人力資源與數位學習科技研究所

Abstract

　　In this talk, we present 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 et al. (2003). 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 and Yang (2002) and similarity-based clustering method (SCM) proposed by Yang and Wu (2004) 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.