An improved edge-based level set method combining local regional fitting information for noisy image segmentation.

Level set methods (LSMs) have been widely used in image segmentation because of their good properties which provide more smooth and accurate segmentation results. The edge-based LSMs use the gradient information of images through edge stop functions (ESFs) to guide the contour curve approaching to object edges. The traditional edge-based LSMs cannot obtain satisfactory segmentation results for noisy images because their regional coefficients are constant and their ESFs are easily influenced by noises. To solve the problems, this paper analyzes the different properties between noise points and object edge points and uses the local regional properties of images points to distinguish noises and object edge points. Based on the local regional properties, we introduce a variable regional coefficient and an improved ESF to overcome shortcomings of the constant regional coefficient and the traditional ESFs. Then we propose an improved edge-based level set method combining local regional fitting information by applying the proposed variable regional coefficient and the improved ESF to the energy function of level set function. The experimental results show that our method obtains accurate segmentation results for noisy images with insensitive to noises and without missing object edges and prove that the method is efficient and robust. Analyze different regional properties between noisy points and edge points.Use local regional properties to distinguish noises and object edges.Variable regional coefficient ensures level set equation has good convergences.The improved edge stop function is insensitive to noises.The improved edge-based level set method is efficient in noisy image segmentation.