Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

CiteULike is a free service for managing and discovering scholarly references - click here to get started.

Sign In to gain access to subscriptions and/or personal tools.
Statistical Modelling
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Husby, O.
Right arrow Articles by Rue, H.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

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

Oddvar Husby

Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway

Haåvard Rue

Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway

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.

Key Words: Gaussian Markov random fields • interval estimates • specular reflection • ultrasound images

Statistical Modelling, Vol. 4, No. 3, 211-226 (2004)
DOI: 10.1191/1471082X04st074oa
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?