Abstract
Many recent medical developments rely on image analysis, however, it is not convenient nor cost-efficient to use professional image acquisition tools in every clinic or laboratory. Hence, a reliable color calibration is necessary; color calibration refers to adjusting the pixel colors to a standard color space.
During a real-life project on neonatal jaundice disease detection, we faced a problem to perform skin color calibration on already taken images of neonatal babies. These images were captured with a smartphone (Samsung Galaxy S7, equipped with a 12 Mega Pixel camera to capture 4032 \(\times \) 3024 resolution images) in the presence of a specific calibration pattern. This post-processing image analysis deprived us from calibrating the camera itself. There is currently no comprehensive study on color calibration methods applied to human skin images, particularly when using amateur cameras (e.g. smartphones). We made a comprehensive study and we proposed a novel approach for color calibration, Gaussian process regression (GPR), a machine learning model that adapts to environmental variables. The results show that the GPR achieves equal results to state-of-the-art color calibration techniques, while also creating more general models.
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Notes
- 1.
However, by specifying a light source from CIE illuminant list, one can approximately define a colorchecker in RGB, but such generic RGB values are not to be fully trusted.
- 2.
For further information please contact Picterus AS at www.picterus.com.
- 3.
It’s worth mentioning that a device (e.g. camera) is to be calibrated while images are to be corrected, hence, color calibration and color correction are slightly different. However, the concept is the same and here we consider them equivalent.
- 4.
NTNU’s hospital (https://stolav.no) located in Trondheim, Norway.
- 5.
We also performed the same evaluation on SpyderCHECKR achieving similar results.
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Amani, M., Falk, H., Jensen, O.D., Vartdal, G., Aune, A., Lindseth, F. (2019). Color Calibration on Human Skin Images. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_20
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