A modified fuzzy c-means algorithm for differentiation in MRI of ophthalmology

Authors:
Wen-Liang Hung;Yen-Chang Chang
Affiliations:
Department of Applied Mathematics, National Hsinchu University of Education, Hsin-Chu, Taiwan, ROC;Department of Business Administration, Vanung University, Chung-Li, Taiwan, ROC
Venue:
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Year:
2006

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Abstract

In this paper we propose an algorithm, called the modified suppressed fuzzy c-means (MS-FCM), that simultaneously performs clustering and parameter selection for the suppressed FCM (S-FCM) proposed by Fan et al. [2]. Numerical examples 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 and alternative FCM (AFCM) proposed by Wu and Yang [14] for these MRI segmentation results, we find that the MS-FCM provides better detection of abnormal tissue than S-FCM and AFCM when based on a window selection. Overall, the MS-FCM clustering algorithm is more efficient and is strongly recommended as an MRI segmentation technique.