Color Image Segmentation Using a Modified Fuzzy C-means Method and Data Fusion Techniques

In this paper, a new color image segmentation method based on modified Fuzzy c-means and data fusion techniques is presented. The proposed segmentation consists in combining many realizations of the same image, to gether, in order to increase the information quality and to get an optimal segmented image. In the first step, the membership degree of each pixel is determined by applying fuzzy c-means clustering to the information coming from the component images to be combined. The idea is to link at the image pixel level, the notion of measurement functions to that of membership functions in fuzzy logic. In the second step, the fuzzy combination theory is employed to merge the component images of the original image, in order to increase the quality of the information and to obtain an optimal segmented image. Segmentation results from the proposed method are validated and classification accuracy for the test date available is evaluated, and then a comparative study versus existing techniques is presented. Experimental segmentation results of color medical and textured images show the effectiveness of the proposed method.