Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal.
We investigate a channelized-ideal observer (CIO) with Laguerre-Gauss (LG) channels to approximate ideal-observer performance in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal. A Markov-chain Monte Carlo approach is employed to determine the performance of both the ideal observer and the CIO using a large number of LG channels. Our results indicate that the CIO with LG channels can approximate ideal-observer performance within error bars, depending on the imaging system, object, and channel parameters. The CIO also outperforms a channelized-Hotelling observer using the same channels. In addition, an alternative approach for estimating the CIO is investigated. This approach makes use of the characteristic functions of channelized data and employs an approximation method to the area under the receiver operating characteristic curve. The alternative approach provides good estimates of the performance of the CIO with five LG channels. However, for large channel cases, more efficient computational methods need to be developed for the CIO to become useful in practice.
Park
Subok
S
National Institute of Biomedical Imaging and Bioengineering/Center for Devices and Radiological Health (CDRH), Division of Imaging and Applied Mathematics, US Food and Drug Administration, Silver Spring, Maryland 20993, USA. subok.park@fda.hhs.gov
Barrett
Harrison H
HH
Clarkson
Eric
E
Kupinski
Matthew A
MA
Myers
Kyle J
KJ
D000465:Algorithms; D019540:Area Under Curve; D016477:Artifacts; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data*; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted