A fast and automatic segmentation method of MR brain images based on genetic fuzzy clustering algorithm.
Image segmentation is the key step for quantitative analysis of brain tissues (white matter, gray matter and cerebrospinal fluid). Based on genetic algorithm and fuzzy C-means (FCM) approach, a fast and fully automatic segmentation method of brain tissues named genetic fuzzy clustering algorithm is introduced in this paper. The method operates slice by slice based on three main steps: The non-brain tissues are removed from the original head MR images at first using an auto-threshold method; then the initial cluster centers of FCM are determined by genetic algorithm; and finally brain tissues are segmented into white matter, grey matter and cerebrospinal fluid by FCM via only one iteration computation. The experiment results have shown that the segmentation method proposed by this paper has faster speed and higher accuracy compared with fast fuzzy c-means algorithm which is commonly used in segmentation of brain tissues.
Nie
Shengdong
S
College of Medical Instrumentation & Foodstuff, University of Shanghai for Science and Technology, Shanghai, China. nsd4647@sohu.com
Zhang
Yingli
Y
Li
Wen
W
Chen
Zhaoxue
Z
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D016000:Cluster Analysis; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity