Evaluation of Cardiac Ultrasound Data by Bayesian Probability Maps

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Evaluation of Cardiac Ultrasound Data by Bayesian Probability Maps

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Publication Conference Paper, peer reviewed
Title Evaluation of Cardiac Ultrasound Data by Bayesian Probability Maps
Author(s) Hansson, Mattias ; Brandt, Sami ; Gudmundsson, Petri ; Lindgren, Finn
Date 2009
English abstract
Abstract. In this paper we present improvements to our Bayesian approach for describing the position distribution of the endocardium in cardiac ultrasound image sequences. The problem is represented as a latent variable model, which represents the inside and outside of the endocardium, for which the posterior density is estimated. We start our construction by assuming a three-component Rayleigh mixture model: for blood, echocardiographic artifacts, and tissue. The Rayleigh distribution has been previously shown to be a suitable model for blood and tissue in cardiac ultrasound images. From the mixture model parameters we build a latent variable model, with two realizations: tissue and endocardium. The model is refined by incorporating priors for spatial and temporal smoothness, in the form of total variation, connectivity, preferred shapes and position, by using the principal components and location distribution of manually segmented training shapes. The posterior density is sampled by a Gibbs method to estimate the expected latent variable image which we call the Bayesian ProbabilityMap, since it describes the probability of pixels being classified as either heart tissue or within the endocardium. By sampling the translation distribution of the latent variables, we improve the convergence rate of the algorithm. Our experiments show promising results indicating the usefulness of the Bayesian Probability Maps for the clinician since, instead of producing a single segmenting curve, it highlights the uncertain areas and suggests possible segmentations.
DOI http://dx.doi.org/10.1007/978-3-642-10520-3_103 (link to publisher's fulltext)
Host/Issue Advances in Visual Computing;5876
Series/Issue Lecture Notes in Computer Science
ISSN 1611-3349
ISBN 978-3-642-10519-7
Pages 1073-1084
Language eng (iso)
Subject(s) Technology
Research Subject Categories::MATHEMATICS::Applied mathematics
Research Subject Categories::MATHEMATICS::Applied mathematics::Optimization, systems theory
Research Subject Categories::MEDICINE::Physiology and pharmacology::Physiology::Clinical physiology
Note 5th International Symposium, ISVC 2009, Las Vegas, NV, USA, November 30-December 2, 2009
Handle http://hdl.handle.net/2043/10097 (link to this page)

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