Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation

This paper presents an algorithm, called the modified suppressed fuzzy c-means (MS-FCM), that simultaneously performs clustering and parameter selection for the suppressed fuzzy c-means (S-FCM) algorithm proposed by [Fan, J.L., Zhen, W.Z., Xie, W.X., 2003. Suppressed fuzzy c-means clustering algorithm. Pattern Recognition Lett. 24, 1607–1612]. The proposed algorithm is computationally simple, and is able to select the parameter α in S-FCM with a prototype-driven learning. The parameter selection is based on the exponential separation strength between clusters. Numerical examples will serve to illustrate the effectiveness of the proposed MS-FCM algorithm. Finally, the S-FCM and MS-FCM algorithms are applied in the segmentation of the magnetic resonance image (MRI) of an ophthalmic patient. In our comparisons of S-FCM, MS-FCM, alternative FCM (AFCM) proposed by [Wu, K.L., Yang, M.S., 2002. Alternative c-means clustering algorithms. Pattern Recognition 35, 2267–2278] and similarity-based clustering method (SCM) proposed by [Yang, M.S., Wu, K.L., 2004. A similarity-based robust clustering method. IEEE Trans. Pattern Anal. Machine Intell. 26, 434–448] for these MRI segmentation results, we find that these four techniques provide useful information as an aid to diagnosis in ophthalmology. However, the MS-FCM provides better detection of abnormal tissue than S-FCM, AFCM and SCM when based on a window selection. Overall, the MS-FCM clustering algorithm is more efficient and is strongly recommended as an MRI segmentation technique.