Tsui S.-YWang C.-YHuang T.-HKUNG-BIN SUNG2021-09-022021-09-02201821567085https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044955836&doi=10.1364%2fBOE.9.001531&partnerID=40&md5=a22ed35aedb32aeecee5a4a6c1b33eabhttps://scholars.lib.ntu.edu.tw/handle/123456789/581530A 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. ? 2018 Optical Society of America.Chromophores; Computer graphics; Graphics processing unit; Intelligent systems; Inverse problems; Neural networks; Program processors; Reflection; Artificial neural network models; Chromophore concentrations; Diffuse reflectance spectroscopy; Diffuse reflectance spectrum; Fitting parameters; Optical parameter; Spatially resolved; Spectral measurement; Monte Carlo methods; article; artificial neural network; diffuse reflectance spectroscopy; Monte Carlo method; oxygen saturation; skin[SDGs]SDG3journal article10.1364/BOE.9.0015312-s2.0-85044955836