Study On Medical Image Segmentation Based On Level Set Active Contour Model

Medical image segmentation is not only the foundation of quantitative analysis, qualities identify of the normal and diseased tissues, but also the key step of computer aided diagnoses. Thus it should have an unambiguous segmentation target and requires high segmentation accuracy. However, due to the complexity, diversity, and many other uncertain effects on medical images, accurate segmentation is a difficult topic in Processing and Analysis of Medical Images.The level-set active contour model-based image segmentation method, by combining of the low-level image information with high-level prior knowledge, has shown its unique advantage and comprehensive applicability in the segmentation of medical images especially when apply to complicated and diverse structures. In this paper, by taking of segmenting Musculoskeletal MRI as instance,we propose some improvements to the level-set geometrical active contour model, and validate the validity of the algorithms in the segmentation of normal bones and focus. The main work and contributions are as follows:(1) We propose a local adaptive active contour model– variational level-set. Compared to the variational level-set without re-initialization which proposed by Li, there are two points of improvement. First, using of the local characteristic of image to implement adaptive evolution of the curve, it can improve the segmentation performance when the initial curve crossing the edge of the object; Second, by applying the global C-V energy to the local region and improving the edge stop function,make the model more robust to the overlapping of intensity distribution,intensity inhomogeneity. Strengthen the applicability of the algorithm. Lastly, based on the characteristics of serial medical images, transform the segmentation result of current slice to Piecewise Constant Function using the Heaviside function. Then cast it to the next slice as the initial curve to implement automatic segmentation of serial images. The proposed method has been applied to segment the tumor focus in musculoskeletal MRI.(2) Due to the complicated structure, low contrast and weak edge between different parenchyma, the level-set method usually can not obtain a satisfying result when segment cartilage in the knee joint MRI, as it only use low-level image information. In this paper, an integrated active model is proposed by blending prior shape information with the level-set method. Firstly, we use the points distributing model to regulate the prior sample shape of the object, and then describe the mean shape as the zero-level of the level-set function. Based on the adaptive active model, integrate the shape information into the level-set framework, and use it to guide and restrict the evolvement of the curve. This algorithm can improve the identification of weak edge and reduce the interference of non-objects'edge. Moreover, it has a good performance of anti-noise. Finally accomplish some elementary researches on the cartilage segmentation of knee joint MRI.