A comparative study of classification methods for automatic multimodal brain tumor segmentation

This paper presents a comparative study of various classification methods in the application of automatic brain tumor segmentation. The data used in the study are 3D MRI volumes from MICCAI2016 brain tumor segmentation (BRATS) benchmark. 30 volumes are chosen randomly as a training set and 57 volumes are randomly chosen as a test set. The volumes are preprocessed and a feature vector is retrieved from each volume's four modalities (T1, T1 contrast-enhanced, T2 and Fluid-attenuated inversion recovery). The popular Dice score is used as an accuracy measure to record each classifier recognition results. All classifiers are implemented in the popular machine learning suit of algorithms, WEKA.