By Topic

Singular Vectors of a Linear Imaging System as Efficient Channels for the Bayesian Ideal Observer

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Park, S. ; 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 ; Witten, J.M. ; Myers, K.J.

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.

Published in:

Medical Imaging, IEEE Transactions on  (Volume:28 ,  Issue: 5 )