20 June 2012 Computational modeling of skin reflectance spectra for biological parameter estimation through machine learning
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Abstract
A computational skin re ectance model is used here to provide the re ectance, absorption, scattering, and transmittance based on the constitutive biological components that make up the layers of the skin. The changes in re ectance are mapped back to deviations in model parameters, which include melanosome level, collagen level and blood oxygenation. The computational model implemented in this work is based on the Kubelka- Munk multi-layer re ectance model and the Fresnel Equations that describe a generic N-layer model structure. This assumes the skin as a multi-layered material, with each layer consisting of specic absorption, scattering coecients, re ectance spectra and transmittance based on the model parameters. These model parameters include melanosome level, collagen level, blood oxygenation, blood level, dermal depth, and subcutaneous tissue re ectance. We use this model, coupled with support vector machine based regression (SVR), to predict the biological parameters that make up the layers of the skin. In the proposed approach, the physics-based forward mapping is used to generate a large set of training exemplars. The samples in this dataset are then used as training inputs for the SVR algorithm to learn the inverse mapping. This approach was tested on VIS-range hyperspectral data. Performance validation of the proposed approach was performed by measuring the prediction error on the skin constitutive parameters and exhibited very promising results.
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Saurabh Vyas, Saurabh Vyas, Hien Van Nguyen, Hien Van Nguyen, Philippe Burlina, Philippe Burlina, Amit Banerjee, Amit Banerjee, Luis Garza, Luis Garza, Rama Chellappa, Rama Chellappa, } "Computational modeling of skin reflectance spectra for biological parameter estimation through machine learning", Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901B (20 June 2012); doi: 10.1117/12.919800; https://doi.org/10.1117/12.919800
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