Journal of Innovative Optical Health Sciences, Volume. 10, Issue 1, 1650027(2017)

Artificial neural networks based estimation of optical parameters by diffuse reflectance imaging under in vitro conditions

Mahmut Ozan Gokkan* and Mehmet Engin
Author Affiliations
  • Department of Electrical & Electronics Engineering Faculty of Engineering Ege University Bornova, Izmir 35100, Turkey
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    Optical parameters (properties) of tissue-mimicking phantoms are determined through nonin-vasive optical imaging. Objective of this study is to decompose obtained diffuse reflectance into these optical properties such as absorption and scattering coefficients. To do so, transmission spectroscopy is firstly used to measure the coefficients via an experimental setup. Next, the optical properties of each characterized phantom are input for Monte Carlo (MC) simulations to get diffuse reflectance. Also, a surface image for each single phantom with its known optical properties is obliquely captured due to reflectance-based geometrical setup using CMOS camera that is positioned at 50 angle to the phantoms. For the illumination of light, a laser light source at 633 nm wavelength is preferred, because optical properties of different components in a biological tissue on that wavelength are nonoverlapped. During in vitro measurements, we prepared 30 different mixture samples adding clinoleic intravenous lipid emulsion (CILE) and evans blue (EB) dye into a distilled water. Finally, all obtained diffuse reflectance values are used to estimate theoptical coefficients by artificial neural networks (ANNs) in inverse modeling. For a biological tissue it is found that the simulated and measured values in our results are in good agreement.

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    Mahmut Ozan Gokkan, Mehmet Engin. Artificial neural networks based estimation of optical parameters by diffuse reflectance imaging under in vitro conditions[J]. Journal of Innovative Optical Health Sciences, 2017, 10(1): 1650027

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    Paper Information

    Received: Oct. 12, 2015

    Accepted: Dec. 31, 2015

    Published Online: Dec. 27, 2018

    The Author Email: Gokkan Mahmut Ozan (ozangokkan@gmail.com)

    DOI:10.1142/s1793545816500279

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