Paper
20 March 2007 A non-intuitive aspect of Swensson's LROC model
Author Affiliations +
Abstract
If the locations of abnormalities (targets) in an image are unknown, the evaluation of human observers' detection performance can be complex. Richard Swensson in 1996 developed a model that unified the various analysis approaches to this problem. For the LROC experiment, the model assumed that a false-positive report-arises from the latent decision variable of the most suspicious non-target location of the target stimuli. The localization scoring was based on the same latent decision variable, i.e., when the latent decision variable at the non-target location was greater than latent decision variable at the target location the response was scored as a miss. Human observer reports vary, i.e., different locations have been identified during replications. A Monte Carlo model was developed to investigate this variation and identified a non-intuitive aspect of Swensson's LROC model. When the number of potentially suspicious locations was 1, the model performance was greater than apparently possible. For example, assume that target expected latent decision variable is 1.0. Both target and non-target standard deviations were assumed to be 1.0. The model predicts the area-under-the-ROC is 0.815, which implies da=1.27. If the target latent decision variable was 0.0, then da=0.61. The reason was the number latent decision variables in the model for the non-target stimuli is one, while the number latent decision variables for the target stimuli is the maximum of 2. The simulation indicated that the parameters of a LROC fit, when the number of suspicious locations is small or the observer performance is low, does not have the same intuitive meaning as ROC parameters of a SKE task.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Philip F. Judy "A non-intuitive aspect of Swensson's LROC model", Proc. SPIE 6515, Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment, 65150Z (20 March 2007); https://doi.org/10.1117/12.710280
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KEYWORDS
Signal to noise ratio

Target detection

Medical imaging

Monte Carlo methods

Performance modeling

Data modeling

Computed tomography

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