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This paper presents a fuzzy clustering-based technique for image segmentation. Many attempts have been put into practice to increase the conventional fuzzy c-means (FCM) performance. In this paper, the sensitivity of the soft membership function of the FCM algorithm to the outlier is considered and the new exponent operator on the Euclidean distance is implemented in the membership function to improve the outlier rejection characteristics of the FCM. The comparative quantitative and qualitative studies are performed among the conventional k-means (KM), moving KM, and FCM algorithms; the latest state-of-the-art clustering algorithms, namely the adaptive fuzzy moving KM , adaptive fuzzy KM, and new weighted FCM algorithms; and the proposed outlier rejection FCM (ORFCM) algorithm. It is revealed from the experimental results that the ORFCM algorithm outperforms the other clustering algorithms in various evaluation functions.

Outlier rejection fuzzy c-means (ORFCM) algorithm for image segmentation