Automated optimization of JPEG 2000 encoder options based on model observer performance for detecting variable signals in X-ray coronary angiograms.

Abstract

Image compression is indispensable in medical applications where inherently large volumes of digitized images are presented. JPEG 2000 has recently been proposed as a new image compression standard. The present recommendations on the choice of JPEG 2000 encoder options were based on nontask-based metrics of image quality applied to nonmedical images. We used the performance of a model observer [non-prewhitening matched filter with an eye filter (NPWE)] in a visual detection task of varying signals [signal known exactly but variable (SKEV)] in X-ray coronary angiograms to optimize JPEG 2000 encoder options through a genetic algorithm procedure. We also obtained the performance of other model observers (Hotelling, Laguerre-Gauss Hotelling, channelized-Hotelling) and human observers to evaluate the validity of the NPWE optimized JPEG 2000 encoder settings. Compared to the default JPEG 2000 encoder settings, the NPWE-optimized encoder settings improved the detection performance of humans and the other three model observers for an SKEV task. In addition, the performance also was improved for a more clinically realistic task where the signal varied from image to image but was not known a priori to observers [signal known statistically (SKS)]. The highest performance improvement for humans was at a high compression ratio (e.g., 30:1) which resulted in approximately a 75% improvement for both the SKEV and SKS tasks.