Abstract
Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm must be estimated by expertise users to determine the cluster number. So, we propose an automatic fuzzy clustering algorithm (AFCM) for automatically grouping the pixels of an image into different homogeneous regions when the number of clusters is not known beforehand. In order to get better segmentation quality, this paper presents an algorithm based on AFCM algorithm, called automatic modified fuzzy c-means cluster segmentation algorithm (AMFCM). AMFCM algorithm 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. Experimental results show that AMFCM algorithm not only can spontaneously estimate the appropriate number of clusters but also can get better segmentation quality.
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Acknowledgments
The authors would like to thank the anonymous referees for their helpful comments and suggestions to improve the presentation of the paper. This research is supported by the National Natural Science Foundation of China (No. 60874031), the Fund for International Cooperation and Exchange of the National Natural Science Foundation of China (No. 60740430664) and the Special Research Fund for the Doctoral Program of Higher Education of China (20070487052).
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Li, Yl., Shen, Y. An automatic fuzzy c-means algorithm for image segmentation. Soft Comput 14, 123–128 (2010). https://doi.org/10.1007/s00500-009-0442-0
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DOI: https://doi.org/10.1007/s00500-009-0442-0