TY - GEN
T1 - Computational modeling of skin reflectance spectra for biological parameter estimation through machine learning
AU - Vyas, Saurabh
AU - Van Nguyen, Hien
AU - Burlina, Philippe
AU - Banerjee, Amit
AU - Garza, Luis
AU - Chellappa, Rama
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Collagen
KW - Hyperspectral Imaging
KW - Melanoma
KW - Melanosomes
KW - Reectance
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=84878395595&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84878395595&partnerID=8YFLogxK
U2 - 10.1117/12.919800
DO - 10.1117/12.919800
M3 - Conference contribution
AN - SCOPUS:84878395595
SN - 9780819490681
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
PB - SPIE
T2 - 18th Annual Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery
Y2 - 23 April 2012 through 27 April 2012
ER -