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Proceedings Paper

Laguerre-Gauss basis functions in observer models
Author(s): Arthur E. Burgess
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Paper Abstract

Observer models based on linear classifiers with basis functions (channels) are useful for evaluation of detection performance with medical images. They allow spatial domain calculations with a covariance matrix of tractable size. The term “channelized Fisher-Hotelling observer” will be used here. It is also called the “channelized Hotelling observer” model. There are an infinite number of basis function (channel ) sets that could be employed. Examples of channel sets that have been used include: difference of Gaussian (DOG) filters, difference of Mesa (DOM) filters and Laguerre-Gauss (LG) basis functions. Another option, sums of LG functions (LGS), will also be presented here. This set has the advantage of having no DC response. The effect of the number of images used to estimate model observer performance will be described, for both filtered 1/f3 noise and GE digital mammogram backgrounds. Finite sample image sets introduce both bias and variance to the estimate. The results presented here agree with previous work on linear classifiers. The LGS basis set gives a small but statistically significant reduction in bias. However, this may not be of much practical benefit. Finally, the effect of varying the number of basis functions included in the set will be addressed. It was found that four LG bases or three LGS bases are adequate.

Paper Details

Date Published: 22 May 2003
PDF: 9 pages
Proc. SPIE 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, (22 May 2003); doi: 10.1117/12.479975
Show Author Affiliations
Arthur E. Burgess, Brigham and Women's Hospital/Harvard Medical School (United States)

Published in SPIE Proceedings Vol. 5034:
Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment
Dev P. Chakraborty; Elizabeth A. Krupinski, Editor(s)

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