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Proceedings Paper

FROC curves using a model of visual search
Author(s): D. P. Chakraborty
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Paper Abstract

The purpose of this paper is to describe FROC (free-response receiver operating characteristic) curves predicted by a recent model of visual search. The model is characterized by three parameters (μ, λ and ν) which quantify perceived lesion signal-to-noise ratio, the average number of non-lesion locations per image considered for marking by the observer, and the probability that a lesion is considered for marking, respectively. An important characteristic of a search-model predicted FROC curve is that it is contained within the rectangle with corners at (0, 0) and (λ, ν). It is shown that λ and ν determine the x and y end-point coordinates of the FROC curve, respectively, and mu determines the sharpness of the transition from vertical slope at the origin to zero slope at (λ, ν). Two figures of merit (FOM) quantifying free-response performance are described. A FOM commonly used by CAD developers is the ordinate of the FROC curve at a specified abscissa. Another FOM, recently introduced by us, measures the ability of the observer to discriminate between normal and abnormal images. The latter is analogous to the Az measure widely used in ROC methodology. The search-model is related to the initial detection and candidate analysis (IDCA) method of fitting FROC curves but a key assumption, the shapes of the fitted curves and the estimation methods are different. The search-model yielded excellent fits to a designer level and to a simulated clinical level CAD data set. Available software implementing these ideas is expected to aid in the optimization of CAD algorithms.

Paper Details

Date Published: 20 March 2007
PDF: 11 pages
Proc. SPIE 6515, Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment, 65150Y (20 March 2007); doi: 10.1117/12.699222
Show Author Affiliations
D. P. Chakraborty, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 6515:
Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment
Yulei Jiang; Berkman Sahiner, Editor(s)

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