An Automatic Parallel Segmentation for CT Image
In this paper, an improved parallel segmentation approach using regional growth with support vector machine is proposed. The conventional regional growth is a difficulty to determine the feed points automatically, and a solo support vector machine is resultful in segmentation, but the speed is slow. In order to solve these problems, an image segmentation method combining support vector machine with regional growth was proposed. Firstly, training the support vector machine classification;then the trained classification is used to search seed points, and a curvature flow filter is used to reduce the noise and get a result with sharp and smoothing boundaries. At last, regional growing with a simple but efficient threshold method is used. The experiment is performed on a parallel environment based on torque. Its result shows that the algorithm is feasible and work better and faster than conventional algorithm.
Support Vector Machine Regional Growth CT Image Parallel Segmentation
Jian-wei Ma Fei-jia Zhu Zhu-ping Wang Yang Cui Ping Xue Lu Liu
School of Automation,Harbin University of Science and Technology,Harbin Heilongjiang,China
国际会议
太原
英文
228-230
2011-02-26（万方平台首次上网日期，不代表论文的发表时间）