Brain MRI segmentation using KFCM and Chan-Vese model |
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| Authors: | Yiquan Wu Wen Hou and Shihua Wu |
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| Institution: | (1) Nanyang Technological University, Singapore, Singapore;(2) Vanderbilt University, Nashville, USA |
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| Abstract: | To extract region of interests (ROI) in brain magnetic resonance imaging (MRI) with more than two objects and improve the
segmentation accuracy, a hybrid model of a kernel-based fuzzy c-means(KFCM) clustering algorithm and Chan-Vese (CV) model
for brain MRI segmentation is proposed. The approach consists of two successive stages. Firstly, the KFCM is used to make
a coarse segmentation, which achieves the automatic selection of initial contour. Then an improved CV model is utilized to
subdivide the image. Fuzzy membership degree from KFCM clustering is incorporated into the fidelity term of the 2-phase piecewise
constant CV model to obtain accurate multi-object segmentation. Experimental results show that the proposed model has advantages
both in accuracy and in robustness to noise in comparison with fuzzy c-means (FCM) clustering, KFCM, and the hybrid model
of FCM and CV on brain MRI segmentation. |
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| Keywords: | brain magnetic resonance imaging image segmentation kernel-based fuzzy c-means clustering Chan-Vese model |
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