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