A multiobjective approach to MR brain image segmentation


This article proposes a novel multiobjective real coded genetic fuzzy clustering scheme for segmentation of multispectral magnetic resonance image (MRI) of the human brain. The proposed technique is able to automatically evolve the number of clusters along with the clustering result. The multiobjective variable string length clustering technique encodes the cluster centers in its chromosomes and simultaneously optimizes the global fuzzy compactness and fuzzy separation among the clusters. In the final generation, it produces a set of non-dominated solutions, from which the best solution in terms of a recently proposed validity index I is chosen to be the final clustering solution. The corresponding chromosome length provides the number of clusters. The proposed method is applied on many simulated T1-weighted, T2-weighted and proton density-weighted normal and MS lesion MRI brain images. Superiority of the proposed method over K-means, Fuzzy C-means, Expectation Maximization, hierarchical clustering, Single Objective Genetic clustering and other recent multiobjective clustering algorithms has been demonstrated quantitatively. The automatic segmentation obtained by the proposed clustering technique is also compared with the available ground truth information.