Brain Tumor Segmentation from Multi-Spectral Magnetic Resonance Image Data Using an Ensemble Learning Approach

The automatic segmentation of medical images represents a research domain of high interest. This paper proposes an automatic procedure for the detection and segmentation of gliomas from multi-spectral MRI data. The procedure is based on a machine learning approach: it uses ensembles of binary decision trees trained to distinguish pixels belonging to gliomas to those that represent normal tissues. The classification employs 100 computed features beside the four observed ones, including morphological, gradients and Gabor wavelet features. The output of the decision ensemble is fed to morphological and structural post-processing, which regularize the shape of the detected tumors and improve the segmentation quality. The proposed procedure was evaluated using the BraTS 2015 train data, both the high-grade (HG) and the low-grade (LG) glioma records. The highest overall Dice scores achieved were 86.5% for HG and 84.6% for LG glioma volumes.