)

    Tan, Sovira; Yao, Jianhua; Ward, Michael M.; Yao, Lawrence; Summers, Ronald M.

    2006-03-01

    Ankylosing Spondylitis is a disease of the vertebra where abnormal bone structures (syndesmophytes) grow at intervertebral disk spaces. Because this growth is so slow as to be undetectable on plain radiographs taken over years, it is necessary to resort to computerized techniques to complement qualitative human judgment with precise quantitative measures on 3-D CT images. Very fine segmentation of the vertebral body is required to capture the small structures caused by the pathology. We propose a segmentation algorithm based on a cascade of three level set stages and requiring no training or prior knowledge. First, the noise inside the vertebral body that often blocks the proper evolution of level set surfaces is attenuated by a sigmoid function whose parameters are determined automatically. The 1st level set (geodesic active contour) is designed to roughly segment the interior of the vertebra despite often highly inhomogeneous and even discontinuous boundaries. The result is used as an initial contour for the 2nd level set (Laplacian level set) that closely captures the inner boundary of the cortical bone. The last level set (reversed Laplacian level set) segments the outer boundary of the cortical bone and also corrects small flaws of the previous stage. We carried out extensive tests on 30 vertebrae (5 from each of 6 patients). Two medical experts scored the results at intervertebral disk spaces focusing on end plates and syndesmophytes. Only two minor segmentation errors at vertebral end plates were reported and two syndesmophytes were considered slightly under-segmented.

  3. Novel multimodality segmentation using level sets and Jensen-Rényi divergence

    SciTech Connect

    Markel, Daniel; Zaidi, Habib; El Naqa, Issam

    2013-12-15

    Purpose: Positron emission tomography (PET) is playing an increasing role in radiotherapy treatment planning. However, despite progress, robust algorithms for PET and multimodal image segmentation are still lacking, especially if the algorithm were extended to image-guided and adaptive radiotherapy (IGART). This work presents a novel multimodality segmentation algorithm using the Jensen-Rényi divergence (JRD) to evolve the geometric level set contour. The algorithm offers improved noise tolerance which is particularly applicable to segmentation of regions found in PET and cone-beam computed tomography. Methods: A steepest gradient ascent optimization method is used in conjunction with the JRD and a level set active contour to iteratively evolve a contour to partition an image based on statistical divergence of the intensity histograms. The algorithm is evaluated using PET scans of pharyngolaryngeal squamous cell carcinoma with the corresponding histological reference. The multimodality extension of the algorithm is evaluated using 22 PET/CT scans of patients with lung carcinoma and a physical phantom scanned under varying image quality conditions. Results: The average concordance index (CI) of the JRD segmentation of the PET images was 0.56 with an average classification error of 65%. The segmentation of the lung carcinoma images had a maximum diameter relative error of 63%, 19.5%, and 14.8% when using CT, PET, and combined PET/CT images, respectively. The estimated maximal diameters of the gross tumor volume (GTV) showed a high correlation with the macroscopically determined maximal diameters, with aR{sup 2} value of 0.85 and 0.88 using the PET and PET/CT images, respectively. Results from the physical phantom show that the JRD is more robust to image noise compared to mutual information and region growing. Conclusions: The JRD has shown improved noise tolerance compared to mutual information for the purpose of PET image segmentation. Presented is a flexible

  4. IFCM Based Segmentation Method for Liver Ultrasound Images.

    PubMed

    Jain, Nishant; Kumar, Vinod

    2016-11-01

    In this paper we have proposed an iterative Fuzzy C-Mean (IFCM) method which divides the pixels present in the image into a set of clusters. This set of clusters is then used to segment a focal liver lesion from a liver ultrasound image. Advantage of IFCM methods is that n-clusters FCM method may lead to non-uniform distribution of centroids, whereas in IFCM method centroids will always be uniformly distributed. Proposed method is compared with the edge based Active contour Chan-Vese (CV) method, and MAP-MRF method by implementing the methods on MATLAB. Proposed method is also compared with region based active contour region-scalable fitting energy (RSFE) method whose MATLAB code is available in author's website. Since no comparison is available on a common database, the performance of three methods and the proposed method have been compared on liver ultrasound (US) images available with us. Proposed method gives the best accuracy of 99.8 % as compared to accuracy of 99.46 %, 95.81 % and 90.08 % given by CV, MAP-MRF and RSFE methods respectively. Computation time taken by the proposed segmentation method for segmentation is 14.25 s as compared to 44.71, 41.27 and 49.02 s taken by CV, MAP-MRF and RSFE methods respectively.

  5. Micro-segmented flow and multisensor-technology for microbial activity profiling.

    PubMed

    Kürsten, Dana; Kothe, Erika; Wetzel, Katharina; Bergmann, Katja; Köhler, J Michael

    2014-01-01

    The combination of micro-segmented flow with miniaturized flow-through multisensor-technology has been utilized for metabolite profiling of soil bacteria. Series of sub-μl segments were generated containing soil sample slurry from historic copper mining sites and exposed to heavy metal salts of copper and nickel. Segments were examined for bacterial growth and spectral properties as well as for the effect of heavy metal-treatment after different incubation times. In order to evaluate microbial growth, extinction was recorded with 4 different spectral channels. Fluorescence was measured using a microflow-through fluorometer to detect both growth and production of fluorescent dyes or metabolites. The incidence of single segments with enhanced absorption in one of the spectral channels or enhanced fluorescence was scored to detect soil microorganisms with interesting properties for further screening. The study could show that the number of vegetated segments, the density of microorganisms in the segments after cultivation and the spectral response are different for separate soil samples and different metals. Thus, the highly parallelized and miniaturized segmented flow method is a promising tool for profiling of soil samples with regard to identifying micro-organisms with interesting profiles for secondary metabolite-production.

  6. Neuronal oscillations form parietal/frontal networks during contour integration.

    PubMed

    Castellano, Marta; Plöchl, Michael; Vicente, Raul; Pipa, Gordon

    2014-01-01

    The ability to integrate visual features into a global coherent percept that can be further categorized and manipulated are fundamental abilities of the neural system. While the processing of visual information involves activation of early visual cortices, the recruitment of parietal and frontal cortices has been shown to be crucial for perceptual processes. Yet is it not clear how both cortical and long-range oscillatory activity leads to the integration of visual features into a coherent percept. Here, we will investigate perceptual grouping through the analysis of a contour categorization task, where the local elements that form contour must be linked into a coherent structure, which is then further processed and manipulated to perform the categorization task. The contour formation in our visual stimulus is a dynamic process where, for the first time, visual perception of contours is disentangled from the onset of visual stimulation or from motor preparation, cognitive processes that until now have been behaviorally attached to perceptual processes. Our main finding is that, while local and long-range synchronization at several frequencies seem to be an ongoing phenomena, categorization of a contour could only be predicted through local oscillatory activity within parietal/frontal sources, which in turn, would synchronize at gamma (>30 Hz) frequency. Simultaneously, fronto-parietal beta (13-30 Hz) phase locking forms a network spanning across neural sources that are not category specific. Both long range networks, i.e., the gamma network that is category specific, and the beta network that is not category specific, are functionally distinct but spatially overlapping. Altogether, we show that a critical mechanism underlying contour categorization involves oscillatory activity within parietal/frontal cortices, as well as its synchronization across distal cortical sites.

  7. A novel approach to extract closed foreground object contours in video surveillance

    NASA Astrophysics Data System (ADS)

    Tzanidou, Giounona; Edirisinghe, Eran A.

    2014-03-01

    In this paper we present a novel approach for the detection of closed contours of foreground objects in videos. The proposed methodology begins with an initial localization of contours that is achieved via background subtraction technique that makes use of mixture of Gaussian distributions to model the background. The features that are used to realize an approximate foreground contour segmentation consist of magnitude of gradient at multiple orientations and phase congruency. In the next stage, canny edges of the incoming frames are computed at multiple scales and thresholds using the saturation and value components of HSV image. The approximate foreground contour is refined by reflecting it on the detected edges. A color ratio based noise and shadow line removal technique has been devised to remove the falsely segmented noise and strong shadow edges. Ultimately, to ensure closed contours, edge completion algorithm by anisotropic diffusion is applied. Once the contour is completed, it undergoes flood fill to define the foreground areas. Detailed experimental results on benchmark dataset showed that the proposed framework performs well in most of the different background scenarios. It effectively tackles the presence of shadows, illumination changes, some cases of dynamic background and thermal videos.

  8. Whole abdominal wall segmentation using augmented active shape models (AASM) with multi-atlas label fusion and level set

    NASA Astrophysics Data System (ADS)

    Xu, Zhoubing; Baucom, Rebeccah B.; Abramson, Richard G.; Poulose, Benjamin K.; Landman, Bennett A.

    2016-03-01

    The abdominal wall is an important structure differentiating subcutaneous and visceral compartments and intimately involved with maintaining abdominal structure. Segmentation of the whole abdominal wall on routinely acquired computed tomography (CT) scans remains challenging due to variations and complexities of the wall and surrounding tissues. In this study, we propose a slice-wise augmented active shape model (AASM) approach to robustly segment both the outer and inner surfaces of the abdominal wall. Multi-atlas label fusion (MALF) and level set (LS) techniques are integrated into the traditional ASM framework. The AASM approach globally optimizes the landmark updates in the presence of complicated underlying local anatomical contexts. The proposed approach was validated on 184 axial slices of 20 CT scans. The Hausdorff distance against the manual segmentation was significantly reduced using proposed approach compared to that using ASM, MALF, and LS individually. Our segmentation of the whole abdominal wall enables the subcutaneous and visceral fat measurement, with high correlation to the measurement derived from manual segmentation. This study presents the first generic algorithm that combines ASM, MALF, and LS, and demonstrates practical application for automatically capturing visceral and subcutaneous fat volumes.

  9. Whole Abdominal Wall Segmentation using Augmented Active Shape Models (AASM) with Multi-Atlas Label Fusion and Level Set

    PubMed Central

    Xu, Zhoubing; Baucom, Rebeccah B.; Abramson, Richard G.; Poulose, Benjamin K.; Landman, Bennett A.

    2016-01-01

    The abdominal wall is an important structure differentiating subcutaneous and visceral compartments and intimately involved with maintaining abdominal structure. Segmentation of the whole abdominal wall on routinely acquired computed tomography (CT) scans remains challenging due to variations and complexities of the wall and surrounding tissues. In this study, we propose a slice-wise augmented active shape model (AASM) approach to robustly segment both the outer and inner surfaces of the abdominal wall. Multi-atlas label fusion (MALF) and level set (LS) techniques are integrated into the traditional ASM framework. The AASM approach globally optimizes the landmark updates in the presence of complicated underlying local anatomical contexts. The proposed approach was validated on 184 axial slices of 20 CT scans. The Hausdorff distance against the manual segmentation was significantly reduced using proposed approach compared to that using ASM, MALF, and LS individually. Our segmentation of the whole abdominal wall enables the subcutaneous and visceral fat measurement, with high correlation to the measurement derived from manual segmentation. This study presents the first generic algorithm that combines ASM, MALF, and LS, and demonstrates practical application for automatically capturing visceral and subcutaneous fat volumes. PMID:27127333

  10. Active fault, fault growth and segment linkage along the Janauri anticline (frontal foreland fold), NW Himalaya, India

    NASA Astrophysics Data System (ADS)

    Malik, Javed N.; Shah, Afroz A.; Sahoo, Ajit K.; Puhan, B.; Banerjee, Chiranjib; Shinde, Dattatraya P.; Juyal, Navin; Singhvi, Ashok K.; Rath, Shishir K.

    2010-03-01

    The 100 km long frontal foreland fold — the Janauri anticline in NW Himalayan foothills represents a single segment formed due to inter-linking of the southern (JS1) and the northern (JS2) Janauri segments. This anticline is a product of the fault related fold growth that facilitated lateral propagation by acquiring more length and linkage of smaller segments giving rise to a single large segment. The linked portion marked by flat-uplifted surface in the central portion represents the paleo-water gap of the Sutlej River. This area is comparatively more active in terms of tectonic activity, well justified by the occurrence of fault scarps along the forelimb and backlimb of the anticline. Occurrence of active fault scarps on either side of the anticline suggests that the slip accommodated in the frontal part is partitioned between the main frontal thrust i.e. the Himalayan Frontal Thrust (HFT) and associated back-thrust. The uplift in the piedmont zone along southern portion of Janauri anticline marked by dissected younger hill range suggests fore-landward propagation of tectonic activity along newly developed Frontal Piedmont Thrust (FPT), an imbricated emergent thrust branching out from the HFT system. We suggests that this happened because the southern segment JS1 does not linked-up with the northwestern end of Chandigarh anticline segment (CS). In the northwestern end of the Janauri anticline, due to no structural asperity the tectonic activity on HFT was taken-up by two (HF1 — in the frontal part and HF2 — towards the hinterland side) newly developed parallel active faults ( Hajipur Fault) branched from the main JS2 segment. The lateral propagation and movements along HF1 and HF2 resulted in uplift of the floodplain as well as responsible for the northward shift of the Beas River. GPR and trench investigations suggest that earthquakes during the recent past were accompanied with surface rupture. OSL (optical stimulated luminescence) dates from the trench

  11. Probabilistic image modeling with an extended chain graph for human activity recognition and image segmentation.

    PubMed

    Zhang, Lei; Zeng, Zhi; Ji, Qiang

    2011-09-01

    Chain graph (CG) is a hybrid probabilistic graphical model (PGM) capable of modeling heterogeneous relationships among random variables. So far, however, its application in image and video analysis is very limited due to lack of principled learning and inference methods for a CG of general topology. To overcome this limitation, we introduce methods to extend the conventional chain-like CG model to CG model with more general topology and the associated methods for learning and inference in such a general CG model. Specifically, we propose techniques to systematically construct a generally structured CG, to parameterize this model, to derive its joint probability distribution, to perform joint parameter learning, and to perform probabilistic inference in this model. To demonstrate the utility of such an extended CG, we apply it to two challenging image and video analysis problems: human activity recognition and image segmentation. The experimental results show improved performance of the extended CG model over the conventional directed or undirected PGMs. This study demonstrates the promise of the extended CG for effective modeling and inference of complex real-world problems.

