Flexible Computing Architecture for Real Time Skin Detection

In both the Air Force and Search and Rescue Communities, there is a current need to detect and characterize persons. Existing methods use red-green-blue (RGB) imagery, but produce high false alarm rates. New technology in multi-spectral skin detection is better than the existing RGB methods, but lacks a control and processing architecture to make them efficient for real time problems. We hypothesize that taking a minimalistic approach to the software design, we can perform image preprocessing, feature computation, and skin detection in real time. A number of applications require accurate detection and characterization of persons, human measurement and signature intelligence (H-MASINT), and SAR in particular. H-MASINT requires it for the detection of persons in images so other processing can be performed. It is useful in the SAR community as a method of finding persons partly obscured, in remote regions, and either living or deceased. We have developed a modular computing architecture to perform the acquisition and processing in real time, as well as separate programs to perform processing and analysis of images post-acquisition. The architecture is flexible, as one can easily add additional functionality to meet growing demands. All programs were organized using a basic Model-View-Controller design, designed using Universal Modeling Language principles, and coded using a bottom-up approach. Based on the results presented in this thesis, image acquisition, processing, skin detection, viewing, and saving can be performed in real time, at nearly 10 fps. Not only does this support the SAR community, the Air Force now has a new capability to help address its H-MASINT mission

Diffuse Reflectance Spectroscopy to Quantify In Vivo Tissue Optical Properties: Applications in Human Epithelium and Subcutaneous Murine Colon Cancer

Colorectal cancer is the 4th most common and 2nd deadliest cancer. Problems exist with predicting which patients will respond best to certain therapy regimens. Diffuse reflectance spectroscopy has been suggested as a candidate to optically monitor a patient’s early response to therapy and has been received favorably in experimentally managing other cancers such as breast and skin. In this dissertation, two diffuse reflectance spectroscopy probes were designed: one with a combined high-resolution microendoscopy modality, and one that was optimized for acquiring data from subcutaneous murine tumors. For both probes, percent errors for estimating tissue optical properties (reduced scattering coefficient and absorption coefficient) were less than 5% and 10%, respectively. Then, studies on tissue-simulating phantoms were performed to test probe sensitivity and to serve as testing platforms for investigators in biomedical optics. Next, the diffuse reflectance spectroscopy probe was applied to subcutaneous murine colon tumors (n=61) undergoing either antibody immunotherapy or standard 5-fluorouracil chemotherapy. Mice treated with a combination of these therapies showed reduced tumor growth compared to saline control, isotype control, immunotherapy, and chemotherapy groups (p\u3c0.001, \u3c0.001, \u3c0.001, and 0.046, respectively) 7 days post-treatment. Additionally, at 7 days post-treatment, oxyhemoglobin, a marker currently being explored as a functional prognostic cancer marker, trended to increase in immunotherapy, chemotherapy, and combination therapy groups compared to controls (p=0.315, 0.149, and 0.190). Also of interest, an oxyhemoglobin flare (averageincrease of 1.44x from baseline, p=0.03 compared to controls) was shown in tumors treated with chemotherapy, indicating that diffuse reflectance spectroscopy may be useful as a complimentary tool to monitor early tumor therapeutic response in colon cancer. However, subject-to-subject variability was high and studies correlating survival to early oxyhemoglobin flares are suggested

Integrating Spectral and Reflectance Transformation Imaging Technologies for the Digitization of Manuscripts and Other Cultural Artifacts

Final report on experiments conducted and lessons learned through the NEH Digital Humanities startup grant that tested methods of combining spectral imaging and RTI

FPI Based Hyperspectral Imager for the Complex Surfaces—Calibration, Illumination and Applications

Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pérot interferometer (FPI) based hyperspectral imager, a specially designed LED module and several sizes of stray light protection cones for reaching and adapting to the complex skin surfaces. The imager is designed for the needs of photometric stereo imaging for providing the skin surface models (3D) for each captured wavelength. The captured HS images contained 33 selected wavelengths (ranging from 477 nm to 891 nm), which were captured simultaneously with accordingly selected LEDs and three specific angles of light. The pre-test results show that the data collected with the new SICSURFIS imager enable the use of the spectral and spatial domains with surface model information. The imager can reach complex skin surfaces. Healthy skin, basal cell carcinomas and intradermal nevi lesions were classified and delineated pixel-wise with promising results, but further studies are needed. The results were obtained with a convolutional neural network

FPI Based Hyperspectral Imager for the Complex Surfaces—Calibration, Illumination and Applications

Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pérot interferometer (FPI) based hyperspectral imager, a specially designed LED module and several sizes of stray light protection cones for reaching and adapting to the complex skin surfaces. The imager is designed for the needs of photometric stereo imaging for providing the skin surface models (3D) for each captured wavelength. The captured HS images contained 33 selected wavelengths (ranging from 477 nm to 891 nm), which were captured simultaneously with accordingly selected LEDs and three specific angles of light. The pre-test results show that the data collected with the new SICSURFIS imager enable the use of the spectral and spatial domains with surface model information. The imager can reach complex skin surfaces. Healthy skin, basal cell carcinomas and intradermal nevi lesions were classified and delineated pixel-wise with promising results, but further studies are needed. The results were obtained with a convolutional neural network

Screening for Neonatal Jaundice by Smartphone Sclera Imaging

Jaundice is observed in over 60% of neonates and must be carefully monitored. Ifsevere cases go unnoticed, death or permanent disability can result. Neonatal jaun-dice causes 100,000 deaths yearly, with low-income countries in Africa and SouthAsia particularly affected. There is an unmet need for an accessible and objectivescreening method. This thesis proposes a smartphone camera-based method forscreening based on quantification of yellow discolouration in the sclera.The primary aim is to develop and test an app to screen for neonatal jaundicethat requires only the smartphone itself. To this end, a novel ambient subtractionmethod is proposed and validated, with less dependence on external hardware orcolour cards than previous app-based methods. Another aim is to investigate thebenefits of screening via the sclera. An existing dataset of newborn sclera images(n=87) is used to show that sclera chromaticity can predict jaundice severity.The neoSCB app is developed to predict total serum bilirubin (TSB) fromambient-subtracted sclera chromaticity via a flash/ no-flash image pair. A studyis conducted in Accra, Ghana to evaluate the app. With 847 capture sessions, thisis the largest study on image-based jaundice detection to date. A model trained onsclera chromaticity is found to be more accurate than one based on skin. The modelis validated on an independent dataset collected at UCLH (n=38).The neoSCB app has a sensitivity of 100% and a specificity of 76% in iden-tifying neonates with TSB≥250μmol/L (n=179). This is equivalent to the TcB(JM-105) data collected concurrently, and as good as the best-performing app in theliterature (BiliCam). Following a one-time calibration, neoSCB works without spe-cialist equipment, which could help widen access to effective jaundice screening