Study On Image Segmentation Based On The Level Set Method

Image segmentation is one of the key problems in computer vision. Classical image segmentation techniques cannot satisfy the requirements of complex image segmentation due to the limitations itself, Under the circumstances, model-driven image segmentation techniques come into being and become a focus of widespread concern. Geometric Active Contour Model is capable of handling changes in the topology of the evolving contour, it is the difficult problem to Parameter Active Contour Mode. And the emergence of the level set method, which has greatly promoted the development of the geometric active contour models.This paper mainly based on the level set method for image segmentation. In reviewing the development of digital image processing, image segmentation process and the current situation based on the research, theory of Active Contour Models, curve evolution and level set method are presented. Traditional level set methods rely on computing the evolution of all the level sets. Using particle swarm optimization and narrow band, Parameter Active Contour model combined with level set, thereby reducing the amount of calculation to save calculation time; Aiming at C-V method based on simplified Mumford-Shah model, proposed algorithm and the corresponding iterative termination criterion, which can improve the speed and reduce the number of iterations; Narrow band method is not used directly in C-V method based on simplified Mumford-Shah model, an segmentation method of narrow band and Mumford-Shah model is presented, calculation is limited in the range of narrow band, so the computational complexity decrease; The velocity field for the moving curves generated by this elastic interaction between image boundaries and the moving curves, this paper proposed a edge-based level set algorithm based on new velocity field, level set method is used for the calculation ofiteration, a length scale is obtained by using nonlinear diffusion instead of solving for the computationally expensive motion by mean curvature.