Metrics of medical image quality: Task-based model observers vs. image discrimination/perceptual difference models

There have been two distinct approaches to develop human vision models that can be used to perform automated evaluation and optimization of medical image quality: linear task based model observers vs. perceptual difference/image discrimination models. Although these two approaches are very different there has been little work directly comparing them in their ability to optimize human performance in clinically relevant tasks. We compared the effectiveness of these two types of metrics of image quality to perform automated computer optimization of JPEG 2000 image compression encoder settings using test images that combined real x-ray coronary angiogram backgrounds with simulated filling defects of 184 different size/shapes. A genetic algorithm was used to optimize the JPEG 2000 encoder settings with respect to: a) a particular task based model observer performance (non-prewhitening matched filter with an eye filter, NPWE; b) a particular perceptual difference/image discrimination model error metric (DCTune2.0; NASA Ames Research Center). A subsequent human psychophysical study was conducted to evaluate the effect of the two different optimized compression encoder settings on visual detection of the simulated filling defect in one of four locations (four alternative forced choice; 4 AFC). Results show that optimizing JPEG 2000 encoder settings with respect to both the NPWE performance and DCTune 2.0 perceptual error lead to improved human task performance relative to human performance with the default encoder settings. However, the NPWE-optimization led to much greater human performance improvement than the perceptual difference model optimization.