Can the non-pre-whitening model observer, including aspects of the human visual system, predict human observer performance in mammography?

ABSTRACT

PURPOSE: In mammography, images are processed prior to display. Current methodologies based on physical image quality measurements are however not designed for the evaluation of processed images. Model observers (MO) might be suitable for this evaluation. The aim of this study was to investigate whether the non-pre-whitening (NPW) MO can be used to predict human observer performance in mammography-like images by including different aspects of the human visual system (HVS). METHODS: The correlation between human and NPW MO performance has been investigated for the detection of disk shaped objects in simulated white noise (WN) and clustered lumpy backgrounds (CLB), representing quantum noise limited and mammography-like images respectively. The images were scored by the MO and five human observers in a 2-alternative forced choice experiment. RESULTS: For WN images it was found that the log likelihood ratio (RLR2), which expresses the goodness of fit, was highest (0.44) for the NPW MO without addition of HVS aspects. For CLB the RLR2 improved from 0.46 to 0.65 with addition of HVS aspects. The correlation was affected by object size and background. CONCLUSIONS: This study shows that by including aspects of the HVS, the performance of the NPW MO can be improved to better predict human observer performance. This demonstrates that the NPW MO has potential for image quality assessment. However, due to the dependencies found in the correlation, the NPW MO can only be used for image quality assessment for a limited range of object sizes and background variability.