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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Unsupervised Image Segmentation Using Penalized Fuzzy Clustering Algorithm
verfasst von : Yong Yang, Feng Zhang, Chongxun Zheng, Pan Lin
Erschienen in: Intelligent Data Engineering and Automated Learning - IDEAL 2005
Verlag: Springer Berlin Heidelberg
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Fuzzy c-means (FCM) clustering algorithm as an unsupervised fuzzy clustering technique has been widely used in image segmentation. However, the conventional FCM algorithm is very sensitive to noise for the reason of incorporating no information about spatial context while segmentation. To overcome this limitation of FCM algorithm, a novel penalized fuzzy c-means (PFCM) algorithm for image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the original FCM algorithm by a penalty term, which is employed to take into account the spatial dependence of the objects. Experiments demonstrate the proposed algorithm is effective and more robust to noise and other artifacts than the standard FCM algorithm.