A generalized level set formulation of the mumford-shah functional for brain MR image segmentation

Authors:
Lishui Cheng;Jie Yang;Xian Fan;Yuemin Zhu
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University (SJTU), Shanghai, P.R.China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University (SJTU), Shanghai, P.R.China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University (SJTU), Shanghai, P.R.China;CNRS UMR 5515 & INSERM Unit 630, CREATIS, Villeurbanne cedex, France
Venue:
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Year:
2005

Citing 10
Cited 1

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
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Contours

International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model

International Journal of Computer Vision
Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review

IEEE Transactions on Information Technology in Biomedicine
Snakes, shapes, and gradient vector flow

IEEE Transactions on Image Processing
Active contours without edges

IEEE Transactions on Image Processing
Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification

IEEE Transactions on Image Processing
RAGS: region-aided geometric snake

IEEE Transactions on Image Processing

A generalized level set formulation of the mumford-shah functional with shape prior for medical image segmentation

CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Brain MR image segmentation is an important research topic in medical image analysis area. In this paper, we propose an active contour model for brain MR image segmentation, based on a generalized level set formulation of the Mumford-Shah functional. The model embeds explicitly gradient information into the Mumford-Shah functional, and incorporates in a generic framework both regional and gradient information into segmentation process simultaneously. The proposed method has been evaluated on real brain MR images and the obtained results have shown the desirable segmentation performance.