Imaging skin structures for health, body mapping, and identification (under the Tucson Skin)
Author(s)Kundu Benjamin, Ina Annesha
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Brian W. Anthony.
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The human skin is dense with features that can be imaged and then analyzed for applications in human health. Applications include using natural body landmarks as a position encoding system for the body, as potential biometric identifiers, and as biomarkers correlated to health. Monitoring these features over time may be useful in the early diagnosis of several health conditions. Two prominent naturally occurring networks of features on the body are the skin microrelief and the superficial vascular structures just below the skin surface. Microreliefs are the fine micrometer scale furrows and ridges that appear like irregular geometric patterns on the skin surface; often, the intersection of the microrelief lines are at the outlets of the sweat ducts, which are used to regulate body temperature. The superficial vasculature is the subdermal network of veins responsible for supplying blood to the body. Since these features have important biological functions, monitoring the evolution of these features over time may be impactful. Long-term monitoring of these networks and network features may be useful for noninvasive methods to aid in computer-assisted diagnosis of numerous dermatological diseases and assessment of overall health. However, it is challenging to accurately observe the microrelief structure and its evolution and variation over time due to its micrometer dimensions distributed over square meters of the body and the 3D non-rigid nature of the body. Imaging the vasculature is difficult as it is below the skin surface. Current optical imaging technology to penetrate the skin surface is expensive. Image processing algorithms often incorrectly identify the subdermal features, mistaking the veins with other similarly shaped skin surface features (like deep wrinkles). Therefore, the evolution of healthy superficial vascular structure has not been well studied. Registration and matching of the skin and vascular regions are further complicated by non-rigid deformation, variations in illumination, and other noise sources. We have designed handheld optical imaging systems in an attempt to address these challenges through a combination of image system design and image processing. The two systems designed and presented here are (1) a high resolution, visible spectrum imaging device to image the skin microrelief and (2) a near-infrared (NIR) imaging system to image the superficial vascular structure. Both systems are ergonomic, lightweight, portable, easily integrated into common computer systems and relatively inexpensive, thereby having the potential to be used in clinical settings. We designed the optical imaging systems to have a convenient form factor while ensuring high-quality imaging of skin features at different length scales. Repeatability experiments were performed over a period of 1 - 2 years. Finally, we developed custom registration and matching algorithms to robustly extract and compare the biological networks on and below the surface of the skin. In an IRB approved study, 16 - 20 healthy volunteers were tested serially in order to characterize the systems. For a controlled set of motions, the vein imaging system correctly finds a position on the body within 5% error (or 0.22 cm of true position) and corrects angular viewpoint variations within 3% error. Vein structures are noted to be stable over a span of 8 months. Using real and synthetic skin images, the microrelief imaging system achieved matching accuracies of 26[mu]m - 80[mu]m. The skin structure is found to be stable over a period of 1 - 2 years. Because it has been shown that these networks are stable over time, they hold promise in being used as a human body positioning system, a non-invasive diagnostic tool, and an accurate biometric identifier.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 127-131).
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology