Fuzzy C-Means Cluster Segmentation Algorithm Based on Modified Membership

Authors:
Yanling Li;Gang Li
Affiliations:
Institute of System Engineering, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China 430074 and College of Computer and Information Technolog ...;College of Computer and Information Technology, Xinyang Normal University, Xinyang, China 464000
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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Abstract

Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is noise sensitive because of not taking into account the spatial information in the image. In this paper, we present fuzzy c-means cluster segmentation algorithm based on modified membership (MFCMp,q) that incorporates spatial information into the membership function for clustering. The spatial function is the weighted summation of the membership function in the neighborhood of each pixel under consideration. The fast MFCMp,q algorithm(FMFCMp,q) which speeds up the convergence of MFCMp,q algorithm is achieved when the MFCMp,q algorithm is initialized by the fast fuzzy c-means algorithm based on statistical histogram. The experiments on the artificial synthetic image and real-world datasets show that MFCMp,q algorithm and FMFCMp,q algorithm can segment images more effectively and provide more robust segmentation results.