On efficient assessment of image-quality metrics based on linear model observers

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Publication Type pre-print
School or College <blank>
Department <blank>
Creator Wunderlich, Adam James
Other Author Noo, Frédéric
Title On efficient assessment of image-quality metrics based on linear model observers
Date 2012-01-01
Description This paper is motivated by the problem of image-quality assessment using model observers for the purpose of development and optimization of medical imaging systems. Specifically, we present a study regarding the estimation of the receiver operating characteristic (ROC) curve for the observer and associated summary measures. This study evaluates the statistical advantage that may be gained in ROC estimates of observer performance by assuming that the difference of the class means for the observer ratings is known. Such knowledge is frequently available in image-quality studies employing known-location lesion detection tasks together with linear model observers. The study is carried out by introducing parametric point and confidence interval estimators that incorporate a known difference of class means. An evaluation of the new estimators for the area under the ROC curve establishes that a large reduction in statistical variability can be achieved through incorporation of knowledge of the difference of class means. Namely, the mean 95% AUC confidence interval length can be as much as seven times smaller in some cases. We also examine how knowledge of the difference of class means can be advantageously used to compare the areas under two correlated ROC curves, and observe similar gains.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Volume 59
Issue 3
First Page 568
Last Page 578
Dissertation Institution University of Utah
Language eng
Bibliographic Citation Wunderlich, A., & Noo, F. (2012). On efficient assessment of image-quality metrics based on linear model observers. IEEE Transactions on Nuclear Science, 59(3), no. 6180203, 568-78.
Rights Management (c)2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 2,693,823 bytes
Identifier uspace,17607
ARK ark:/87278/s6805mbt
Setname ir_uspace
Date Created 2012-08-28
Date Modified 2021-05-06
ID 708064
Reference URL https://collections.lib.utah.edu/ark:/87278/s6805mbt
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