원문정보
초록
영어
In medical Image analysis, the parallel segmentation is the core technology. As one of the classical methods, regional growth algorithms have some problems: it is hard to confirm the feed points automatically. To solve this defect, a new parallel segmentation algorithm with regional growth and support vector machine (SVM) is proposed. SVMs have a good result in segmentation (classification) but a non-ideal convergence rate which is the advantage of regional growth method. So that, combining them and the idea of the algorithm is: classify by SVM to search the seed points, segment by regional growth method. A curvature flow filter is also used in this algorithm to reduce the noise. The experiments are performed on a parallel environment based on torque. The results show that the algorithm is faster than conventional algorithms and the results are better.
목차
1. Introduction
2. Relevant Works
2.1. Support Vector Machine
2.2. Image Segmentation based on Region Growing Method
3. Method and Realization
3.1. Combined Support Vector Machine and Regional Growth
3.2. Parallel Image Segmentation based on Torque
4. Experiments and Result
5. Conclusions
Acknowledgements
References