Abstract
An improved hybrid model (FCM_MS) for medical image segmentation is proposed by combining fuzzy C-means (FCM) clustering and Mumford-Shah (MS) algorithm. In the proposed model, fuzzy membership degree from FCM clustering is firstly used to initialize the contour placement, and then incorporated into the fidelity term of the 2-phase piecewise constant MS model to obtain multi-object segmentation. Meanwhile penalizing energy term is introduced into the energy functional to eliminate re-initialization of level set and thus to fasten convergent speed on curve evolution. Experimental results show that the proposed model has advantages both in accuracy and in robustness to noise in comparison with the standard FCM or the classical MS model on medical image segmentation.
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