Bayesian 2D deconvolution: A model for diffuse ultrasound scattering

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 di...

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Bibliographic Details
Published in:Modeling, identification and control identification and control, 2001-10, Vol.22 (4), p.227-242
Main Authors: HUSBY, Oddvar, LIE, Torgrim, LANGØ, Thomas, HOKLAND, Jørn, RUES, Havard
Format: Article
Language:English
Subjects:
Biological and medical sciences
Computerized, statistical medical data processing and models in biomedicine
Degradation
diffuse scattering
Exact sciences and technology
Image analysis
Investigative techniques, diagnostic techniques (general aspects)
Markov processes
Markov random fields
Mathematics
Medical sciences
Medical ultrasound
Miscellaneous. Technology
Models and simulation
Monte Carlo methods
Probability and statistics
Probability theory and stochastic processes
Radiodiagnosis. Nmr imagery. Nmr spectrometry
restoration
Sciences and techniques of general use
Ultrasonic scattering
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Summary: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.
ISSN:0332-7353
1890-1328
DOI:10.4173/mic.2001.4.3