摘要
针对CV模型的多水平集算法需要较高的数值稳定性以及曲线演化速度慢的缺点,根据人脑MR图像的特征,提出一种快速CV双水平集算法,统计被2条曲线划分成4类的直方图,构造符号矩阵,依次将直方图上的点放入其他类中,根据能量的变化更改该点对应点的符号,得到粗分割结果,并对粗分割结果进行优化。对MR图像进行的分割实验表明,其分割效果更好,速度有大幅度的提高。
Multiphase level set method of Chan-Vese（CV） model is not suitable for real-time application for having numerical stability constraints and its low efficiency. Aiming at this disability and based on the specialty of human brain, a fast method to solve is developed. This approach computes the histograms of four areas which can be used to distinguish the area cut by two curves, and constructs signed tables. The points of the histogram are changed to other clusters and the sign of the points are checked according to the change of the energy. And the improved mean of small neighborhood method is used to optimize the results and get the final edges. Experimental results show that the new model can get the better results in an efficient way.
出处
《计算机工程》
CAS
CSCD
北大核心
2009年第14期181-183,共3页
Computer Engineering
基金
江苏省教育厅"青蓝工程"基金资助项目(2006)
关键词
CV模型
直方图
图像分割
Chan-Vese（CV） model
histogram
image segmentation
作者简介
詹天明（1984-），男，硕士研究生，主研方向：图像分析与处理，模式识别； E—mail：zhantianming1984@sina．com
张建伟，教授、博士；
陈允杰，博士研究生；
王宇,硕士研究生
吴玲玲，硕士研究生
