Quantification and classification of magnetic resonance spectroscopic data for brain tumor diagnosis

Magnetic Resonance Spectroscopy has been successfully used in brain tumor diagnosis and represents a complementary aid to the well-known technique, Magnetic Resonance Imaging, by providing metabolic information that is not available with the latter. Both Imaging and Spectroscopy can be used for the grading and typing of brain tumors. Classifying brain tumors from spectroscopic data is not trivial and requires several steps. The common main steps are preprocessing, feature extraction and, finally, classification of the data. The preprocessing step aims to clean up the data and to normalize them in order to facilitate the extraction of the relevant features. These features, once selected and extracted, are used in a classifier, whose output is a brain tumor class. The challenge is to improve brain tumor diagnosis based on spectroscopic data. In this thesis, we analyzed methods used in each of the steps of the procedure in order to extract their advantages and limitations. Due to the complexity and diversity of the data and the still limited amount of available data, there is no gold standard procedure which would provide the best classification results. However, this thesis aims to identify the problems that can be encountered during the whole procedure (preprocessing, feature extraction and classification) and to provide the reader with possible solutions. In particular, a large part of this thesis is devoted to the quantification of MRS data, which remains very complicated, especially when dealing with in vivo MRS(I) data.