A vascular imaging system for longitudinal registration and mapping of superficial vessels with quantitative analysis

Abstract

Superficial vasculature presented on human skin is a stable and unique network across different individuals and contains important physiological information that is less understood and studied. Potential clinical applications include monitoring the progress of the peripheral arterial disease, assessment of revascularization during surgical interventions, and early assessment of skin cancer from melanoma imagery. Some non-clinical applications include biometric scanning and relocalization for ultrasound imaging. To bridge the knowledge gap between the technology and these potential applications, a reliable, robust, and versatile platform is necessary. My thesis project involves the design and development of a platform for longitudinal superficial vasculature imaging, as well as robust computational algorithms to characterize and quantify vasculature networks. The technology used for the system includes near-infrared (NIR) optics and illumination source in the biological tissue window (750nm-940nm) optimized for hemoglobin absorption. The algorithms used, including segmentation, registration, and graph-based network analysis, are developed and implemented in Matlab. Some of my results include evidence of longitudinal vascular stability, relocalization capability, vasculature features on different parts of the human body.