  12. Fault barriers favor activation of backthrusts near segment ends of megathrust ruptures

    NASA Astrophysics Data System (ADS)

    Xu, S.; Fukuyama, E.; Ben-Zion, Y.; Ampuero, J. P.

    2013-12-01

    Increasing evidence indicates that backthrusts may become active during or after megathrust ruptures in subduction zones, such as in Chile and Sumatra areas (Melnick et al., 2012; Singh et al., 2011). Previous studies on relevant mechanisms mainly focused on the interaction between forethrusts and the megathrust. Here we aim to investigate through dynamic rupture simulations how backthrusts may be activated by megathrust ruptures in subduction zone environment. Assuming a single backthrust branch, our preliminary results show that the activation of backthrust is difficult if the megathrust rupture can easily pass through the fault junction, owing to a quickly established stress shadow zone in the wake of the megathrust rupture front. In contrast, if the megathrust rupture is arrested or delayed around the junction, a resultant backward stress lobe of the type discussed by Xu and Ben-Zion (2013) can load the backthrust over a considerable amount of time and facilitates rupture activation along the backthrust. A number of candidates can serve to arrest or delay megathrust ruptures, such as the velocity-strengthening frictional behavior and off-fault weak materials in the shallow portion of subduction zones, fault bend or ramp, and subducted seamount. Moreover, these features are also found capable of generating backthrusts during the long-term quasi-static process, which provide pre-existing weakness to be reactivated by later dynamic ruptures. Our results agree, from a different point of view, with the study based on the critical taper theory (Cubas et al., 2013) that an increase of friction towards the trench favors the activation of backthrusts near the up-dip limit of megathrust ruptures. The results highlight the role of fault geometric or strength heterogeneities in controlling the strain partitioning on and off the main fault plane. Accordingly, activated backthrusts may be treated as markers that reflect the limits of seismogenic zones, and thus may be used

  13. Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications

    NASA Astrophysics Data System (ADS)

    Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lu, Weiguo; Yan, Yulong; Jiang, Steve B.; Timmerman, Robert; Abdulrahman, Ramzi; Nedzi, Lucien; Gu, Xuejun

    2016-12-01

    The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8

  14. Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications.

    PubMed

    Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lu, Weiguo; Yan, Yulong; Jiang, Steve B; Timmerman, Robert; Abdulrahman, Ramzi; Nedzi, Lucien; Gu, Xuejun

    2016-12-21

    The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8

  15. SU-E-T-134: Dosimetric Implications From Organ Segmentation

    SciTech Connect

    Wu, Z; Turian, J; Chu, J

    2014-06-01

    Purpose: To evaluate the dosimetric implications resulting from organ segmentation performed by different clinical experts Methods: Twelve patients received SBRT treatment to thoracic region within the past year were selected for this study. Three physicians contoured a set of organs following RTOG guideline. DVHs of all contours were generated from the approved plans used for treatment, and were compared to those produced during planning. Most OARs were evaluated on their max dose, some, such as heart and chest wall, were also evaluated on metrics such as max dose to 4cc of volume, or 30Gy volume dose. Results: In general, there is a greater dosimetric difference between the RTOG contour sets and clinical contour sets than among the three RTOG contour sets themselves for each patient. For example, there was no difference in esophagus max dose between the RTOG contour sets for ten patients. However, they showed an average of 2.3% higher max dose than the clinical contour set, with a standard deviation of 6.6%. The proximal bronchial tree (PBT) showed a similar behavior. The average difference of PBT max dose for seven patients is 0% between the three RTOG contour sets, with standard deviation of 1%. They showed an average of 16.1% higher max dose than the clinical contour set, with a standard deviation of 126%. Conclusion: This study shows that using RTOG contouring standards improves segmentation consistency between different physicians; most of the contours examined showed less than 1% dose difference. When RTOG contour sets were compared to the clinical contour set, the differences are much more significant. Thus it is important to standardize contouring guidelines in radiation therapy treatment planning. This will reduce uncertainties in clinical outcome analysis and research studies.

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

    PubMed

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

    2014-01-01

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

  17. Both predictability and familiarity facilitate contour integration.

    PubMed

    Sassi, Michaël; Demeyer, Maarten; Machilsen, Bart; Putzeys, Tom; Wagemans, Johan

    2014-05-30

    Research has shown that contour detection is impaired in the visual periphery for snake-shaped Gabor contours but not for circular and elliptical contours. This discrepancy in findings could be due to differences in intrinsic shape properties, including shape closure and curvature variation, as well as to differences in stimulus predictability and familiarity. In a detection task using only circular contours, the target shape is both more familiar and more predictable to the observer compared with a detection task in which a different snake-shaped contour is presented on each trial. In this study, we investigated the effects of stimulus familiarity and predictability on contour integration by manipulating and disentangling the familiarity and predictability of snakelike stimuli. We manipulated stimulus familiarity by extensively training observers with one particular snake shape. Predictability was varied by alternating trial blocks with only a single target shape and trial blocks with multiple target shapes. Our results show that both predictability and familiarity facilitated contour integration, which constitutes novel behavioral evidence for the adaptivity of the contour integration mechanism in humans. If familiarity or predictability facilitated contour integration in the periphery specifically, this could explain the discrepant findings obtained with snake contours as compared with circles or ellipses. However, we found that their facilitatory effects did not differ between central and peripheral vision and thus cannot explain that particular discrepancy in the literature.

  18. Improving semi-automated segmentation by integrating learning with active sampling

    NASA Astrophysics Data System (ADS)

    Huo, Jing; Okada, Kazunori; Brown, Matthew

    2012-02-01

    Interactive segmentation algorithms such as GrowCut usually require quite a few user interactions to perform well, and have poor repeatability. In this study, we developed a novel technique to boost the performance of the interactive segmentation method GrowCut involving: 1) a novel "focused sampling" approach for supervised learning, as opposed to conventional random sampling; 2) boosting GrowCut using the machine learned results. We applied the proposed technique to the glioblastoma multiforme (GBM) brain tumor segmentation, and evaluated on a dataset of ten cases from a multiple center pharmaceutical drug trial. The results showed that the proposed system has the potential to reduce user interaction while maintaining similar segmentation accuracy.

  19. Classification of Benign and Malignant Breast Tumors in Ultrasound Images with Posterior Acoustic Shadowing Using Half-Contour Features.

    PubMed

    Zhou, Zhuhuang; Wu, Shuicai; Chang, King-Jen; Chen, Wei-Ren; Chen, Yung-Sheng; Kuo, Wen-Hung; Lin, Chung-Chih; Tsui, Po-Hsiang

    Posterior acoustic shadowing (PAS) can bias breast tumor segmentation and classification in ultrasound images. In this paper, half-contour features are proposed to classify benign and malignant breast tumors with PAS, considering the fact that the upper half of the tumor contour is less affected by PAS. Adaptive thresholding and disk expansion are employed to detect tumor contours. Based on the detected full contour, the upper half contour is extracted. For breast tumor classification, six quantitative feature parameters are analyzed for both full contours and half contours, including standard deviation of degree (SDD), which is proposed to describe tumor irregularity. Fifty clinical cases (40 with PAS and 10 without PAS) were used. Tumor circularity (TC) and SDD were both effective full- and half-contour parameters in classifying images without PAS. Half-contour TC [74 % accuracy, 72 % sensitivity, 76 % specificity, 0.78 area under the receiver operating characteristic curve (AUC), p > 0.05] significantly improved the classification of breast tumors with PAS compared to that with full-contour TC (54 % accuracy, 56 % sensitivity, 52 % specificity, 0.52 AUC, p > 0.05). Half-contour SDD (72 % accuracy, 76 % sensitivity, 68 % specificity, 0.81 AUC, p < 0.05) improved the classification of breast tumors with PAS compared to that with full-contour SDD (62 % accuracy, 80 % sensitivity, 44 % specificity, 0.61 AUC, p > 0.05). The proposed half-contour TC and SDD may be useful in classifying benign and malignant breast tumors in ultrasound images affected by PAS.

  20. Earthquake swarm activity in the Oaxaca segment of Middle American Subduction Zone

    NASA Astrophysics Data System (ADS)

    Brudzinski, M. R.; Cabral, E.; Arciniega-Ceballos, A.

    2013-05-01

    An outstanding question in geophysics is the degree to which the newly discovered family of slow fault slip behaviors is related to more traditional earthquakes, especially since theoretical predictions indicate slip in the deeper transitional zone promotes failure in the shallower seismogenic zone. The Oaxacan segment of the Middle American Subduction zone is a natural region to pursue detailed studies of the spectrum of fault slip due to the unusually shallow subduction angle and short trench-to-coast distances that bring broad portions of the seismogenic and transitional zones of the plate interface inland. A deployment of broadband seismometers in this region has improved the network coverage to ~70 km station spacing since 2006, providing new opportunities to investigate smaller seismic phenomena. While characterization of tectonic tremor has been a prominent focus of this deployment, the improved network has also revealed productive earthquake swarms, whose sustained periods of similar magnitude earthquakes are also thought to be driven by slow slip. We identify a particularly productive earthquake swarm in July 2006 (~600 similar earthquakes detected), which occurred during a week-long episode of tectonic tremor and geodetically detected slow slip. Using a multi-station "template matching" waveform cross correlation technique, we have been able to detect and locate swarm earthquakes several orders of magnitude smaller than that of traditional processing, particularly during periods of increased background activity, because the detector is finely tuned to events with similar hypocentral location and focal mechanism. When we scan for repeats of the event families detected in the July 2006 sequence throughout the 6+ years since, we find these families were also activated during several other slow slip episodes, which indicates a link between slow slip in the transition zone and earthquakes at the downdip end of the seismogenic portion of the megathrust.

  1. Automated skin segmentation in ultrasonic evaluation of skin toxicity in breast cancer radiotherapy.

    PubMed

    Gao, Yi; Tannenbaum, Allen; Chen, Hao; Torres, Mylin; Yoshida, Emi; Yang, Xiaofeng; Wang, Yuefeng; Curran, Walter; Liu, Tian

    2013-11-01

    Skin toxicity is the most common side effect of breast cancer radiotherapy and impairs the quality of life of many breast cancer survivors. We, along with other researchers, have recently found quantitative ultrasound to be effective as a skin toxicity assessment tool. Although more reliable than standard clinical evaluations (visual observation and palpation), the current procedure for ultrasound-based skin toxicity measurements requires manual delineation of the skin layers (i.e., epidermis-dermis and dermis-hypodermis interfaces) on each ultrasound B-mode image. Manual skin segmentation is time consuming and subjective. Moreover, radiation-induced skin injury may decrease image contrast between the dermis and hypodermis, which increases the difficulty of delineation. Therefore, we have developed an automatic skin segmentation tool (ASST) based on the active contour model with two significant modifications: (i) The proposed algorithm introduces a novel dual-curve scheme for the double skin layer extraction, as opposed to the original single active contour method. (ii) The proposed algorithm is based on a geometric contour framework as opposed to the previous parametric algorithm. This ASST algorithm was tested on a breast cancer image database of 730 ultrasound breast images (73 ultrasound studies of 23 patients). We compared skin segmentation results obtained with the ASST with manual contours performed by two physicians. The average percentage differences in skin thickness between the ASST measurement and that of each physician were less than 5% (4.8 ± 17.8% and -3.8 ± 21.1%, respectively). In summary, we have developed an automatic skin segmentation method that ensures objective assessment of radiation-induced changes in skin thickness. Our ultrasound technology offers a unique opportunity to quantify tissue injury in a more meaningful and reproducible manner than the subjective assessments currently employed in the clinic.

  2. AUTOMATED SKIN SEGMENTATION IN ULTRASONIC EVALUATION OF SKIN TOXICITY IN BREAST CANCER RADIOTHERAPY

    PubMed Central

    Gao, Yi; Tannenbaum, Allen; Chen, Hao; Torres, Mylin; Yoshida, Emi; Yang, Xiaofeng; Wang, Yuefeng; Curran, Walter; Liu, Tian

    2013-01-01

    Skin toxicity is the most common side effect of breast cancer radiotherapy and impairs the quality of life of many breast cancer survivors. We, along with other researchers, have recently found quantitative ultrasound to be effective as a skin toxicity assessment tool. Although more reliable than standard clinical evaluations (visual observation and palpation), the current procedure for ultrasound-based skin toxicity measurements requires manual delineation of the skin layers (i.e., epidermis-dermis and dermis-hypodermis interfaces) on each ultrasound B-mode image. Manual skin segmentation is time consuming and subjective. Moreover, radiation-induced skin injury may decrease image contrast between the dermis and hypodermis, which increases the difficulty of delineation. Therefore, we have developed an automatic skin segmentation tool (ASST) based on the active contour model with two significant modifications: (i) The proposed algorithm introduces a novel dual-curve scheme for the double skin layer extraction, as opposed to the original single active contour method. (ii) The proposed algorithm is based on a geometric contour framework as opposed to the previous parametric algorithm. This ASST algorithm was tested on a breast cancer image database of 730 ultrasound breast images (73 ultrasound studies of 23 patients). We compared skin segmentation results obtained with the ASST with manual contours performed by two physicians. The average percentage differences in skin thickness between the ASST measurement and that of each physician were less than 5% (4.8 ± 17.8% and −3.8 ± 21.1%, respectively). In summary, we have developed an automatic skin segmentation method that ensures objective assessment of radiation-induced changes in skin thickness. Our ultrasound technology offers a unique opportunity to quantify tissue injury in a more meaningful and reproducible manner than the subjective assessments currently employed in the clinic. PMID:23993172

  3. A Neurocomputational account of the role of contour facilitation in brightness perception.

    PubMed

    Domijan, Dražen

    2015-01-01

    A new filling-in model is proposed in order to account for challenging brightness illusions, where inducing background elements are spatially separated from the gray target such as dungeon, cube and grating illusions, bullseye display and ring patterns. This model implements the simple idea that neural response to low-contrast contour is enhanced (facilitated) by the presence of collinear or parallel high-contrast contours in its wider neighborhood. Contour facilitation is achieved via dendritic inhibition, which enables the computation of maximum function among inputs to the node. Recurrent application of maximum function leads to the propagation of the neural signal along collinear or parallel contour segments. When a strong global-contour signal is accompanied with a weak local-contour signal at the same location, conditions are met to produce brightness assimilation within the Filling-in Layer. Computer simulations showed that the model correctly predicts brightness appearance in all of the aforementioned illusions as well as in White's effect, Benary's cross, Todorović's illusion, checkerboard contrast, contrast-contrast illusion and various variations of the White's effect. The proposed model offers new insights on how geometric factors (contour colinearity or parallelism), together with contrast magnitude contribute to the brightness perception.

