Medical Image Segmentation using Enhanced Feature Weight Learning Based FCM Clustering

Image segmentation is a way to simplify and analyze images by separating them into different segments. Fuzzy c-means (FCM) is the most widely used clustering algorithm, as it can handle data with blurry boundaries; where points belong to multiple clusters with varying strengths. The segmentation performance of this method is sensitive to the initial cluster centers. The fact that every feature in the image contributes equally and is given equal weight is another issue with this algorithm. In this paper, an image segmentation technique based on Fuzzy C-means (FCM) method is proposed. The proposed technique uses an extended feature set consisting of homogeneity, CIELAB, texture and edge is used for feature extraction in order to enhance segmentation quality. Further, weight optimization is done to help clustering process leverage the strengths of each feature, while downplaying less significant ones. The subjective and objective performance analysis of the proposed algorithm on medical images show improved performance as compared to existing standard image segmentation techniques.