Computational modeling of skin reflectance spectra for biological parameter estimation through machine learning

Saurabh Vyas, Hien Van Nguyen, Philippe Burlina, Amit Banerjee, Luis Garza, Rama Chellappa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
PublisherSPIE
ISBN (Print)9780819490681
DOIs
StatePublished - Jan 1 2012
Event18th Annual Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery - Baltimore, MD, United States
Duration: Apr 23 2012Apr 27 2012

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8390
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

Other18th Annual Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery
CountryUnited States
CityBaltimore, MD
Period4/23/124/27/12

Keywords

  • Collagen
  • Hyperspectral Imaging
  • Melanoma
  • Melanosomes
  • Reectance
  • Support Vector Machine

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Vyas, S., Van Nguyen, H., Burlina, P., Banerjee, A., Garza, L., & Chellappa, R. (2012). Computational modeling of skin reflectance spectra for biological parameter estimation through machine learning. In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII [83901B] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8390). SPIE. https://doi.org/10.1117/12.919800