  4. Brightness alteration with interweaving contours.

    PubMed

    Roncato, Sergio

    2012-01-01

    Chromatic induction is observed whenever the perceived colour of a target surface shifts towards the hue of a neighbouring surface. Some vivid manifestations may be seen in a white background where thin coloured lines have been drawn (assimilation) or when lines of different colours are collinear (neon effect) or adjacent (watercolour) to each other. This study examines a particular colour induction that manifests in concomitance with an opposite effect of colour saturation (or anti-spread). The two phenomena can be observed when a repetitive pattern is drawn in which outline thin contours intercept wider contours or surfaces, colour spreading appear to fill the surface occupied by surfaces or thick lines whereas the background traversed by thin lines is seen as brighter or filled of a saturated white. These phenomena were first observed by Bozzi (1975) and Kanizsa (1979) in figural conditions that did not allow them to document their conjunction. Here we illustrate various manifestations of this twofold phenomenon and compare its effects with the known effects of brightness and colour induction. Some conjectures on the nature of these effects are discussed.

  5. Antenna surface contour control system

    NASA Technical Reports Server (NTRS)

    Ahl, Elvin L. (Inventor); Miller, James B. (Inventor)

    1989-01-01

    The invention is a system for automatically controlling the surface contour of a deployable and restowable antenna having a mesh reflector surface supported by a circular, folding hoop affixed to a central, telescoping column. The antenna, when deployed, forms a quad-aperture reflector with each quadrant of the mesh surface shaped to provide an offset parabolic radio frequency (RF) reflector. The hoop is supported and positioned by quartz support cords attached to the top of a column and by lower graphite hoop control cords that extend between the hoop and base of the column. The antenna, an RF reflective surface, is a gold plated molybdenum wire mesh supported on a graphite cord truss structure that includes the hoop control cords and a plurality of surface control cords attached at selected points on the surface and to the base of the column. The contour of the three-dimensional surface of the antenna is controlled by selectively adjusting the lengths of the surface control cords and the graphite hoop control cords by means of novel actuator assemblies that automatically sense and change the lengths of the lower hoop control cords and surface control cords.

  6. Projection moire for remote contour analysis

    NASA Technical Reports Server (NTRS)

    Doty, J. L.

    1983-01-01

    Remote projection and viewing of moire contours are examined analytically for a system employing separate projection and viewing optics, with specific attention paid to the practical limitations imposed by the optical systems. It is found that planar contours are possible only when the optics are telecentric (exit pupil at infinity) but that the requirement for spatial separability of the contour fringes from extraneous fringes is independent of the specific optics and is a function only of the angle separating the two optic axes. In the nontelecentric case, the contour separation near the object is unchanged from that of the telecentric case, although the contours are distorted into low-eccentricity (near-circular) ellipses. Furthermore, the minimum contour spacing is directly related to the depth of focus through the resolution of the optics.

  7. Lung tumor segmentation in PET images using graph cuts.

    PubMed

    Ballangan, Cherry; Wang, Xiuying; Fulham, Michael; Eberl, Stefan; Feng, David Dagan

    2013-03-01

    The aim of segmentation of tumor regions in positron emission tomography (PET) is to provide more accurate measurements of tumor size and extension into adjacent structures, than is possible with visual assessment alone and hence improve patient management decisions. We propose a segmentation energy function for the graph cuts technique to improve lung tumor segmentation with PET. Our segmentation energy is based on an analysis of the tumor voxels in PET images combined with a standardized uptake value (SUV) cost function and a monotonic downhill SUV feature. The monotonic downhill feature avoids segmentation leakage into surrounding tissues with similar or higher PET tracer uptake than the tumor and the SUV cost function improves the boundary definition and also addresses situations where the lung tumor is heterogeneous. We evaluated the method in 42 clinical PET volumes from patients with non-small cell lung cancer (NSCLC). Our method improves segmentation and performs better than region growing approaches, the watershed technique, fuzzy-c-means, region-based active contour and tumor customized downhill.

  8. Contouring variability of human- and deformable-generated contours in radiotherapy for prostate cancer

    NASA Astrophysics Data System (ADS)

    Gardner, Stephen J.; Wen, Ning; Kim, Jinkoo; Liu, Chang; Pradhan, Deepak; Aref, Ibrahim; Cattaneo, Richard, II; Vance, Sean; Movsas, Benjamin; Chetty, Indrin J.; Elshaikh, Mohamed A.

    2015-06-01

    This study was designed to evaluate contouring variability of human-and deformable-generated contours on planning CT (PCT) and CBCT for ten patients with low-or intermediate-risk prostate cancer. For each patient in this study, five radiation oncologists contoured the prostate, bladder, and rectum, on one PCT dataset and five CBCT datasets. Consensus contours were generated using the STAPLE method in the CERR software package. Observer contours were compared to consensus contour, and contour metrics (Dice coefficient, Hausdorff distance, Contour Distance, Center-of-Mass [COM] Deviation) were calculated. In addition, the first day CBCT was registered to subsequent CBCT fractions (CBCTn: CBCT2-CBCT5) via B-spline Deformable Image Registration (DIR). Contours were transferred from CBCT1 to CBCTn via the deformation field, and contour metrics were calculated through comparison with consensus contours generated from human contour set. The average contour metrics for prostate contours on PCT and CBCT were as follows: Dice coefficient—0.892 (PCT), 0.872 (CBCT-Human), 0.824 (CBCT-Deformed); Hausdorff distance—4.75 mm (PCT), 5.22 mm (CBCT-Human), 5.94 mm (CBCT-Deformed); Contour Distance (overall contour)—1.41 mm (PCT), 1.66 mm (CBCT-Human), 2.30 mm (CBCT-Deformed); COM Deviation—2.01 mm (PCT), 2.78 mm (CBCT-Human), 3.45 mm (CBCT-Deformed). For human contours on PCT and CBCT, the difference in average Dice coefficient between PCT and CBCT (approx. 2%) and Hausdorff distance (approx. 0.5 mm) was small compared to the variation between observers for each patient (standard deviation in Dice coefficient of 5% and Hausdorff distance of 2.0 mm). However, additional contouring variation was found for the deformable-generated contours (approximately 5.0% decrease in Dice coefficient and 0.7 mm increase in Hausdorff distance relative to human-generated contours on CBCT). Though deformable contours provide a reasonable starting point for contouring on

  9. Enhanced Th1 and Th17 responses in peripheral blood in active non-segmental vitiligo.

    PubMed

    Zhen, Yu; Yao, Lei; Zhong, Shuxia; Song, Yang; Cui, Yan; Li, Shanshan

    2016-12-01

    Accumulating studies have indicated that vitiligo, especially non-segmental vitiligo (NSV), is one kind of autoimmune diseases and CD4(+) T cells play important roles in the pathogenesis. However, there have been very limited data on the detailed changes of each of the CD4(+) T cell subsets in periphery in active NSV. To clarify this issue, we collected the peripheral blood mononuclear cells (PBMCs) from 30 patients with active NSV and 30 age- and sex-matched healthy controls. The percentages of circulating Th1, Th2, Th17 and Tregs were evaluated using flow cytometry and the expressions of their specific transcription factors T-bet, GATA3, RORγt and FOXP3 at mRNA level and protein levels were qualified by qPCR and flow cytometry, respectively. Meanwhile, the expression levels of IFN-γ, IL-4, TGF-β, and IL-17A in serum were measured. We found that in patients with NSV, the percentages and absolute numbers of circulating Th1 and Th17 were both significantly higher than those of healthy controls, while the percentages of Th2 and Tregs and absolute numbers showed no significant difference compared to healthy controls. Moreover, the ratios of Th1/Tregs and Th17/Tregs in circulation were both statistically elevated in active NSV. Similar results were got in qualification of their corresponding transcription factors at mRNA level and protein levels. Compared with healthy controls, the expression level of IL-17A was significantly increased in serum of patients with NSV, while the productions of IFN-γ, IL-4, TGF-β had no significant change. These data suggested that in circulating CD4(+) T cell subsets, Th1 and Th17 played the major role in cellular immunity in the progression of vitiligo. The immune lever in circulation was inclined to effector CD4(+) T cells not suppressor CD4(+) T cells that may result in the loss of self-tolerance to melanocytes.

  10. Contour Extraction in Prostate Ultrasound Images Using the Wavelet Transform and Snakes

    DTIC Science & Technology

    2007-11-02

    signal noise levels. In this paper we present a semi-automatic prostate contour extraction scheme, which is based on the wavelet transform and active...contour models, or snakes. The ultrasound image is first decomposed into edge naps at different resolutions via the wavelet transform . Seed points are

  11. 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.

  12. A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation.

    PubMed

    Zhang, Rui; Zhu, Shiping; Zhou, Qin

    2016-10-21

    Infrared image segmentation is a challenging topic because infrared images are characterized by high noise, low contrast, and weak edges. Active contour models, especially gradient vector flow, have several advantages in terms of infrared image segmentation. However, the GVF (Gradient Vector Flow) model also has some drawbacks including a dilemma between noise smoothing and weak edge protection, which decrease the effect of infrared image segmentation significantly. In order to solve this problem, we propose a novel generalized gradient vector flow snakes model combining GGVF (Generic Gradient Vector Flow) and NBGVF (Normally Biased Gradient Vector Flow) models. We also adopt a new type of coefficients setting in the form of convex function to improve the ability of protecting weak edges while smoothing noises. Experimental results and comparisons against other methods indicate that our proposed snakes model owns better ability in terms of infrared image segmentation than other snakes models.

  13. A Hybrid Approach for Segmentation and Tracking of Myxococcus xanthus Swarms.

    PubMed

    Chen, Jianxu; Alber, Mark S; Chen, Danny Z

    2016-03-30

    Cell segmentation and motion tracking in timelapse images are fundamental problems in computer vision, and are also crucial for various biomedical studies. Myxococcus xanthus is a type of rod-like cells with highly coordinated motion. The segmentation and tracking of M. xanthus are challenging, because cells may touch tightly and form dense swarms that are difficult to identify individually in an accurate manner. The known cell tracking approaches mainly fall into two frameworks, detection association and model evolution, each having its own advantages and disadvantages. In this paper, we propose a new hybrid framework combining these two frameworks into one and leveraging their complementary advantages. Also, we propose an active contour model based on the Ribbon Snake, which is seamlessly integrated with our hybrid framework. Evaluated by 10 different datasets, our approach achieves considerable improvement over the state-of-the-art cell tracking algorithms on identifying complete cell trajectories, and higher segmentation accuracy than performing segmentation in individual 2D images.

  14. A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation

    PubMed Central

    Zhang, Rui; Zhu, Shiping; Zhou, Qin

    2016-01-01

    Infrared image segmentation is a challenging topic because infrared images are characterized by high noise, low contrast, and weak edges. Active contour models, especially gradient vector flow, have several advantages in terms of infrared image segmentation. However, the GVF (Gradient Vector Flow) model also has some drawbacks including a dilemma between noise smoothing and weak edge protection, which decrease the effect of infrared image segmentation significantly. In order to solve this problem, we propose a novel generalized gradient vector flow snakes model combining GGVF (Generic Gradient Vector Flow) and NBGVF (Normally Biased Gradient Vector Flow) models. We also adopt a new type of coefficients setting in the form of convex function to improve the ability of protecting weak edges while smoothing noises. Experimental results and comparisons against other methods indicate that our proposed snakes model owns better ability in terms of infrared image segmentation than other snakes models. PMID:27775660

  15. Pre-Processing Effect on the Accuracy of Event-Based Activity Segmentation and Classification through Inertial Sensors.

    PubMed

    Fida, Benish; Bernabucci, Ivan; Bibbo, Daniele; Conforto, Silvia; Schmid, Maurizio

    2015-09-11

    Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. For monitoring and fitness applications, it is crucial to develop methods able to segment each activity cycle, e.g., a gait cycle, so that the successive classification step may be more accurate. To increase detection accuracy, pre-processing is often used, with a concurrent increase in computational cost. In this paper, the effect of pre-processing operations on the detection and classification of locomotion activities was investigated, to check whether the presence of pre-processing significantly contributes to an increase in accuracy. The pre-processing stages evaluated in this study were inclination correction and de-noising. Level walking, step ascending, descending and running were monitored by using a shank-mounted inertial sensor. Raw and filtered segments, obtained from a modified version of a rule-based gait detection algorithm optimized for sequential processing, were processed to extract time and frequency-based features for physical activity classification through a support vector machine classifier. The proposed method accurately detected >99% gait cycles from raw data and produced >98% accuracy on these segmented gait cycles. Pre-processing did not substantially increase classification accuracy, thus highlighting the possibility of reducing the amount of pre-processing for real-time applications.

