An automatic glioma grading method based on multi-feature extraction and fusion.

BACKGROUND: An accurate assessment of tumor malignancy grade in the preoperative situation is important for clinical management. However, the manual grading of gliomas from MRIs is both a tiresome and time consuming task for radiologists. Thus, it is a priority to design an automatic and effective computer-aided diagnosis (CAD) tool to assist radiologists in grading gliomas. OBJECTIVE: To design an automatic computer-aided diagnosis for grading gliomas using multi-sequence magnetic resonance imaging. METHODS: The proposed method consists of two steps: (1) the features of high and low grade gliomas are extracted from multi-sequence magnetic resonance images, and (2) then, a KNN classifier is trained to grade the gliomas. In the feature extraction step, the intensity, volume, and local binary patterns (LBP) of the gliomas are extracted, and PCA is used to reduce the data dimension. RESULTS: The proposed "Intensity-Volume-LBP-PCA-KNN" method is validated on the MICCAI 2015 BraTS challenge dataset, and an average grade accuracy of 87.59% is obtained. CONCLUSIONS: The proposed method is an effective method for automatically grading gliomas and can be applied to real situations.