Application of clustering Technique for Tissue image segmentation and comparison

through this paper we proposed the methodology that incorporates the K-means and fuzzy c means algorithm for the color image segmentation. The image segmentation may be defined as the process of dividing the given image into different parts. Here we are taking color image as the input and we are supposed to segment the given image on the basis of its color. The clustering algorithm also proved to be very efficient in the process of image segmentation. The clustering can be defined as the method of grouping the similar kind of objects from the input. The grouping can be on the basis of the attributes like color, shape, texture, size and other. The motive behind the segmentation is to extract the some meaning information from the input like image so that it can be utilized in effective manner. The Kmeans and fuzzy c means are the most popular clustering algorithm. The k-means use the iterative approach to partition the given image into k clusters while in the fuzzy c means clustering method of image segmentation a group of images are allowed to form a cluster on the basis of similarity checks.