A New Parallel Segmentation Algorithm for Medical Image

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.