# 水平集方法在医学图像分割中的一个应用Application of Level Set Method in Medical Image Segmentation

DOI: 10.12677/AAM.2016.51010, PDF, HTML, XML, 下载: 1,817  浏览: 5,382  国家自然科学基金支持

Abstract: Image segmentation is the basic of object detection and resource classification, and it has an im-portant application in the field of medical diagnosis, video monitoring and computer vision. Level set method describes the evolution of geometric active contour in a compact way and provides a stable numerical algorithm. Chan and Vese first introduced the active contour model based sim-plified Mumford-Shah model, which could well detect the vacuum of object, but could represent only two phases or segments in the image. Vese-Chan variational level set model was proposed by Vese-Chan as the generalization of C-V model, which needed multiple level set functions for n phases image segmentation; it can represent boundaries with complex topologies. In this paper, based on the C-V model and Vese-Chan variational level set model, we show how to do medical image segmentation through one level set function and two-level set functions respectively. We discuss the advantages and disadvantages of both methods. Our numerical results validate our theoretical predication.

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