3D Medical Image Segmentation Based On Level Set Method

3D segmentation is an important part of computer-based medical applications for diagnosis and analysis of anatomical data. With rapid advances in medical imaging modalities and volume visualization techniques, computer-based diagnosis is fast becoming a reality. These computer-based tools allow scientists and physicians to understand and diagnose anatomical structures by virtually interacting with them. 3D segmentation plays a critical role by facilitating automatic or semi-automatic extraction of the anatomical organ or region-of-interest. A deep study is made in level set methods and it covers four parts which is organized as follows:Firstly, the basic theory on level set method and C-V Model is systematically researched and their application in image segmentation field is discussed. And their application of single level set method and multiphase level set method in image segmentation field is pointed out through experiments which lays a solid foundation for the following research.Secondly, several representative multiphase level set methods, especially variational n multiphase level set method in 3D image segmentation , are presented. For this goal, a generic characteristic function for region partitioning based on n level set functions for n regions and their Heaviside functions is designed, and a generic energy functional includes region-based model, edge-based model and constraint term enforcing the level set functions as signed distance functions are suggested also.In this thesis, an multiphase level set method is proposed and used to segment Ultrasound 3D image whose noise abides by Rayleigh Probability Distribution Model respectively. Thirdly, an multiphase level set method is realized and used to segment Ultrasound 3D image whose noise abides by Rayleigh Probability Distribution Model respectively by the simulation process.Fourthly, the future research direction is put forward.