Color Image Segmentation Based on a Modified Fuzzy C-means Technique and Statistical Features

R. Harrabi and E. Ben Braiek CEREP, ESSTT, 5 Av. Taha Hussein, 1008, Tunis, Tunisia Abstract: In this paper, a novel method of color image segmentation based on the Fuzzy C-means algorithm and statistical features is presented. The role of including first order statistical feature vector in the Fuzzy C-means technique is studied in this paper to obtain the optimally segmented image. Instead of using the simple pixel value, feature vectors are extracted from sliding window centered on the pixels. The Fuzzy C-means (FCM) algorithm is used to cluster the obtained feature vectors into several classes corresponding to the different regions of the image. Classification accuracies of the proposed technique are compared with those of the recent techniques in literature for the same image data. The experimental results on medical and textures color images demonstrate the superiority of combining statistical features and the standard Fuzzy C-Means algorithm for image segmentation.