Automatic centroids selection in K-means clustering based image segmentation

This paper proposes a K-means clustering based image segmentation algorithm which select the centroids automatically. It eliminates the limitations associated with K-means clustering such as selection of initial centroids and dead centers. As image histogram is one of the best ways to represent the distribution of image gray levels, the proposed approach selects centroids as the gray levels corresponding to the peaks of the image histogram. With these initial centroids, K-means clustering is performed. The result is post processed by some morphological operations. The proposed algorithm uniformly segments the regions of interest over randomly selected centroids. The performance of proposed algorithm and random centroids selection is compared with some validity parameters like SSIM, MSE, PSNR, IF, SC and CC. Comparison with the existing algorithms confirms the improvement in qualitative parameters. The tool used in this work is MATLAB R2012a.