Incorporating human contrast sensitivity in model observers for detection tasks.
Contrast sensitivity of the human visual system is a characteristic that can adversely affect human performance in detection tasks. In this paper, we propose a method for incorporating human contrast sensitivity in anthropomorphic model observers. In our method, we model human contrast sensitivity using the Barten model with the mean luminance of a region of interest centered at the signal location. In addition, one free parameter is varied to control the effect of the contrast sensitivity on the model observer's performance. We investigate our model of human contrast sensitivity in a channelized-Hotelling observer (CHO) with difference-of-Gaussian channels. We call the CHO incorporating the contrast sensitivity a contrast-sensitive CHO (CS-CHO). The human data from a psychophysical study by Park et al. [1] are used for comparing the performance of the CS-CHO to human performance. That study used Gaussian signals with six different signal intensities in non-Gaussian lumpy backgrounds. A value of the free parameter is chosen to match the performance of the CS-CHO to the mean human performance only at the strongest signal. Results show that the CS-CHO with the chosen value of the free parameter predicts the mean human performance at the five lower signal intensities. Our results show that the CS-CHO predicts human performance well as a function of signal intensity.
Park
Subok
S
NIBIB/CDRH Laboratory for the Assessment of Medical Imaging System, Division of Imaging and Applied Mathematics, Center for Devices and Radiological Health, Food and Drug Administration, White Oak, MD 20993, USA. subok.park@fda.hhs.gov
Badano
Aldo
A
Gallas
Brandon D
BD
Myers
Kyle J
KJ
D000465:Algorithms; D019540:Area Under Curve; D003198:Computer Simulation; D015350:Contrast Sensitivity; D006801:Humans; D008954:Models, Biological; D016011:Normal Distribution; D019370:Observation / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D018709:Statistics, Nonparametric