A conventional FCM algorithm does not fully utilize the spatial information in the image. In this paper, we present a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The advantages of the new method are the following: (1) it yields regions more homogeneous than those of other methods, (2) it reduces the spurious blobs, (3) it removes noisy spots, and (4) it is less sensitive to noise than other techniques. This technique is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information. q
REF applied the spatial information into the FCM algorithm by incorporating spatial function into the membership function to take advantage of correlation in image pixels neighbourhood to reduce the effect of noise and obtain more homogeneous clustering results.
Fuzzy c-means clustering with spatial information for image segmentation
FSKD (1)
Mathematics Medicine Computer Science