Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
On active contour models and balloons
CVGIP: Image Understanding
International Journal of Computer Vision
A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
SIAM Journal on Scientific Computing
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
A Statistical Approach to Snakes for Bimodal and Trimodal Imagery
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Structure-Texture Image Decomposition--Modeling, Algorithms, and Parameter Selection
International Journal of Computer Vision
Fast Global Minimization of the Active Contour/Snake Model
Journal of Mathematical Imaging and Vision
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Minimization of Region-Scalable Fitting Energy for Image Segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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In this paper, a new region-based active contour model is proposed for magnetic resonance image segmentation and denoising based on the global minimization framework and level set evolution. A new region fitting energy based on Nadaraya-Watson estimator and local image information is defined to enforce the curve evolution. By this improved region fitting term, the images with noise and intensity un-uniformity can be segmented and denoised. Inspired by the Perona-Malik diffusion equation, an edge-preserving regularization term is defined through the duality formulation to penalize the length of region boundaries. By this new regularization term, the edge information is utilized to improve the contour@?s ability of capturing the edge and remaining smooth during the evolution. The energy functional of the proposed model is minimized by an efficient dual algorithm avoiding the inefficiency of the gradient descent method. Experiments on medical images demonstrate the proposed model provides a hybrid way to perform image segmentation and image denoising simultaneously.