Improved Binarized Statistical Image Features for MRI Brain Tumor Identification and Classification

MRI brain tumor identification and classification is costly and time-consuming owing to the difficulty and complication of tumors. The process is highly influenced by the experience and knowledge of radiologists and neurologists. For this reason, automating this process becomes indispensable to overcome the drawbacks. In this work, we propose a new model, called DRB-BSIF (i.e., Deep Rule Based Classifier using Binarized Statistical Image Features), to enhance classification performances and reduce the complexity implicated in the diagnostic decision. Specifically, the tumor region is augmented by image dilation and used as the Region of Interest (ROI) instead of the initial tumor region. Then, features are extracted using BSIF image descriptor. Furthermore, we have constructed a Bank-BSIF, which is founded by the best parameters of BSIF filters. In the classification step, a deep-rule based classifier (DRB) has been used. The main of the BRD classifier is a self-organized set of IF and THEN fuzzy rules guided by the prototypes. These fuzzy rules are generated by DRB classifier and represented its ‘engine’. We studied classification of different kinds of brain tumors (e.g., Meningioma, Glioma, and Pituitary tumor). The proposed model is evaluated on publicly available brain CE-MRI images via different measures such as performance accuracy, sensitivity and specificity. Experimental results demonstrated that the DRB-BSIF is effective and can be used in computer aided brain tumor classification.