Image Segmentation Algorithm Based On Level Set

Image segmentation applications are wide. However, due to the diversity and qualityof the image itself reason processing target image processing becomes a difficult.traditional image segmentation method, applied widely, but has its own advantages anddisadvantages. Osher and Sethian level set based level set of image processing methodarises become a hot research topic in recent years, the use of the level set method forimage segmentation has been widely used. The basic idea of the level set is that lowone-dimensional curve embedded in the high-level set of one-dimensional function, curveevolution, constantly updated level set function zero level set changes, and ultimately thedivision of the outline of the image. Level set method for image segmentation showed agood performance. This paper studies the image segmentation algorithm based on thelevel set method. For several geometric active contour model proposed a few monthsedges and regional image segmentation model。This paper focuses on elaborating image segmentation, describing the imageprocessing model based on the level set method. Boundary-based active contour modeland Region-based active contour model has been given simulation experiments, and theseexperiments have been compared from the gray, noise and the number of phases.(1) We have introduced the curve evolution theory, the level set definition andevolution equation and the value of the level set method. We also introduced parametricactive contour model and its computer simulation.(2) Elaborated several geometric active contour model (geodesic contour model, MSmodel CV model, LBF model, LCV model) from the definition and theory, and variousmodels were simulated.(3) This paper proposes a new combination of regional and Edge Informationgeometric active contour model, The model is compared with other models in the iterationprocess without re-initialization so the iteration time is shorter, more convenient operation,noise immunity, the effect is good. Then according to each model theory for various typesof images using computer computer simulations carried out, such as noise, gray, numberof phases, etc. And according to the experimental results were analyzed...