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Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study.

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Title: Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study.
Author: Imani, Farhad; Daoud, Mohammad; Moradi, Mehdi; Abolmaesumi, Purang; Mousavi, Parvin
Issue Date: 2011
Publicly Available in cIRcle 2011-08-16
Publisher Society of Photo-Optical Instrumentation Engineers
Citation: Imani, Farhad; Daoud, Mohammad; Moradi, Mehdi; Abolmaesumi, Purang; Mousavi, Parvin. Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study. Medical Imaging 2011: Ultrasonic Imaging, Tomography, and Therapy, edited by Jan D'hooge, Marvin M. Doyley, Proceedings of SPIE, Volume 7968, 79680F, 2011. http://dx.doi.org/10.1117/12.877845
Abstract: Time series analysis of ultrasound radio-frequency (RF) signals has been shown to be an effective tissue classification method. Previous studies of this method for tissue differentiation at high and clinical-frequencies have been reported. In this paper, analysis of RF time series is extended to improve tissue classification at the clinical frequencies by including novel features extracted from the time series spectrum. The primary feature examined is the Mean Central Frequency (MCF) computed for regions of interest (ROIs) in the tissue extending along the axial axis of the transducer. In addition, the intercept and slope of a line fitted to the MCF-values of the RF time series as a function of depth have been included. To evaluate the accuracy of the new features, an in vitro animal study is performed using three tissue types: bovine muscle, bovine liver, and chicken breast, where perfect two-way classification is achieved. The results show statistically significant improvements over the classification accuracies with previously reported features. Copyright 2011 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited
Affiliation: Electrical and Computer Engineering, Dept of
URI: http://hdl.handle.net/2429/36698
Peer Review Status: Reviewed
Scholarly Level: Faculty

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