Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
ICCV '95 Proceedings of the Fifth International Conference on 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
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
Dynamical Statistical Shape Priors for Level Set-Based Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear Dynamical Shape Priors for Level Set Segmentation
Journal of Scientific Computing
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
An efficient local Chan-Vese model for image segmentation
Pattern Recognition
Active contours driven by local image fitting energy
Pattern Recognition
Active contours with selective local or global segmentation: A new formulation and level set method
Image and Vision Computing
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
Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction
Journal of Scientific Computing
Medical image segmentation based on novel local order energy
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
A local region-based Chan-Vese model for image segmentation
Pattern Recognition
Level set segmentation based on local gaussian distribution fitting
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Correntropy: Properties and Applications in Non-Gaussian Signal Processing
IEEE Transactions on Signal Processing
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Minimization of Region-Scalable Fitting Energy for Image Segmentation
IEEE Transactions on Image Processing
Level set evolution with locally linear classification for image segmentation
Pattern Recognition
Region-based image segmentation with local signed difference energy
Pattern Recognition Letters
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It is still a challenging task to segment real-world images, since they are often distorted by unknown noise and intensity inhomogeneity. To address these problems, we propose a novel segmentation algorithm via a local correntropy-based K-means (LCK) clustering. Due to the correntropy criterion, the clustering algorithm can decrease the weights of the samples that are away from their clusters. As a result, LCK based clustering algorithm can be robust to the outliers. The proposed LCK clustering algorithm is incorporated into the region-based level set segmentation framework. The iteratively re-weighted algorithm is used to solve the LCK based level set segmentation method. Extensive experiments on synthetic and real images are provided to evaluate our method, showing significant improvements on both noise sensitivity and segmentation accuracy, as compared with the state-of-the-art approaches.