%0 Journal Article
%T Fast B-ultrasound Image Segmentation Based on a Convex Relaxation Method<br>基于凸松弛方法的医学B超图像快速分割
%A HUANG Jie
%A YANG Xiao-Ping
%A <br>黄杰
%A 杨孝平
%J 自动化学报
%D 2012
%I 
%X One main drawback of active contour method applied to image segmentation is that the objective function is not convex. The solution of a non-convex minimization problem is prone to get stuck in a local minima, and some fast algorithms to convex optimization problems can not be used in a non-convex active contour model. Using a Bayesian risk method, this paper presents a new level set model for B-ultrasound image segmentation based on a Rayleigh distribution. The directly obtained model is not convex. However, we can get a new relaxed convex model by using a convex relaxation method. The relation between the directly obtained model and the relaxed convex model is given by a theorem. Then, a split Bregman algorithm is incorporated to propose a fast algorithm to solve the relaxed convex model. Compared with the traditional gradient descent method, the proposed method can not only get a global minima, but also is quite faster than gradient descent method.
%K Medical B ultrasound
%K active contour
%K Bayesian risk
%K convex relaxation
%K split Bregman<br>医学B超
%K 活动轮廓
%K 贝叶斯风险
%K 凸松弛
%K 分裂Bregman
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=28538CA98C300078D6C2A209ED216D6B&yid=99E9153A83D4CB11&vid=16D8618C6164A3ED&iid=E158A972A605785F&sid=90773C2285A2F0BB&eid=C7461453A367FC85&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=26