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

Farhad Imani; Mohammad Daoud; Mehdi Moradi; Purang Abolmaesumi; Parvin Mousavi

doi:10.1117/12.877845

25 March 2011 Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study

Farhad Imani, Mohammad Daoud, Mehdi Moradi, Purang Abolmaesumi, Parvin Mousavi

Proceedings Volume 7968, Medical Imaging 2011: Ultrasonic Imaging, Tomography, and Therapy; 79680F (2011) https://doi.org/10.1117/12.877845
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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.

Citation Download Citation

Farhad Imani, Mohammad Daoud, Mehdi Moradi, Purang Abolmaesumi, and Parvin Mousavi "Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study", Proc. SPIE 7968, Medical Imaging 2011: Ultrasonic Imaging, Tomography, and Therapy, 79680F (25 March 2011); https://doi.org/10.1117/12.877845

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