Bayesian 2D deconvolution: A model for diffuse ultrasound scattering

Oddvar Husby; Torgrim Lie; Thomas Langø; Jørn Hokland; Håvard Rue

doi:10.4173/mic.2001.4.3

Bayesian 2D deconvolution: A model for diffuse ultrasound scattering

Oddvar Husby^*, Torgrim Lie, Thomas Langø, Jørn Hokland, Håvard Rue

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Observed medical ultrasound images are degraded representations of the true acoustic tissue reflectance. The degradation is due to blur and speckle, and significantly reduces the diagnostic value of the images. In order to remove both blur and speckle we have developed a new statistical model for diffuse scattering in 2D ultrasound radio-frequency images, incorporating both spatial smoothness constraints and a physical model for diffuse scattering. The modeling approach is Bayesian in nature, and we use Markov chain Monte Carlo methods to obtain the restorations. The results from restorations of some real and simulated radio-frequency ultrasound images are presented, and compared with results produced by Wiener filtering.

Original language	English (US)
Pages (from-to)	227-242
Number of pages	16
Journal	Modeling, Identification and Control
Volume	22
Issue number	4
DOIs	https://doi.org/10.4173/mic.2001.4.3
State	Published - Oct 2001
Externally published	Yes

Keywords

Diffuse scattering
Markov chain Monte Carlo
Markov random fields
Medical ultrasound
Restoration

ASJC Scopus subject areas

Software
Control and Systems Engineering
Modeling and Simulation
Computer Science Applications

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10.4173/mic.2001.4.3

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title = "Bayesian 2D deconvolution: A model for diffuse ultrasound scattering",

abstract = "Observed medical ultrasound images are degraded representations of the true acoustic tissue reflectance. The degradation is due to blur and speckle, and significantly reduces the diagnostic value of the images. In order to remove both blur and speckle we have developed a new statistical model for diffuse scattering in 2D ultrasound radio-frequency images, incorporating both spatial smoothness constraints and a physical model for diffuse scattering. The modeling approach is Bayesian in nature, and we use Markov chain Monte Carlo methods to obtain the restorations. The results from restorations of some real and simulated radio-frequency ultrasound images are presented, and compared with results produced by Wiener filtering.",

keywords = "Diffuse scattering, Markov chain Monte Carlo, Markov random fields, Medical ultrasound, Restoration",

author = "Oddvar Husby and Torgrim Lie and Thomas Lang{\o} and J{\o}rn Hokland and H{\aa}vard Rue",

year = "2001",

month = oct,

doi = "10.4173/mic.2001.4.3",

language = "English (US)",

volume = "22",

pages = "227--242",

journal = "Modeling, Identification and Control",

issn = "0332-7353",

publisher = "Research Council of Norway",

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TY - JOUR

T1 - Bayesian 2D deconvolution

T2 - A model for diffuse ultrasound scattering

AU - Husby, Oddvar

AU - Lie, Torgrim

AU - Langø, Thomas

AU - Hokland, Jørn

AU - Rue, Håvard

PY - 2001/10

Y1 - 2001/10

N2 - Observed medical ultrasound images are degraded representations of the true acoustic tissue reflectance. The degradation is due to blur and speckle, and significantly reduces the diagnostic value of the images. In order to remove both blur and speckle we have developed a new statistical model for diffuse scattering in 2D ultrasound radio-frequency images, incorporating both spatial smoothness constraints and a physical model for diffuse scattering. The modeling approach is Bayesian in nature, and we use Markov chain Monte Carlo methods to obtain the restorations. The results from restorations of some real and simulated radio-frequency ultrasound images are presented, and compared with results produced by Wiener filtering.

AB - Observed medical ultrasound images are degraded representations of the true acoustic tissue reflectance. The degradation is due to blur and speckle, and significantly reduces the diagnostic value of the images. In order to remove both blur and speckle we have developed a new statistical model for diffuse scattering in 2D ultrasound radio-frequency images, incorporating both spatial smoothness constraints and a physical model for diffuse scattering. The modeling approach is Bayesian in nature, and we use Markov chain Monte Carlo methods to obtain the restorations. The results from restorations of some real and simulated radio-frequency ultrasound images are presented, and compared with results produced by Wiener filtering.

KW - Diffuse scattering

KW - Markov chain Monte Carlo

KW - Markov random fields

KW - Medical ultrasound

KW - Restoration

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U2 - 10.4173/mic.2001.4.3

DO - 10.4173/mic.2001.4.3

M3 - Article

AN - SCOPUS:0035493856

SN - 0332-7353

VL - 22

SP - 227

EP - 242

JO - Modeling, Identification and Control

JF - Modeling, Identification and Control

IS - 4

ER -

Bayesian 2D deconvolution: A model for diffuse ultrasound scattering

Abstract

Keywords

ASJC Scopus subject areas

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