Literature DB >> 18059918

Estimation receiver operating characteristic curve and ideal observers for combined detection/estimation tasks.

Eric Clarkson1.   

Abstract

The localization receiver operating characteristic (LROC) curve is a standard method to quantify performance for the task of detecting and locating a signal. This curve is generalized to arbitrary detection/estimation tasks to give the estimation ROC (EROC) curve. For a two-alternative forced-choice study, where the observer must decide which of a pair of images has the signal and then estimate parameters pertaining to the signal, it is shown that the average value of the utility on those image pairs where the observer chooses the correct image is an estimate of the area under the EROC curve (AEROC). The ideal LROC observer is generalized to the ideal EROC observer, whose EROC curve lies above those of all other observers for the given detection/estimation task. When the utility function is nonnegative, the ideal EROC observer is shown to share many mathematical properties with the ideal observer for the pure detection task. When the utility function is concave, the ideal EROC observer makes use of the posterior mean estimator. Other estimators that arise as special cases include maximum a posteriori estimators and maximum-likelihood estimators.

Mesh:

Year:  2007        PMID: 18059918      PMCID: PMC2575755          DOI: 10.1364/josaa.24.000b91

Source DB:  PubMed          Journal:  J Opt Soc Am A Opt Image Sci Vis        ISSN: 1084-7529            Impact factor:   2.129


  7 in total

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Authors:  Matthew A Kupinski; John W Hoppin; Eric Clarkson; Harrison H Barrett
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3.  Bounds on the area under the receiver operating characteristic curve for the ideal observer.

Authors:  Eric Clarkson
Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  2002-10       Impact factor: 2.129

4.  Decision strategies that maximize the area under the LROC curve.

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5.  Using Fisher information to approximate ideal-observer performance on detection tasks for lumpy-background images.

Authors:  Fangfang Shen; Eric Clarkson
Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  2006-10       Impact factor: 2.129

6.  Approximations to ideal-observer performance on signal-detection tasks.

Authors:  E Clarkson; H H Barrett
Journal:  Appl Opt       Date:  2000-04-10       Impact factor: 1.980

7.  Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions.

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Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  1998-06       Impact factor: 2.129

  7 in total
  20 in total

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Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  2010-10-01       Impact factor: 2.129

2.  A Task-Based Approach to Adaptive and Multimodality Imaging: Computation techniques are proposed for figures-of-merit to establish feasibility and optimize use of multiple imaging systems for disease diagnosis and treatment-monitoring.

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Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2008-03       Impact factor: 10.961

3.  Objective assessment of image quality VI: imaging in radiation therapy.

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Review 4.  Task-based measures of image quality and their relation to radiation dose and patient risk.

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7.  Scanning linear estimation: improvements over region of interest (ROI) methods.

Authors:  Meredith K Kupinski; Eric W Clarkson; Harrison H Barrett
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8.  Shannon information for joint estimation/detection tasks and complex imaging systems.

Authors:  Eric Clarkson; Johnathan B Cushing
Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  2016-03       Impact factor: 2.129

9.  Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods.

Authors:  Weimin Zhou; Hua Li; Mark A Anastasio
Journal:  IEEE Trans Med Imaging       Date:  2020-11-30       Impact factor: 10.048

10.  The efficiency of the human observer for lesion detection and localization in emission tomography.

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Journal:  Phys Med Biol       Date:  2009-04-08       Impact factor: 3.609

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