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 journalArticlepeer-review


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 languageEnglish (US)
Pages (from-to)227-242
Number of pages16
JournalModeling, Identification and Control
Issue number4
StatePublished - Oct 2001
Externally publishedYes


  • 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


Dive into the research topics of 'Bayesian 2D deconvolution: A model for diffuse ultrasound scattering'. Together they form a unique fingerprint.

Cite this