Real-coded differential crisp clustering for MRI brain image segmentation

Abstract-- In this paper, a segmentation technique of multispectral magnetic resonance image of the brain using a new differential evolution based crisp clustering is proposed. Realcoded encoding of the cluster centres is used for this purpose. Here assignments of points to different clusters are made based on the Euclidean distance. The proposed method is applied on several simulated T1-weighted, T2-weighted and proton density for normal and MS lesion magnetic resonance brain images. Superiority of the proposed method over genetic algorithm based crisp clustering, simulated annealing based crisp clustering, Kmeans and average linkage are demonstrated quantitatively. Segmentation obtained by differential evolution based crisp clustering technique is also compared with the available ground truth information. Also statistical analysis has been conducted to judge the effectiveness. Matlab version of the software is available at http://bio.icm.edu.pl/darman/MRI.

• Type: Journal
• Year: 2010
• Where: CEC
• Authors: Indrajit Saha, Ujjwal Maulik, Sanghamitra Bandyopadhyay, Dariusz Plewczynski
• Keywords: Artificial Intelligence, CEC 2010, Crisp Clustering, Magnetic Resonance, Multispectral Magnetic Resonance