Affiliation:
1. Department of Mathematical Sciences, Norwegian University of Science and
Technology, Trondheim, Norway
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.
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Cited by
3 articles.
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1. Ultrasound Image Segmentation through a Fast Active Contour Based Algorithm;VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014;2015
2. Detection of an anomalous cluster in a network;The Annals of Statistics;2011-02-01
3. Ultrasound image segmentation: a survey;IEEE Transactions on Medical Imaging;2006-08