  16. Creation of digital contours that approach the characteristics of cartographic contours

    USGS Publications Warehouse

    Tyler, Dean J.; Greenlee, Susan K.

    2012-01-01

    The capability to easily create digital contours using commercial off-the-shelf (COTS) software has existed for decades. Out-of-the-box raw contours are suitable for many scientific applications without pre- or post-processing; however, cartographic applications typically require additional improvements. For example, raw contours generally require smoothing before placement on a map. Cartographic contours must also conform to certain spatial/logical rules; for example, contours may not cross waterbodies. The objective was to create contours that match as closely as possible the cartographic contours produced by manual methods on the 1:24,000-scale, 7.5-minute Topographic Map series. This report outlines the basic approach, describes a variety of problems that were encountered, and discusses solutions. Many of the challenges described herein were the result of imperfect input raster elevation data and the requirement to have the contours integrated with hydrographic features from the National Hydrography Dataset (NHD).

  17. Three-Dimensional Blood Vessel Segmentation and Centerline Extraction based on Two-Dimensional Cross-Section Analysis.

    PubMed

    Kumar, Rahul Prasanna; Albregtsen, Fritz; Reimers, Martin; Edwin, Bjørn; Langø, Thomas; Elle, Ole Jakob

    2015-05-01

    The segmentation of tubular tree structures like vessel systems in volumetric datasets is of vital interest for many medical applications. In this paper we present a novel, semi-automatic method for blood vessel segmentation and centerline extraction, by tracking the blood vessel tree from a user-initiated seed point to the ends of the blood vessel tree. The novelty of our method is in performing only two-dimensional cross-section analysis for segmentation of the connected blood vessels. The cross-section analysis is done by our novel single-scale or multi-scale circle enhancement filter, used at the blood vessel trunk or bifurcation, respectively. The method was validated for both synthetic and medical images. Our validation has shown that the cross-sectional centerline error for our method is below 0.8 pixels and the Dice coefficient for our segmentation is 80% ± 2.7%. On combining our method with an optional active contour post-processing, the Dice coefficient for the resulting segmentation is found to be 94% ± 2.4%. Furthermore, by restricting the image analysis to the regions of interest and converting most of the three-dimensional calculations to two-dimensional calculations, the processing was found to be more than 18 times faster than Frangi vesselness with thinning, 8 times faster than user-initiated active contour segmentation with thinning and 7 times faster than our previous method.

  18. Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages.

    PubMed

    Nunez-Iglesias, Juan; Kennedy, Ryan; Plaza, Stephen M; Chakraborty, Anirban; Katz, William T

    2014-01-01

    The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.

  19. Plasma Characterization of Hall Thruster with Active and Passive Segmented Electrodes

    SciTech Connect

    Raitses, Y.; Staack, D.; Fisch, N.J.

    2002-09-04

    Non-emissive electrodes and ceramic spacers placed along the Hall thruster channel are shown to affect the plasma potential distribution and the thruster operation. These effects are associated with physical properties of the electrode material and depend on the electrode configuration, geometry and the magnetic field distribution. An emissive segmented electrode was able to maintain thruster operation by supplying an additional electron flux to sustain the plasma discharge between the anode and cathode neutralizer. These results indicate the possibility of new configurations for segmented electrode Hall thruster.

  20. 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 evaluation of a four-phase three-dimensional active contour implemented with a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to speed up numerical computation and avoid the need for a priori information. This random initialization ensures robustness of the method to variation of user expertise, biased a priori information and errors in input information that could be influenced by variations in image quality. Experimentation on three MRI brain data sets showed that an optimal partitioning successfully labeled regions that accurately identified white matter, gray matter and cerebrospinal fluid in the ventricles. Quantitative evaluation of the segmentation was performed with comparison to manually labeled data and computed false positive and false negative assignments of voxels for the three organs. We report high accuracy for the two comparison cases. These results demonstrate the efficiency and flexibility of this segmentation framework to perform the challenging task of automatically extracting brain tissue volume contours.

  1. Comparing Demographic, Health Status and Psychosocial Strategies of Audience Segmentation to Promote Physical Activity

    ERIC Educational Resources Information Center

    Boslaugh, Sarah E.; Kreuter, Matthew W.; Nicholson, Robert A.; Naleid, Kimberly

    2005-01-01

    The goal of audience segmentation is to identify population subgroups that are homogeneous with respect to certain variables associated with a given outcome or behavior. When such groups are identified and understood, targeted intervention strategies can be developed to address their unique characteristics and needs. This study compares the…

  2. Age, Episodicity and Migration of Hydrothermal Activity within the Axial Valley, Endeavour Segment, Juan de Fuca Ridge

    NASA Astrophysics Data System (ADS)

    Jamieson, J. W.; Hannington, M. D.; Kelley, D. S.; Clague, D. A.; Holden, J. F.; Tivey, M. K.; Delaney, J. R.

    2011-12-01

    Hydrothermal sulfide deposits record the history of high-temperature venting along the Endeavour Segment. Active venting is currently located within five discreet vent fields, with minor diffuse venting occurring between the fields. However, inactive and/or extinct sulfide structures are found throughout the entire axial valley of the ridge segment, suggesting that hydrothermal activity has been more vigorous in the past or focused venting has migrated with time. Here, we present age constraints from U-series dating of 44 sulfide samples collected by manned submersible from between the Mothra Field in the south to Sasquatch in the north. Samples are dated using 226Ra/Ba ratios from hydrothermal barite that precipitates along with the sulfide minerals. Most samples have been collected from within or near the active vent fields. Fifteen samples from the Main Endeavour Field (MEF) show a spectrum of ages from present to 2,430 years old, indicating that this field has been continuously active for at least ~2,400 years. MEF appears to be oldest currently active field. This minimum value for the age of hydrothermal activity also provides a minimum age of the axial valley itself. Ages from thirteen samples from the High-Rise Field indicate continuous venting for at least the past ~1,250 years. These age data are used in conjunction with age constraints of the volcanic flows to develop an integrated volcanic, hydrothermal and tectonic history of the Endeavour Segment. The total volume of hydrothermal sulfide within the axial valley, determined from high-resolution bathymetry, is used in conjunction with the age constraints of the sulfide material to determine the mass accumulation rates of sulfide along the Endeavour Segment. These data can be used to calibrate the efficiency of sulfide deposition from the hydrothermal vents, and provide a time-integrated history of heat, fluid and chemical fluxes at the ridge-segment scale. The comparison of time-integrated rates with

  3. Interval and Contour Processing in Autism

    ERIC Educational Resources Information Center

    Heaton, Pamela

    2005-01-01

    High functioning children with autism and age and intelligence matched controls participated in experiments testing perception of pitch intervals and musical contours. The finding from the interval study showed superior detection of pitch direction over small pitch distances in the autism group. On the test of contour discrimination no group…

  4. Tongue Motion Averaging from Contour Sequences

    ERIC Educational Resources Information Center

    Li, Min; Kambhamettu, Chandra; Stone, Maureen

    2005-01-01

    In this paper, a method to get the best representation of a speech motion from several repetitions is presented. Each repetition is a representation of the same speech captured at different times by sequence of ultrasound images and is composed of a set of 2D spatio-temporal contours. These 2D contours in different repetitions are time aligned…

  5. An adipose segmentation and quantification scheme for the intra abdominal region on minipigs

    NASA Astrophysics Data System (ADS)

    Engholm, Rasmus; Dubinskiy, Aleksandr; Larsen, Rasmus; Hanson, Lars G.; Christoffersen, Berit Østergaard

    2006-03-01

    This article describes a method for automatic segmentation of the abdomen into three anatomical regions: subcutaneous, retroperitoneal and visceral. For the last two regions the amount of adipose tissue (fat) is quantified. According to recent medical research, the distinction between retroperitoneal and visceral fat is important for studying metabolic syndrome, which is closely related to diabetes. However previous work has neglected to address this point, treating the two types of fat together. We use T1-weighted three-dimensional magnetic resonance data of the abdomen of obese minipigs. The pigs were manually dissected right after the scan, to produce the "ground truth" segmentation. We perform automatic segmentation on a representative slice, which on humans has been shown to correlate with the amount of adipose tissue in the abdomen. The process of automatic fat estimation consists of three steps. First, the subcutaneous fat is removed with a modified active contour approach. The energy formulation of the active contour exploits the homogeneous nature of the subcutaneous fat and the smoothness of the boundary. Subsequently the retroperitoneal fat located around the abdominal cavity is separated from the visceral fat. For this, we formulate a cost function on a contour, based on intensities, edges, distance to center and smoothness, so as to exploit the properties of the retroperitoneal fat. We then globally optimize this function using dynamic programming. Finally, the fat content of the retroperitoneal and visceral regions is quantified based on a fuzzy c-means clustering of the intensities within the segmented regions. The segmentation proved satisfactory by visual inspection, and closely correlated with the manual dissection data. The correlation was 0.89 for the retroperitoneal fat, and 0.74 for the visceral fat.

  6. Comparison of cell body size and oxidative enzyme activity in motoneurons between the cervical and lumbar segments in the rat spinal cord after spaceflight and recovery.

    PubMed

    Ishihara, A; Yamashiro, J; Matsumoto, A; Higashibata, A; Ishioka, N; Shimazu, T; Ohira, Y

    2006-03-01

    The cell body sizes and succinate dehydrogenase (SDH) activities of motoneurons in the dorsolateral region of the ventral horn at the cervical and lumbar segments in the rat spinal cord were determined following 9 days of spaceflight with or without 10 days of recovery on Earth. The motoneurons were divided into three types based on their cell body sizes; small-, medium-, and large-sized motoneurons. In control rats, there was no difference in the cell body size or SDH activity of small- and large-sized motoneurons between the cervical and lumbar segments. The SDH activity of medium-sized motoneurons in control rats was higher in the lumbar segment than in the cervical segment, while the cell body sizes of medium-sized motoneurons were identical. The SDH activity of medium-sized motoneurons in the lumbar segment decreased to a level similar to that in the cervical segment of control rats following spaceflight. In addition, the decreased SDH activity of medium-sized motoneurons persisted for at least 10 days of recovery on Earth. It is concluded that spaceflight selectively affects the SDH activity of medium-sized motoneurons in the lumbar segment of the spinal cord, which presumably innervate skeletal muscles having an antigravity function.

  7. Spikelets in Pyramidal Neurons: Action Potentials Initiated in the Axon Initial Segment That Do Not Activate the Soma

    PubMed Central

    Michalikova, Martina; Kempter, Richard

    2017-01-01

    Spikelets are small spike-like depolarizations that can be measured in somatic intracellular recordings. Their origin in pyramidal neurons remains controversial. To explain spikelet generation, we propose a novel single-cell mechanism: somato-dendritic input generates action potentials at the axon initial segment that may fail to activate the soma and manifest as somatic spikelets. Using mathematical analysis and numerical simulations of compartmental neuron models, we identified four key factors controlling spikelet generation: (1) difference in firing threshold, (2) impedance mismatch, and (3) electrotonic separation between the soma and the axon initial segment, as well as (4) input amplitude. Because spikelets involve forward propagation of action potentials along the axon while they avoid full depolarization of the somato-dendritic compartments, we conjecture that this mode of operation saves energy and regulates dendritic plasticity while still allowing for a read-out of results of neuronal computations. PMID:28068338

  8. Orientation Histogram-Based Center-Surround Interaction: An Integration Approach for Contour Detection.

    PubMed

    Zhao, Rongchang; Wu, Min; Liu, Xiyao; Zou, Beiji; Li, Fangfang

    2017-01-01

    Contour is a critical feature for image description and object recognition in many computer vision tasks. However, detection of object contour remains a challenging problem because of disturbances from texture edges. This letter proposes a scheme to handle texture edges by implementing contour integration. The proposed scheme integrates structural segments into contours while inhibiting texture edges with the help of the orientation histogram-based center-surround interaction model. In the model, local edges within surroundings exert a modulatory effect on central contour cues based on the co-occurrence statistics of local edges described by the divergence of orientation histograms in the local region. We evaluate the proposed scheme on two well-known challenging boundary detection data sets (RuG and BSDS500). The experiments demonstrate that our scheme achieves a high [Formula: see text]-measure of up to 0.74. Results show that our scheme achieves integrating accurate contour while eliminating most of texture edges, a novel approach to long-range feature analysis.

  9. Contour tracking in echocardiographic sequences via sparse representation and dictionary learning.

    PubMed

    Huang, Xiaojie; Dione, Donald P; Compas, Colin B; Papademetris, Xenophon; Lin, Ben A; Bregasi, Alda; Sinusas, Albert J; Staib, Lawrence H; Duncan, James S

    2014-02-01

    This paper presents a dynamical appearance model based on sparse representation and dictionary learning for tracking both endocardial and epicardial contours of the left ventricle in echocardiographic sequences. Instead of learning offline spatiotemporal priors from databases, we exploit the inherent spatiotemporal coherence of individual data to constraint cardiac contour estimation. The contour tracker is initialized with a manual tracing of the first frame. It employs multiscale sparse representation of local image appearance and learns online multiscale appearance dictionaries in a boosting framework as the image sequence is segmented frame-by-frame sequentially. The weights of multiscale appearance dictionaries are optimized automatically. Our region-based level set segmentation integrates a spectrum of complementary multilevel information including intensity, multiscale local appearance, and dynamical shape prediction. The approach is validated on twenty-six 4D canine echocardiographic images acquired from both healthy and post-infarct canines. The segmentation results agree well with expert manual tracings. The ejection fraction estimates also show good agreement with manual results. Advantages of our approach are demonstrated by comparisons with a conventional pure intensity model, a registration-based contour tracker, and a state-of-the-art database-dependent offline dynamical shape model. We also demonstrate the feasibility of clinical application by applying the method to four 4D human data sets.

