摘要
提出了基于曲线演化的MR边界轮廓精确提取方法.针对MR图像边缘模糊和高噪声问题,改进Mum-ford-Shah曲线演化模型,将模糊聚类引入到轮廓演化能量模型中,降低对非规则细节和噪声的敏感性;利用水平集和半隐式的加性-乘性算子分裂数值方案进行轮廓线演化的迭代计算,提高精度和计算效率.实验表明这种方法可以对边界模糊和高噪声的轮廓进行有效提取.
This paper proposes a high-accuracy contour extraction algorithm based on curve evolution model. Face to the blurred edges and high noise in MR image, Mumford-shah model is improved here and fuzzy clustering is used to establish active contour model to lower sensitivity to irregular details and noises. The derivation of the level set and the semi-implicit implementation based on the additive-multiplicative operator splitting is performed in order to improve the computing efficiency and accuracy. Experimental results are given to demonstrate the feasibility of the proposed method in extracting contour from the blurred edge and high-noise images.
出处
《计算机学报》
EI
CSCD
北大核心
2009年第2期268-274,共7页
Chinese Journal of Computers
基金
国家自然科学基金(60773053,60673153,60673003)资助~~
关键词
曲线演化
轮廓提取
水平集
算子分裂
curve evolution
contour extraction
level set
operator splitting
