•Preprocessing is proposed to compensate inherent noise and low contrast.•A minimal set of highly representative regional features are employed.•The problem of class imbalance at a regional level is addressed for the first time.•Random Forest performance is much better than Support Vector Machine. This paper presents a fully automated brain tissue classification method for normal and abnormal tissues and its associated region from Fluid Attenuated Inversion Recovery modality of Magnetic Resonance (MR) images. The proposed regional classification method is able to simultaneously detect and segment tumours to pixel-level accuracy. The region-based features considered in this study are statistical, texton histograms, and fractal features. This is the first study to address the class imbalance problem at the regional level using Random Majority Down-sampling-Synthetic Minority Over-sampling Technique (RMD-SMOTE). A comparison of benchmark supervised techniques including Support Vector Machine, AdaBoost and Random Forest (RF) classifiers is presented, where the RF-based regional classifier is selected in the proposed approach due to its better generalization performance. The robustness of the proposed method is evaluated on the standard publicly available BRATS 2012 dataset using five standard benchmark measures. We demonstrate that the proposed method consistently outperforms three benchmark tumour classification methods in terms of Dice score and obtains significantly better results as compared to its SVM and AdaBoost counterparts in terms of precision and specificity at the 5% confidence interval. The promising results of the proposed method support its application for early detection and diagnosis of brain tumours in clinical settings.