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12 December 2018 Machine learning approach for rapid and accurate estimation of optical properties using spatial frequency domain imaging
Swapnesh Panigrahi, Sylvain Gioux
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Journal of Biomedical Optics, Vol. 24, Issue 7, 071606 (December 2018). https://doi.org/10.1117/1.JBO.24.7.071606
Abstract
Fast estimation of optical properties from reflectance measurements at two spatial frequencies could pave way for real-time, wide-field and quantitative mapping of vital signs of tissues. We present a machine learning-based approach for estimating optical properties in the spatial frequency domain, where a random forest regression algorithm is trained over data obtained from Monte-Carlo photon transport simulations. The algorithm learns the nonlinear mapping between diffuse reflectance at two spatial frequencies, and the absorption and reduced scattering coefficient of the tissue under consideration. Using this method, absorption and reduced scattering properties could be obtained over a 1 megapixel image in 450 ms with errors as low as 0.556% in absorption and 0.126% in reduced scattering.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Swapnesh Panigrahi and Sylvain Gioux "Machine learning approach for rapid and accurate estimation of optical properties using spatial frequency domain imaging," Journal of Biomedical Optics 24(7), 071606 (12 December 2018). https://doi.org/10.1117/1.JBO.24.7.071606
Received: 20 August 2018; Accepted: 16 October 2018; Published: 12 December 2018
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Cited by 39 scholarly publications.
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KEYWORDS
Optical properties
Machine learning
Spatial frequencies
Diffuse reflectance spectroscopy
Scattering
Absorption
Tissues
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