Medical Image Segmentation Based On Deformable Model And Their Application In Liver Perfusion Analysis

Medical image segmentation is a fundamental problem in medical image processing and analysis, and is the basis of computer aided diagnosis and treatment as well. The general segmentation problem is the process of partitioning an image or data-set into a number of uniformity or homogeneous segments. Image segmentation is important for medical image analysis, for example, 3D Visualization, Computer Aided Operation, and Radiology Treatment are all assume that Region of Interesting (ROI) are well segmented. Also automate liver perfusion analysis's first step is liver segmentation. Perfusion analysis is based on the segmentation result.Deformable model segmentation method has generally become one of the most active and successful section in medical image segmentation research. The basis idea of deformable model is to construct an energy function for the model and let the curve or surface evolve under the model's inner control force and outside image force. When the energy function reaches its minimum, the evolving curve or surface reach its target region. The advantage of the deformable mode is image data, initial contour and target contour are included by one uniform mathematical mode. One of the most popular methods is deformable model with prior shape information. By incorporating the prior shape information, it can improve the accuracy of deformable model. Here, we propose a deformable model with mean shape prior information, and its numerical realization.Level Set Methods is the mathematical foundation of deformable model, and it is very popular in medical image segmentation. Its advantage is that it is independent of detail parameters during the evolving process. The evolving curve or surface can be represented as the zero level set of a higher dimensional function, which can deal with the topological change of region of interesting automate. But Level Set Methods often will produce under-segmentation, over-segmentation and leakage problems. How to solve these problems becomes a big challenge for researchers. To solve these problems, we use multiply initialization for level set and improve the speed function's definition.The main works are described as follows:1. Overview of the popular medical image segmentation methods, which emphasis on deformable model and Level Set Methods.2. Propose a multiple initialization Level Set Methods. We use the multiple initializations and an improved speed function to solve the under-segmentation, over-segmentation and leakage problems.3. Propose a deformable segmentation model with mean shape prior information. In this thesis, we propose a deformable segmentation model with a mean shape prior and its numerical realization.4. Realize liver segmentation in abdomen MR images. We use the multiple initialization Level Set Methods to segment the liver in the noisy and low contrast abdomen MR images, and the segmentation results are compared with other segmentation methods.5. Realize automate liver perfusion analysis. We use Chamfer Match to trace the perfusion position in each slice of a whole abdomen MR series, and draw the perfusion curve for radiologist.