Estimating blood vessel areas in ultrasound images using a deformable template model

Oddvar Husby, Haavard Rue

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

We consider the problem of obtaining interval estimates of vessel areas from ultrasound images of cross sections through the carotid artery. Robust and automatic estimates of the cross sectional area is of medical interest and of help in diagnosing atherosclerosis, which is caused by plaque deposits in the carotid artery. We approach this problem by using a deformable template to model the blood vessel outline, and use recent developments in ultrasound science to model the likelihood. We demonstrate that by using an explicit model for the outline, we can easily adjust for an important feature in the data: strong edge reflections called specular reflection. The posterior is challenging to explore, and naive standard MCMC algorithms simply converge too slowly. To obtain an efficient MCMC algorithm we make extensive use of computational efficient Gaussian Markov random fields, and use various block sampling constructions that jointly update large parts of the model.

Original languageEnglish (US)
Pages (from-to)211-226
Number of pages16
JournalStatistical Modeling
Volume4
Issue number3
DOIs
StatePublished - Jan 1 2004

Keywords

  • Gaussian Markov random fields
  • interval estimates
  • specular reflection
  • ultrasound images

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Estimating blood vessel areas in ultrasound images using a deformable template model'. Together they form a unique fingerprint.

Cite this