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Rationale and Objectives. The investigators developed an efficient method for optimizing cathode ray tube (CRT) monitor performance for digital mammography, based on the correlation between the performance of human observers and the performance of a mathematical computer model of the human visual system. The investigators examined observer performance on soft-copy display of mammographic images that were either unprocessed or processed to compensate for modulation transfer function (MTF) deficiencies in the CRT display. The results were used to validate the human visual system model.Materials and Methods. Six radiologists viewed a series of 250 mammographic images with microcalcification clusters with different contrast levels on a CRT monitor. The images were viewed twice: once without image processing and once with processing designed to compensate for MTF deficiencies in the CRT monitor. The images were analyzed with the JNDmetrix Visual Discrimination Model, which is based on the principles of just-noticeable difference measurement and frequency-channel vision modeling. Receiver operating characteristic (ROC) curves were generated for the human observers and compared statistically with the model observers’ performance.Results. Both human and model performance was better overall with the MTF-compensated images, especially for microcalcifications in the midlevel contrast range. There was a very high correlation between human and model observers.Conclusion. The use of image-processing methods to compensate for limitations in the MTF of CRT monitors can improve the detection performance of radiologists searching for microcalcifications in mammographic images, and a model based on characteristics of the human visual system can be used to predict human observer results accurately. [Copyright &y& Elsevier]