Fuzzy c-means clustering with spatial information for color image segmentation

Spatial information enhances the quality of clustering which is not utilized in the conventional FCM. Normally fuzzy c-means (FCM) algorithm is not used for color image segmentation and also it is not robust against noise. In this paper, we presented a modified version of fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering of color images [7, 8]. We used HSV model for decomposition of color image and then FCM is applied separately on each component of HSV model. For optimal clustering, grayscale image is used. Additionally, spatial information is incorporated in each model separately. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The advantages of this new method are: (a) it yields regions more homogeneous than those of other methods for color images; (b) it reduces the spurious blobs; and (c) it removes noisy spots. It is less sensitive to noise as compared with other techniques. This technique is a powerful method for noisy color image segmentation and works for both single and multiple-feature data with spatial information.