Background: Hyperspectral imaging for in vivo human skin study has shown great potential by providing non-invasive measurement from which information usually invisible to the human eye can be revealed. In particular, maps of skin parameters including oxygen rate, blood volume fraction, and melanin concentration can be estimated from a hyperspectral image by using an optical model and an optimization algorithm. These applications, relying on hyperspectral images acquired with a high-resolution camera especially dedicated to skin measurement, have yielded promising results. However, the data analysis process is relatively expensive in terms of computation cost, with calculation of full-face skin property maps requiring up to 5 hours for 3-megapixels hyperspectral images. Such a computation time prevents punctual previewing and quality assessment of the maps immediately after acquisition.
Methods: To address this issue, we have implemented a neural network that models the optimization-based analysis algorithm. This neural network has been trained on a set of hyperspectral images, acquired from 204 patients and their corresponding skin parameter maps, which were calculated by optimization.
Results: The neural network is able to generate skin parameter maps that are visually very faithful to the reference maps much more quickly than the optimization-based algorithm, with computation times as short as 2 seconds for a 3-megapixel image representing a full face and 0.5 seconds for a 1-megapixel image representing a smaller area of skin. The average deviation calculated on selected areas shows the network's promising generalization ability, even on wide-field full-face images.
Conclusion: Currently, the network is adequate for preview purposes, providing relatively accurate results in a few seconds.
Keywords: hyperspectral; imaging; in vivo; machine learning; non-invasive.
© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.