Abstract
We present the results of an animal tissue characterization study to demonstrate the effectiveness of a novel approach in collecting and analyzing ultrasound echo signals. In this approach, we continuously record RF echo signals backscattered from a tissue sample, while the imaging probe and the tissue are fixed in position. The continuously recorded RF data generates a time series of RF signal samples. The Higuchi fractal dimension of the resulting time series at each spatial coordinate of the RF frame, averaged over a region of interest, serves as our tissue characterizing feature. The proposed feature is used along with Bayesian classifiers and feed-forward neural networks to distinguish different types of animal tissue. Pairwise classification of four different types of animal tissue are performed. Accuracies are in the range of 68%-96% and are significantly higher than the natural split of the data. The promising results of this study show that analysis of RF time series as proposed here, can potentially give rise to effective measures for ultrasound-based tissue characterization.
Published Version
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