Application of a new line symmetry distance based genetic clustering technique for automatic MR brain image segmentation

In this paper, an automatic segmentation technique of multispectral magnetic resonance image of the brain using a new line symmetry based genetic clustering technique is proposed. The proposed real-coded variable string length genetic clustering technique (VGALS clustering) is able to evolve the number of clusters present in the data set automatically. Here assignment of points to different clusters are done based on the line symmetry based distance rather than the Euclidean distance. The cluster centers are encoded in the chromosomes, whose value may vary. A newly developed line symmetry based cluster validity index, LineSym-index, is used as a measure of dasiagoodnesspsila of the corresponding partitioning. This validity index is able to correctly indicate the presence of clusters of different sizes as long as they are line symmetrical. A Kd-tree based data structure is used to reduce the complexity of computing the line symmetry distance. The proposed method is applied on several simulated T1-weighted, T2-weighted and proton density normal magnetic resonance brain images. The proposed method is able to detect most of the regions well. Superiority of the proposed method over Fuzzy C-means and Expectation Maximization clustering algorithms are demonstrated quantitatively. The automatic segmentation obtained by VGALS clustering technique is also compared with the available ground truth information.