  10. MRI Brain Tumor Segmentation and Necrosis Detection Using Adaptive Sobolev Snakes

    PubMed Central

    Nakhmani, Arie; Kikinis, Ron; Tannenbaum, Allen

    2014-01-01

    Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at different points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D diffusion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation. PMID:25302005

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

    PubMed

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  13. Differential contribution of early visual areas to the perceptual process of contour processing.

    PubMed

    Schira, Mark M; Fahle, Manfred; Donner, Tobias H; Kraft, Antje; Brandt, Stephan A

    2004-04-01

    We investigated contour processing and figure-ground detection within human retinotopic areas using event-related functional magnetic resonance imaging (fMRI) in 6 healthy and naïve subjects. A figure (6 degrees side length) was created by a 2nd-order texture contour. An independent and demanding foveal letter-discrimination task prevented subjects from noticing this more peripheral contour stimulus. The contour subdivided our stimulus into a figure and a ground. Using localizers and retinotopic mapping stimuli we were able to subdivide each early visual area into 3 eccentricity regions corresponding to 1) the central figure, 2) the area along the contour, and 3) the background. In these subregions we investigated the hemodynamic responses to our stimuli and compared responses with or without the contour defining the figure. No contour-related blood oxygenation level-dependent modulation in early visual areas V1, V3, VP, and MT+ was found. Significant signal modulation in the contour subregions of V2v, V2d, V3a, and LO occurred. This activation pattern was different from comparable studies, which might be attributable to the letter-discrimination task reducing confounding attentional modulation. In V3a, but not in any other retinotopic area, signal modulation corresponding to the central figure could be detected. Such contextual modulation will be discussed in light of the recurrent processing hypothesis and the role of visual awareness.

  14. Spiral Light Beams and Contour Image Processing

    NASA Astrophysics Data System (ADS)

    Kishkin, Sergey A.; Kotova, Svetlana P.; Volostnikov, Vladimir G.

    Spiral beams of light are characterized by their ability to remain structurally unchanged at propagation. They may have the shape of any closed curve. In the present paper a new approach is proposed within the framework of the contour analysis based on a close cooperation of modern coherent optics, theory of functions and numerical methods. An algorithm for comparing contours is presented and theoretically justified, which allows convincing of whether two contours are similar or not to within the scale factor and/or rotation. The advantages and disadvantages of the proposed approach are considered; the results of numerical modeling are presented.

  15. Changes in multi-segmented body movements and EMG activity while standing on firm and foam support surfaces.

    PubMed

    Fransson, P A; Gomez, S; Patel, M; Johansson, L

    2007-09-01

    Postural control ensures stability during both static posture and locomotion by initiating corrective adjustments in body movement. This is particularly important when the conditions of the support surface change. We investigated the effects of standing on a compliant foam surface using 12 normal subjects (mean age 26 years) in terms of: linear movements at the head, shoulder, hip and knee; EMG activity of the tibialis anterior and gastrocnemius muscles and torques towards the support surface. As subjects repeated the trials with eyes open or closed, we were also able to determine the effects of vision on multi-segmented body movements during standing upon different support surface conditions. As expected, EMG activity, torque variance values and body movements at all measured positions increased significantly when standing on foam compared with the firm surface. Linear knee and hip movements increased more, relative to shoulder and head movements while standing on foam. Vision stabilized the head and shoulder movements more than hip and knee movements while standing on foam support surface. Moreover, vision significantly reduced the tibialis anterior EMG activity and torque variance during the trials involving foam. In conclusion, the foam support surface increased corrective muscle and torque activity, and changed the firm-surface multi-segmented body movement pattern. Vision improved the ability of postural control to handle compliant surface conditions. Several essential features of postural control have been found from recording movements from multiple points on the body, synchronized with recording torque and EMG.

  16. Hydrothermal activity along the slow-spreading Lucky Strike ridge segment (Mid-Atlantic Ridge): Distribution, heatflux, and geological controls

    NASA Astrophysics Data System (ADS)

    Escartin, J.; Barreyre, T.; Cannat, M.; Garcia, R.; Gracias, N.; Deschamps, A.; Salocchi, A.; Sarradin, P.-M.; Ballu, V.

    2015-12-01

    We have reviewed available visual information from the seafloor, and recently acquired microbathymetry for several traverses across the Lucky Strike segment, to evaluate the distribution of hydrothermal activity. We have identified a new on-axis site with diffuse flow, Ewan, and an active vent structure ∼1.2 km from the axis, Capelinhos. These sites are minor relative to the Main field, and our total heatflux estimate for all active sites (200-1200 MW) is only slightly higher than previously published estimates. We also identify fossil sites W of the main Lucky Strike field. A circular feature ∼200 m in diameter located on the flanks of a rifted off-axis central volcano is likely a large and inactive hydrothermal edifice, named Grunnus. We find no indicator of focused hydrothermal activity elsewhere along the segment, suggesting that the enhanced melt supply and the associated melt lenses, required to form central volcanoes, also sustain hydrothermal circulation to form and maintain large and long-lived hydrothermal fields. Hydrothermal discharge to the seafloor occurs along fault traces, suggesting focusing of hydrothermal circulation in the shallow crust along permeable fault zones.

  17. Modulation of synaptic transmission from segmental afferents by spontaneous activity of dorsal horn spinal neurones in the cat.

    PubMed

    Manjarrez, E; Rojas-Piloni, J G; Jimenez, I; Rudomin, P

    2000-12-01

    We examined, in the anaesthetised cat, the influence of the neuronal ensembles producing spontaneous negative cord dorsum potentials (nCDPs) on segmental pathways mediating primary afferent depolarisation (PAD) of cutaneous and group I muscle afferents and on Ia monosynaptic activation of spinal motoneurones. The intraspinal distribution of the field potentials associated with the spontaneous nCDPs indicated that the neuronal ensembles involved in the generation of these potentials were located in the dorsal horn of lumbar segments, in the same region of termination of low-threshold cutaneous afferents. During the occurrence of spontaneous nCDPs, transmission from low-threshold cutaneous afferents to second order neurones in laminae III-VI, as well as transmission along pathways mediating PAD of cutaneous and Ib afferents, was facilitated. PAD of Ia afferents was instead inhibited. Monosynaptic reflexes of flexors and extensors were facilitated during the spontaneous nCDPs. The magnitude of the facilitation was proportional to the amplitude of the 'conditioning' spontaneous nCDPs. This led to a high positive correlation between amplitude fluctuations of spontaneous nCDPs and fluctuations of monosynaptic reflexes. Stimulation of low-threshold cutaneous afferents transiently reduced the probability of occurrence of spontaneous nCDPs as well as the fluctuations of monosynaptic reflexes. It is concluded that the spontaneous nCDPs were produced by the activation of a population of dorsal horn neurones that shared the same functional pathways and involved the same set of neurones as those responding monosynaptically to stimulation of large cutaneous afferents. The spontaneous activity of these neurones was probably the main cause of the fluctuations of the monosynaptic reflexes observed under anaesthesia and could provide a dynamic linkage between segmental sensory and motor pathways.

  18. Modulation of synaptic transmission from segmental afferents by spontaneous activity of dorsal horn spinal neurones in the cat

    PubMed Central

    Manjarrez, E; Rojas-Piloni, J G; Jiménez, I; Rudomin, P

    2000-01-01

    We examined, in the anaesthetised cat, the influence of the neuronal ensembles producing spontaneous negative cord dorsum potentials (nCDPs) on segmental pathways mediating primary afferent depolarisation (PAD) of cutaneous and group I muscle afferents and on Ia monosynaptic activation of spinal motoneurones. The intraspinal distribution of the field potentials associated with the spontaneous nCDPs indicated that the neuronal ensembles involved in the generation of these potentials were located in the dorsal horn of lumbar segments, in the same region of termination of low-threshold cutaneous afferents. During the occurrence of spontaneous nCDPs, transmission from low-threshold cutaneous afferents to second order neurones in laminae III-VI, as well as transmission along pathways mediating PAD of cutaneous and Ib afferents, was facilitated. PAD of Ia afferents was instead inhibited. Monosynaptic reflexes of flexors and extensors were facilitated during the spontaneous nCDPs. The magnitude of the facilitation was proportional to the amplitude of the ‘conditioning’ spontaneous nCDPs. This led to a high positive correlation between amplitude fluctuations of spontaneous nCDPs and fluctuations of monosynaptic reflexes. Stimulation of low-threshold cutaneous afferents transiently reduced the probability of occurrence of spontaneous nCDPs as well as the fluctuations of monosynaptic reflexes. It is concluded that the spontaneous nCDPs were produced by the activation of a population of dorsal horn neurones that shared the same functional pathways and involved the same set of neurones as those responding monosynaptically to stimulation of large cutaneous afferents. The spontaneous activity of these neurones was probably the main cause of the fluctuations of the monosynaptic reflexes observed under anaesthesia and could provide a dynamic linkage between segmental sensory and motor pathways. PMID:11101653

  19. Two YxxL segments of a single immunoreceptor tyrosine-based activation motif in the CD3zeta molecule differentially activate calcium mobilization and mitogen-activated protein kinase family pathways.

    PubMed

    Tsuchihashi, N; Matsuda, S; Reinherz, E L; Koyasu, S

    2000-06-01

    Immunoreceptor tyrosine-based activation motifs (ITAM), consisting of two YxxL segments, transmit signals leading to IL-2 gene activation in T cells. We investigated here the functional difference in signal transduction between these two YxxL segments in the CD3zeta membrane-proximal ITAM. N-terminal YxxL mutants failed to induce ZAP-70 phosphorylation, elevation of intracellular Ca2+ concentration ([Ca2+]i) or extracellular signal-regulated kinase (ERK) activation even in the presence of CD28 co-stimulation, whereas a mutant of the leucine residue at the C-terminal YxxL segment retained the ability to induce these events although this mutation abrogated the ability to induce IL-2 gene activation. In marked contrast to ERK activation, c-Jun N-terminal kinase (JNK) activation was observed in all mutants when co-stimulated with CD28. The mutant of the leucine residue at the C-terminal YxxL segment had a defect in the transcriptional activation at the NF-AT cis-element, which was restored to wild-type level by addition of a Ca2+ ionophore, suggesting that the intensity and/or duration of [Ca2+]i elevation defines the threshold of T cell activation in this mutant. Our data collectively indicate that the activation pathways of ERK, JNK and Ca2+ mobilization are differentially regulated through YxxL segments of an ITAM.

  20. Analysis of Breast Contour using Rotated Catenary.

    PubMed

    Lee, Juhun; Beahm, Elisabeth K; Crosby, Melissa A; Reece, Gregory P; Markey, Mia K

    2010-11-13

    Surgical reconstruction of natural-appearing breasts is a challenging task. Currently, surgical planning is limited to the surgeon's subjective assessment of breast morphology. Therefore, it is useful to develop objective measurements of breast contour. In this paper, a novel quantitative measure of the breast contour based on catenary theory is introduced. A catenary curve is fitted on the breast contour (lateral and inferior) and the key parameter determining the shape of the curve is extracted. The new catenary analysis was applied to pre- and post-operative clinical photographs of women who underwent tissue expander/implant (TE/Implant) reconstruction. A logistic regression model was developed to predict the probability that the observed contour is that of a TE/Implant reconstruction from the catenary parameter, patient age, and patient body mass index. It was demonstrated that the parameters contain useful information for distinguishing TE/Implant reconstructed breasts from pre-operative breasts.

  1. Holding fixture for variable-contour parts

    NASA Technical Reports Server (NTRS)

    Haynie, C. C.; Packer, P. N.; Zebus, P. P.

    1979-01-01

    Array of vacuum cups on spindles holds parts for safe machining and other processings. Variable-contour part resting on fixture is held firmly enough for machining, coating, or other mechanical treatment.

  2. Extreme_SeaState_Contour_v1

    SciTech Connect

    2015-10-19

    This software generates environmental contours of extreme sea states using buoy observations of significant wave height and energy period or peak period. The code transforms these observations using principal component analysis (PCA) to create an uncorrelated representation of the data. The subsequent components are modeled using probability distributions and parameter fitting functions. The inverse first-order reliability method (I-FORM) is then applied to these models in order to generate an extreme event contour based on a given return period (i.e., 100 years).The subsequent contour is then transformed back into the original input space defined by the variables of interest in order to create an environmental contour of extreme sea states.

  3. Contour based object detection using part bundles

    PubMed Central

    Lu, ChengEn; Adluru, Nagesh; Ling, Haibin; Zhu, Guangxi; Latecki, Longin Jan

    2016-01-01

    In this paper we propose a novel framework for contour based object detection from cluttered environments. Given a contour model for a class of objects, it is first decomposed into fragments hierarchically. Then, we group these fragments into part bundles, where a part bundle can contain overlapping fragments. Given a new image with set of edge fragments we develop an efficient voting method using local shape similarity between part bundles and edge fragments that generates high quality candidate part configurations. We then use global shape similarity between the part configurations and the model contour to find optimal configuration. Furthermore, we show that appearance information can be used for improving detection for objects with distinctive texture when model contour does not sufficiently capture deformation of the objects.

