To solve the problems about noise sensitivity and unclosed segmentation object boundaries in Fuzzy C-means Method (FCM), this paper proposes a fast image segmentation model combined with FCM and curve evolution, based on the pseudo level set formulations and object boundary curves, which are defined on the membership matrixes of FCM. To get the smooth and closed segmentation object boundaries, the Gaussian filter is performed on the pseudo level sets to approximate the function of the curve length regularization term. To eliminate the influence of Gaussian filter on the results of FCM, the gray values of the noisy points?are corrected, according to a new introduced edge-stop function and the mapping relationship between the gray value and membership degree. The FCM and the smoothing object boundary stage are performed alternately, which improves the robustness of this model. The experimental results show that the proposed model can overcome the influence of noise and get better segmentation results.
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