Identifying the Margin of Glioma Using 1H-MRSI Data

Glioma is one of malign tumors due to the special construction of the glia cell and its character of infiltration. The treatment, such as surgical resection and radiotherapy, needs the precise tumor boundary. To identify noninvasively the margin of the tumor, using metabolic information by proton magnetic resonance spectroscopic imaging (¹H-MRSI) has been approved to be a powerful tool. In this paper we adopt ¹H-MRSI data for feature extraction and employ support vector machine(SVM) to classify every voxel in the region of interest (ROI) into either glioma or normal tissue, and then to infer the margin of glioma. Experimental results on ¹H-MRSI glioma data demonstrate that proposed method is effective and show a better performance compared with recent popular method.