Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast level set method for propagating interfaces
Journal of Computational Physics
International Journal of Computer Vision
SIAM Journal on Scientific Computing
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Initialization Techniques for Segmentation with the Chan-Vese Model
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
A variational formulation for segmenting desired objects in color images
Image and Vision Computing
Image and Vision Computing
On the statistical interpretation of the piecewise smooth Mumford-Shah functional
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Efficient segmentation of piecewise smooth images
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Γ-convergence approximation to piecewise smooth medical image segmentation
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Image segmentation and selective smoothing by using Mumford-Shah model
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Localizing Region-Based Active Contours
IEEE Transactions on Image Processing
A fast level set-like algorithm for region-based active contours
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Texture segmentation via non-local non-parametric active contours
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Journal of Mathematical Imaging and Vision
Small object detection in cluttered image using a correlation based active contour model
Pattern Recognition Letters
A local region-based Chan-Vese model for image segmentation
Pattern Recognition
Segmentation of interest region in medical volume images using geometric deformable model
Computers in Biology and Medicine
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
A robust medical image segmentation method using KL distance and local neighborhood information
Computers in Biology and Medicine
Segmentation of histological images using a metaheuristic-based level set approach
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A novel multi-scale local region model for segmenting image with intensity inhomogeneity
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
A new level set method for inhomogeneous image segmentation
Image and Vision Computing
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In this paper, a new local Chan-Vese (LCV) model is proposed for image segmentation, which is built based on the techniques of curve evolution, local statistical function and level set method. The energy functional for the proposed model consists of three terms, i.e., global term, local term and regularization term. By incorporating the local image information into the proposed model, the images with intensity inhomogeneity can be efficiently segmented. In addition, the time-consuming re-initialization step widely adopted in traditional level set methods can be avoided by introducing a new penalizing energy. To avoid the long iteration process for level set evolution, an efficient termination criterion is presented which is based on the length change of evolving curve. Particularly, we proposed constructing an extended structure tensor (EST) by adding the intensity information into the classical structure tensor for texture image segmentation. It can be found that by combining the EST with our LCV model, the texture image can be efficiently segmented no matter whether it presents intensity inhomogeneity or not. Finally, experiments on some synthetic and real images have demonstrated the efficiency and robustness of our model. Moreover, comparisons with the well-known Chan-Vese (CV) model and recent popular local binary fitting (LBF) model also show that our LCV model can segment images with few iteration times and be less sensitive to the location of initial contour and the selection of governing parameters.