Robust principal component analysis and clustering methods for automated classification of tissue response to ARFI excitation.

We introduce a new method for automatic classification of acoustic radiation force impulse (ARFI) displacement profiles using what have been termed "robust" methods for principal component analysis (PCA) and clustering. Unlike classical approaches, the robust methods are less sensitive to high variance outlier profiles and require no a priori information regarding expected tissue response to ARFI excitation. We first validate our methods using synthetic data with additive noise and/or outlier curves.