A variational level set approach to multiphase motion
Journal of Computational Physics
Pattern Recognition Letters
Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
Multiscale Segmentation of Three-Dimensional MR Brain Images
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Neural Computation
Threshold dynamics for the piecewise constant Mumford-Shah functional
Journal of Computational Physics
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Phase-Field Relaxation of Topology Optimization with Local Stress Constraints
SIAM Journal on Control and Optimization
A Nonlinear Elastic Shape Averaging Approach
SIAM Journal on Imaging Sciences
Region based image segmentation using a modified Mumford-Shah algorithm
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Energy minimization based segmentation and denoising using a multilayer level set approach
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Hi-index | 0.00 |
We proposed a novel framework of multiphase segmentation based on stochastic theory and phase transition theory. Our main contribution lies in the introduction of a constructed function so that its composition with phase function forms membership functions. In this way, it saves memory space and also avoids the general simplex constraint problem for soft segmentations. The framework is then applied to partial volume segmentation. Although the partial volume segmentation in this paper is focused on brain MR image, the proposed framework can be applied to any segmentation containing partial volume caused by limited resolution and overlapping.