Application of GAC Model Driven by the Local Entropy on Medical Image Segmentation
Wang Shun-fen · Dianzi xuebao · 2013
The geodesic active contour model(GAC)can′t identify the object of the images with complex background such as noise and intensity inhomogeneities successfully.For this reason,this paper proposes GAC model driven by the local entropy.First of all,the local information entropy of image is abstracted to describe the local intensity variation.Then,the signed pressure force function based on the local entropy are structured,which guides the contour curve close to the boundary of the object and achieves the segmentation of the object.In order to reduce the computational complexity and improve the robustness of the proposed model to different level sets,the proposed method is implemented by the binary level set method.The experimental results show that this method can overcome the influence of complex background to the segmentation results,and realize fast and accurate segmentation.