Visual discrimination model for digital mammography

Numerous studies have been conducted to determine experimentally the effects of image processing and display parameters on the diagnostic performance of radiologists. Comprehensive optimization of imaging systems for digital mammography based solely on measurements of reader performance is impractical, however, due to the large number of interdependent variables to be tested. A reliable, efficient alternative is needed to improve the evaluation and optimization of new imaging technologies. The Sarnoff JNDmetrix^TM Visual Discrimination Model (VDM) is a computational, just-noticeable difference model of human vision that has been applied successfully to predict performance in various nonmedical detection and rating tasks. To test the applicability of the VDM to specific detection tasks in digital mammography, two observer performance studies were conducted. In the first study, effects of display tone scale and peak luminance on the detectability of microcalcifications were evaluated. The VDM successfully predicted improvements in reader performance for perceptually linearized tone scales and higher display luminances. In the second study, the detectability of JPEG and wavelet compression artifacts was evaluated, and performance ratings were again found to be highly correlated with VDM predictions. These results suggest that the VDM would be useful in the assessment and optimization of new imaging and compression technologies for digital mammography.