Automated brain tumor segmentation from multi-modality MRI data based on Tamura texture feature and SVM model

Abstract

The precise segmentation of brain tumors is the most important and critical step in the diagnosis and radiotherapy of brain tumors. An automatic segmentation algorithm of brain tumor MRI image based on Tamura texture feature and SVM model is proposed in this paper. Firstly, the local grayscale features of four modal magnetic resonance images are combined with the Tamura texture metrics, which are roughness, contrast, directionality, and regularity. In this way, the information in the image is extracted as much as possible. Then, the known samples are put into the SVM model and classifier training is performed. Finally, other brain tumor images are processed with the trained SVM model. The performance of this method is evaluated using the 2013 BRATS test data-set. The encouraging evaluation results is obtained.