Segmentation of B-mode cardiac ultrasound data by Bayesian Probability Maps

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Segmentation of B-mode cardiac ultrasound data by Bayesian Probability Maps

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Publication Article, peer reviewed scientific
Title Segmentation of B-mode cardiac ultrasound data by Bayesian Probability Maps
Author(s) Hansson, Mattias ; Brandt, Sami ; Lindström, Johan ; Gudmundsson, Petri ; Jujic, Amra ; Malmgren, Andreas ; Cheng, Yuanji
Date 2014
English abstract
In this paper we present a model for describing the position distribution of the endocardium in the two-chamber apical long-axis view of the heart in clinical B-mode ultrasound cycles. We propose a novel Bayesian formulation, including priors for spatial and temporal smoothness, and preferred shapes and position. The shape model takes into account both endocardium, atrial region and apex. The likelihood is built using a statistical signal model, which attempts to closely model a censored signal. In addition, the use of a censored Gamma mixture model with unknown censoring point, to handle artefacts resulting from left-censoring of the in US clinical B-mode, is to our knowledge novel. The posterior density is sampled by the Gibbs method to estimate the expected latent variable representation of the endocardium, which we call the Bayesian Probability Map; the map describes the probability of pixels being classified as being within the endocardium. The regularization parameters of the model are estimated by cross-validation, and the results are compared against the two-chamber apical model of Chen et al.
DOI http://dx.doi.org.proxy.mah.se/10.1016/j.media.2014.06.004 (link to publisher's fulltext)
Publisher Elsevier
Host/Issue Medical Image Analysis;7
Volume 18
ISSN 1361-8415
Pages 1184-1199
Language eng (iso)
Subject(s) Bayes
Segmentation
Cardiac
B-mode ultrasound
Censored data
Medicine
Research Subject Categories::MEDICINE
Handle http://hdl.handle.net/2043/18225 (link to this page)

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