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Journal of Electronic Imaging

Bayesian model for intensity mapping in magnetic resonance imaging image registration
Author(s): Alexei Manso Correa Machado; Mario F.M. Campos; James C. Gee
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Paper Abstract

We present a likelihood model for Bayesian nonrigid image registration that relates the distinct acquisition models of different MRI (magnetic resonance imaging) scanners. The model is derived from a Bayesian network that represents the imaging situation under consideration to construct the appropriate similarity measure for the given situation. The method is compared to the cross-correlation and mutual information measures in a set of registration experiments on different images and over different synthetically generated geometric and intensity distortions. The probability-based similarity measure yields, on average, more accurate and robust registrations than either the cross-correlation or mutual information measures.

Paper Details

Date Published: 1 January 2003
PDF: 9 pages
J. Electron. Imag. 12(1) doi: 10.1117/1.1526845
Published in: Journal of Electronic Imaging Volume 12, Issue 1
Show Author Affiliations
Alexei Manso Correa Machado, Pontifical Catholic Univ. of Minas Gerais (Brazil)
Mario F.M. Campos, Univ. Federal de Minas Gerais (Brazil)
James C. Gee, Univ. of Pennsylvania (United States)

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