Preliminary observations on a history-based image display optimizer for chest images
Harold L. Kundel, Calvin F. Nodine, Inna Brikman, Sridhar Belavadi Seshadri, Ronald L. Arenson · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
A major difficulty with currently available display systems is contrast rendition. The number of gray steps that can be displayed by a digital system driving an analog display is determined by the luminance range of the display and the signal-to-noise ratio (Schreiber, 1986). For a good display this might be 128 steps (7 bits). The minimal detectable contrast is determined by the average luminance at the eye and the image noise. Pizer and Chan (1979) using ROC techniques found the "perceived dynamic range" of a "high quality W monitor" to be about 87 steps. In their experiment, the eye was adapted to each luminance level. In a display like the chest with large bright and dark areas (heart and mediastinum vs. lungs) the eye will be adapted to some intermediate level and not optimally coupled to either the bright or the dark area. For this reason gray scale image processing is used for matching correctly the display to the observer' s perceptual system (Kundel, 1986). There have been some attempts to develop "perceptually relevant" gray scales (Johnston et al.,1985). Clinicians do not seem willing to spend the time needed to adjust optimally the controls at a display console. Even when the controls are fairly simple they still prefer images that technicians have recorded on film using some prescription for window and level. Perhaps they do not know how to optimize the contrast in the image and prefer to leave it to someone else or perhaps the controls are not as simple as they look. We have been working on a rule-based system that will select the proper gray scale transfer characteristic (GSTC) for the initial look at the image. It is based on the notion that there is an optimal GSTC for each diagnostic finding on the image and that for most images the diagnostic findings can be predicted from prompts derived directly from the clinical history. If this is the case, radiologists who receive diagnostic prompts should be able to predict the appropriate GSTC. This experiment was done to determine if radiologists can agree about the GSTC that will optimize the video display for particular diagnoses.