Medical Image Segmentation Based On Level Set CV

With the progress of sciences and technology, the demand for information increases more and more. However most of these information comes from the visual image information. In order to obtain the information form digital images we need to process these images. In image analysis and processing, image segmentation is fundamental, and occupies an important position.Due to the importance of image segmentation, scholars have done a lot of research. Although there are many image segmentation algorithms, a general image segmentation theory does not exist. Most of the methods are proposed according to the actual situation of the specific image. In recent years, segmentation algorithm based on the level set model becomes a hot field of image segmentation research (especially for medical image). The level set based segmentation algorithm shows a good performance, and most likely becomes a unified framework for image segmentation. Up to now, the level set based segmentation algorithm has almost mature and complete, but there are still some shortcoming. In this context, this paper proposes a modified Chan-Vese (C-V) level set based image segmentation algorithm, and applies it to medical image segmentation. The works carried out in this article include:(1) We comprehensively overview state of the art of level set based image segmentation algorithms, and its applications. The shortcomings of the different algorithms have been analyzed.(2) We describe the basic theory of curve evolution theory and level set method. The level set method is applied to image segmentation of medical images. Numerical experiment shows the advantages and disadvantages of the methods.(3) The basic theory of Mumford-Shah Model is discussed in detail. Based on the discussion, the C-V Level Set Model is explained. We apply the C-V level set based segmentation algorithm to medical image segmentation. The relative merits are compared according to experimental results.(4) The traditional C-V model is sensitive to noise and inaccurate positioning of the edges in an image. This paper proposes a C-V model based on the Gaussian Laplace operator. The experiments show that the proposed algorithm can achieve the better actual contour and edge localization of segmentation image. It has the obvious superiority comparing with the traditional method.