Segmentation And Parameter Measure On Pathological Regions Of CT Image Based On Deformable Contour Model

Image segmentation is the basic technique in image process and computer vision fields, and also is the important part of most image analysis and computer vision system. Medical-image segmentation is the main applied domain in image segmentation. It is being more and more important technique during clinical diagnosis and treatment. Practice and study of medical clinical are often used to measure the figures, borderlines and distance between focus and acreage of. section. By this way, it can be gained important information of the tissue pathology or function. Accurately measure has main clinical meaning to morbid diagnose and treatment.Due to both the imaging characteristic of medical images and the variation in image quality, these techniques either fail probably. To address these difficulties, deformable models have been extensively studied and widely used in medical-image segmentation, with promising results.The article first to analyze the methods used widely in medical-image segmentation and discuss the characteristics and range of application of these ways, and then, to study emphatically extensive uses of deformable models in medical-image segmentation. A new modified active contour model based on GVFmodel is proposed for CT image segmentation. The method by introducing contour center technique and an external force can overcome the problems of segmenting object with the concave regions. The experiment results demonstrate that this method is better than traditional snake models and GVF models.After the focus been divided up, the paragraph are scaled and analyzed. It is include the figure, size and the distance among the focuses. This can provide some gist for setting down the means of surgeries and treatment.