Glioma grade classification using wavelet transform-local binary pattern based statistical texture features and geometric measures extracted from MRI

The objective of this study is to perform brain glioma grade classification by identifying an optimal quantitative feature set from conventional magnetic resonance images. In this work, a hybrid feature set comprising of statistical texture and geometric features is computed over entire segmented tumor volume. Discrete wavelet transform (DWT) and local binary pattern (LBP) techniques are combined to extract texture information from segmented tumour volume at multiple resolutions. Statistical texture features comprising of skewness, kurtosis and entropy are then computed from DWT-LBP transformed images. Geometric features are calculated from (i) fractal dimension (FD) of three dimensional (3D) volumes of tumour region, tumour border and tumour skeleton, and (ii) convexity parameters over complete segmented tumour volume. Statistical analysis revealed that extracted texture features are significantly different between high grade (HG) and low grade (LG) glioma patients (p < 0.05). FD-based geometric parameters are significantly higher for HG glioma patients in comparison to LG glioma patients. Our results reflect that HG glioma has more structural complexity than LG glioma. The optimised feature set comprising of DWT-LBP-based texture features and FD-based measures extracted from segmented tumour volume achieved 96% accuracy, 97% sensitivity and 95% specificity for glioma classification with Naive Bayes classifier.