Multi-kernel SVM based classification for brain tumor segmentation of MRI multi-sequence

In this paper, the multi-kernel SVM (Support Vector Machine) classification, integrated with a fusion process, is proposed to segment brain tumor from multi-sequence MRI images (T2, PD, FLAIR). The objective is to quantify the evolution of a tumor during a therapeutic treatment. As the procedure develops, a manual learning process about the tumor is carried out just on the first MRI examination. Then the follow-up on coming examinations adapts the learning automatically and delineates the tumor. Our method consists of two steps. The first one classifies the tumor region using a multi-kernel SVM which performs on multi-image sources and obtains relative multi-result. The second one ameliorates the contour of the tumor region using both the distance and the maximum likelihood measures. Our method has been tested on real patient images. The quantification evaluation proves the effectiveness of the proposed method.