Non-local weighted fuzzy energy-based active contour model with level set evolution starting with a constant function

Non-local weighted fuzzy energy-based active contour model with level set evolution starting with a constant function

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In the traditional active contour models, global region-based methods fail to segment images with intensity inhomogeneity, and local region-based methods are sensitive to initial contour. In this study, a novel fuzzy energy-based active contour model is proposed to segment medical images, which are always corrupted by intensity inhomogeneity. In order to deal with intensity inhomogeneity, a local energy term is first constructed by substituting a non-local weight for Gaussian kernel widely used in traditional local region-based models. Second, the defined adaptive force can drive the level set function to adaptively increase or decrease according to image intensity information. Therefore, the initial contour can be initialised as a constant function, which eliminates the problem caused by contour initialisation. Moreover, the proposed active contour model is a convex function. Thus, the problem, resulting from optimising a non-convex functional in the traditional active contour models, can be avoided. Experimental results validate the superiorities and effectiveness of the proposed model for image segmentation with comparisons of those yielded by several state-of-the-art techniques.


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