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    NTHUR > College of Nuclear Science  > Biomedical Engineering and Environmental Sciences > NS Journal / Magazine Articles  >  Fuzzy c-means clustering with spatial information for image segmentation

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    Title: Fuzzy c-means clustering with spatial information for image segmentation
    Authors: Chuang KS;Tzeng HL;Chen S;Wu J;Chen TJ
    Teacher: 莊克士
    Date: 2006
    Publisher: ELSEVIER
    Relation: COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, Elsevier, Volume 30, Issue 1, JAN 2006, Pages 9-15
    Keywords: fuzzy c-means
    spatial information
    image segmentation
    Abstract: ©2006 Elsevier - 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.
    Appears in Collections:[Biomedical Engineering and Environmental Sciences] NS Journal / Magazine Articles

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