Human Visual System
In order to optimize a display system to allow for the detection of small changes in radiographic density and small spatial features in a digital image, it is necessary that we understand how the human visual system works.
The human visual system does not respond linearly to the amount of incident light. At low levels of incident light, we can notice small changes in luminance. At higher light levels, the change needs to be much greater before we perceive the difference from one level to the next. That is to say, the human eye adjusts to the average luminance to which it is exposed, and as the luminance diverges from the point of adaptation, subtle contrast changes (such as lesions in radiographic images) are more difficult to perceive. The contrast sensitivity of the human visual system can be quantized using just noticeable differences (JND) or detection thresholds, which represent perceivable changes in luminance. Most of the seminal work on modeling contrast sensitivity was done by Peter Barten. He determined an average human visual system response based on data collected from a large subject sample and showed that the human visual system is nonlinear.
What does this mean? To optimize the perceptibility of diagnostic image information, we need calibration methods that account for the limitations of the human visual system. Barten’s model provides a means to accomplish this by producing perceptual linearity so that contrasts are not lost across the grayscale range, from the darkest to the brightest shades. It ensures that information at low luminance levels is not hidden to preserve information at high levels and vice versa.
The Barten model of the human visual system’s response to contrast stimuli has been used to develop the Digital Imaging and Communication in Medicine (DICOM) Grayscale Standard Display Function (GSDF). The standard provides a mathematical definition of the luminance output versus digital input, which ensures perceptually equivalent contrast throughout the grayscale range of the display. In this way, a display’s response characteristic curve can be calibrated to the DICOM GSDF and provide for a much higher degree of gray level separation at low gray levels. The display can be optimized for the human visual system. An added benefit of calibration is that a given gray level will appear the same from one display to the next. The graph below demonstrates the measured display response characteristic curve and the GSDF standard curve.
The calibration of any diagnostic display system to the DICOM Grayscale Standard Display Function is part of a Display QA program.
Barten, PMJ: Physical model for the Contrast Sensitivity of the human eye. Proc. SPIE 1666, 57-72, 1992.
Barten, PMJ: Spatio-temporal model for the Contrast Sensitivity of the human eye and its temporal aspects. Proc. SPIE 1913-01, 1993.
Barten PGJ. Contrast Sensitivity of the Human Eye and Its Effects on Image Quality. Bellingham, WA: SPIE Press, 1999.
Digital Imaging and Communications in Medicine (DICOM) Part 14: Grayscale Standard Display Function. National Electrical Manufacturers Association (NEMA), 2017.
Fetterly, KA, Blume, HR, Flynn, MJ, Samei, E: Introduction to Grayscale Calibration and Related Aspects of Medical Imaging Grade Liquid Crystal Displays. Journal of Digital Imaging; 21(2):193-201, 2008.