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<p>In recent years, hyperspectral imaging technologies have been broadly applied to evaluate complex plant physiological features such as leaf moisture content, nutrient level and disease stress. A  critical  component  of  this  technique  is  white  referencing  used  to  remove  the  effect  of  non-uniform  lighting  intensity  in  different  wavelengths  on  raw  hyperspectral  images. Based  on  the  literature,  the leaf  geometry (e.g.,  tilt  angles)  and its interaction  with  the  illumination  severely impact  the  plant  reflectance  spectra  and vegetation  indices  such  as  the  normalized  difference  vegetation index (NDVI).  This thesis is  aimed to address the issues caused by the tilt angles across the leaf surface. To achieve this, two methods based on the fusion of the hyperspectral images and 3D  point  clouds  were  proposed.  The  first  method  was  to  build  a  3D  white  reference  library  in  which a point with almost the same tilt angle, height and position with the pixel on the plant leaf can be found, and then the white reference spectrum at that point can be used to calibrate the raw spectrum of the leaf pixel. The second method was to observe and summarize how the plant spectra and NDVI values changed with the leaf angles. Using the changing trends, the original NDVI and spectra  of  leaf  pixels  at  different  angles  can  be  calibrate  to  a  same  standard  as  if  the  leaf  was  imaged  at  a  flat  and  horizontal  surface.  The  approach  was  called  3D  calibration.  The  results  showed  that  the NDVI  values significantly  changed  with  leaf  angles  and  the  changing  trends differed  between  the  corn  and  soybean  species.  To evaluate the  performance  of  3D  calibration, 180 soybean plants with different genotypes, nitrogen (N), phosphorus (P) and water treatments were  grown  in  the  greenhouse. Each  plant  was  imaged  in three systems:  the high-throughput greenhouse hyperspectral imaging system, the indoor desktop imaging system with a visible-near infrared  (VINIR)  hyperspectral camera and  an  Intel  RealSense  depth  camera  and  the handheld device hyperspectral imaging system. In the greenhouse system, the whole canopy was captured. In the indoor desktop system, the partial canopy was captured because of the space limitation and the  top-matured  leaf  (the  middle  leaf  of  the  uppermost  matured  trifoliate)  was  focused.  The proposed  3D  calibration  was  applied  on  the  top-matured  leaf  to  remove  angle  impacts.  In  the  handheld device system, the flat top-matured leaf was captured. After done with imaging work, the plants were harvested to collect the ground truth data such as relative water content (RWC), N content and P content. Combined with the ground truth data, the NDVI values from three systems were  used  to  discriminate  different  genotypes  and  biochemical treatments,  whereas,  the  spectra from three systems were used to build partial least squares regression (PLSR) models for N, P and RWC. The results showed that the averaged tilt angles of top-matured leaves were impacted by different treatments. For instance, the low-nitrogen (LN) plants showed significantly higher leaf angles than high-nitrogen (HN) plants; the leaf angles on water-stressed (WS) plants were higher than those on well-watered (WW) plants. The leaf angles carried some signals that influenced not only the NDVI discrimination but also the PLSR modelling results. The signals were lost after 3D calibration.  For  the  top-matured  leaves,  the  discrimination  and  modelling  results  after  3D  calibration in the indoor desktop system were close to those from the flat leaves in the handheld device  system.  The  proposed  3D  calibration  approach  has  a  potential  to  eliminate  leaf  angle  impacts.</p>

