Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Bayesian Spectral Estimation Applied to Echo Signals from Nonlinear Ultrasound Scatterers | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 146175 10 pages doi 2011 146175 Research Article Bayesian Spectral Estimation Applied to Echo Signals from Nonlinear Ultrasound Scatterers Yan Yan 1 James R. Hopgood EURASIP Member 1 and Vassilis Sboros2 1 Institute for Digital Communications School of Engineering University of Edinburgh EH9 3JL Edinburgh UK 2Department of Medical Physics University of Edinburgh EH16 4TJ Edinburgh UK Correspondence should be addressed to James R. Hopgood Received 15 July 2010 Accepted 27 October 2010 Academic Editor Elias Aboutanios Copyright 2011 Yan Yan et al. 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. 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 .
