The Experts below are selected from a list of 24342 Experts worldwide ranked by ideXlab platform
Laurent D Cohen - One of the best experts on this subject based on the ideXlab platform.
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Anisotropic tubular Minimal Path model with fast marching front freezing scheme
Pattern Recognition, 2020Co-Authors: Li Liu, Laurent D Cohen, Da Chen, Michel Paques, Huazhong ShuAbstract:Abstract In this work, we introduce an anisotropic Minimal Path model based on a new Riemannian tensor integrating the crossing-adaptive anisotropic radius-lifted tensor field and the front freezing indicator by appearance and Path features. The non-local Path feature only can be obtained during the geodesic distance computation process by the fast marching method. The predefined criterion derived from Path feature is able to steer the front evolution by freezing the point causing high bending of the geodesic to solve the shortcut problem. We performed qualitative and quantitative experiments on synthetic and real images (including retinal vessels, rivers and roads) and compare with the Minimal Path models with classical anisotropic Riemannian metric and dynamic isotropic metric, which demonstrated the proposed method can detect desired targets from complex tubular tree structures.
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Vessel Extraction Using Crossing-Adaptive Minimal Path Model With Anisotropic Enhancement And Curvature Constraint
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019Co-Authors: Da Chen, Laurent D Cohen, Michel PaquesAbstract:In this work, we propose a new Minimal Path model with a dynamic Riemannian metric to overcome the shortcuts problem in vessel extraction. The invoked metric consists of a crossing-adaptive anisotropic radius-lifted tensor field and a front freezing indicator. It is able to reduce the anisotropy of the metric on the crossing points and steer the front evolution by freezing the points causing high curvature of a geodesic. We validate our model on the DRIVE and IOSTAR datasets, and the segmentation accuracy is 0.861 and 0.881, respectively. The proposed method can extract the centreline position and vessel width efficiently and accuracy.
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Minimal Paths for Tubular Structure Segmentation With Coherence Penalty and Adaptive Anisotropy
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2018Co-Authors: Da Chen, Jiong Zhang, Laurent D CohenAbstract:The Minimal Path method has proven to be particularly useful and efficient in tubular structure segmentation applications. In this paper, we propose a new Minimal Path model associated with a dynamic Riemannian metric embedded with an appearance feature coherence penalty and an adaptive anisotropy enhancement term. The features that characterize the appearance and anisotropy properties of a tubular structure are extracted through the associated orientation score. The proposed the dynamic Riemannian metric is updated in the course of the geodesic distance computation carried out by the efficient single-pass fast marching method. Compared to the state-of-the-art Minimal Path models, the proposed Minimal Path model is able to extract the desired tubular structures from a complicated vessel tree structure. In addition, we propose an efficient prior Path-based method to search for vessel radius value at each centerline position of the target. Finally, we perform the numerical experiments on both synthetic and real images. The quantitive validation is carried out on retinal vessel images. The results indicate that the proposed model indeed achieves a promising performance.
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an isotropic Minimal Path based framework for segmentation and quantification of vascular networks
Energy Minimization Methods in Computer Vision and Pattern Recognition, 2017Co-Authors: Laurent D Cohen, Emmanuel Cohen, Thomas Deffieux, Mickael TanterAbstract:Minimal Path approaches for image analysis aim to extract curves minimizing an energy functional. The energy of a Path corresponds to its weighted curve length according to a relevant metric function. In this study, we design a binary isotropic metric model with the use of a Hessian-based vascular enhancement filter in order to extract geometrical features from vascular networks. We introduce a constrained keypoint search method able to extract subpixel vessel centrelines, diameters and bifurcations. Experiments on retinal images demonstrated that the proposed framework achieves similar even better segmentation performances as compared with methods using more sophisticated metric designs.
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A New Coherence-Penalized Minimal Path Model with Application to Retinal Vessel Centerline Delineation
arXiv: Computational Geometry, 2017Co-Authors: Da Chen, Laurent D CohenAbstract:In this paper, we propose a new Minimal Path model for Minimally interactive retinal vessel centerline extraction. The main contribution lies at the construction of a novel coherence-penalized Riemannian metric in a lifted space, dependently of the local geometry of tubularity and an external scalar-valued reference feature map. The globally minimizing curves associated to the proposed metric favour to pass through a set of retinal vessel segments with low variations of the feature map, thus can avoid the short branches combination problem and shortcut problem, commonly suffered by the existing Minimal Path models in the application of retinal imaging. We validate our model on a series of retinal vessel patches obtained from the DRIVE and IOSTAR datasets, showing that our model indeed get promising results.
Da Chen - One of the best experts on this subject based on the ideXlab platform.
