Research Of Level Set Method For Image Segmentation And Its Applications In Medical Images

In recent years,medical imaging technique achieves a large development,on which computer aid-diagnosis system(CAD) research is becoming a hot research point.Medical image segmentation is one of most important technique for CAD system.Image segmentation generally contains two item questions.One is the given tissue recognition of body reigion from medical image,and the other is method of description and extraction of object region.In most occation,medical image segmentation is more difficult than other image segmentation,because of the complexity and diversity of the medical imaging techniques.Image segmentation technique is generally considered as the bottleneck of CAD system.Active contours model has been widely used in medical image segmentation,as it is based on human vision theory and image information.Level set method coupled with curve evolution theory conquers many limitations of general active contours model,which widens the utilization of active contours model.This dissertation analyses the latest level set method,and its major contents think much of level set evolving theory and medical segmentation method.The main research work and contribution of this dissertation can be summarized as follows:1) The general active contours model has many limitations,an improved method based on Balloon model and Gradient Vector Flow model is introduced to conquer those limitations,which can segment images with sunken region at a fast convergence speed.2) To realize drawbacks of genearl geometric active contours model,a novel multiphase level set function segmentation method is proposed.As level set methods based on image edge information sometimes get into local optimization, a novel global optimization edge-based level set method is introduced into liver CT image segmentation.3) A new level set segmentation framework is researched and explains Chan-Vese model in a new way.For the lung segmentation problem from high resolution CT images,a novel united active contours model is proposed by a Bayes-statistical approach.The method is found to be much more efficient in lung segmentation than other methods that are only based on boundary or region.4) Medical image segmentation particularity is found to be consistent with fuzzy connectedness segmentation theory in this dissertation,so a new driven forece of geometric active contours model is introduced into medical segmentation.The experimental results of medical image segmentation prove the validity of this method.The correlative contents provide a new method for active contours model research and medical image segmentation.5) High resolution CT slice images of chest contain amount of texture information, which provide powerful datasets for research of computer aid-diagnosis system. But the extraction of lung tissue textures is a challenge task.A novel method based on level set is proposed for the extraction of lung tissue tree textures.The method is fully automatic and effectual.A number of contrast experiments are performed,and the results of 3d surface reconstruction shows the efficacies and advantages of our method for the segmentation of fine lung tree texture structures.At last,the author appreciates National Natural Science Foundation of China (60771007) and Natural Science Foundation of Anhui Education Department (2006KJ097A).