Research Of New Approaches In Medical Image Segmentation Based On Gibbs Random Fields

Image segmentation is to separate an image into a lot of un-overlapped and homogeneous regions. As a fundamental technique, image segmentation has being played most key role in the image processing field, such as image analysis, image compression and so on. Especially, precisely segmenting the regions for some medical images is essential to clinical diagnosis.The Gibbs random prior model is often used to solve the ill-posed inverse problems in regularization for degraded image, and also to medical Bayesian segmentation due to providing an excellent spatial contextual constraints information. However, the classical GRFs model must be revised in the process of regularization to meet the clinical needs because of the complicated structure and degraded phenomenon in medical image. In the paper, some researches about the model have been developped deeply and systematically, and a series of approaches have been proposed to address them correspondingly.Firstly, in order to perform the parameters estimation about segmentation based on GRFs, a method fusion of maximum likelihood with maximum a posterior has been introduced after training data of an image and getting image statistic to solve the problems of parameters estimation and Bayesian segmentation based on GRFs during the iteration.Secondly, a hybrid pyramid Gibbs random model is provided, by extending a single MRFs to a multi one, to overcome the embarrassment derived from high neighborhood system used to describing the spatial contextual constraints. By using the proposed model, second order neighborhood system is enough to solve the problems on segmentation precise and its efficiency which are performed well only by a high one for a single MRFs.Thirdly, a novel generalized fuzzy Gibbs random model is constructed to overcome the bottleneck brought by multi-class fuzzy segmentation in medical images. Moreover, a series of theories and techniques about fuzzy segmentation are derived from the models of prior and likelihood.Additionally, an adaptive speed term based on generalized fuzzy operator is proposed to replace the traditional gradient-based edge map applied in level set segmentation in order to solve the problem of boundary leakage, which is expected to provide more robust edge estimation and more reliable information used as stopping criteria for curve evolution in dealing with the topology changing of the shape and the complexity of medical structures.A lot of experiments are also provided to prove the validity of the models and their corresponding approaches mentioned in the paper.