Abstract
In this paper, we present a new color image segmentation method, based on a modified Fuzzy C-Means technique and different color spaces which aim at including the informations provided by different color spaces in the Fuzzy C-means technique in order to increase the information quality and to get a more reliable and accurate segmentation result. In the first phase of segmentation, a classification accuracy degree is employed to identify the most significant pieces of the used color spaces. In the second phase, the Fuzzy C-means (FCM) algorithm is used to cluster these different pieces of information into homogeneous regions.
The classification accuracy of the proposed method is evaluated and a comparative study versus existing techniques is presented. The experimental results on medical and textures color images demonstrate the superiority of combining different pieces of color spaces and the standard Fuzzy C-Means algorithm for image segmentation.