Segmentation of multispectral magnetic resonance image using penalized fuzzy competitive learning network.
Segmentation (tissue classification) of the medical images obtained from Magnetic resonance (MR) images is a primary step in most applications of computer vision to medical image analysis. This paper describes a penalized fuzzy competitive learning network designed to segment multispectral MR spin echo images. The proposed approach is a new unsupervised and winner-takes-all scheme based on a neural network using the penalized fuzzy clustering technique. Its implementation consists of the combination of a competitive learning network and penalized fuzzy clustering methods in order to make parallel implementation feasible. The penalized fuzzy competitive learning network could provide an acceptable result for medical image segmentation in parallel processing using the hardware implementation. The experimental results show that a promising solution can be obtained using the penalized fuzzy competitive learning neural network based on least squares criteria.
Lin
J S
JS
Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, 70101, Republic of China.
Cheng
K S
KS
Mao
C W
CW
D000328:Adult; D000465:Algorithms; D001921:Brain / Q000473:pathology; D003201:Computers; D005240:Feasibility Studies; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016018:Least-Squares Analysis; D008279:Magnetic Resonance Imaging; D008297:Male; D016571:Neural Networks, Computer; D010537:Peritoneum / Q000473:pathology