Evaluation of cardiac ultrasound data by Bayesian probability maps

Mattias Hansson*, Sami Brandt, Petri Gudmundsson, Finn Lindgren

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 Probability Map, 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.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 5th International Symposium, ISVC 2009, Proceedings
Pages1073-1084
Number of pages12
EditionPART 2
DOIs
Publication statusPublished - 2009
Event5th International Symposium on Advances in Visual Computing, ISVC 2009 - Las Vegas, NV, United States
Duration: 30 Nov 20092 Dec 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5876 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Symposium on Advances in Visual Computing, ISVC 2009
Country/TerritoryUnited States
CityLas Vegas, NV
Period30/11/092/12/09

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