A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints.
Image segmentation is often required as a preliminary and indispensable stage in the computer aided medical image process, particularly during the clinical analysis of magnetic resonance (MR) brain images. In this paper, we present a modified fuzzy c-means (FCM) algorithm for MRI brain image segmentation. In order to reduce the noise effect during segmentation, the proposed method incorporates both the local spatial context and the non-local information into the standard FCM cluster algorithm using a novel dissimilarity index in place of the usual distance metric. The efficiency of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other state of the art algorithms.
Wang
Jianzhong
J
School of Mathematics and Statistics, Northeast Normal University, Changchun, China. wangjz019@nenu.edu.cn
Kong
Jun
J
Lu
Yinghua
Y
Qi
Miao
M
Zhang
Baoxue
B
D001921:Brain / Q000033:anatomy & histology*; D016000:Cluster Analysis; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D014894:Weights and Measures