Artificial neural networks based estimation of optical parameters by diffuse reflectance imaging under in vitro conditions
Optical parameters (properties) of tissue-mimicking phantoms are determined through noninvasive 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 f...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
World Scientific Publishing
2017-01-01
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| Series: | Journal of Innovative Optical Health Sciences |
| Subjects: | |
| Online Access: | http://www.worldscientific.com/doi/pdf/10.1142/S1793545816500279 |
| Summary: | Optical parameters (properties) of tissue-mimicking phantoms are determined through noninvasive 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 5∘ angle to the phantoms. For the illumination of light, a laser light source at 633nm 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 the optical 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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| ISSN: | 1793-5458 1793-7205 |