0000000000814531
AUTHOR
Jean-baptiste Poullet
Combining HR-MAS and In Vivo MRI and MRSI Information for Robust Brain Tumor Recognition
In this study we propose to classify short echotime brain MRSI data by using multimodal information coming from magnetic resonance imaging (MRI), magnetic resonance spectroscopic imaging (MRSI) and high resolution magic angle spinning (HR-MAS), and to develop an advanced pattern recognition method that could help clinicians in diagnosing brain tumors. We study the impact of using HR-MAS information in combination with in vivo information for classifying brain tumors and we investigate which parameters influence our classification results.
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 ((1)H 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 extr…