Level Set Based Image Denoising And Segmentation Algorithm

Image filtering and segmentation are important parts of image processing. When denoising noise, try not to damage the edge,small-scale structures and other important information of images. The denoising methods based on geometric partial differential equation could meet such requests. Image segmentation should extract the objects accurately and effectively. The quality of image filtering and segmentation directly affects the follow-up image analysis,identification and other applications.In this article, we introduced the theory of curve evolution and level set method, discussed the geometric active contour model. We systematically studied the general algorithm of level set ant its application in image denoising; we improved the speed function of curve evolution process, realized preserving edge details while denoising the noise. Then we give a liver segmentation algorithm based on level set.Chan-Vese is a typical image segmentation model of geometric active contour model. Chan-Vese is a two phase segmentation model using one level set function. This model can detect the interior contour of object and it is not necessary for the initial contour to be close enough to the true boundary. In order to carry out multi-object image hierarchical segmentation, a Chan-Vese model is adopted serially in this paper to segment an image into two subregions including object and background regions. If any of the resulting subregions require additional segmentation, Chan-Vese model can be applied to those regions again until all objects in the image are segmented. This method is simpler than using Mumford-Shah model, and it is accurate for object location. Each segmentation step can get significant regions. The experimental results show that the method can segment the multi-objects image effectively and accurately.