Morphometry based methods allow the detection of subtle anatomical differences in the Magnetic Resonance Images (MRI) between healthy subjects and Alzheimer's Disease (AD) patients. However, anatomical volumes are rarely used for clinical diagnosis as the changes induced by AD are hard to differentiate from normal brain aging.

We present a morphometry method which uses brain models generated using Nonnegative Matrix Factorization (NMF) characterized by signatures calculated from perceptual features such as intensities, edges or orientations, of salient regions. The Earth Mover's Distance (EMD), a robust measure of the cost of transforming signature A into signature B, is used to calculate volume-models distances. The discerning power of these distances is tested by using them as features for a Support Vector Machine classifier.

This work shows the usefulness of the EMD as a metric in medical image applications as it has proven to be robust to bin selection, takes into account cross bin relations, and allows high sensitivity with lower dimensionality. This method is able to find discerning regions which, besides aiding in classification, may provide new insights of the disease's development.