A fuzzy-soft competitive learning algorithm for ophthalmological MRI segmentation

Authors:
Miin-Shen Yang;Karen Chia-Ren Lin;Hsiu-Chih Liuc;Jiing-Feng Lirng
Affiliations:
Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li, Taiwan;Department of Management Information System, Nanya Institute of Technology, Chung-Li, Taiwan;Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan;Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
Venue:
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Year:
2008

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Abstract

In this paper, we consider a fuzzy-soft competitive learning algorithm (FS-CLA) which is a sequential type of the fuzzy-soft learning vector quantization (FS-LVQ). The FS-CLA is a competitive learning with a fuzzy relaxation technique by using fuzzy membership functions as kernel type neighborhood interaction functions. We then apply the FS-CLA to magnetic resonance image (MRI) segmentation for a real case of ophthalmology recommended by a Neurologist with MR image data. The algorithm is used in segmenting the ophthalmological MRI data for reducing medical image noise effects with a learning mechanism. These segmentation results demonstrate that the proposed FS-CLA is useful for use in MRI segmentation as an aid for support diagnoses.