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The main objective of this paper is to calculate the performance of quality measures towards the segmentation of medical images using K-means clustering. Four types of medical images such as MRI, X-rays, CT and Ultrasonic images are studied. The K-means clustering shows that the non-intactness of the clusters. As cluster size increases the edges are brittle and compactness of the clusters get altered The quality measures like PSNR, average difference, structural content, image fidelity and normalize coefficients are calculated for K-means methods. The K-means (K) increase in PSNR (db) values than the K-means clustering.