Multispectral magnetic resonance images segmentation using fuzzy Hopfield neural network.
This paper demonstrates a fuzzy Hopfield neural network for segmenting multispectral MR brain images. The proposed approach is a new unsupervised 2-D Hopfield neural network based upon the fuzzy clustering technique. Its implementation consists of the combination of 2-D Hopfield neural network and fuzzy c-means clustering algorithm in order to make parallel implementation for segmenting multispectral MR brain images feasible. For generating feasible results, a fuzzy c-means clustering strategy is included in the Hopfield neural network to eliminate the need for finding weighting factors in the energy function which is formulated and based on a basic concept commonly used in pattern classification, called the 'within-class scatter matrix' principle. The suggested fuzzy c-means clustering strategy has also been proven to be convergent and to allow the network to learn more effectively than the conventional Hopfield neural network. The experimental results show that a near optimal solution can be obtained using the fuzzy Hopfield neural network based on the within-class scatter matrix.
Department of Electrical Engineering, National Cheng Kung University, Tainan.Taiwan, ROC.
J S
JS
Lin
K S
KS
Cheng
C W
CW
Mao
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D002544:Cerebral Infarction / Q000175:diagnosis*; D016000:Cluster Analysis; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008279:Magnetic Resonance Imaging; D016571:Neural Networks, Computer; D012016:Reference Values; D012680:Sensitivity and Specificity