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We propose a geometric convexity shape prior preservation method forvariational level set based image segmentation methods. Our method is builtupon the fact that the level set of a convex signed distanced function must beconvex. This property enables us to transfer a complicated geometricalconvexity prior into a simple inequality constraint on the function. An activeset based Gauss-Seidel iteration is used to handle this constrainedminimization problem to get an efficient algorithm. We apply our method toregion and edge based level set segmentation models including Chan-Vese (CV)model with guarantee that the segmented region will be convex. Experimentalresults show the effectiveness and quality of the proposed model and algorithm.

Convexity Shape Prior for Level Set based Image Segmentation Method