Visual perception studies and observer models in medical imaging

Most academic radiologists will be familiar with receiver operating characteristic (ROC) studies. Fundamental studies of human observer performance are now usually performed by forced-choice methods. Both methods are based on signal detection theory. The ROC method gives an operating curve of true-positive versus false-positive probabilities. The area under the curve, A(Z), can be used a summary performance measure. In the forced-choice method, observers are given 2 or more images with one containing the signal. The observer's task is to select the option most likely to contain the signal. The percentage of correct responses, PC, is a summary performance measure. Precise comparison of the 2 methods is limited to very controlled experiments in which signals (simulated lesions for example) are carefully designed and detection or discrimination is limited by true random noise. Under these conditions, theory predicts a simple relationship between summary measures and human results are consistent with theory. There will be a description of forced-choice experimental methods and data analysis. There has also been considerable work on development of theoretic observer models. Human experiment results have used to evaluate the models. Models that correlate well with human performance in turn can be used for preliminary design of new imaging systems and for selection of image quality metrics for comparing equipment performance, this article will provide a summary of work during the last 30 years on evaluating human signal detection capabilities, observer models and image quality metrics.