Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
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Quantitative analysis on diffusion tensor imaging (DTI) has shown be useful in the study of disease-related degeneration. More and more studies perform voxel-by-voxel comparisons of fractional anisotropy (FA) values, aiming at detecting white matter alterations. Overall, there is no agreement about how the normalization stage should be performed. The purpose of this study was to evaluate the effect of the normalization strategy on voxel-based analysis of DTI images, using the performance of a classification approach as objective measure of normalization quality. This is achieved by using a Support Vector Machine (SVM) which constructs a decision surface that allows binary classification with two types of regions, generated after a statistical evaluation of the grey level values of regions detected as statistically significant in a FA analysis.