• V.Divya
• P. Hariharan

In computer vision, image compression mainly refers to the problem of reducing the memory size of a digital image. Mainly problems regarding data transferring over internet might require an image data to be of comparatively lesser memory size. Moreover high bandwidth is also required for transmission of high quality image data. Image compression provides solution to this problem. Now in order to retrieve the image of lesser size there is a considerable amount of loss of image data. For this problem optimized method is chosen to provide a final image which is comparatively of smaller memory size than the original image, yet it is quite visually similar to the original image. In this paper, we are trying to segment the original image using K-Means clustering method. To overcome the noise sensitiveness of conventional clustering algorithm, a novel FCM algorithm for image segmentation is presented in this paper. The algorithm is developed by modifying the objective function of the standard FCM algorithm. Experimental results on segmentation of synthetic and real images demonstrate that the proposed algorithm is effective and robust

Fuzzy C means, Clustering, K-means, Compression, Segmentation

"COMPRESSION AND SEGMENTATION OF MEDICAL IMAGES USING K-MEAN CLUSTERING ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 8, page no.559-564, August-2018, Available :http://www.jetir.org/papers/JETIR1808559.pdf

"COMPRESSION AND SEGMENTATION OF MEDICAL IMAGES USING K-MEAN CLUSTERING ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 8, page no. pp559-564, August-2018, Available at : http://www.jetir.org/papers/JETIR1808559.pdf