Theoretical Research On The Level Set Method For Active Contour Model And Its Applications

Compared with the traditional image segmentation method, the active contour models based on the PDE equation which combine the level set method and the curve evolution theory, have shown unique advantage and comprehensive applicability in the segmentation of images with various styles and complicated structures. But there are some questions in the active contour model, such as the un-convex and local minimum of the energy functional. How to perfect its theory and settle the questions in the image segmentation is the main task of this dissertation.Previous research works in recent years on the active contour model for image segmentation and its numerical difference methods are studied extensively and classified. Then three kind of active contour model in which the curve is described by the way of non-parameter: Geodesic active contour based on the region information, active contour model with a global minimum and active contour model driven by the local energy, are studied in detail and its corresponding improved method are provied.The main research work and contribution of this dissertation can be summarized as follows:1). The traditional GAC whose curve is driven by the information of image gradient, is sensitive to noise and can not converge to the expected object boundary when the edges of image are blur. To realize the drawback of GAC, a novel improved model of GAC ,named as GAC-CV model which is integrated with image region information and direction information is proposed. The GAC-CV model has faster and more stable segmentation result compared with the traditional GAC.2). To overcome the non-convex of the energy functional in the GAC, a new GMGAC model which combines the classical non-parameter ROS model with the GAC-CV model, is established and the existence of a global minimum of the new model is provided. The fast algorithm based on the dual formulation of TV-norm is deduced. The experiments on the synthetic images and noised medical images demonstrate that the GMGAC model is robust to noise and accurately segment the whole geometry structures in the image and the speed is very high.3). The extended studies on the CV model. The methods of multi-phase image segmentation are studied in detail and a novel segmentation based on the classic CV model is proposed. To handle the local limitation of the energy functional of CV, a fast method for bi-model segmentation is offered, in which the data fitting terms in the CV model are adjusted and a shifted competition function is introduced. In this novel model the curve can be initialized to a arbitrarily function with two reverse values and the re-initialization is not needed. The experiment results show that it is superior to the traditional CV.4). Most active contour models based region information depend on the supposing or the condition that the image intensity conform to homogeneity or a statistical distributing, which result in a inaccurate result. Based on the local descriptor which is studied respectively from the image Cartesian coordinate space and the image pixel intensity space, a novel region-based active contour model is propose whose curve is driven by the local image information around the curve. The experiment for real images and medical images results demonstrate that the active contour model driven by the local image information can cope with the image segmentation with intensity inhomogeneity and show desirable performances.