Studies On Image Segmentation Based On Variational Level Set

As one of the key technologies in areas from image processing to image analysis, Image segmentation has always been a fundamental problem in the field of image processing and computer vision. Recently,more and more methods and models have been offered. Compared to traditional image segmentation approaches, the active contour models, which are based on the theory of surface evolution and geometric flows, have become hot research topics.The active contour models can be classified into two categories: parametric deformable models and geometric deformable models based on the level set. Level set method is an important means for image segmentation. It can automatically deal with the changes in images'topological structure, and is an accurate and steady algorithm. We mainly introduced and researched the geometric active contour models based on the variational level set method, and then proposed a new region-based active contour model that embeds the image local information in the paper, that is the local image fitting model. The primary research content and innovation are as follows:Firstly, summarized the development situation of the image segmentation, introduced the theory of curve evolution and the level set method, and analyzed the applications in the field of image segmentation.Secondly, introduced several classic models, such as GAC model, a variational level set model without re-initialization, Mumford-Shah model, Chan-Vese model and so on, and analyzed the advantage and defect by the experiments on the proposed models in the paper.Lastly, proposed the local image fitting model, offered new models by integrated the double level set function Chan-Vese model, which show a good performance, and analyzed the advantage and disadvantage over traditional variational level set method.