Opto-Physiological Modelling: On Light Interaction in Skin Tissue

The future of medical care is digital and mobile based, rooted in smartphone-based imaging and sensor technology. Essential to the development of these next-generation technologies is a rigorous understanding of the mathematical underpinnings of how light and skin tissue interact. In this dissertation we present a mathematical framework and multi-stage methodology to model the interaction of light in skin tissue and recover underlying physiological information from images and videos of skin tissue. In the first portion of the dissertation, we construct the individual building blocks of our methodology. We review Gaussian Process inference as a tool for regression, study the radiative transport equation as a model of light-interaction in skin tissue, and reconstruct the in-camera imaging processing pipeline from smartphone digital cameras. The second portion of the dissertation combines these components together into a multi-stage methodology to render skin pixel values in digital images as a function of the underlying pigment generating chromophores, and then recover an inverse chromophore map. We apply this map to both simulated and real skin images to determine the underlying chromophore concentration, and recover vasculature maps depending on the chromophore concentration. In the final portion, we apply the inverse chromophore map to video frames of skin tissue for imaging diagnostics. We effectively recover the heart rate of a user with excellent accuracy. Finally, we propose a novel methodology to recover a user's electrocardiogram wavefrom from a facial blood flow signal, introducing a method for contactless imaging electrocardiography.

Author dc:creator

• Gold, Nathan

Advisor dc:contributor.advisor

• Huang, Huaxiong

• Biomedical engineering

Statement dc:rights

• Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.

Language dc:language

en

Handle dc:identifier.uri

http://hdl.handle.net/10315/38178

OAI identifier oai:identifier

oai:yorkspace.library.yorku.ca:10315/38178