Abstract

Modern imaging systems rely on complicated hardware and sophisticated image-processing methods to produce images. Owing to this complexity in the imaging chain, there are numerous variables in both the hardware and the software that need to be determined. We advocate a task-based approach to measuring and optimizing image quality in which one analyzes the ability of an observer to perform a task. Ideally, a task-based measure of image quality would account for all sources of variation in the imaging system, including object variability. Often, researchers ignore object variability even though it is known to have a large effect on task performance. The more accurate the statistical description of the objects, the more believable the task-based results will be. We have developed methods to fit statistical models of objects, using only noisy image data and a well-characterized imaging system. The results of these techniques could eventually be used to optimize both the hardware and the software components of imaging systems.

© 2003 Optical Society of America

Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques

Matthew A. Kupinski, John W. Hoppin, Eric Clarkson, and Harrison H. Barrett
J. Opt. Soc. Am. A 20(3) 430-438 (2003)

Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal

Subok Park, Harrison H. Barrett, Eric Clarkson, Matthew A. Kupinski, and Kyle J. Myers
J. Opt. Soc. Am. A 24(12) B136-B150 (2007)

Validating the use of channels to estimate the ideal linear observer

Brandon D. Gallas and Harrison H. Barrett
J. Opt. Soc. Am. A 20(9) 1725-1738 (2003)

Efficiency of the human observer detecting random signals in random backgrounds

Subok Park, Eric Clarkson, Matthew A. Kupinski, and Harrison H. Barrett
J. Opt. Soc. Am. A 22(1) 3-16 (2005)

Objective assessment of image quality. IV. Application to adaptive optics

Harrison H. Barrett, Kyle J. Myers, Nicholas Devaney, and Christopher Dainty
J. Opt. Soc. Am. A 23(12) 3080-3105 (2006)