Fast Global Minimization of the Active Contour/Snake Model Export

@article{citeulike:1596492, abstract = {Abstract\ \  The active contour/snake model is one of the most successful variational models in image segmentation. It consists of evolving a contour in images toward the boundaries of objects. Its success is based on strong mathematical properties and efficient numerical schemes based on the level set method. The only drawback of this model is the existence of local minima in the active contour energy, which makes the initial guess critical to get satisfactory results. In this paper, we propose to solve this problem by determining a global minimum of the active contour model. Our approach is based on the unification of image segmentation and image denoising tasks into a global minimization framework. More precisely, we propose to unify three well-known image variational models, namely the snake model, the Rudin–Osher–Fatemi denoising model and the Mumford–Shah segmentation model. We will establish theorems with proofs to determine the existence of a global minimum of the active contour model. From a numerical point of view, we propose a new practical way to solve the active contour propagation problem toward object boundaries through a dual formulation of the minimization problem. The dual formulation, easy to implement, allows us a fast global minimization of the snake energy. It avoids the usual drawback in the level set approach that consists of initializing the active contour in a distance function and re-initializing it periodically during the evolution, which is time-consuming. We apply our segmentation algorithms on synthetic and real-world images, such as texture images and medical images, to emphasize the performances of our model compared with other segmentation models.}, author = {Bresson, Xavier and Esedoḡlu, Selim and Vandergheynst, Pierre and Thiran, Jean-Philippe and Osher, Stanley}, citeulike-article-id = {1596492}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1286063}, citeulike-linkout-1 = {http://dx.doi.org/10.1007/s10851-007-0002-0}, day = {23}, doi = {10.1007/s10851-007-0002-0}, journal = {Journal of Mathematical Imaging and Vision}, keywords = {active-contours, algorithm, level-sets, segmentation}, month = {June}, number = {2}, pages = {151--167}, posted-at = {2007-08-27 15:37:12}, priority = {5}, title = {Fast Global Minimization of the Active Contour/Snake Model}, url = {http://dx.doi.org/10.1007/s10851-007-0002-0}, volume = {28}, year = {2007} }

TY - JOUR ID - citeulike:1596492 L3 - citeulike-article-id:1596492 N2 - Abstract   The active contour/snake model is one of the most successful variational models in image segmentation. It consists of evolving a contour in images toward the boundaries of objects. Its success is based on strong mathematical properties and efficient numerical schemes based on the level set method. The only drawback of this model is the existence of local minima in the active contour energy, which makes the initial guess critical to get satisfactory results. In this paper, we propose to solve this problem by determining a global minimum of the active contour model. Our approach is based on the unification of image segmentation and image denoising tasks into a global minimization framework. More precisely, we propose to unify three well-known image variational models, namely the snake model, the Rudin–Osher–Fatemi denoising model and the Mumford–Shah segmentation model. We will establish theorems with proofs to determine the existence of a global minimum of the active contour model. From a numerical point of view, we propose a new practical way to solve the active contour propagation problem toward object boundaries through a dual formulation of the minimization problem. The dual formulation, easy to implement, allows us a fast global minimization of the snake energy. It avoids the usual drawback in the level set approach that consists of initializing the active contour in a distance function and re-initializing it periodically during the evolution, which is time-consuming. We apply our segmentation algorithms on synthetic and real-world images, such as texture images and medical images, to emphasize the performances of our model compared with other segmentation models. IS - 2 JF - Journal of Mathematical Imaging and Vision EP - 167 TI - Fast Global Minimization of the Active Contour/Snake Model VL - 28 SP - 151 KW - active-contours KW - algorithm KW - level-sets KW - segmentation AU - Bresson, Xavier AU - Esedoḡlu, Selim AU - Vandergheynst, Pierre AU - Thiran, Jean-Philippe AU - Osher, Stanley PY - 2007/06/23/ UR - http://dx.doi.org/10.1007/s10851-007-0002-0 ER -