Processing radio frequency ultrasound images : a robust method for local spectral features estimation by a spatially constrained parametric approach

Spectral estimation is a major component in studies aiming at characterizing biological tissues through the analysis of backscattered radio frequency (RF) ultrasonic signals and images. However, conventional spectral estimation techniques yield a well-known trade-off between spatial resolution and v...

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Veröffentlicht in:IEEE transactions on ultrasonics, ferroelectrics, and frequency control. - 1986. - 49(2002), 12 vom: 24. Dez., Seite 1704-19
1. Verfasser: Gorce, Jean-Marie (VerfasserIn)
Weitere Verfasser: Friboulet, Denis, Dydenko, Igor, D'hooge, Jan, Bijnens, Bart H, Magnin, Isabelle E
Format: Aufsatz
Sprache:English
Veröffentlicht: 2002
Zugriff auf das übergeordnete Werk:IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Schlagworte:Comparative Study Evaluation Study Journal Article Validation Study
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100 1 |a Gorce, Jean-Marie  |e verfasserin  |4 aut 
245 1 0 |a Processing radio frequency ultrasound images  |b a robust method for local spectral features estimation by a spatially constrained parametric approach 
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520 |a Spectral estimation is a major component in studies aiming at characterizing biological tissues through the analysis of backscattered radio frequency (RF) ultrasonic signals and images. However, conventional spectral estimation techniques yield a well-known trade-off between spatial resolution and variance. The backscattered signals are stochastic by nature, so short-term local analysis results in a high variance of the estimates, which cannot efficiently be reduced through conventional spatial averaging. We address this issue by describing a spectral estimation technique that reduces the variance of the estimates (by smoothing the local estimates in spectrally homogeneous regions) while preserving spectral discontinuities (i.e., the smoothing is not performed across regions with different spectral contents). The proposed approach is set in a Bayesian framework and is based on local autoregressive (AR) estimation, constrained by smoothness priors. These smoothness priors are introduced through a Markov random field in which the associated potential functions are nonquadratic, allowing thereby to preserve discontinuity. The method is validated on simulated RF images and tested on echocardiographic images acquired in vivo. The results are compared to the estimates provided by the conventional Burg technique. These results clearly demonstrate the ability of the proposed approach to improve spectral estimation in terms of variance reduction and discontinuity detection 
650 4 |a Comparative Study 
650 4 |a Evaluation Study 
650 4 |a Journal Article 
650 4 |a Validation Study 
700 1 |a Friboulet, Denis  |e verfasserin  |4 aut 
700 1 |a Dydenko, Igor  |e verfasserin  |4 aut 
700 1 |a D'hooge, Jan  |e verfasserin  |4 aut 
700 1 |a Bijnens, Bart H  |e verfasserin  |4 aut 
700 1 |a Magnin, Isabelle E  |e verfasserin  |4 aut 
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