Abstract

We studied the influence of signal variability on human and model observers for detection tasks with realistic simulated masses superimposed on real patient mammographic backgrounds and synthesized mammographic backgrounds (clustered lumpy backgrounds, CLB). Results under the signal-known-exactly (SKE) paradigm were compared with signal-known-statistically (SKS) tasks for which the observers did not have prior knowledge of the shape or size of the signal. Human observers' performance did not vary significantly when benign masses were superimposed on real images or on CLB. Uncertainty and variability in signal shape did not degrade human performance significantly compared with the SKE task, while variability in signal size did. Implementation of appropriate internal noise components allowed the fit of model observers to human performance.

© 2009 Optical Society of America

Human linear template with mammographic backgrounds estimated with a genetic algorithm

Cyril Castella, Craig K. Abbey, Miguel P. Eckstein, Francis R. Verdun, Karen Kinkel, and François O. Bochud
J. Opt. Soc. Am. A 24(12) B1-B12 (2007)

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)

Channelized model observer for the detection and estimation of signals with unknown amplitude, orientation, and size

Lu Zhang, Bart Goossens, Christine Cavaro-Ménard, Patrick Le Callet, and Di Ge
J. Opt. Soc. Am. A 30(11) 2422-2432 (2013)

Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds

Subok Park, Brandon D. Gallas, Aldo Badano, Nicholas A. Petrick, and Kyle J. Myers
J. Opt. Soc. Am. A 24(4) 911-921 (2007)

Effect of random background inhomogeneity on observer detection performance

J. P. Rolland and H. H. Barrett
J. Opt. Soc. Am. A 9(5) 649-658 (1992)