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Proceedings Paper

A nonparametric approach to comparing the areas under correlated LROC curves
Author(s): Adam Wunderlich; Frédéric Noo
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Paper Abstract

In contrast to the ROC assessment paradigm, localization ROC (LROC) analysis provides a means to jointly assess the accuracy of visual search and detection in an observer study. In a typical multireader, multicase (MRMC) evaluation, the data sets are paired so that correlations arise in observer performance both between observers and between image reconstruction methods (or modalities). Therefore,MRMC evaluations motivate the need for a statistical methodology to compare correlated LROC curves. In this work, we suggest a nonparametric strategy for this purpose. Specifically, we find that seminal work of Sen on U-statistics can be applied to estimate the covariance matrix for a vector of LROC area estimates. The resulting covariance estimator is the LROC analog of the covariance estimator given by DeLong et al. for ROC analysis. Once the covariance matrix is estimated, it can be used to construct confidence intervals and/or confidence regions for purposes of comparing observer performance across reconstruction methods. The utility of our covariance estimator is illustrated with a human-observer LROC evaluation of three reconstruction strategies for fan-beam CT.

Paper Details

Date Published: 22 February 2012
PDF: 8 pages
Proc. SPIE 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment, 83180F (22 February 2012);
Show Author Affiliations
Adam Wunderlich, The Univ. of Utah (United States)
Frédéric Noo, The Univ. of Utah (United States)

Published in SPIE Proceedings Vol. 8318:
Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Claudia R. Mello-Thoms, Editor(s)

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