The Research Of Image Segmentation Based On Active Contour Model And Closed-form

Image segmentation is not only an important part but also a prevalent puzzle in image processing, image recognition and computer-vision research field; especially in the segmentation problems for complex images (such as medical, natural types), there is not an uniform and effective solution.In recent years, image segmentation based on the active contour models has received widespread concern since it has rigorous mathematical theory framework, flexible numerical programs as well as superior performance. It uses mathematical models to represent the problem in image segmentation. According to the given initial contours, by minimizing the energy functional with image information, the contours are evolved to achieve the minimum in the target boundary. In addition, transparency-related image matting technique is now a research hotspot. This method treats transparency as an intrinsic nature. By seeking the optimal solution, it manages to obtain the target partition purpose. Closed-form solution performs well in segmenting the targets from blurred images. This solution treats images foreground and background through establishing the mathematical function to get the transparency when the energy function is at its lowest level.This thesis first gives a brief introduction to the significance and literature of image segmentation, which includes a brief classification of methods and a comprehensive overview over image segmentation and image matting technology. Then it introduces the geometric active contour model-curve evolution theory and level set method, presents briefly the basic principle of several kinds of representative active contour model and the theoretical basis of the Closed-form solution method. Motivated by others’recent work, this thesis presents two improvements and applications lying in the two methods:Firstly, GAC model:based on the global and local information. This thesis investigates SBGFRLS which is based on the GAC model:binary level set method and the SPF model. Then aiming at defects that SPF Model cannot handle non homogeneous image, a new active contour model is proposed to combine with global and local gray information. Finally, by comparing experiments, it proves that the new model overcomes the shortcomings of the SPF model and has better noise immunity, and makes it far more efficient than the LBF model using binary level set method.Secondly, brain tumor segmentation:closed-form solution. Brain tumor tissues have blurred edges and are uneven. The traditional segmentation method is hard to extract the brain tumor. This thesis proposes an application of closed-form solution for the extraction of brain tumors, which achieves desirable results. The traditional manual marking is cumbersome and may cause mal-segmentation. KNN method is used to pre-split and automatically mark brain tumor and background. Experiments show improved closed-form solution method reduces requirements for the users, but also improves the efficiency and accuracy of the segmentation.