Statistical Modelling 4 (2004), 211–226
Estimating blood vessel areas in ultrasound
images using a deformable template model
Oddvar Husby and Håvard Rue
Department of Mathematical Sciences,
Norwegian University of Science and Technology,
N–7491 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.
Keywords:
Gaussian Markov random fields; interval estimates;
specular reflection; ultrasound images
Data and software:
Concerning the availability of data and computer code, please contact
Oddvar Husby at oddhu@statoil.com.
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