DocumentCode :
979486
Title :
Singular Vectors of a Linear Imaging System as Efficient Channels for the Bayesian Ideal Observer
Author :
Park, Subok ; Witten, Joel M. ; Myers, Kyle J.
Author_Institution :
Center for Devices & Radiol. Health, Div. of Imaging & Appl. Math., NIBIB/CDRH Lab. for Assessment of Med. Imaging Syst., Food & Drug Adm., White Oak, MD
Volume :
28
Issue :
5
fYear :
2009
fDate :
5/1/2009 12:00:00 AM
Firstpage :
657
Lastpage :
668
Abstract :
The Bayesian ideal observer provides an absolute upper bound for diagnostic performance of an imaging system and hence should be used for the assessment of image quality whenever possible. However, computation of ideal-observer performance in clinical tasks is difficult since the probability density functions of the data required for this observer are often unknown in tasks involving realistic, complex backgrounds. Moreover, the high dimensionality of the integrals that need to be calculated for the observer makes the computation more difficult. The ideal observer constrained to a set of channels, which we call a channelized-ideal observer (CIO), can reduce the dimensionality of the problem. These channels are called efficient if the CIO can approximate ideal-observer performance. In this paper, we propose a method to choose efficient channels for the ideal observer based on a singular value decomposition of a linear imaging system. As a demonstration, we test our method on detection tasks using non-Gaussian lumpy backgrounds and signals of Gaussian and elliptical profiles. Our simulation results show that singular vectors associated with either the background or the signal are highly efficient for the ideal observer for detecting both types of signals. In addition, this CIO outperforms a channelized-Hotelling observer with the same channels.
Keywords :
Bayes methods; image classification; medical image processing; medical signal detection; probability; singular value decomposition; Bayesian ideal observer; Gaussian profiles; absolute upper bound; binary signal detection; channelized-Hotelling observer; channelized-ideal observer; clinical tasks; elliptical profiles; image classification; image quality; linear imaging system; nonGaussian lumpy backgrounds; probability density functions; singular value decomposition; singular vectors; Bayesian methods; Biomedical imaging; Drugs; Image quality; Mathematics; Probability density function; Signal detection; Singular value decomposition; Upper bound; Vectors; Bayesian ideal observer; efficient channels; image quality; linear imaging system; Algorithms; Area Under Curve; Bayes Theorem; Computer Simulation; Data Interpretation, Statistical; Image Processing, Computer-Assisted; Normal Distribution;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2008.2008967
Filename :
4667659
Link To Document :
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