The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Jian Yang - One of the best experts on this subject based on the ideXlab platform.
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local global Active Contour Model based on tensor based representation for 3d ultrasound vessel segmentation
Physics in Medicine and Biology, 2021Co-Authors: Jiahui Dong, Yongtian Wang, Jingfan Fan, Qiaoling Deng, Hong Song, Zhigang Cheng, Ping Liang, Jian YangAbstract:Three-dimensional (3D) vessel segmentation can provide full spatial information about an anatomic structure to help physicians gain increased understanding of vascular structures, which plays an utmost role in many medical image-processing and analysis applications. The purpose of this paper aims to develop a 3D vessel-segmentation method that can improve segmentation accuracy in 3D ultrasound images. We propose a 3D tensor-based Active Contour Model method for accurate 3D vessel segmentation. With our method, the contrast-independent multiscale bottom-hat tensor representation and local-global information are captured. This strategy ensures the effective extraction of the boundaries of vessels from inhomogeneous and homogeneous regions without being affected by the noise and low-contrast of the 3D ultrasound images. Experimental results in clinical 3D ultrasound and public 3D Multiphoton Microscopy datasets are used for quantitative and qualitative comparison with several state-of-the-art vessel segmentation methods. Clinical experiments demonstrate that our method can achieve a smoother and more accurate boundary of the vessel object than competing methods. The mean SE, SP and ACC of the proposed method are: 0.7768±0.0597, 0.9978±0.0013 and 0.9971±0.0015 respectively. Experiments on the public dataset show that our method can segment complex vessels in different medical images with noise and low- contrast.
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saliency driven vasculature segmentation with infinite perimeter Active Contour Model
Neurocomputing, 2017Co-Authors: Yitian Zhao, Jian Yang, Jingliang Zhao, Yifan Zhao, Yalin Zheng, Yongtian WangAbstract:Abstract Automated detection of retinal blood vessels plays an important role in advancing the understanding of the mechanism, diagnosis and treatment of cardiovascular disease and many systemic diseases, such as diabetic retinopathy and age-related macular degeneration. Here, we propose a new framework for precisely segmenting retinal vasculatures. The proposed framework consists of three steps. A non-local total variation Model is adapted to the Retinex theory, which aims to address challenges presented by intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The image is then divided into superpixels, and a compactness-based saliency detection method is proposed to locate the object of interest. For better general segmentation performance, we then make use of a new infinite Active Contour Model to segment the vessels in each superpixel. The proposed framework has wide applications, and the results show that our Model outperforms its competitors.
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a novel multiphase Active Contour Model for inhomogeneous image segmentation
Multimedia Tools and Applications, 2014Co-Authors: Shangbing Gao, Jian Yang, Yunyang YanAbstract:The problem of image segmentation has been investigated with a focus on inhomogeneous multiphase image segmentation. Intensity inhomogeneity is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) and natural images segmentation. The complex images usually contain an arbitrary number of objects. This paper presents a new multiphase Active Contour Model method for simultaneous regions classification of MR images and natural images without bias field correction. In this Model, a simple and effective initialization method is taken to speed up the curve evolution toward final results; a new multiphase level set method is proposed to segment the multiple regions. This Model not only extracts multiple objects simultaneously, but also provides smooth and accurate boundaries of the objects. The results for experiments on several synthetic and real images demonstrate the effectiveness and accuracy of our Model.
Martin D Levine - One of the best experts on this subject based on the ideXlab platform.
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tracking deformable objects in the plane using an Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993Co-Authors: Frederic Fol Leymarie, Martin D LevineAbstract:The problems of segmenting a noisy intensity image and tracking a nonrigid object in the plane are discussed. In evaluating these problems, a technique based on an Active Contour Model commonly called a snake is examined. The technique is applied to cell locomotion and tracking studies. The snake permits both the segmentation and tracking problems to be simultaneously solved in constrained cases. A detailed analysis of the snake Model, emphasizing its limitations and shortcomings, is presented, and improvements to the original description of the Model are proposed. Problems of convergence of the optimization scheme are considered. In particular, an improved terminating criterion for the optimization scheme that is based on topographic features of the graph of the intensity image is proposed. Hierarchical filtering methods, as well as a continuation method based on a discrete sale-space representation, are discussed. Results for both segmentation and tracking are presented. Possible failures of the method are discussed. >
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simulating the grassfire transform using an Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992Co-Authors: Frederic Fol Leymarie, Martin D LevineAbstract:A method for shape description of planar objects that integrates both region and boundary features is presented. The method is an implementation of a 2D dynamic grassfire that relies on a distance surface on which elastic Contours minimize an energy function. The method is based on an Active Contour Model. Numerous implementation aspects of the shape description method were optimized. A Euclidean metric was used for optimal accuracy, and the Active Contour Model permits bypassing some of the discretization limitations inherent in using a digital grid. Noise filtering was performed on the basis of both Contour feature measures and region measures, that is, curvature extremum significance and ridge support, respectively, to obtain robust shape descriptors. Other improvements and variations of the algorithmic implementation are proposed. >
Baba C Vemuri - One of the best experts on this subject based on the ideXlab platform.
