Multi-kernel SVM based classification for tumor segmentation by fusion of MRI images

Tumor segmentation, a significant application in the field of medical imaging and pattern recognition, is still a very difficult and unsolved problem up to now. In this paper, an improved SVM algorithm-multi-kernel SVM, integrated with data fusion process, is proposed to segment the tumors from the MRI image sequence. Three kinds of MRI image sequence-T2, PD, FLAIR are used as input sources in learning and classifying process. Then a region growing step is exploited for a refinement of the tumor contour. At last, according to the follow-up result of the same patient at five different periods, it is obvious that the tumor's volume becomes smaller, and an evaluation percentage is given to prove the effectiveness of the therapy. The quantification of result demonstrates the effectiveness of the proposed method.