  4. Evaluation of Automatic Atlas-Based Lymph Node Segmentation for Head-and-Neck Cancer

    SciTech Connect

    Stapleford, Liza J.; Lawson, Joshua D.; Perkins, Charles; Edelman, Scott; Davis, Lawrence

    2010-07-01

    Purpose: To evaluate if automatic atlas-based lymph node segmentation (LNS) improves efficiency and decreases inter-observer variability while maintaining accuracy. Methods and Materials: Five physicians with head-and-neck IMRT experience used computed tomography (CT) data from 5 patients to create bilateral neck clinical target volumes covering specified nodal levels. A second contour set was automatically generated using a commercially available atlas. Physicians modified the automatic contours to make them acceptable for treatment planning. To assess contour variability, the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm was used to take collections of contours and calculate a probabilistic estimate of the 'true' segmentation. Differences between the manual, automatic, and automatic-modified (AM) contours were analyzed using multiple metrics. Results: Compared with the 'true' segmentation created from manual contours, the automatic contours had a high degree of accuracy, with sensitivity, Dice similarity coefficient, and mean/max surface disagreement values comparable to the average manual contour (86%, 76%, 3.3/17.4 mm automatic vs. 73%, 79%, 2.8/17 mm manual). The AM group was more consistent than the manual group for multiple metrics, most notably reducing the range of contour volume (106-430 mL manual vs. 176-347 mL AM) and percent false positivity (1-37% manual vs. 1-7% AM). Average contouring time savings with the automatic segmentation was 11.5 min per patient, a 35% reduction. Conclusions: Using the STAPLE algorithm to generate 'true' contours from multiple physician contours, we demonstrated that, in comparison with manual segmentation, atlas-based automatic LNS for head-and-neck cancer is accurate, efficient, and reduces interobserver variability.

  5. Interval and contour processing in autism.

    PubMed

    Heaton, Pamela

    2005-12-01

    High functioning children with autism and age and intelligence matched controls participated in experiments testing perception of pitch intervals and musical contours. The finding from the interval study showed superior detection of pitch direction over small pitch distances in the autism group. On the test of contour discrimination no group differences emerged. These findings confirm earlier studies showing facilitated pitch processing and a preserved ability to represent small-scale musical structures in autism.

  6. Contour detection based on wavelet differentiation

    NASA Astrophysics Data System (ADS)

    Bezuglov, D.; Kuzin, A.; Voronin, V.

    2016-05-01

    This work proposes a novel algorithm for contour detection based on high-performance algorithm of wavelet analysis for multimedia applications. To solve the noise effect on the result of peaking in this paper we consider the direct and inverse wavelet differentiation. Extensive experimental evaluation on noisy images demonstrates that our contour detection method significantly outperform competing algorithms. The proposed algorithm provides a means of coupling our system to recognition application such as detection and identification of vehicle number plate.

  7. Parallel algorithms for contour extraction and coding

    NASA Astrophysics Data System (ADS)

    Dinstein, Its'hak; Landau, Gad M.

    1990-07-01

    A parallel approach to contour extraction and coding on an Exclusive Read Exclusive Write (EREW) Parallel Random Access Machine (PRAM) is presented and analyzed. The algorithm is intended for binary images. The labeled contours can be represented by lists of coordinates, and/or chain codes, and/or any other user designed codes. Using O(n2/log n) processors, the algorithm runs in O(logn) time, where n by n is the size of the processed binary image.

  8. Pin guidance of reconstruction plate contour: an expanded role of external fixation.

    PubMed

    Jaquet, Yves; Higgins, Kevin M; Enepekides, Danny J

    2011-09-01

    This article presents a modification of intraoperative external fixation for mandibular reconstruction with free tissue flaps. This technique is indicated when preregistration of the reconstruction plate is not possible due to transmandibular tumor extension. Once standard external fixation has been carried out and prior to segmental mandibulectomy, additional pins are fixed to the connecting rod that delineate the mandibular contour in three-dimensional (3D) space. Following mandibulectomy, these pins allow accurate contouring of the reconstruction plate and improved restoration of mandibular contour, projection, and dental occlusion. A step-by-step description of the technique using models and intraoperative photos is presented. This method of mandibular reconstruction is a simple and time-effective alternative to intraoperative computer navigation and 3D modeling in select cases of oral carcinoma where tumor infiltration of the outer mandibular cortex precludes prebending of the reconstruction plates.

  9. Right-hemisphere specialization for contour grouping.

    PubMed

    Volberg, Gregor

    2014-01-01

    Previous studies often revealed a right-hemisphere specialization for processing the global level of compound visual stimuli. Here we explore whether a similar specialization exists for the detection of intersected contours defined by a chain of local elements. Subjects were presented with arrays of randomly oriented Gabor patches that could contain a global path of collinearly arranged elements in the left or in the right visual hemifield. As expected, the detection accuracy was higher for contours presented to the left visual field/right hemisphere. This difference was absent in two control conditions where the smoothness of the contour was decreased. The results demonstrate that the contour detection, often considered to be driven by lateral coactivation in primary visual cortex, relies on higher-level visual representations that differ between the hemispheres. Furthermore, because contour and non-contour stimuli had the same spatial frequency spectra, the results challenge the view that the right-hemisphere advantage in global processing depends on a specialization for processing low spatial frequencies.

  10. Isolating contour information from arbitrary images

    NASA Technical Reports Server (NTRS)

    Jobson, Daniel J.

    1989-01-01

    Aspects of natural vision (physiological and perceptual) serve as a basis for attempting the development of a general processing scheme for contour extraction. Contour information is assumed to be central to visual recognition skills. While the scheme must be regarded as highly preliminary, initial results do compare favorably with the visual perception of structure. The scheme pays special attention to the construction of a smallest scale circular difference-of-Gaussian (DOG) convolution, calibration of multiscale edge detection thresholds with the visual perception of grayscale boundaries, and contour/texture discrimination methods derived from fundamental assumptions of connectivity and the characteristics of printed text. Contour information is required to fall between a minimum connectivity limit and maximum regional spatial density limit at each scale. Results support the idea that contour information, in images possessing good image quality, is (centered at about 10 cyc/deg and 30 cyc/deg). Further, lower spatial frequency channels appear to play a major role only in contour extraction from images with serious global image defects.

  11. Isolating contour information from arbitrary images

    NASA Astrophysics Data System (ADS)

    Jobson, Daniel J.

    1989-11-01

    Aspects of natural vision (physiological and perceptual) serve as a basis for attempting the development of a general processing scheme for contour extraction. Contour information is assumed to be central to visual recognition skills. While the scheme must be regarded as highly preliminary, initial results do compare favorably with the visual perception of structure. The scheme pays special attention to the construction of a smallest scale circular difference-of-Gaussian (DOG) convolution, calibration of multiscale edge detection thresholds with the visual perception of grayscale boundaries, and contour/texture discrimination methods derived from fundamental assumptions of connectivity and the characteristics of printed text. Contour information is required to fall between a minimum connectivity limit and maximum regional spatial density limit at each scale. Results support the idea that contour information, in images possessing good image quality, is (centered at about 10 cyc/deg and 30 cyc/deg). Further, lower spatial frequency channels appear to play a major role only in contour extraction from images with serious global image defects.

  12. Tracking fuzzy borders using geodesic curves with application to liver segmentation on planning CT

    SciTech Connect

    Yuan, Yading Chao, Ming; Sheu, Ren-Dih; Rosenzweig, Kenneth; Lo, Yeh-Chi

    2015-07-15

    Purpose: This work aims to develop a robust and efficient method to track the fuzzy borders between liver and the abutted organs where automatic liver segmentation usually suffers, and to investigate its applications in automatic liver segmentation on noncontrast-enhanced planning computed tomography (CT) images. Methods: In order to track the fuzzy liver–chestwall and liver–heart borders where oversegmentation is often found, a starting point and an ending point were first identified on the coronal view images; the fuzzy border was then determined as a geodesic curve constructed by minimizing the gradient-weighted path length between these two points near the fuzzy border. The minimization of path length was numerically solved by fast-marching method. The resultant fuzzy borders were incorporated into the authors’ automatic segmentation scheme, in which the liver was initially estimated by a patient-specific adaptive thresholding and then refined by a geodesic active contour model. By using planning CT images of 15 liver patients treated with stereotactic body radiation therapy, the liver contours extracted by the proposed computerized scheme were compared with those manually delineated by a radiation oncologist. Results: The proposed automatic liver segmentation method yielded an average Dice similarity coefficient of 0.930 ± 0.015, whereas it was 0.912 ± 0.020 if the fuzzy border tracking was not used. The application of fuzzy border tracking was found to significantly improve the segmentation performance. The mean liver volume obtained by the proposed method was 1727 cm{sup 3}, whereas it was 1719 cm{sup 3} for manual-outlined volumes. The computer-generated liver volumes achieved excellent agreement with manual-outlined volumes with correlation coefficient of 0.98. Conclusions: The proposed method was shown to provide accurate segmentation for liver in the planning CT images where contrast agent is not applied. The authors’ results also clearly

  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.

  14. Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT.

    PubMed

    Zhou, Jinghao; Yan, Zhennan; Lasio, Giovanni; Huang, Junzhou; Zhang, Baoshe; Sharma, Navesh; Prado, Karl; D'Souza, Warren

    2015-12-01

    To resolve challenges in image segmentation in oncologic patients with severely compromised lung, we propose an automated right lung segmentation framework that uses a robust, atlas-based active volume model with a sparse shape composition prior. The robust atlas is achieved by combining the atlas with the output of sparse shape composition. Thoracic computed tomography images (n=38) from patients with lung tumors were collected. The right lung in each scan was manually segmented to build a reference training dataset against which the performance of the automated segmentation method was assessed. The quantitative results of this proposed segmentation method with sparse shape composition achieved mean Dice similarity coefficient (DSC) of (0.72, 0.81) with 95% CI, mean accuracy (ACC) of (0.97, 0.98) with 95% CI, and mean relative error (RE) of (0.46, 0.74) with 95% CI. Both qualitative and quantitative comparisons suggest that this proposed method can achieve better segmentation accuracy with less variance than other atlas-based segmentation methods in the compromised lung segmentation.

  15. Conditional trimerization and lytic activity of HIV-1 gp41 variants containing the membrane-associated segments.

    PubMed

    Dai, Zhou; Tao, Yisong; Liu, Nina; Brenowitz, Michael D; Girvin, Mark E; Lai, Jonathan R

    2015-03-03

    Fusion of host and viral membranes is a critical step during infection by membrane-bound viruses. The HIV-1 glycoproteins gp120 (surface subunit) and gp41 (fusion subunit) represent the prototypic system for studying this process; in the prevailing model, the gp41 ectodomain forms a trimeric six-helix bundle that constitutes a critical intermediate and provides the energetic driving force for overcoming barriers associated with membrane fusion. However, most structural studies of gp41 variants have been performed either on ectodomain constructs lacking one or more of the membrane-associated segments (the fusion peptide, FP, the membrane-proximal external region, MPER, and the transmembrane domain, TM) or on variants consisting of these isolated segments alone without the ectodomain. Several recent reports have suggested that the HIV-1 ectodomain, as well as larger construct containing the membrane-bound segments, dissociates from a trimer to a monomer in detergent micelles. Here we compare the properties of a series of gp41 variants to delineate the roles of the ectodomain, FP, and MPER and TM, all in membrane-mimicking environments. We find that these proteins are prone to formation of a monomer in detergent micelles. In one case, we observed exclusive monomer formation at pH 4 but conditional trimerization at pH 7 even at low micromolar (∼5 μM) protein concentrations. Liposome release assays demonstrate that these gp41-related proteins have the capacity to induce content leakage but that this activity is also strongly modulated by pH with much higher activity at pH 4. Circular dichroism, nuclear magnetic resonance, and binding assays with antibodies specific to the MPER provide insight into the structural and functional roles of the FP, MPER, and TM and their effect on structure within the larger context of the fusion subunit.

  16. Segmentation of humeral head from axial proton density weighted shoulder MR images

    NASA Astrophysics Data System (ADS)

    Sezer, Aysun; Sezer, Hasan Basri; Albayrak, Songul

    2015-01-01

    The purpose of this study is to determine the effectiveness of segmentation of axial MR proton density (PD) images of bony humeral head. PD sequence images which are included in standard shoulder MRI protocol are used instead of T1 MR images. Bony structures were reported to be successfully segmented in the literature from T1 MR images. T1 MR images give more sharp determination of bone and soft tissue border but cannot address the pathological process which takes place in the bone. In the clinical settings PD images of shoulder are used to investigate soft tissue alterations which can cause shoulder instability and are better in demonstrating edema and the pathology but have a higher noise ratio than other modalities. Moreover the alteration of humeral head intensity in patients and soft tissues in contact with the humeral head which have the very similar intensities with bone makes the humeral head segmentation a challenging problem in PD images. However segmentation of the bony humeral head is required initially to facilitate the segmentation of the soft tissues of shoulder. In this study shoulder MRI of 33 randomly selected patients were included. Speckle reducing anisotropic diffusion (SRAD) method was used to decrease noise and then Active Contour Without Edge (ACWE) and Signed Pressure Force (SPF) models were applied on our data set. Success of these methods is determined by comparing our results with manually segmented images by an expert. Applications of these methods on PD images provide highly successful results for segmentation of bony humeral head. This is the first study to determine bone contours in PD images in literature.

  17. A Multi-Marker Model for Detecting Chromosomal Segments Displaying Qtl Activity

    PubMed Central

    Rodolphe, F.; Lefort, M.

    1993-01-01

    A statistical method is presented for detecting quantitative trait loci (QTLs), based on the linear model. Unlike methods able to detect a few well separated QTLs and to estimate their effects and positions, this method considers the genome as a whole and enables the detection of chromosomal segments involved in the differences between two homozygous lines, and their backcross, doubled haploid, or F(2) progenies, for a quantitative trait. Genetic markers must be codominant, but missing markers are accepted, provided they are missing independently from the experiment. Asymptotic properties, which are of practical use, are developed. This method does not rely on strong genetic hypotheses, and thus does not permit any precise genetic analysis of the trait under study, but it does assess which regions of the genome are involved, whatever the complexity of the genetic determinism (number, effects and interactions among QTLs). Simultaneous use of several methods, including this one, should lead to better efficiency in QTL detection. PMID:8375662

  18. Segmentation propagation for the automated quantification of ventricle volume from serial MRI

    NASA Astrophysics Data System (ADS)

    Linguraru, Marius George; Butman, John A.

