Paradigm for achieving color‐reproduction accuracy in LCDs for medical imaging

Silverstein, Louis D.; Hashmi, Syed F.; Lang, Karl; Krupiński, Elizabeth A.

doi:10.1889/jsid20.1.53

Cited by 17 publications

(11 citation statements)

References 10 publications

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“…Currently, dermatologists want to identify melanoma at the earliest stage of the disease, when it is, in most cases, similar to a benign pigment mole in the naked eye examination. Dermoscopy improves diagnosis accuracy significantly, even in early stages of melanoma [52][53][54][55][56][57]. To detect melanoma at an early stage, the most subtle colour shades (especially brown) and structures need to be identified by dermoscopy, often only in a small part of the lesion [58][59][60].…”

Section: Discussionmentioning

confidence: 99%

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Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection

Dugonik

Marovt

et al. 2020

Applied Sciences

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The fast-growing incidence of skin cancer, especially melanoma, is the guiding principle for intense development of various digital image-capturing devices providing easier recognition of melanoma by dermatologists. Handheld and digital dermoscopy, following of mole changes with smartphones and digital analysing of mole images, is based on evaluation of the colours, shape and deep structures in the skin moles. Incorrect colour information of an image, under- or overexposed images, lack of sharpness and low resolution of the images, can lead to melanoma misdiagnosis. The purpose of our study was to determine the colour error in the image according to the given lighting conditions and different camera settings. We focused on measuring the image quality parameters of smartphones and high-resolution cameras to compare them with the results of state-of-the-art dermoscopy device systems. We applied standardised measuring methods. The spatial frequency response method was applied for measuring the sharpness and resolution of the tested camera systems. Colour images with known reference values were captured from the test target, to evaluate colour error as a CIELAB (Commission Internationale de l’Eclairage) ΔE*ab colour difference as seen by a human observer. The results of our measurements yielded two significant findings. First, all tested cameras produced inaccurate colours when operating in automatic mode, and second, the amount of sharpening was too intensive. These deficiencies can be eliminated through adjusting the camera parameters manually or by image post-production. The presented two-step camera calibration procedure improves the colour accuracy of captured clinical and dermoscopy images significantly.

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Section: Discussionmentioning

confidence: 99%

“…A calibrator detects the colours emitted by a screen and creates a custom software ICC profile. The use of ICC profiles for achieving a good colour reproduction across devices [52] is already a well-established practice in other fields of Medicine.…”

Section: Displaysmentioning

confidence: 99%

Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection

Dugonik

Marovt

et al. 2020

Applied Sciences

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“…This will ensure that the colours on the screen are displayed accurately in accordance with the RGB values. See Silverstein et al (2012) and Yagi (2011) for detailed reviews of colour calibration for monitors.…”

Section: Recommendationsmentioning

confidence: 99%

Accurate representation of interference colours (Michel–Lévy chart): from rendering to image colour correction

Johnsen

Bollmann

Lee

et al. 2017

Journal of Microscopy

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Here a work flow towards an accurate representation of interference colours (Michel-Lévy chart) digitally captured on a polarised light microscope using dry and oil immersion objectives is presented. The work flow includes accurate rendering of interference colours considering the colour temperature of the light source of the microscope and chromatic adaptation to white points of RGB colour spaces as well as the colour correction of the camera using readily available colour targets. The quality of different colour correction profiles was tested independently on an IT8.7/1 target. The best performing profile was using the XYZ cLUT algorithm and it revealed a ΔE of 1.9 (6.4 no profile) at 5× and 1.1 (8.4 no profile) at 100× magnification, respectively. The overall performance of the workflow was tested by comparing rendered interference colours with colour-corrected images of a quartz wedge captured over a retardation range from 80-2500 nm at 5× magnification. Uncorrected images of the quartz wedge in sRGB colour space revealed a mean ΔE of 12.3, which could be reduced to a mean of 4.9 by applying a camera correction profile based on an IT8.7/1 target and the Matrix only algorithm (ΔE < 1.0 signifies colour differences imperceptible by the human eye). ΔE varied significantly over the retardation range of 80-2500 nm of the quartz wedge, but the reasons for this variation is not well understood and the quality of colour correction might be further improved in future by using custom made colour targets specifically designed for the analysis of high-order interference colours.

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“…Inspired by a paradigm recently published to improve color reproduction accuracy for LCD displays in medical applications [14], but without aiming at specific display calibration methods for medical applications such as gray-scale standard display function (GSDF) [15], we derive the following method to achieve the same goal for mobile displays. We use equation (4) to account for displays with different colorimetric properties than those listed in Table 1.…”

Section: B the Mobile Display Characterization And Illumination Modelmentioning

confidence: 99%

Improving color reproduction accuracy of a mobile liquid crystal display

et al. 2016

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The default method for color representation on displays involves sRGB as device-independent encoding color space. For improving color reproduction accuracy, we develop a device-specific display characterization model for the Apple iPad Air 2. In separate articles, we will evaluate the same method for other devices and for other display technologies. This model is combined with an easy-to-implement new method to account for the influence of illuminance from ambient light. The combination is called the mobile display characterization and illumination model (MDCIM) for the iPad Air 2, representing modern liquid crystal displays. Seven observers performed psychophysical tests at ambient illuminance levels from 600 to 3000 lx. They visually compared colors of calculated images with those of physical samples. The MDCIM model achieves similar color reproduction accuracy as when using the default method involving sRGB encoding at 1000 lx, while considerably improving color accuracy at other illuminance levels. At 600 lx, 98% of the observers prefer images directly generated with the MDCIM model over those created using the default method. The average color reproduction accuracy improves by two categories on a five-point scale. At 3000 lx, the percentage of colors that is represented at least reasonably well, according to the subjective assessment of visual observers, increased from 0% to 60%.

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Paradigm for achieving color‐reproduction accuracy in LCDs for medical imaging

Cited by 17 publications

References 10 publications

Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection

Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection

Accurate representation of interference colours (Michel–Lévy chart): from rendering to image colour correction

Improving color reproduction accuracy of a mobile liquid crystal display

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