Image Segmentation Methods Based On Active Contours Model

Image segmentation, one of the key issues in Image processing and Computer Vision, has received considerable attention of more and more researchers and many methods about it have been proposed. The first important method is active contours model. It is a top-down processing with prior knowledge and provides a theoretically uniform framework to a series of problems, such as contour extraction, stereo matching and object tracking. So the method has been successfully applied to image segmentation, medical image processing, human-computer interaction and many other research and practical fields. The second is geometrical method based on active contours model. It is first proposed by Osher and Sethian to solve the changing process of blaze following the thermodynamics equation. So the second method has much advantage in image segmentation about the object with complex topology changing and has been applied in medical diagnosis. However these methods still have many shortcomings and need to do in-depth study.The paper illuminates the foundation of active contours model, and studies the improved algorithm of energy minimizing of traditional parametric active contours model. First because active contour model can not solve boundary concavities better. To solve the problem, we introduce the gradient vector flow (GVF). It can solve the concavity problem better than the active contours model. But the deep boundary concavities problem is still not solved. On the basis of analysing the force field of gradient vector flow model; we normalize the force vector of GVF in the diffusion operations. This makes the external forces have no influence from the distance between the points of the contour and the boundary of object. So this method can solve deep boundary concavities better. At the same time introducing direction vector in the GVF model, in the region specified by this vector we can shield the force fields of the objects that we don't want, and then can segment the interested objects more precisely. The Mumford-Shah model is one of the most successful image segmentation models. In this paper, based on M-S model and level-set method, a new method for initializing level-set function and hierarchical constant segmentation is proposed in order to overcome the shortcomings in the Chan-Vese model . First, we introduce a well-designed basin hopping scheme which uses global updates to escape from local traps. Secondly, this paper presents a hierarchical constant segmentation method for multi-phase segmentations, using estimated energy to determine whether the sub-regions need to further segmentations.The experiment results show that the active model,the GVF model and the Mumford-Shah model have their advantage and disadvantages. we should utilize their advantage in different occasions to serve us.