    2009-02-01

    Accurate ventricle volume estimates could potentially improve the understanding and diagnosis of communicating hydrocephalus. Postoperative communicating hydrocephalus has been recognized in patients with brain tumors where the changes in ventricle volume can be difficult to identify, particularly over short time intervals. Because of the complex alterations of brain morphology in these patients, the segmentation of brain ventricles is challenging. Our method evaluates ventricle size from serial brain MRI examinations; we (i) combined serial images to increase SNR, (ii) automatically segmented this image to generate a ventricle template using fast marching methods and geodesic active contours, and (iii) propagated the segmentation using deformable registration of the original MRI datasets. By applying this deformation to the ventricle template, serial volume estimates were obtained in a robust manner from routine clinical images (0.93 overlap) and their variation analyzed.

  19. Segmentation of thermographic images of hands using a genetic algorithm

    NASA Astrophysics Data System (ADS)

    Ghosh, Payel; Mitchell, Melanie; Gold, Judith

    2010-01-01

    This paper presents a new technique for segmenting thermographic images using a genetic algorithm (GA). The individuals of the GA also known as chromosomes consist of a sequence of parameters of a level set function. Each chromosome represents a unique segmenting contour. An initial population of segmenting contours is generated based on the learned variation of the level set parameters from training images. Each segmenting contour (an individual) is evaluated for its fitness based on the texture of the region it encloses. The fittest individuals are allowed to propagate to future generations of the GA run using selection, crossover and mutation. The dataset consists of thermographic images of hands of patients suffering from upper extremity musculo-skeletal disorders (UEMSD). Thermographic images are acquired to study the skin temperature as a surrogate for the amount of blood flow in the hands of these patients. Since entire hands are not visible on these images, segmentation of the outline of the hands on these images is typically performed by a human. In this paper several different methods have been tried for segmenting thermographic images: Gabor-wavelet-based texture segmentation method, the level set method of segmentation and our GA which we termed LSGA because it combines level sets with genetic algorithms. The results show a comparative evaluation of the segmentation performed by all the methods. We conclude that LSGA successfully segments entire hands on images in which hands are only partially visible.

  20. Chip, a widely expressed chromosomal protein required for segmentation and activity of a remote wing margin enhancer in Drosophila

    PubMed Central

    Morcillo, Patrick; Rosen, Christina; Baylies, Mary K.; Dorsett, Dale

    1997-01-01

    The mechanisms allowing remote enhancers to regulate promoters several kilobase pairs away are unknown but are blocked by the Drosophila suppressor of Hairy-wing protein (Suhw) that binds to gypsy retrovirus insertions between enhancers and promoters. Suhw bound to a gypsy insertion in the cut gene also appears to act interchromosomally to antagonize enhancer–promoter interactions on the homologous chromosome when activity of the Chip gene is reduced. This implicates Chip in enhancer–promoter communication. We cloned Chip and find that it encodes a homolog of the recently discovered mouse Nli/Ldb1/Clim-2 and Xenopus Xldb1 proteins that bind nuclear LIM domain proteins. Chip protein interacts with the LIM domains in the Apterous homeodomain protein, and Chip interacts genetically with apterous, showing that these interactions are important for Apterous function in vivo. Importantly, Chip also appears to have broad functions beyond interactions with LIM domain proteins. Chip is present in all nuclei examined and at numerous sites along the salivary gland polytene chromosomes. Embryos without Chip activity lack segments and show abnormal gap and pair–rule gene expression, although no LIM domain proteins are known to regulate segmentation. We conclude that Chip is a ubiquitous chromosomal factor required for normal expression of diverse genes at many stages of development. We suggest that Chip cooperates with different LIM domain proteins and other factors to structurally support remote enhancer–promoter interactions. PMID:9334334

  1. Architecture-Driven Level Set Optimization: From Clustering to Subpixel Image Segmentation.

    PubMed

    Balla-Arabe, Souleymane; Gao, Xinbo; Ginhac, Dominique; Brost, Vincent; Yang, Fan

    2016-12-01

    Thanks to their effectiveness, active contour models (ACMs) are of great interest for computer vision scientists. The level set methods (LSMs) refer to the class of geometric active contours. Comparing with the other ACMs, in addition to subpixel accuracy, it has the intrinsic ability to automatically handle topological changes. Nevertheless, the LSMs are computationally expensive. A solution for their time consumption problem can be hardware acceleration using some massively parallel devices such as graphics processing units (GPUs). But the question is: which accuracy can we reach while still maintaining an adequate algorithm to massively parallel architecture? In this paper, we attempt to push back the compromise between, speed and accuracy, efficiency and effectiveness, to a higher level, comparing with state-of-the-art methods. To this end, we designed a novel architecture-aware hybrid central processing unit (CPU)-GPU LSM for image segmentation. The initialization step, using the well-known k -means algorithm, is fast although executed on a CPU, while the evolution equation of the active contour is inherently local and therefore suitable for GPU-based acceleration. The incorporation of local statistics in the level set evolution allowed our model to detect new boundaries which are not extracted by the used clustering algorithm. Comparing with some cutting-edge LSMs, the introduced model is faster, more accurate, less subject to giving local minima, and therefore suitable for automatic systems. Furthermore, it allows two-phase clustering algorithms to benefit from the numerous LSM advantages such as the ability to achieve robust and subpixel accurate segmentation results with smooth and closed contours. Intensive experiments demonstrate, objectively and subjectively, the good performance of the introduced framework both in terms of speed and accuracy.

  2. Multi-object segmentation using coupled nonparametric shape and relative pose priors

    NASA Astrophysics Data System (ADS)

    Uzunbas, Mustafa Gökhan; Soldea, Octavian; Çetin, Müjdat; Ünal, Gözde; Erçil, Aytül; Unay, Devrim; Ekin, Ahmet; Firat, Zeynep

    2009-02-01

    We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes.

  3. Refined contour analysis of giant unilamellar vesicles

    NASA Astrophysics Data System (ADS)

    Pécréaux, J.; Döbereiner, H.-G.; Prost, J.; Joanny, J.-F.; Bassereau, P.

    2004-03-01

    The fluctuation spectrum of giant unilamellar vesicles is measured using a high-resolution contour detection technique. An analysis at higher q vectors than previously achievable is now possible due to technical improvements of the experimental setup and of the detection algorithm. The global fluctuation spectrum is directly fitted to deduce the membrane tension and the bending modulus of lipid membranes. Moreover, we show that the planar analysis of fluctuations is valid for spherical objects, even at low wave vectors. Corrections due to the integration time of the video camera and to the section of a 3D object by the observation plane are introduced. A precise calculation of the error bars has been done in order to provide reliable error estimate. Eventually, using this technique, we have measured bending moduli for EPC, SOPC and \\chem{SOPC:CHOL} membranes confirming previously published values. An interesting application of this technique can be the measurement of the fluctuation spectra for non-equilibrium membranes, such as “active membranes”.

  4. A murine monoclonal antibody that binds N-terminal extracellular segment of human protease-activated receptor-4.

    PubMed

    Sangawa, Takeshi; Nogi, Terukazu; Takagi, Junichi

    2008-10-01

    Abstract A monoclonal antibody that recognizes native G protein coupled receptors (GPCR) is generally difficult to obtain. Protease-activated receptor-4 (PAR4) is a GPCR that plays an important role in platelet activation as a low-affinity thrombin receptor. By immunizing peptide corresponding to the N-terminal segment of human PAR4, we obtained a monoclonal antibody that recognizes cell surface expressed PAR4. Epitope mapping using a series of artificial fusion proteins that carry PAR4-derived peptide revealed that the recognition motif is fully contained within the 6-residue portion adjacent to the thrombin cleavage site. The antibody blocked PAR4 peptide cleavage by thrombin, suggesting its utility in the functional study of PAR4 signaling.

  5. Fast automatic myocardial segmentation in 4D cine CMR datasets.

    PubMed

    Queirós, Sandro; Barbosa, Daniel; Heyde, Brecht; Morais, Pedro; Vilaça, João L; Friboulet, Denis; Bernard, Olivier; D'hooge, Jan

    2014-10-01

    A novel automatic 3D+time left ventricle (LV) segmentation framework is proposed for cardiac magnetic resonance (CMR) datasets. The proposed framework consists of three conceptual blocks to delineate both endo and epicardial contours throughout the cardiac cycle: (1) an automatic 2D mid-ventricular initialization and segmentation; (2) an automatic stack initialization followed by a 3D segmentation at the end-diastolic phase; and (3) a tracking procedure. Hereto, we propose to adapt the recent B-spline Explicit Active Surfaces (BEAS) framework to the properties of CMR images by integrating dedicated energy terms. Moreover, we extend the coupled BEAS formalism towards its application in 3D MR data by adapting it to a cylindrical space suited to deal with the topology of the image data. Furthermore, a fast stack initialization method is presented for efficient initialization and to enforce consistent cylindrical topology. Finally, we make use of an anatomically constrained optical flow method for temporal tracking of the LV surface. The proposed framework has been validated on 45 CMR datasets taken from the 2009 MICCAI LV segmentation challenge. Results show the robustness, efficiency and competitiveness of the proposed method both in terms of accuracy and computational load.

  6. Automatic segmentation of equine larynx for diagnosis of laryngeal hemiplegia

    NASA Astrophysics Data System (ADS)

    Salehin, Md. Musfequs; Zheng, Lihong; Gao, Junbin

    2013-10-01

    This paper presents an automatic segmentation method for delineation of the clinically significant contours of the equine larynx from an endoscopic image. These contours are used to diagnose the most common disease of horse larynx laryngeal hemiplegia. In this study, hierarchal structured contour map is obtained by the state-of-the-art segmentation algorithm, gPb-OWT-UCM. The conic-shaped outer boundary of equine larynx is extracted based on Pascal's theorem. Lastly, Hough Transformation method is applied to detect lines related to the edges of vocal folds. The experimental results show that the proposed approach has better performance in extracting the targeted contours of equine larynx than the results of using only the gPb-OWT-UCM method.

  7. Prostate Contouring Variation: Can It Be Fixed?

    SciTech Connect

    Khoo, Eric L.H.; Schick, Karlissa; Plank, Ashley W.; Poulsen, Michael; Wong, Winnie W.G.; Middleton, Mark; Martin, Jarad M.

    2012-04-01

    Purpose: To assess whether an education program on CT and MRI prostate anatomy would reduce inter- and intraobserver prostate contouring variation among experienced radiation oncologists. Methods and Materials: Three patient CT and MRI datasets were selected. Five radiation oncologists contoured the prostate for each patient on CT first, then MRI, and again between 2 and 4 weeks later. Three education sessions were then conducted. The same contouring process was then repeated with the same datasets and oncologists. The observer variation was assessed according to changes in the ratio of the encompassing volume to intersecting volume (volume ratio [VR]), across sets of target volumes. Results: For interobserver variation, there was a 15% reduction in mean VR with CT, from 2.74 to 2.33, and a 40% reduction in mean VR with MRI, from 2.38 to 1.41 after education. A similar trend was found for intraobserver variation, with a mean VR reduction for CT and MRI of 9% (from 1.51 to 1.38) and 16% (from 1.37 to 1.15), respectively. Conclusion: A well-structured education program has reduced both inter- and intraobserver prostate contouring variations. The impact was greater on MRI than on CT. With the ongoing incorporation of new technologies into routine practice, education programs for target contouring should be incorporated as part of the continuing medical education of radiation oncologists.

  8. Portable FORTRAN contour-plotting subprogram

    SciTech Connect

    Haskell, K.H.

    1983-07-01

    In this report we discuss a contour plotting Fortran subprogram. While contour plotting subroutines are available in many commercial plotting packages, this routine has the following advantages: (1) since it uses the Weasel and VDI plot routines developed at Sandia, it occupies little storage and can be used on most of the Sandia time-sharing systems as part of a larger program. In the past, the size of plotting packages often forced a user to perform plotting operations in a completely separate program; (2) the contour computation algorithm is efficient and robust, and computes accurate contours for sets of data with low resolution; and (3) the subprogram is easy to use. A simple contour plot can be produced with a minimum of information provided by a user in one Fortran subroutine call. Through the use of a wide variety of subroutine options, many additional features can be used. These include such items as plot titles, grid lines, placement of text on the page, etc. The subroutine is written in portable Fortran 77, and is designed to run on any system which supports the Weasel and VDI plot packages. It also uses routines from the SLATEC mathematical subroutine library.

  9. An Efficient Correction Algorithm for Eliminating Image Misalignment Effects on Co-Phasing Measurement Accuracy for Segmented Active Optics Systems.

    PubMed

    Yue, Dan; Xu, Shuyan; Nie, Haitao; Wang, Zongyang

    2016-01-01

    The misalignment between recorded in-focus and out-of-focus images using the Phase Diversity (PD) algorithm leads to a dramatic decline in wavefront detection accuracy and image recovery quality for segmented active optics systems. This paper demonstrates the theoretical relationship between the image misalignment and tip-tilt terms in Zernike polynomials of the wavefront phase for the first time, and an efficient two-step alignment correction algorithm is proposed to eliminate these misalignment effects. This algorithm processes a spatial 2-D cross-correlation of the misaligned images, revising the offset to 1 or 2 pixels and narrowing the search range for alignment. Then, it eliminates the need for subpixel fine alignment to achieve adaptive correction by adding additional tip-tilt terms to the Optical Transfer Function (OTF) of the out-of-focus channel. The experimental results demonstrate the feasibility and validity of the proposed correction algorithm to improve the measurement accuracy during the co-phasing of segmented mirrors. With this alignment correction, the reconstructed wavefront is more accurate, and the recovered image is of higher quality.

