Segmentation in MRI of ophthalmology using a robust-type clustering algorithm

Authors:
Wen-Liang Hung;Miin-Shen Yang;De-Hua Chen
Affiliations:
Graduate Institute of Computer Science, National Hsinchu University of Education, Hsin-Chu, Taiwan;Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li, Taiwan;Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li, Taiwan
Venue:
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Year:
2009

Citing 5
Cited 0

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Artificial Intelligence in Medicine

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Abstract

In this paper we propose a robust-type clustering algorithm that has more accuracy than the alternative FCM (AFCM) proposed by Wu and Yang [7]. Moreover, to speed up the proposed algorithm, we use the suppressed idea to modify it. The modified robust-type clustering algorithm presents fast convergence speed and also robustness. Finally, this algorithm is applied in the segmentation of the magnetic resonance image (MRI) of an ophthalmic patient. In our comparisons of the proposed algorithm with the AFCM for these MRI segmentation results, we find that the proposed algorithm provides better detection of abnormal tissue than AFCM.