Brain MR images segmentation using statistical ratio: mapping between watershed and competitive Hopfield clustering network algorithms.
Conventional watershed segmentation methods invariably produce over-segmented images due to the presence of noise or local irregularities in the source images. In this paper, a robust medical image segmentation technique is proposed, which combines watershed segmentation and the competitive Hopfield clustering network (CHCN) algorithm to minimize undesirable over-segmentation. In the proposed method, a region merging method is presented, which is based on employing the region adjacency graph (RAG) to improve the quality of watershed segmentation. The relation of inter-region similarities is then investigated using image mapping in the watershed and CHCN images to determine more appropriate region merging. The performance of the proposed technique is presented through quantitative and qualitative validation experiments on benchmark images. Significant and promising segmentation results were achieved on brain phantom simulated data.
Kuo
Wen-Feng
WF
Department of Computer Science & Information Engineering, National Cheng Kung University, No. 1, Ta-Hsueh Road, Tainan 701, Taiwan.
Lin
Chi-Yuan
CY
Sun
Yung-Nien
YN
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity