An alternate method for using a visual discrimination model (VDM) to optimize soft-copy display image quality.

Researchers have developed visual discrimination models (VDMs) that can predict a human observer's ability to detect a target object superposed on an image. These models incorporate sophisticated knowledge of the properties of the human visual system. In the predictive approach, termed conventional VDM usage, two input images with and without a target are analyzed by an algorithm that calculates a just-noticeable-difference (JND) index, which is a taken as a measure of the detectability of the target. A new method of using the VDM is described, termed channelized VDM, which involves finding the linear combination of the VDM-generated channels (which are not used in conventional VDM analysis) that has optimal classification ability between normal and abnormal images. The classification ability can be measured using receiver operating characteristic (ROC) or two alternative forced choice (2AFC) experiments, and in special cases they can also be predicted by signal detection theory (SDT) based model-observer methods. In this study simulated background and nodule containing regions were used to validate the new method. It was found that the channelized VDM predictions were in excellent qualitative agreement with human-observer validated SDT predictions. Either VDM method (conventional or channelized) has potential applicability to soft-copy display optimization. An advantage of any VDM-based approach is that complex effects, such as visual masking, are automatically accounted for, which effects are usually not included in SDT-based methods.