Fuzzy c-means clustering with spatial information for image segmentation.
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.
Chuang
Keh-Shih
KS
Department of Nuclear Science, National Tsing-Hua University, Hsinchu 30013 Taiwan. kschuang@mx.nthu.edu.tw
Tzeng
Hong-Long
HL
Chen
Sharon
S
Wu
Jay
J
Chen
Tzong-Jer
TJ
D000465:Algorithms; D001921:Brain / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D013624:Taiwan