IMAGE SEGMENTATION USING FUZZY CLUSTERING MEANS TECHNIQUES

摘要

Different image processing applications, such as remote sensing, medical imaging, robot artificial viewing, etc.apply the segmentation process in the image. Segmentation should subdivide an image into its constituentregions or objects. The level of details, to which the subdivision is carried on, depends on the problem beingsolved [1]. So, the segmentation should stop when the objects or regions of interest in an application have beendetected. Segmentation accuracy determines the eventual success or failure of computerized analysis procedures,and for this reason a considerable care is taken to improve the probability of accurate segmentation. The toolused in this work for the segmentation process in medical imaging is the fuzzy logic theory, or more precisely,the Fuzzy Clustering Means (FCM) algorithm, which is one of the most effective methods applied in the imagesegmentation. [2] This algorithm is feed with the number of clusters that we want to create and searches for itscenters to determine quantity of data belong to each a cluster. The algorithm posses the form explained in Fig.1.