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Journal of Biomedical Optics

Estimating physiological skin parameters from hyperspectral signatures
Author(s): Saurabh Vyas; Amit Banerjee; Philippe Burlina
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Paper Abstract

We describe an approach for estimating human skin parameters, such as melanosome concentration, collagen concentration, oxygen saturation, and blood volume, using hyperspectral radiometric measurements (signatures) obtained from in vivo skin. We use a computational model based on Kubelka-Munk theory and the Fresnel equations. This model forward maps the skin parameters to a corresponding multiband reflectance spectra. Machine-learning-based regression is used to generate the inverse map, and hence estimate skin parameters from hyperspectral signatures. We test our methods using synthetic and in vivo skin signatures obtained in the visible through the short wave infrared domains from 24 patients of both genders and Caucasian, Asian, and African American ethnicities. Performance validation shows promising results: good agreement with the ground truth and well-established physiological precepts. These methods have potential use in the characterization of skin abnormalities and in minimally-invasive prescreening of malignant skin cancers.

Paper Details

Date Published: 30 May 2013
PDF: 14 pages
J. Biomed. Opt. 18(5) 057008 doi: 10.1117/1.JBO.18.5.057008
Published in: Journal of Biomedical Optics Volume 18, Issue 5
Show Author Affiliations
Saurabh Vyas, Johns Hopkins Univ. Applied Physics Lab. (United States)
Amit Banerjee, Johns Hopkins Univ. Applied Physics Lab. (United States)
Philippe Burlina, Johns Hopkins Univ. Applied Physics Lab. (United States)


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