Human visual system intrascene luminance dynamic range

A model of human retinal cones was studied as a function of the adapting luminance to predict the intrascene luminance dynamic range (LDR). It has shown that the human retinal cones do not have a unique perceptual characteristic because the adapting luminance is dependent on the data visualization (tone scale) and ambient lighting. Using the minimum retinal response and maximum luminance of the display, a relationship is derived that specifies the intrascene LDR as a function of the adapting luminance. It is concluded that an intrascene LDR of about 100 is acceptable for primary interpretation (viewing to generate the radiology report) provided the adapting luminance is less than half of the display maximum luminance. However, due to excessively high ambient lighting and lower maximum luminance of displays typically used for secondary interpretation (viewing after radiology report is available), an intrascene LDR of about 50 is recommended for this setting. As the retinal cones provide high spatial frequency response, the minimum display luminance should be greater than 1 cd/m2 to ensure fully operational retinal cones. Finally, it is noted that fixing LDR and minimum luminance provides an opportunity to present images with true consistency for image distribution throughout an enterprise.

[1]  Hartwig R. Blume,et al.  Display of medical images on CRT soft-copy displays: a tutorial , 1995, Medical Imaging.

[2]  D. Hood,et al.  Psychophysical tests of models of the response function , 1979, Vision Research.

[3]  R. Normann,et al.  Changes in lesion detectability caused by light adaptation in retinal photoreceptors. , 1982, Investigative radiology.

[4]  Donald C. Hood,et al.  12 – Psychophysical and Physiological Tests of Proposed Physiological Mechanisms of Light Adaptation1 , 1978 .

[5]  Najoua Belaïd Perceptual linearization of soft-copy displays for achromatic images , 1999 .

[6]  A R Cowen,et al.  The computer enhancement of digital grey-scale fluorography images. , 1988, The British journal of radiology.

[7]  D. Hubel Eye, brain, and vision , 1988 .

[8]  Michael J. Flynn,et al.  Diagnostic performance with enhanced digital mammographic films , 1993, Medical Imaging.

[9]  J H Kim,et al.  Improved visualization of stimulated nodules by adaptive enhancement of digital chest radiography. , 1994, Academic radiology.

[10]  D. Field,et al.  Visual sensitivity, blur and the sources of variability in the amplitude spectra of natural scenes , 1997, Vision Research.

[11]  Marco Eichelberg,et al.  Consistency of softcopy and hardcopy: preliminary experiences with the new DICOM extensions for image display , 2000, Medical Imaging.

[12]  Michael J. Berry,et al.  Adaptation of retinal processing to image contrast and spatial scale , 1997, Nature.

[13]  Hartwig R. Blume,et al.  Presentation of medical images on CRT displays: a renewed proposal for a display function standard , 1993, Medical Imaging.

[14]  P. Whittle Brightness, discriminability and the “Crispening Effect” , 1992, Vision Research.

[15]  R. Gregory Eye and Brain: The Psychology of Seeing , 1966 .

[16]  Scott J. Daly,et al.  Visible differences predictor: an algorithm for the assessment of image fidelity , 1992, Electronic Imaging.

[17]  Richard A. Normann,et al.  Photoreceptor contributions to contrast sensitivity: Applications in radiological diagnosis , 1983, IEEE Transactions on Systems, Man, and Cybernetics.