  In this paper, we propose a novel learning based method for automated
segmentation of brain tumor in multimodal MRI images, which incorporates two
sets of machine -learned and hand crafted features. Fully convolutional
networks (FCN) forms the machine learned features and texton based features are
considered as hand-crafted features. Random forest (RF) is used to classify the
MRI image voxels into normal brain tissues and different parts of tumors, i.e.
edema, necrosis and enhancing tumor. The method was evaluated on BRATS 2017
challenge dataset. The results show that the proposed method provides promising
segmentations. The mean Dice overlap measure for automatic brain tumor
segmentation against ground truth is 0.86, 0.78 and 0.66 for whole tumor, core
and enhancing tumor, respectively.
