Automatic magnetic resonance image segmentation by fuzzy intercluster hostility index based genetic algorithm: An application

Segmentation of magnetic resonance (MR) images plays an important role in the medical science or clinical research. In this article, an application of a genetic algorithm (GA) based segmentation algorithm is presented for automatic grouping of unlabeled pixels of the MR images into different homogeneous clusters. Before the segmentation, the information about the optimal number of segments as well as the underlying pixel distribution of an image is not required in this method. The centroid of different segments is demarcated as active/inactive centroid by the fuzzy intercluster hostility index. After that, the test images are segmented by the selected active centroids. The optimal number of segments and their respective centroids are determined by this method. A performance comparison is manifested between the fuzzy intercluster hostility index based GA method and the well-known automatic clustering using differential evolution (ACDE) algorithm and one genetic algorithm based non-automatic algorithm with the help of two real life MR images. The comparison depicted the superiority of the GA based automatic image segmentation method with the help of fuzzy intercluster hostility index over other two algorithms.

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