Ultrasound backscatter signal characterization and classification using autoregressive modeling and machine learning algorithms

This research explores the possibility of monitoring apoptosis and classifying clusters of apoptotic cells based on the changes in ultrasound backscatter signals from the tissues. The backscatter from normal and apoptotic cells, using a high frequency ultrasound instrument are modeled through an autoregressive (AR) modeling technique. The proper model order is calculated by tracking the error criteria in the reconstruction of the original signal. The AR model coefficients, which are assumed to contain the main statistical features of the signal, are passed as the input to linear and nonlinear machine classifiers (Fisher linear discriminant, conditional Gaussian classifier, Naive Bayes classifier and neural networks with nonlinear activation functions). In addition, an adaptive signal segmentation method (least squares lattice filter) is used to differentiate the data from layers of different cell types into stationary parts ready for modeling and classification.