Open Access

Bayesian Spectral Estimation Applied to Echo Signals from Nonlinear Ultrasound Scatterers

  • Yan Yan1,
  • James R. Hopgood (EURASIP Member)1Email author and
  • Vassilis Sboros2
EURASIP Journal on Advances in Signal Processing20102011:146175

Received: 15 July 2010

Accepted: 27 October 2010

Published: 2 November 2010


The understanding and exploitation of acoustic echo signals from nonlinear ultrasound scatterers is an active research area that aims to improve the sensitivity and specificity of diagnostic imaging. Discriminating between acoustic echoes from linear scatterers, such as tissue, and nonlinear scatterers, such as contrast microbubbles, based on their frequency content is also an important topic in ultrasound contrast imaging. In order to achieve these objectives, a fundamental preliminary stage is to extract information about the reflected signals in the frequency domain with high accuracy: this is essentially a feature extraction and estimation problem. In this paper, a parametric Bayesian spectral estimation method is utilised for the analysis of the backscattered echo signals from microbubbles. In contrast to existing nonparametric discrete-Fourier-transform- (DFT-) based spectral estimation techniques used in the ultrasonic literature, this method is able to estimate the number of spectral components as well as their amplitudes and frequencies. The Bayesian spectral analysis technique has improved frequency resolution compared with the DFT for short multiple-component signals at low signal-to-noise ratios. The performance of the method is demonstrated with simulated signals, as well as analysing experimentally measured echo signals from nonlinear microbubble scatterers.

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Authors’ Affiliations

Institute for Digital Communications, School of Engineering, University of Edinburgh
Department of Medical Physics, University of Edinburgh


© Yan Yan et al. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.