Advanced search
1 file | 847.42 KB Add to list

Nonparametric estimation receiver operating characteristic analysis for performance evaluation on combined detection and estimation tasks

(2014) JOURNAL OF MEDICAL IMAGING. 1(3). p.031002-1-031002-8
Author
Organization
Abstract
In an effort to generalize task-based assessment beyond traditional signal detection, there is a growing interest in performance evaluation for combined detection and estimation tasks, in which signal parameters, such as size, orientation, and contrast are unknown and must be estimated. One motivation for studying such tasks is their rich complexity, which offers potential advantages for imaging system optimization. To evaluate observer performance on combined detection and estimation tasks, Clarkson introduced the estimation receiver operating characteristic (EROC) curve and the area under the EROC curve as a summary figure of merit. This work provides practical tools for EROC analysis of experimental data. In particular, we propose nonparametric estimators for the EROC curve, the area under the EROC curve, and for the variance/covariance matrix of a vector of correlated EROC area estimates. In addition, we show that reliable confidence intervals can be obtained for EROC area, and we validate these intervals with Monte Carlo simulation. Application of our methodology is illustrated with an example comparing magnetic resonance imaging k -space sampling trajectories. MATLAB® software implementing the EROC analysis estimators described in this work is publicly available at http://code.google.com/p/iqmodelo/.
Keywords
confidence intervals., receiver operating characteristic, image quality, U-statistics

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 847.42 KB

Citation

Please use this url to cite or link to this publication:

MLA
Wunderlich, Adam, and Bart Goossens. “Nonparametric Estimation Receiver Operating Characteristic Analysis for Performance Evaluation on Combined Detection and Estimation Tasks.” JOURNAL OF MEDICAL IMAGING 1.3 (2014): 031002–1–031002–8. Print.
APA
Wunderlich, A., & Goossens, B. (2014). Nonparametric estimation receiver operating characteristic analysis for performance evaluation on combined detection and estimation tasks. JOURNAL OF MEDICAL IMAGING, 1(3), 031002–1–031002–8.
Chicago author-date
Wunderlich, Adam, and Bart Goossens. 2014. “Nonparametric Estimation Receiver Operating Characteristic Analysis for Performance Evaluation on Combined Detection and Estimation Tasks.” Journal of Medical Imaging 1 (3): 031002–1–031002–8.
Chicago author-date (all authors)
Wunderlich, Adam, and Bart Goossens. 2014. “Nonparametric Estimation Receiver Operating Characteristic Analysis for Performance Evaluation on Combined Detection and Estimation Tasks.” Journal of Medical Imaging 1 (3): 031002–1–031002–8.
Vancouver
1.
Wunderlich A, Goossens B. Nonparametric estimation receiver operating characteristic analysis for performance evaluation on combined detection and estimation tasks. JOURNAL OF MEDICAL IMAGING. SPIE; 2014;1(3):031002–1–031002–8.
IEEE
[1]
A. Wunderlich and B. Goossens, “Nonparametric estimation receiver operating characteristic analysis for performance evaluation on combined detection and estimation tasks,” JOURNAL OF MEDICAL IMAGING, vol. 1, no. 3, pp. 031002-1-031002–8, 2014.
@article{5835615,
  abstract     = {In an effort to generalize task-based assessment beyond traditional signal detection, there is a growing interest in performance evaluation for combined detection and estimation tasks, in which signal parameters, such as size, orientation, and contrast are unknown and must be estimated. One motivation for studying such tasks is their rich complexity, which offers potential advantages for imaging system optimization. To evaluate observer performance on combined detection and estimation tasks, Clarkson introduced the estimation receiver operating characteristic (EROC) curve and the area under the EROC curve as a summary figure of merit. This work provides practical tools for EROC analysis of experimental data. In particular, we propose nonparametric estimators for the EROC curve, the area under the EROC curve, and for the variance/covariance matrix of a vector of correlated EROC area estimates. In addition, we show that reliable confidence intervals can be obtained for EROC area, and we validate these intervals with Monte Carlo simulation. Application of our methodology is illustrated with an example comparing magnetic resonance imaging k -space sampling trajectories. MATLAB® software implementing the EROC analysis estimators described in this work is publicly available at http://code.google.com/p/iqmodelo/.},
  articleno    = {14037SSPR},
  author       = {Wunderlich, Adam and Goossens, Bart},
  issn         = {2329-4302},
  journal      = {JOURNAL OF MEDICAL IMAGING},
  keywords     = {confidence intervals.,receiver operating characteristic,image quality,U-statistics},
  language     = {eng},
  number       = {3},
  pages        = {14037SSPR:031002-1--14037SSPR:031002-8},
  publisher    = {SPIE},
  title        = {Nonparametric estimation receiver operating characteristic analysis for performance evaluation on combined detection and estimation tasks},
  url          = {http://dx.doi.org/10.1117/1.JMI.1.3.031002},
  volume       = {1},
  year         = {2014},
}

Altmetric
View in Altmetric