  10. An Efficient Correction Algorithm for Eliminating Image Misalignment Effects on Co-Phasing Measurement Accuracy for Segmented Active Optics Systems

    PubMed Central

    Yue, Dan; Xu, Shuyan; Nie, Haitao; Wang, Zongyang

    2016-01-01

    The misalignment between recorded in-focus and out-of-focus images using the Phase Diversity (PD) algorithm leads to a dramatic decline in wavefront detection accuracy and image recovery quality for segmented active optics systems. This paper demonstrates the theoretical relationship between the image misalignment and tip-tilt terms in Zernike polynomials of the wavefront phase for the first time, and an efficient two-step alignment correction algorithm is proposed to eliminate these misalignment effects. This algorithm processes a spatial 2-D cross-correlation of the misaligned images, revising the offset to 1 or 2 pixels and narrowing the search range for alignment. Then, it eliminates the need for subpixel fine alignment to achieve adaptive correction by adding additional tip-tilt terms to the Optical Transfer Function (OTF) of the out-of-focus channel. The experimental results demonstrate the feasibility and validity of the proposed correction algorithm to improve the measurement accuracy during the co-phasing of segmented mirrors. With this alignment correction, the reconstructed wavefront is more accurate, and the recovered image is of higher quality. PMID:26934045

  11. Heteroclinic contours in oscillatory ensembles.

    PubMed

    Komarov, M A; Osipov, G V; Zhou, C S

    2013-02-01

    In this work, we study the onset of sequential activity in ensembles of neuronlike oscillators with inhibitorylike coupling between them. The winnerless competition (WLC) principle is a dynamical concept underlying sequential activity generation. According to the WLC principle, stable heteroclinic sequences in the phase space of a network model represent sequential metastable dynamics. We show that stable heteroclinic sequences and stable heteroclinic channels, connecting saddle limit cycles, can appear in oscillatory models of neural activity. We find the key bifurcations which lead to the occurrence of sequential activity as well as heteroclinic sequences and channels.

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

    PubMed

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

    2014-09-01

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

  13. Retinal vessel segmentation: an efficient graph cut approach with retinex and local phase.

    PubMed

    Zhao, Yitian; Liu, Yonghuai; Wu, Xiangqian; Harding, Simon P; Zheng, Yalin

    2015-01-01

    Our application concerns the automated detection of vessels in retinal images to improve understanding of the disease mechanism, diagnosis and treatment of retinal and a number of systemic diseases. We propose a new framework for segmenting retinal vasculatures with much improved accuracy and efficiency. The proposed framework consists of three technical components: Retinex-based image inhomogeneity correction, local phase-based vessel enhancement and graph cut-based active contour segmentation. These procedures are applied in the following order. Underpinned by the Retinex theory, the inhomogeneity correction step aims to address challenges presented by the image intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The local phase enhancement technique is employed to enhance vessels for its superiority in preserving the vessel edges. The graph cut-based active contour method is used for its efficiency and effectiveness in segmenting the vessels from the enhanced images using the local phase filter. We have demonstrated its performance by applying it to four public retinal image datasets (3 datasets of color fundus photography and 1 of fluorescein angiography). Statistical analysis demonstrates that each component of the framework can provide the level of performance expected. The proposed framework is compared with widely used unsupervised and supervised methods, showing that the overall framework outperforms its competitors. For example, the achieved sensitivity (0:744), specificity (0:978) and accuracy (0:953) for the DRIVE dataset are very close to those of the manual annotations obtained by the second observer.

  14. Geomorphic signatures of active tectonics in the Trans-Yamuna segment of the western Doon valley, northwest Himalaya, India

    NASA Astrophysics Data System (ADS)

    Philip, George; Sah, Madho P.

    Being involved in the late orogenic movements of the sub-Himalaya, the Doon valley and its Quaternary formations have received considerable attention from Earth scientists in the study of active tectonics and paleoseismic events. Study of aerial photographs and satellite data, and selected field checks not only confirmed neotectonic features already reported by various authors but also revealed the presence of more such features. In response to active tectonics, these features have affected very young terraces and Quaternary sediments in the Trans-Yamuna segment of the Doon valley in the western sub-Himalaya. In the present study, an attempt has been made to understand the neotectonic implications of these movements on landforms in and around Sataun-Sirmuri Tal. Ground evidence indicates that the area has experienced at least three major tectonic impulses since the generation of the Main Boundary Thrust. The major tectonic disturbances are most likely due to co-seismic activity along the ongoing Himalayan tectonic processes. In this paper, we discuss some of the strong geomorphic signatures, such as lineament and active fault traces, pressure ridges, sag ponds, alluvial fans, river terraces and finally landslides, which are indicative of active tectonics in this area. On the basis of the present-day geomorphic configuration of this sub-Himalayan basin, a possible evolutionary history is also presented.

  15. Analysis of muscle activation in each body segment in response to the stimulation intensity of whole-body vibration

    PubMed Central

    Lee, Dae-Yeon

    2017-01-01

    [Purpose] The purpose of this study was to investigate the effects of a whole-body vibration exercise, as well as to discuss the scientific basis to establish optimal intensity by analyzing differences between muscle activations in each body part, according to the stimulation intensity of the whole-body vibration. [Subjects and Methods] The study subjects included 10 healthy men in their 20s without orthopedic disease. Representative muscles from the subjects’ primary body segments were selected while the subjects were in upright positions on exercise machines; electromyography electrodes were attached to the selected muscles. Following that, the muscle activities of each part were measured at different intensities. No vibration, 50/80 in volume, and 10/25/40 Hz were mixed and applied when the subjects were on the whole-vibration exercise machines in upright positions. After that, electromyographic signals were collected and analyzed with the root mean square of muscular activation. [Results] As a result of the analysis, it was found that the muscle activation effects had statistically meaningful differences according to changes in exercise intensity in all 8 muscles. When the no-vibration status was standardized and analyzed as 1, the muscle effect became lower at higher frequencies, but became higher at larger volumes. [Conclusion] In conclusion, it was shown that the whole-body vibration stimulation promoted muscle activation across the entire body part, and the exercise effects in each muscle varied depending on the exercise intensities. PMID:28265155

  16. Curved contours and the associative response.

    PubMed

    Zusne, L

    1975-02-01

    72 random polygons and their curvilinear transformations were exposed for 3 sec. to 40 subjects who produced written associations during a 10-sec. interval. The number of associations varied, in general, directly with the amount of curved contour as well as with the degree of contour dispersion. The amount of variance accounted for by these two variables was small, however. Differences in curvature produced much greater differences in the content of the associations, greater degrees of curvature evoking more associations that were curved, man-made objects or living things and fewer associations that were straight-edged, man-made objects. A significant and inverse relationship was also established between contour dispersion and associations that were non-living, natural objects. It is concluded that physical form dimensions, especially curvature, affect less the association value (connotative meaning) of visual forms and much more their denotative meaning.

  17. Surface reconstruction from sparse fringe contours

    SciTech Connect

    Cong, G.; Parvin, B.

    1998-08-10

    A new approach for reconstruction of 3D surfaces from 2D cross-sectional contours is presented. By using the so-called ''Equal Importance Criterion,'' we reconstruct the surface based on the assumption that every point in the region contributes equally to the surface reconstruction process. In this context, the problem is formulated in terms of a partial differential equation (PDE), and we show that the solution for dense contours can be efficiently derived from distance transform. In the case of sparse contours, we add a regularization term to insure smoothness in surface recovery. The proposed technique allows for surface recovery at any desired resolution. The main advantage of the proposed method is that inherent problems due to correspondence, tiling, and branching are avoided. Furthermore, the computed high resolution surface is better represented for subsequent geometric analysis. We present results on both synthetic and real data.

  18. Modified contour-improved perturbation theory

    SciTech Connect

    Cvetic, Gorazd; Loewe, Marcelo; Martinez, Cristian; Valenzuela, Cristian

    2010-11-01

    The semihadronic tau decay width allows a clean extraction of the strong coupling constant at low energies. We present a modification of the standard ''contour-improved'' method based on a derivative expansion of the Adler function. The new approach has some advantages compared to contour-improved perturbation theory. The renormalization scale dependence is weaker by more than a factor of 2 and the last term of the expansion is reduced by about 10%, while the renormalization scheme dependence remains approximately equal. The extracted QCD coupling at the tau mass scale is by 2% lower than the contour-improved value. We find {alpha}{sub s}(M{sub Z}{sup 2})=0.1211{+-}0.0010.

  19. Perceptual Grouping of Object Contours Survives Saccades

    PubMed Central

    Demeyer, Maarten; De Graef, Peter; Verfaillie, Karl; Wagemans, Johan

    2011-01-01

    Human observers explore scenes by shifting their gaze from object to object. Before each eye movement, a peripheral glimpse of the next object to be fixated has however already been caught. Here we investigate whether the perceptual organization extracted from such a preview could guide the perceptual analysis of the same object during the next fixation. We observed that participants were indeed significantly faster at grouping together spatially separate elements into an object contour, when the same contour elements had also been grouped together in the peripheral preview display. Importantly, this facilitation occurred despite a change in the grouping cue defining the object contour (similarity versus collinearity). We conclude that an intermediate-level description of object shape persists in the visual system across gaze shifts, providing it with a robust basis for balancing efficiency and continuity during scene exploration. PMID:21713007

  20. Affect From Mere Perception: Illusory Contour Perception Feels Good.

    PubMed

    Erle, Thorsten M; Reber, Rolf; Topolinski, Sascha

    2017-02-16

    Can affect be evoked by mere perception? Earlier work on processing fluency, which manipulated the dynamics of a running perceptual process, has shown that efficient processing can indeed trigger positive affect. The present work introduces a novel route by not manipulating the dynamics of an ongoing perceptual process, but by blocking or allowing the whole process in the first place. We used illusory contour perception as one very basic such process. In 5 experiments (total N = 422), participants briefly (≤100 ms) viewed stimuli that either allowed illusory contour perception, so-called Kanizsa shapes, or proximally identical control shapes that did not allow for this process to occur. Self-reported preference ratings (Experiments 1, 2, and 4) and facial muscle activity (Experiment 3) showed that participants consistently preferred Kanizsa over these control shapes. Moreover, even within Kanizsa shapes, those that most likely instigated illusory contour perception (i.e., those with the highest support ratio) were liked the most (Experiment 5). At the same time, Kanizsa stimuli with high support ratios were objectively and subjectively the most complex, rendering a processing fluency explanation of this preference unlikely. These findings inform theorizing in perception about affective properties of early perceptual processes that are independent from perceptual fluency and research on affect about the importance of basic perception as a source of affectivity. (PsycINFO Database Record

  1. Enzyme activities and morphological appearance in functioning and excluded segments of the small intestine after shunt operation for obesity.

    PubMed Central

    Asp, N G; Gudmand-Høyer, E; Andersen, B; Berg, N O

    1979-01-01

    Five patients in whom small-intestinal bypass was performed for severe obesity had a second operation 11-19 months later because of insufficient weight loss. Mucosal enzyme activities and histological appearance were investigated in biopsies from different parts of the functioning and excluded small intestine. These were compared with biopsies form corresponding sites obtained at the first operation. In addition to a prominent increase in length, circumference, and mucosal thickness in the functioning shunt, the disaccharidases and two intracellular beta-galactosidases increased in specific activity,, especially in the distal ileal part of the shunt. In the excluded segment of the small intestine different enzymes showed a different response: trehalase increased and alkaline phosphate decreased significantly. Other enzymes that were measured showed a varied pattern. The results indicated that not only the luminal content but also other, presumably hormonal, factors regulated the enzyme activities, and that different regulating factors influenced the various enzymes differently. The marked adaptive increase in mucosal surface of the functioning shunt could be one factor in explaining the weight stabilisation and, in some cases, weight increase after the initial rapid weight loss after the operation for small-intestinal bypass. The increase in specific enzyme activities would further increase the digestive capacity of the shunt. Images Fig. 1 PMID:488750

  2. The velocity snake: Deformable contour for tracking in spatio-velocity space

    SciTech Connect

    Peterfreund, N.

    1997-06-01

    The author presents a new active contour model for boundary tracking and position prediction of nonrigid objects, which results from applying a velocity control to the class of elastodynamical contour models, known as snakes. The proposed control term minimizes an energy dissipation function which measures the difference between the contour velocity and the apparent velocity of the image. Treating the image video-sequence as continuous measurements along time, it is shown that the proposed control results in an unbiased tracking. This is in contrast to the original snake model which is proven to be biased due to the image (object) velocity, thus resulting in high sensitivity to image clutter. The motion estimation further allows for position prediction of nonrigid boundaries. Based on the proposed control approach, the author proposes a new class of real time tracking contours, varying from models with batch-mode control estimation to models with real time adaptive controllers.

  3. Contour junctions underlie neural representations of scene categories in high-level human visual cortex.

    PubMed

    Choo, Heeyoung; Walther, Dirk B

    2016-07-15

    Humans efficiently grasp complex visual environments, making highly consistent judgments of entry-level category despite their high variability in visual appearance. How does the human brain arrive at the invariant neural representations underlying categorization of real-world environments? We here show that the neural representation of visual environments in scene-selective human visual cortex relies on statistics of contour junctions, which provide cues for the three-dimensional arrangement of surfaces in a scene. We manipulated line drawings of real-world environments such that statistics of contour orientations or junctions were disrupted. Manipulated and intact line drawings were presented to participants in an fMRI experiment. Scene categories were decoded from neural activity patterns in the parahippocampal place area (PPA), the occipital place area (OPA) and other visual brain regions. Disruption of junctions but not orientations led to a drastic decrease in decoding accuracy in the PPA and OPA, indicating the reliance of these areas on intact junction statistics. Accuracy of decoding from early visual cortex, on the other hand, was unaffected by either image manipulation. We further show that the correlation of error patterns between decoding from the scene-selective brain areas and behavioral experiments is continge