Classification and Pathologic Diagnosis of Gliomas in MR Brain Images

Abstract

Detection and classification of a brain tumour in medical images is always a challenging task, as the treatment may lead to Radiosurgery depending upon the exact shape, size, and position of a tumour. To provide a complete characterization of glioma and the degree of malignancy, classification and grading are vital. In some complicated cases, localization of the tumour, comparison of tumour tissues with adjacent regions, to make it clear for detection, and finally classification without human intervention is need of the hour. Out of the available numerous imaging techniques to detect and classify brain tumours, MRI is the most suitable non-invasive technique and has superior image quality. The edge over other techniques is its superior soft-tissue resolution and the ability to acquire different images employing contrast-enhanced agents. This research work classifies Low-Grade Glioma (LGG) and High-Grade Glioma(HGG) tumours on the basis of features obtained from extracted tumor region. The results of the classification of HGG and LGG tumours using the most prominent features are validated by five-fold cross-validation. Satisfactory and encouraging classification results are obtained using large and publicly available clinical datasets. Using the extracted features, a computer-assisted algorithm is developed that uses a machine-learning algorithm along with Gradient-Based Kernel Selection Graph Cut to provide binary classification with an accuracy of 94.6% with T1ce sequence of MRI. The proposed models can be employed to assist physicians and radiologists in the early pathological detection and classification of gliomas. The proposed framework also can be a model for validating brain tumours and their initial screening for their grading classification.