Quantification and classification of high-resolution magic angle spinning data for brain tumor diagnosis

The goal of this work is to propose a complete protocol (preprocessing, processing and classification) for classifying brain tumors with proton high-resolution magic-angle spinning (H-1 HR-MAS) data. The different steps of the procedure are detailed and discussed. Feature extraction techniques such as peak integration, including also the automated quantitation method AQSES, were combined with linear (LDA) and non-linear (least-squares support vector machine or LS-SVM) classifiers. Classification accuracy was assessed using a stratified random sampling scheme. The results suggest that LS-SVM performs better than LDA while AQSES performs better than the standard peak integration feature extraction method.