Classification of 3T MRS spectra using support vector machines

Recent advances in the power and resolution capabilities of MR scanners have extended the reach of Magnetic Resonance Spectroscopy as a powerful non-invasive diagnostic tool. Coupled with MRI techniques it can provide accurate identification and quantification of biologically important compounds in soft tissue. In practice sensor calibration issues, magnetic field homogeneity effects and measurement noise induce distortion into the obtained spectra. Therefore a combination of robust preprocessing models and nonlinear pattern analysis algorithms is needed in order to evaluate and map the underlying relations of the measured metabolites'. In this work we evaluate a set of support vector machine classifiers in the task of brain tumor classification. We aim at providing the human expert with easily interpretable probabilistic metrics to assist in the time, volume and accuracy demanding diagnostic process.