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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Hung, WL., Chang, YC. (2006). A Modified Fuzzy C-Means Algorithm for Differentiation in MRI of Ophthalmology. In: Torra, V., Narukawa, Y., Valls, A., Domingo-Ferrer, J. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2006. Lecture Notes in Computer Science(), vol 3885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11681960_33
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DOI: https://doi.org/10.1007/11681960_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32780-6
Online ISBN: 978-3-540-32781-3
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