Bayesian multiscale analysis of images modeled as Gaussian Markov random fields

Kevin Thon, Hvard Rue, Stein Olav Skrøvseth*, Fred Godtliebsen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

A Bayesian multiscale technique for the detection of statistically significant features in noisy images is proposed. The prior is defined as a stationary intrinsic Gaussian Markov random field on a toroidal graph, which enables efficient computation of the relevant posterior marginals. Hence the method is applicable to large images produced by modern digital cameras. The technique is demonstrated in two examples from medical imaging.

Original languageEnglish (US)
Pages (from-to)49-61
Number of pages13
JournalComputational Statistics and Data Analysis
Volume56
Issue number1
DOIs
StatePublished - Jan 1 2012

Bibliographical note

Funding Information:
We thank M.D. Thomas Schopf for sharing his expertise in dermatology, and Dr. med. Herbert Kirchesch for supplying us with the dermatoscopic skin lesion images used in the examples. We would also like to thank Dr. Maciel Zortea for doing the hair detection with the Dullrazor software. The research was funded by the Norwegian Research Council and the Centre for Research Driven Innovation, Tromsø Telemedicine Laboratory (TTL) .

Keywords

  • Bayesian analysis
  • Gaussian Markov random fields
  • Multi-resolution analysis
  • Scale space

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Bayesian multiscale analysis of images modeled as Gaussian Markov random fields'. Together they form a unique fingerprint.

Cite this