Tissue characterization using fractal dimension of high frequency ultrasound RF time series

Authors:
Mehdi Moradi;Parvin Mousavi;Purang Abolmaesumi
Affiliations:
School of Computing, Queen's University, Kingston, Canada;School of Computing, Queen's University, Kingston, Canada;School of Computing, Queen's University, Kingston, Canada and Department of Electrical and Computer Engineering, Queen's University
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Year:
2007

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Abstract

This paper is the first report on the analysis of ultrasound RF echo time series acquired using high frequency ultrasound. We show that variations in the intensity of one sample of RF echo over time is correlated with tissue microstructure. To form the RF time series, a high frequency probe and a tissue sample were fixed in position and RF signals backscattered from the tissue were continuously recorded. The fractal dimension of RF time series was used as a feature for tissue classification. Feature values acquired from different areas of one tissue type were statistically similar. For animal tissues with different cellular microstructure, we successfully used the fractal dimension of RF time series to distinguish segments as small as 20 microns with accuracies as high as 98%. The results of this study demonstrate that the analysis of RF time series is a promising approach for distinguishing tissue types with different cellular microstructure.