
Proceedings Paper
Computational modeling of skin reflectance spectra for biological parameter estimation through machine learningFormat | Member Price | Non-Member Price |
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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
Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)
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)
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)
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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