Abstract

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.

Highlights

  • High frequency ultrasound (US) has been shown to detect the structural changes cells and tissues undergo during cell death

  • Research has shown that the ultrasound backscatter signals from apoptotic' acute myeloid leukemia(AML) cells differ in intensity and frequency spectrum as the result of the change in size, spatial distribution and acoustic impedance of the scattering sources within the cell [ l ] (Fig. 1)

  • The accuracy of different classifiers has been studied and it was found that non-linear neural networks were most successful in classification

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Summary

INTRODUCTION

High frequency ultrasound (US) has been shown to detect the structural changes cells and tissues undergo during cell death. Research has shown that the ultrasound backscatter signals from apoptotic' acute myeloid leukemia(AML) cells differ in intensity and frequency spectrum as the result of the change in size, spatial distribution and acoustic impedance of the scattering sources within the cell [ l ] (Fig. 1). We assume that pulse echo data from different cell types contain distinguishable statistical regularities. In this work we attempt to classify normal and apoptotic cancerous cells by tracking the statistics of the ultrasound backscatter signals from tissues by using Autoregressive (AR) method for time series modeling of ultrasound signals

Autoregressive (AR) Modeling of US signals
Data Acquisition
Choosing the proper Model Order
Adaptive signal Segmentation
RESULTS
CONCLUSION
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