Evaluation of modified adaptive k-means segmentation algorithm

Segmentation is the act of partitioning an image into different regions by creating boundaries between regions. k-means image segmentation is the simplest prevalent approach. However, the segmentation quality is contingent on the initial parameters(the cluster centers and their number). In this paper, a convolution-based modified adaptive k-means(MAKM)approach is proposed and evaluated using images collected from different sources(MATLAB, Berkeley image database, VOC2012, BGH, MIAS, and MRI).The evaluation shows that the proposed algorithm is superior to k-means++, fuzzy c-means, histogrambased k-means, and subtractive k-means algorithms in terms of image segmentation quality(Q-value),computational cost, and RMSE. The proposed algorithm was also compared to state-of-the-art learning-based methods in terms of IoU and MIoU; it achieved a higher MIoU value.