This paper proposes a fully-automatic method for extraction of high-throughput MRI radiomics features in Glioblastoma Multiforme (GBM). First, the tumor subregions are automatically segmented from multi-modality MR images using a proposed random forest classifier with a conditional random field spatial regulation, where the importances of the multi-modality features are considered. Within the segmented subregions, 44928 high-order texture features are extracted at different voxel sizes by using different quantization methods with varying gray levels. The DICE score, sensitivity and specificity of the proposed automatic segmentation algorithm are calculated. The reproducibility of the extracted features against the changes in voxel sizes, quantization methods and gray levels are assessed quantitatively. The proposed radiomics method can be used for exploring of imaging biomarker and facilitate the pre-treatment care of GBM patients.