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Proceedings Paper

Development of an inverse approach for the characterization of in-vivo optical properties of human skin based on artificial neural networks (Conference Presentation)
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Paper Abstract

Due to the complex and inhomogeneous structure of biological tissues, the analysis of imaging data collected with various optical biopsy methods is often complicated and time consuming. The major challenge here is to understand the peculiarities of light propagation and link it with advanced image/data classification pipelines. This presentation considers the application of the novel Artificial Intelligence (AI) based methods to the inverse problem of light transport in scattering media such as human skin. A spectral image classification pipeline based on Artificial Neural Networks (ANNs) has been developed by implementing and training several configurations of ANNs classifiers that fit for the scattering and absorption properties of the tissues. The training of the ANNs has been performed by the further developed unified Monte Carlo-based computational framework for light transport in scattering media. The hyperspectral data is acquired at each pixel as a function of time, by varying the illumination/detection wavelength and polarization of light. The results of nearly real-time chromophore mappings for parameters such as distributions of melanin, blood vessels, oxygenation, simulation of BSSRDFs, reflectance spectra of human tissues, corresponding colours and 3D rendering examples of human skin appearance will be presented and compared with the exact analytical solutions, phantom studies, traditional diffuse reflectance spectroscopic point measurements and advanced Spatial Frequency Domain Imaging (SFDI) technique. Computer simulation and training are accelerated by parallel computing on Graphics Processing Units (GPUs) using Compute Unified Device Architecture (CUDA) and Cloud-based environment. Open-source machine learning frameworks (e.g. Tensorflow) are used to measure and validate each ANN’s performance.

Paper Details

Date Published: 17 September 2018
PDF
Proc. SPIE 10743, Optical Modeling and Performance Predictions X, 107430K (17 September 2018); doi: 10.1117/12.2320814
Show Author Affiliations
Alexander Doronin, Yale Univ. (United States)
Holly Rushmeier, Yale Univ. (United States)
Alexander Bykov, Univ. of Oulu (Finland)
Igor Meglinski, Univ. of Oulu (Finland)


Published in SPIE Proceedings Vol. 10743:
Optical Modeling and Performance Predictions X
Mark A. Kahan; Marie B. Levine-West, Editor(s)

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