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Proceedings Paper

Computational modeling of skin reflectance spectra for biological parameter estimation through machine learning
Author(s): Saurabh Vyas; Hien Van Nguyen; Philippe Burlina; Amit Banerjee; Luis Garza; Rama Chellappa
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Paper 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.

Paper Details

Date Published: 20 June 2012
PDF: 7 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901B (20 June 2012); doi: 10.1117/12.919800
Show Author Affiliations
Saurabh Vyas, The Johns Hopkins Univ. Applied Physics Lab. (United States)
The Johns Hopkins Univ. (United States)
Hien Van Nguyen, Univ. of Maryland, College Park (United States)
Philippe Burlina, The Johns Hopkins Univ. Applied Physics Lab. (United States)
The Johns Hopkins Univ. (United States)
Amit Banerjee, The Johns Hopkins Univ. Applied Physics Lab. (United States)
Luis Garza, The Johns Hopkins Univ. (United States)
Rama Chellappa, Univ. of Maryland, College Park (United States)


Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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