University of Oulu

E. Zherebtsov, V. Dremin, A. Popov, A. Doronin, D. Kurakina, M. Kirillin, I. Meglinski, and A. Bykov, "Hyperspectral imaging of human skin aided by artificial neural networks," Biomed. Opt. Express 10, 3545-3559 (2019).

Hyperspectral imaging of human skin aided by artificial neural networks

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Author: Zherebtsov, Evgeny1; Dremin, Viktor1; Popov, Alexey1;
Organizations: 1Opto-Electronics and Measurement Techniques Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, PO Box 4500, 90014 Oulu, Finland
2School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, 6140 Wellington, New Zealand
3Institute of Applied Physics of the Russian Academy of Sciences, 46 Ul’yanov Street, 603950 Nizhny Novgorod, Russia
4Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia
5Institute of Engineering Physics for Biomedicine, National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 115409 Moscow, Russia
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 7.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019081324070
Language: English
Published: Optical Society of America, 2019
Publish Date: 2019-08-13
Description:

Abstract

We developed a compact, hand-held hyperspectral imaging system for 2D neural network-based visualization of skin chromophores and blood oxygenation. State-of-the-art micro-optic multichannel matrix sensor combined with the tunable Fabry-Perot micro interferometer enables a portable diagnostic device sensitive to the changes of the oxygen saturation as well as the variations of blood volume fraction of human skin. Generalized object-oriented Monte Carlo model is used extensively for the training of an artificial neural network utilized for the hyperspectral image processing. In addition, the results are verified and validated via actual experiments with tissue phantoms and human skin in vivo. The proposed approach enables a tool combining both the speed of an artificial neural network processing and the accuracy and flexibility of advanced Monte Carlo modeling. Finally, the results of the feasibility studies and the experimental tests on biotissue phantoms and healthy volunteers are presented.

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Series: Biomedical optics express
ISSN: 2156-7085
ISSN-E: 2156-7085
ISSN-L: 2156-7085
Volume: 10
Issue: 7
Pages: 3545 - 3559
DOI: 10.1364/BOE.10.003545
OADOI: https://oadoi.org/10.1364/BOE.10.003545
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
Subjects:
Funding: Academy of Finland (290596, 314369, 318281); Ministry of Science and Higher Education of Russian Federation (0035-2019-0014).
Academy of Finland Grant Number: 290596
314369
318281
Detailed Information: 290596 (Academy of Finland Funding decision)
314369 (Academy of Finland Funding decision)
318281 (Academy of Finland Funding decision)
Copyright information: © 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.