摘要：The basic idea of active contour model upon the image segmentation problem is evolved into a closed curve about the functional minimization problems. Active contour model based on edge information takes advantage of gradient information, and it has some shortcomings such as cannot separate weak boundary, fuzzy boundary, and discontinuous boundary object. Chan–Vese active contour model without edges can overcome the shortcomings of model based on gradient, but it cannot separate gray inhomogeneous target and evolves slowly, moreover, segmentation efficiency is low. The aim of this article is to overcome the shortage of the geodesic active contour model such as lower convergence rate and more easily trapped in local minimum .The article puts forward a new active contour model control system where the edge information is combined with regional one effectively, and it makes good use of the gradient information and the area information. A large amount of simulation results show that the proposed algorithm’s convergence speed is much faster than geodesic active contour model, and it can segment serious noisy image. It inherited the edge and the region information as well, so the new model performs well in resisting noise and has high segmentation efficiency.
关键词：Image segmentation; active contour model; level set method; geodesic active contour model
