The effect of non-linear human visual system components on linear model observers

Linear model observers have been used successfully to predict human performance in clinically relevant visual tasks for a variety of backgrounds. On the other hand, there has been another family of models used to predict human visual detection of signals superimposed on one of two identical backgrounds (masks). These masking models usually include a number of non-linear components in the channels that reflect properties of the firing of cells in the primary visual cortex (V1). The relationship between these two traditions of models has not been extensively investigated in the context of detection in noise. In this paper, we evaluated the effect of including some of these non-linear components into a linear channelized Hotelling observer (CHO), and the associated practical implications for medical image quality evaluation. In particular, we evaluate whether the rank order evaluation of two compression algorithms (JPEG vs. JPEG 2000) is changed by inclusion of the non-linear components. The results show: a) First that the simpler linear CHO model observer outperforms CHO model with the nonlinear components investigated. b) The rank order of model observer performance for the compression algorithms did not vary when the non-linear components were included. For the present task, the results suggest that the addition of the physiologically based channel non-linearities to a channelized Hotelling might add complexity to the model observers without great impact on medical image quality evaluation.