The Technology Of Medical Image Segmentation Based On Snake Model

Medical image segmentation is a classical difficult problem in medical image processing. The development of medical image segmentation techniques not only influences on other techniques in image processing, but also plays an important role in the analysis of biology medical image. Traditional image segmentation techniques have many defects for complex image segmentation. The Snake model is driven to the desired image features by an energy formulation, which consists of prior models and image data. This model inherits the prior knowledge of the upper layer and integrates the characteristics of the lower layer of images. Thus, it can be applied effectively to the medical image segmentation.Based on the traditional Snake model presented by Kass, after some introduction of basic theories and methods of Snakes, this paper analyzes and researches some correlative theories and methods and their application in medical image segmentation, after that some advanced methods are given.Firstly, based on the GVF Snake model, this paper researches its application in medical image segmentation. Some related image processing methods, such as image filter, image enhancement are compared combined with both the characteristics of the Snake model and medical image, especially the contrast enhancement algorithm based on wavelet transform and its evaluation.Secondly, the Snake model is applied in the segmentation of ultrasound tumor image, we analyzes and researches the pre-segmentation processing, initialization method, segmentation progress and the evaluation of the result. A new initialization method based on OTSU which can fit ultrasound tumor image well is presented.At last, the method of mathematical morphology and its application in Snake segmentation is researched, we just take the morphology gradient and morphology reconstruction methods as emphasis. In this part, the advancements of mathematical morphology and Snake model are combined and have reached the primary goal of assisting Snake model segmentation and improving the effect of segmentation.