Share Email Print
cover

Proceedings Paper

Efficient channels for the ideal observer
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

For a signal-detection task, the Bayesian ideal observer is optimal among all observers because it incorporates all the statistical information of the raw data from an imaging system. The ideal observer test statistic, the likelihood ratio, is difficult to compute when uncertainties are present in backgrounds and signals. In this work, we propose a new approximation technique to estimate the likelihood ratio. This technique is a dimensionality-reduction scheme we will call the channelized-ideal observer (CIO). We can reduce the high-dimensional integrals of the ideal observer to the low-dimensional integrals of the CIO by applying a set of channels to the data. Lumpy backgrounds and circularly symmetric Gaussian signals are used for simulations studies. Laguerre-Gaussian (LG) channels have been shown to be useful for approximating ideal linear observers with these backgrounds and signals. For this reason, we choose to use LG channels for our data. The concept of efficient channels is introduced to closely approximate ideal-observer performance with the CIO for signal-known-exactly (SKE) detection tasks. Preliminary results using one to three LG channels show that the performance of the CIO is better than the channelized-Hotelling observer for the SKE detection tasks.

Paper Details

Date Published: 4 May 2004
PDF: 10 pages
Proc. SPIE 5372, Medical Imaging 2004: Image Perception, Observer Performance, and Technology Assessment, (4 May 2004); doi: 10.1117/12.531614
Show Author Affiliations
Subok Park, Univ. of Arizona (United States)
Matthew A. Kupinski, Optical Sciences Ctr./Univ. of Arizona (United States)
Eric Clarkson, Optical Sciences Ctr./Univ. of Arizona (United States)
Harrison H. Barrett, Optical Sciences Ctr./Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 5372:
Medical Imaging 2004: Image Perception, Observer Performance, and Technology Assessment
Dev P. Chakraborty; Miguel P. Eckstein, Editor(s)

© SPIE. Terms of Use
Back to Top