Detection of multiple sclerosis lesions in MRIs with an SVM classifier
Annarita D’Addabbo, Nicola Ancona, Palma N. Blonda, Roberto A. De Blasi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003
The purpose of this paper is to test the effectiveness of a Support Vector Machine (SVM) classifier, with gaussian kernel function, in the automatic detection of small lesions from Magnetic Resonance Images (MRIs) of a patientt affected by multiple sclerosis. The data set consists of Proton Density, T2 (the spin-spin relaxation time) Spin-Echo images and a three-dimensional T1-weighted gradient echo sequence, called Magnetization-Prepared RApid Gradient Echo, that can be generated from contiguous and very thin sections, allowing detection of small lesions typically affected by partial volume effects and intersection gaps in T1 weighted Spin-Echol sequences. In this context of classification, SVM with Gaussian kernel function exhibited a good classification accuracy, higher than accuracies obtained, on the same data set, with a traditional RBF, confirming its high generalization capability and its effectiveness when applied to low-dimensional multi-spectral images.