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Anisotropic tubular Minimal Path model with fast marching front freezing scheme
Pattern Recognition, 2020Co-Authors: Li Liu, Laurent D Cohen, Da Chen, Michel Paques, Huazhong ShuAbstract:Abstract In this work, we introduce an anisotropic Minimal Path model based on a new Riemannian tensor integrating the crossing-adaptive anisotropic radius-lifted tensor field and the front freezing indicator by appearance and Path features. The non-local Path feature only can be obtained during the geodesic distance computation process by the fast marching method. The predefined criterion derived from Path feature is able to steer the front evolution by freezing the point causing high bending of the geodesic to solve the shortcut problem. We performed qualitative and quantitative experiments on synthetic and real images (including retinal vessels, rivers and roads) and compare with the Minimal Path models with classical anisotropic Riemannian metric and dynamic isotropic metric, which demonstrated the proposed method can detect desired targets from complex tubular tree structures.
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Vessel Extraction Using Crossing-Adaptive Minimal Path Model With Anisotropic Enhancement And Curvature Constraint
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019Co-Authors: Da Chen, Laurent D Cohen, Michel PaquesAbstract:In this work, we propose a new Minimal Path model with a dynamic Riemannian metric to overcome the shortcuts problem in vessel extraction. The invoked metric consists of a crossing-adaptive anisotropic radius-lifted tensor field and a front freezing indicator. It is able to reduce the anisotropy of the metric on the crossing points and steer the front evolution by freezing the points causing high curvature of a geodesic. We validate our model on the DRIVE and IOSTAR datasets, and the segmentation accuracy is 0.861 and 0.881, respectively. The proposed method can extract the centreline position and vessel width efficiently and accuracy.
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Minimal Paths for Tubular Structure Segmentation With Coherence Penalty and Adaptive Anisotropy
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2018Co-Authors: Da Chen, Jiong Zhang, Laurent D CohenAbstract:The Minimal Path method has proven to be particularly useful and efficient in tubular structure segmentation applications. In this paper, we propose a new Minimal Path model associated with a dynamic Riemannian metric embedded with an appearance feature coherence penalty and an adaptive anisotropy enhancement term. The features that characterize the appearance and anisotropy properties of a tubular structure are extracted through the associated orientation score. The proposed the dynamic Riemannian metric is updated in the course of the geodesic distance computation carried out by the efficient single-pass fast marching method. Compared to the state-of-the-art Minimal Path models, the proposed Minimal Path model is able to extract the desired tubular structures from a complicated vessel tree structure. In addition, we propose an efficient prior Path-based method to search for vessel radius value at each centerline position of the target. Finally, we perform the numerical experiments on both synthetic and real images. The quantitive validation is carried out on retinal vessel images. The results indicate that the proposed model indeed achieves a promising performance.
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A New Coherence-Penalized Minimal Path Model with Application to Retinal Vessel Centerline Delineation
arXiv: Computational Geometry, 2017Co-Authors: Da Chen, Laurent D CohenAbstract:In this paper, we propose a new Minimal Path model for Minimally interactive retinal vessel centerline extraction. The main contribution lies at the construction of a novel coherence-penalized Riemannian metric in a lifted space, dependently of the local geometry of tubularity and an external scalar-valued reference feature map. The globally minimizing curves associated to the proposed metric favour to pass through a set of retinal vessel segments with low variations of the feature map, thus can avoid the short branches combination problem and shortcut problem, commonly suffered by the existing Minimal Path models in the application of retinal imaging. We validate our model on a series of retinal vessel patches obtained from the DRIVE and IOSTAR datasets, showing that our model indeed get promising results.
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Global Minimum for a Finsler Elastica Minimal Path Approach
International Journal of Computer Vision, 2016Co-Authors: Da Chen, Jeanmarie Mirebeau, Laurent D CohenAbstract:In this paper, we propose a novel curvature penalized Minimal Path model via an orientation-lifted Finsler metric and the Euler elastica curve. The original Minimal Path model computes the globally Minimal geodesic by solving an Eikonal partial differential equation (PDE). Essentially, this first-order model is unable to penalize curvature which is related to the Path rigidity property in the classical active contour models. To solve this problem, we present an Eikonal PDE-based Finsler elastica Minimal Path approach to address the curvature-penalized geodesic energy minimization problem. We were successful at adding the curvature penalization to the classical geodesic energy (Caselles et al. in Int J Comput Vis 22(1):61---79, 1997; Cohen and Kimmel in Int J Comput Vis 24(1):57---78, 1997). The basic idea of this work is to interpret the Euler elastica bending energy via a novel Finsler elastica metric that embeds a curvature penalty. This metric is non-Riemannian, anisotropic and asymmetric, and is defined over an orientation-lifted space by adding to the image domain the orientation as an extra space dimension. Based on this orientation lifting, the proposed Minimal Path model can benefit from both the curvature and orientation of the Paths. Thanks to the fast marching method, the global minimum of the curvature-penalized geodesic energy can be computed efficiently. We introduce two anisotropic image data-driven speed functions that are computed by steerable filters. Based on these orientation-dependent speed functions, we can apply the proposed Finsler elastica Minimal Path model to the applications of closed contour detection, perceptual grouping and tubular structure extraction. Numerical experiments on both synthetic and real images show that these applications of the proposed model indeed obtain promising results.
