Automatic generation of training data for brain tissue classification from MRI

A fully automatic procedure for brain tissue classification from 3D magnetic resonance head images (MRI) is described. The procedure uses feature space proximity measures, and does not make any assumptions about the tissue intensity data distributions. As opposed to existing methods for automatic tissue classification, which are often sensitive to anatomical variability and pathology, the proposed procedure is robust against morphological deviations from the model. A novel method for automatic generation of classifier training samples, using a minimum spanning tree graph-theoretic approach, is proposed in this thesis. Starting from a set of samples generated from prior tissue probability maps (the "model") in a standard, brain-based coordinate system ("stereotaxic space"), the method reduces the fraction of incorrectly labelled samples in this set from 25% down to 2%. The corrected set of samples is then used by a supervised classifier for classifying the entire 3D image. Validation experiments were performed on both real and simulated MRI data; the kappa similarity measure increased from 0.90 to 0.95