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