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Proceedings Paper

Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study
Author(s): Farhad Imani; Mohammad Daoud; Mehdi Moradi; Purang Abolmaesumi; Parvin Mousavi
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Paper 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.

Paper Details

Date Published: 25 March 2011
PDF: 7 pages
Proc. SPIE 7968, Medical Imaging 2011: Ultrasonic Imaging, Tomography, and Therapy, 79680F (25 March 2011); doi: 10.1117/12.877845
Show Author Affiliations
Farhad Imani, Queen's Univ. (Canada)
Mohammad Daoud, The Univ. of British Columbia (Canada)
Mehdi Moradi, The Univ. of British Columbia (Canada)
Purang Abolmaesumi, The Univ. of British Columbia (Canada)
Parvin Mousavi, Queen's Univ. (Canada)


Published in SPIE Proceedings Vol. 7968:
Medical Imaging 2011: Ultrasonic Imaging, Tomography, and Therapy
Jan D'hooge; Marvin M. Doyley, Editor(s)

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