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Clustering as a segmentation technique gives a vector of N measurements describing each pixel or group of pixels (i.e., region) in an image, a similarity of the measurement vectors and therefore their clustering in the N-dimensional measurement space implies similarity of the corresponding pixels or pixel groups. Therefore, clustering in measurement space may be an indicator of similarity of image regions, and may be used for segmentation purposes. This book investigates efficient and effective clustering and soft computing algorithms for image segmentation.The improved algorithm for K-medoids clustering incorporates histogram equalization as its first step to reduce the number of centroids. The algorithm calculates the best optimal medoids and uses them for segmentation to reduce the time complexity without much affecting the intercluster similarity.