Research Of Medical Image Segmentation On Active Contour Model

Currently, cardiovascular disease has become the second death cause except malignant tumors in China, the clinical diagnosis and medical studies show that it is important for cardiovascular disease people to diagnose and control disease progression with the mature computer technology. Medical segmentation for the area of targets of interest is the important step for image analysis and understanding. Due to the imaging mechanism and other factors, the majority of medical images contain noise, weak border, intensity inhomogeneity (bias field phenomena) as well as complex background. For early image segmentation methods, such as classification methods based on image feature space, the regional approaches and the methods based on graph theory, they are difficult to get better segmentation results. Compared to the early image segmentation methods, active contour models based on level set, which has the framework of the rigorous mathematical theory, merges prior information of the anatomical organization, could use smooth contours to describe the target boundary, they gradually become the focus of medical image segmentation scholars.The active contour models based on image segmentation could be divided into the models based on edge and the models based on region.Segmentation models based on edge, which use gradient information as driving force, have limited capacity of dealing with image the weak boundary or noise, thus fall into local optimums or cause the border to vent. and require higher initial curve set conditions.The models based on region such as local statistical models. The models use small neighborhood statistical information as driving force to overcome the bias field, but they are sensitive to the size of a small neighborhood window parameter, isotropy of the neighborhood, and quality of image background. These factors are easy to make the models fall into local optimums.We deeply study active contour models in order to segment the area of targets of interest. Introducing statistical information of image to improve the models based on edge, we study small neighborhood statistical information to construct a new driving force which could overcome the bias field, reflect the target gray information, and improve the freedom of the initial contour. Main study content and innovations for this paper include the following aspects:(1). Image segmentation model based on region adaptive level set evolution was presented. Signed pressure force function based on regional information was introduced so that the contour shrieked when outside the object or expanded when inside the object, improved the shortage of artificial setting initial contour, overcame the influence of parameters of the weighted area.(2). A new model of GAC (ggeodesic aactive ccontour) based on local region was presented. Information of the local mean was used to make pressure force to segment images with intensity inhomogeneity. Experimental results with different medical images show that the new model can get the better results in efficient way.(3). A new model of GAC based on local statistical was presented, which has original GAC model with directional segment feature in order to reduce image background of the impact of segmentation results. The local statistics signed pressure force function is used to external force in order to describe the local image intensity distribution. Experimental results with left ventricular and brain tumor images show that the new model can get the better results in efficient way.(4). We proposes image segmentation active contour model based on local and global features. The local fitting term based on means and variances of local intensities to cope with intensity inhomogeneity. The global intensity fitting term, which conquers the unexpected local minimum stemming from image local intensity, deals with weak. The experiment results demonstrate that this algorithm is effective for segmenting the left ventricle MR images.