Open AccessModelling spatially-resolved diffuse reflectance spectra of a multi-layered skin model by artificial neural networks trained with Monte Carlo simulations
Author(s) -
Sheng-Yang Tsui,
Chiao-Yi Wang,
Tsan-Hsueh Huang,
Kung-Bin Sung
Publication year - 2018
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.9.001531
Subject(s) - monte carlo method , diffuse reflectance infrared fourier transform , artificial neural network , computation , computer science , graphics processing unit , biological system , inverse , diffuse reflection , optics , image processing , spectral line , chromophore , artificial intelligence , materials science , algorithm , chemistry , physics , image (mathematics) , mathematics , geometry , biochemistry , statistics , photocatalysis , biology , operating system , catalysis , organic chemistry , astronomy
A robust modelling method was proposed to extract chromophore information in multi-layered skin tissue with spatially-resolved diffuse reflectance spectroscopy. Artificial neural network models trained with a pre-simulated database were first built to map geometric and optical parameters into diffuse reflectance spectra. Nine fitting parameters including chromophore concentrations and oxygen saturation were then determined by solving the inverse problem of fitting spectral measurements from three different parts of the skin. Compared to the Monte Carlo simulation accelerated by a graphics processing unit, the proposed modelling method not only reduced the computation time, but also achieved a better fitting performance.