MR brain image segmentation based on modified fuzzy C-means clustering using fuzzy GIbbs random field.
- Author:
Liang LIAO
1
;
Tusheng LIN
;
Bi LI
;
Weidong ZHANG
Author Information
1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China. liaoliangis@126.com
- Publication Type:Journal Article
- MeSH:
Algorithms;
Brain;
anatomy & histology;
Cluster Analysis;
Fuzzy Logic;
Humans;
Image Interpretation, Computer-Assisted;
methods;
Magnetic Resonance Imaging;
instrumentation;
methods;
Pattern Recognition, Automated;
methods
- From:
Journal of Biomedical Engineering
2008;25(6):1264-1270
- CountryChina
- Language:Chinese
-
Abstract:
A modified algorithm using fuzzy Gibbs random field model and fuzzy c-means (FCM) clustering is proposed for segmentation of Magnetic resonance(MR) brain images. Spatial constraints using the definitions of homogeneity of cliques and fuzzy Gibbs clique potential are introduced in this algorithm. A new modified objective function , which is established by introducing the spatial constraints into the traditional intensity based FCM algorithm, leads to the establishment of new iterative formulas for membership matrix and centroids. This algorithm can improve the performance of corresponding traditional one by modifying the original intensity based segmentation model. Experiments on synthetic images and MR phantoms show the validation of the proposed algorithm, which is usually a better alternative for segmenting medical MR images corrupted by noise.
