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Fuzzy c-means clustering with spatial information for image segmentation

✍ Scribed by Keh-Shih Chuang; Hong-Long Tzeng; Sharon Chen; Jay Wu; Tzong-Jer Chen

Publisher: Elsevier Science
Year: 2006
Tongue: English
Weight: 466 KB
Volume: 30
Category: Article
ISSN: 0895-6111
DOI: 10.1016/j.compmedimag.2005.10.001

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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