D Cohenlaurent - One of the best experts on this subject based on the ideXlab platform.
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Tubular Structure Segmentation Based on Minimal Path Method and Anisotropic Enhancement
International Journal of Computer Vision, 2011Co-Authors: Benmansourfethallah, D CohenlaurentAbstract:We present a new interactive method for tubular structure extraction. The main application and motivation for this work is vessel tracking in 2D and 3D images. The basic tools are Minimal Paths sol...
Ghassan Hamarneh - One of the best experts on this subject based on the ideXlab platform.
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globally optimal spinal cord segmentation using a Minimal Path in high dimensions
International Symposium on Biomedical Imaging, 2013Co-Authors: Jeremy Kawahara, Chris Mcintosh, Roger Tam, Ghassan HamarnehAbstract:Spinal cord segmentation is an important step to empirically quantify spinal cord atrophy that can occur in neurological diseases such as multiple sclerosis (MS). In this work, we propose a novel method to find the globally optimal segmentation of the spinal cord using a high dimensional Minimal Path search. The spinal cord cross-sectional shapes are represented using principal component analysis (in the probability simplex) which captures most of spinal cord's axial cross-sectional variation and partial volume effects. We propose modifications to the A* Minimal Path search algorithm that drastically reduce the required memory and run-time to make our high dimensional Minimal Path optimization computationally feasible. Finally, we validate our results over five vertebrae levels of both healthy and MS clinical MR volumes (20 volumes total) and show improvements on volume agreement with expert segmentations and less user interaction when compared to current state-of-the-art methods.
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ISBI - Globally optimal spinal cord segmentation using a Minimal Path in high dimensions
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013Co-Authors: Jeremy Kawahara, Chris Mcintosh, Roger Tam, Ghassan HamarnehAbstract:Spinal cord segmentation is an important step to empirically quantify spinal cord atrophy that can occur in neurological diseases such as multiple sclerosis (MS). In this work, we propose a novel method to find the globally optimal segmentation of the spinal cord using a high dimensional Minimal Path search. The spinal cord cross-sectional shapes are represented using principal component analysis (in the probability simplex) which captures most of spinal cord's axial cross-sectional variation and partial volume effects. We propose modifications to the A* Minimal Path search algorithm that drastically reduce the required memory and run-time to make our high dimensional Minimal Path optimization computationally feasible. Finally, we validate our results over five vertebrae levels of both healthy and MS clinical MR volumes (20 volumes total) and show improvements on volume agreement with expert segmentations and less user interaction when compared to current state-of-the-art methods.
Karl Rohr - One of the best experts on this subject based on the ideXlab platform.
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Progressive Minimal Path Method for Segmentation of 2D and 3D Line Structures
IEEE transactions on pattern analysis and machine intelligence, 2017Co-Authors: Wei Liao, Stefan Wörz, Chang-ki Kang, Zang-hee Cho, Karl RohrAbstract:We propose a novel Minimal Path method for the segmentation of 2D and 3D line structures. Minimal Path methods perform propagation of a wavefront emanating from a start point at a speed derived from image features, followed by Path extraction using backtracing. Usually, the computation of the speed and the propagation of the wave are two separate steps, and point features are used to compute a static speed. We introduce a new continuous Minimal Path method which steers the wave propagation progressively using dynamic speed based on Path features . We present three instances of our method, using an appearance feature of the Path, a geometric feature based on the curvature of the Path, and a joint appearance and geometric feature based on the tangent of the wavefront. These features have not been used in previous continuous Minimal Path methods. We compute the features dynamically during the wave propagation, and also efficiently using a fast numerical scheme and a low-dimensional parameter space. Our method does not suffer from discretization or metrication errors. We performed qualitative and quantitative evaluations using 2D and 3D images from different application areas.
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globally Minimal Path method using dynamic speed functions based on progressive wave propagation
Asian Conference on Computer Vision, 2012Co-Authors: Wei Liao, Stefan Wörz, Karl RohrAbstract:In this paper, we propose a novel framework which extends the classical Minimal Path methods. Usually, Minimal Path methods can be interpreted as the simulation of the outward propagation of a wavefront emanating from a specific start point at a certain speed derived from an image. In previous methods, either a static speed is computed before the wavefront starts to propagate, or the normal of the wavefront is used to update the speed dynamically. We generalize the latter methods by introducing more general dynamic speed functions: During the outward propagation of the wavefront, features of the region already visited by the wavefront are used to update the speed dynamically. Our framework can incorporate both the fast marching method and Dijkstra's algorithm. We prove that the global optimum can be found using our approach and demonstrate its advantage experimentally by applying it for segmentation of tubular structures in synthetic and real images.