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Volume: 29 | Article ID: art00017
Model-based Skin Pigment Cartography by High-Resolution Hyperspectral Imaging
  DOI :  10.2352/ISSN.2470-1173.2017.8.MAAP-281  Published OnlineJanuary 2017

The determination of local components in human skin from in vivo spectral reflectance measurements is crucial for medical applications, especially for aiding the diagnostic of skin diseases. Hyperspectral imaging is a convenient technique since one spectrum is acquired in each pixel of the image, and by inverting a light scattering model, we can retrieve the concentrations of skin components in each pixel. The good performance of the method presented in this article comes from both the imaging system and the model. The hyperspectral camera that we conceived uses polarizing filters in order to remove gloss effects generated by the stratum corneum; it provides a high-resolution image (1120 × 900 pixels), with a thin spectral sampling of 10 nm over the visible spectrum. The acquisition time of 2 seconds is short enough to prevent movement effects of the imaged area, which is usually the main issue in hyperspectral imaging. The model relies on a two-layer model for the skin, and the Kubelka–Munk theory with Saunderson correction for the light reflection. An optimization method enables computing, in less than one hour, several skin parameters in each of the million of pixels. These parameters (blood, melanin and bilirubin volume fractions, oxygen saturation…) are then displayed under the form of density images. Different skin structures, such as veins, blood capillaries, hematoma or pigmented spots, can be highlighted. The deviation between the measured spectrum and the one computed from the fitted parameters is evaluated in each pixel. © 2016 Society for Imaging Science and Technology.

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Pierre Seroul, Mathieu Hébert, Marie Cherel, Romain Vernet, Raphael Clerc, Matthieu Jomier, "Model-based Skin Pigment Cartography by High-Resolution Hyperspectral Imagingin Proc. IS&T Int’l. Symp. on Electronic Imaging: Material Appearance,  2017,  pp 108 - 114,

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Copyright © Society for Imaging Science and Technology 2017
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