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tensor field segmentation using region based Active Contour Model
European Conference on Computer Vision, 2004Co-Authors: Zhizhou Wang, Baba C VemuriAbstract:Tensor fields (matrix valued data sets) have recently attracted increased attention in the fields of image processing, computer vision, visualization and medical imaging. Tensor field segmentation is an important problem in tensor field analysis and has not been addressed adequately in the past. In this paper, we present an effective region-based Active Contour Model for tensor field segmentation and show its application to diffusion tensor magnetic resonance images (MRI) as well as for the texture segmentation problem in computer vision. Specifically, we present a variational principle for an Active Contour using the Euclidean difference of tensors as a discriminant. The variational formulation is valid for piecewise smooth regions, however, for the sake of simplicity of exposition, we present the piecewise constant region Model in detail. This variational principle is a generalization of the region-based Active Contour to matrix valued functions. It naturally leads to a curve evolution equation for tensor field segmentation, which is subsequently expressed in a level set framework and solved numerically. Synthetic and real data experiments involving the segmentation of diffusion tensor MRI as well as structure tensors obtained from real texture data are shown to depict the performance of the proposed Model.
Yalin Zheng - One of the best experts on this subject based on the ideXlab platform.
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saliency driven vasculature segmentation with infinite perimeter Active Contour Model
Neurocomputing, 2017Co-Authors: Yitian Zhao, Jian Yang, Jingliang Zhao, Yifan Zhao, Yalin Zheng, Yongtian WangAbstract:Abstract Automated detection of retinal blood vessels plays an important role in advancing the understanding of the mechanism, diagnosis and treatment of cardiovascular disease and many systemic diseases, such as diabetic retinopathy and age-related macular degeneration. Here, we propose a new framework for precisely segmenting retinal vasculatures. The proposed framework consists of three steps. A non-local total variation Model is adapted to the Retinex theory, which aims to address challenges presented by intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The image is then divided into superpixels, and a compactness-based saliency detection method is proposed to locate the object of interest. For better general segmentation performance, we then make use of a new infinite Active Contour Model to segment the vessels in each superpixel. The proposed framework has wide applications, and the results show that our Model outperforms its competitors.
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automated vessel segmentation using infinite perimeter Active Contour Model with hybrid region information with application to retinal images
IEEE Transactions on Medical Imaging, 2015Co-Authors: Yitian Zhao, Lavdie Rada, Ke Chen, Simon P Harding, Yalin ZhengAbstract:Automated detection of blood vessel structures is becoming of crucial interest for better management of vascular disease. In this paper, we propose a new infinite Active Contour Model that uses hybrid region information of the image to approach this problem. More specifically, an infinite perimeter regularizer, provided by using ${\cal L}^{2}$ Lebesgue measure of the $\gamma$ -neighborhood of boundaries, allows for better detection of small oscillatory (branching) structures than the traditional Models based on the length of a feature's boundaries (i.e., ${\cal H}^{1}$ Hausdorff measure). Moreover, for better general segmentation performance, the proposed Model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map. The local phase based enhancement map is used for its superiority in preserving vessel edges while the given image intensity information will guarantee a correct feature's segmentation. We evaluate the performance of the proposed Model by applying it to three public retinal image datasets (two datasets of color fundus photography and one fluorescein angiography dataset). The proposed Model outperforms its competitors when compared with other widely used unsupervised and supervised methods. For example, the sensitivity (0.742), specificity (0.982) and accuracy (0.954) achieved on the DRIVE dataset are very close to those of the second observer's annotations.
Yongtian Wang - One of the best experts on this subject based on the ideXlab platform.
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local global Active Contour Model based on tensor based representation for 3d ultrasound vessel segmentation
Physics in Medicine and Biology, 2021Co-Authors: Jiahui Dong, Yongtian Wang, Jingfan Fan, Qiaoling Deng, Hong Song, Zhigang Cheng, Ping Liang, Jian YangAbstract:Three-dimensional (3D) vessel segmentation can provide full spatial information about an anatomic structure to help physicians gain increased understanding of vascular structures, which plays an utmost role in many medical image-processing and analysis applications. The purpose of this paper aims to develop a 3D vessel-segmentation method that can improve segmentation accuracy in 3D ultrasound images. We propose a 3D tensor-based Active Contour Model method for accurate 3D vessel segmentation. With our method, the contrast-independent multiscale bottom-hat tensor representation and local-global information are captured. This strategy ensures the effective extraction of the boundaries of vessels from inhomogeneous and homogeneous regions without being affected by the noise and low-contrast of the 3D ultrasound images. Experimental results in clinical 3D ultrasound and public 3D Multiphoton Microscopy datasets are used for quantitative and qualitative comparison with several state-of-the-art vessel segmentation methods. Clinical experiments demonstrate that our method can achieve a smoother and more accurate boundary of the vessel object than competing methods. The mean SE, SP and ACC of the proposed method are: 0.7768±0.0597, 0.9978±0.0013 and 0.9971±0.0015 respectively. Experiments on the public dataset show that our method can segment complex vessels in different medical images with noise and low- contrast.
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saliency driven vasculature segmentation with infinite perimeter Active Contour Model
Neurocomputing, 2017Co-Authors: Yitian Zhao, Jian Yang, Jingliang Zhao, Yifan Zhao, Yalin Zheng, Yongtian WangAbstract:Abstract Automated detection of retinal blood vessels plays an important role in advancing the understanding of the mechanism, diagnosis and treatment of cardiovascular disease and many systemic diseases, such as diabetic retinopathy and age-related macular degeneration. Here, we propose a new framework for precisely segmenting retinal vasculatures. The proposed framework consists of three steps. A non-local total variation Model is adapted to the Retinex theory, which aims to address challenges presented by intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The image is then divided into superpixels, and a compactness-based saliency detection method is proposed to locate the object of interest. For better general segmentation performance, we then make use of a new infinite Active Contour Model to segment the vessels in each superpixel. The proposed framework has wide applications, and the results show that our Model outperforms its competitors.