The time required for the identification of pathogens is an important determinant of infection-related mortality rates and disease spread for species of relevance in agriculture. Conventional identification methods require a processing time of at least one to twenty days. Therefore, inaccurate empirical treatments are often provided while awaiting further identification, such that most cases progress with further aggravation of symptoms, contamination of other animals or plants, generating economic loss from decreased yield, and increased mitigation costs. Thus, there is a need for innovative, non-destructive, and rapid analytical techniques that provide reagent-free, portable, reliable, and holistic approaches to detect diseases in real-time. Vibrational spectroscopy techniques have shown the capacity to provide relevant information for disease detection. In near infrared spectroscopy (NIRS), the absorbance from a sample is measured across several hundred wavelengths in the near infrared band (750-2500 nm) and is directly influenced by the number and type of chemical bonds present. In order to make NIRS an effective tool for field-based studies, a simplified procedure is needed such that NIRS can be used in minimally processed samples found in situ. Here, experiments involving the agricultural important bovine herpesvirus-1 (BoHV-1), bovine respiratory syncytial virus (BRSV), Mannheimia haemolytica (MH), Xanthomonas citri pv. malvacearum (Xcm) and Rhizoctonia solani (Rs) were carried out to determine if biological spectral signatures can be differentiated between samples from two classes (i.e., healthy vs. sick, control sample vs. test sample). The specific objectives were to (1) create a spectral library for each evaluated pathogen and disease, (2) identify and establish the characteristic NIR spectral signatures and trends by aquaphotomics and chemometrics-based MVA methods, (3) generate and evaluate models for discriminating representative spectra, (4) provide new biochemical information by the correlation of the results with pathogen development and disease states in living systems, and (5) support the groundwork for a portable, fast, non-destructive, and accurate diagnostic tool capable of reducing the existing time necessary for pathogen and disease detection.

Computational modeling of light-matter interactions is a valuable approach for simulating photon paths in highly scattering media such as biological tissues. Monte Carlo (MC) models are considered to be the gold standard of implementation and can offer insights into light flux, absorption, and emission through tissues. Monte Carlo modeling is a computationally intensive approach, but this burden has been alleviated in recent years due to the parallelizable nature of the algorithm and the recent implementation of graphics processing unit (GPU) acceleration. Despite impressive translational applications, the relatively recent emergence of GPU-based acceleration of MC models can still be utilized to address some pressing challenges in biomedical optics beyond DOT and PDT. The overarching goal of the current dissertation is to advance the applications and abilities of GPU accelerated MC models to include low-cost devices and model Raman scattering phenomena as they relate to clinical diagnoses. The massive increase in computational capacity afforded by GPU acceleration dramatically reduces the time necessary to model and optimize optical detection systems over a wide range of real-world scenarios. Specifically, the development of simplified optical devices to meet diagnostic challenges in low-resource settings is an emerging area of interest in which the use of MC modeling to better inform device design has not yet been widely reported. In this dissertation, GPU accelerated MC modeling is utilized to guide the development of a mobile phone-based approach for diagnosing neonatal jaundice. Increased computational capacity makes the incorporation of less common optical phenomena such as Raman scattering feasible in realistic time frames. Previous Raman scattering MC models were simplistic by necessity. As a result, it was either challenging or impractical to adequately include model parameters relevant to guiding clinical translation. This dissertation develops a Raman scattering MC model and validates it in biological tissues. The high computational capacity of a GPU-accelerated model can be used to dramatically decrease the model’s grid size and potentially provide an understanding of measured signals in Raman spectroscopy that span multiple orders of magnitude in spatial scale. In this dissertation, a GPU-accelerated Raman scattering MC model is used to inform clinical measurements of millimeter-scale bulk tissue specimens based on Raman microscopy images. The current study further develops the MC model as a tool for designing diffuse detection systems and expands the ability to use the MC model in Raman scattering in biological tissues. / Bioengineering

ABSTRACT Erythema is a well-documented early indicator of tissue injury resulting from exposure to high doses of ionizing radiation. Close monitoring of radiation-induced injury to the skin can help identify opportunities for early interventions that may prevent or reduce more severe reactions. The gold standard for monitoring erythema is visual assessment (VA) by a trained clinician. This method has been criticized for being subjective and designed with very broad categorical descriptors. This work introduces a newly developed VA scale called the clinician erythema assessment for radiation therapy (CEA-RT).The reliability and accuracy of the CEA-RT scale was tested among 20 radiation therapists who trained to use the scale on digital images of radiation induced erythema. CEA-RT demonstrated to be highly reliable when therapist’s grades were compared to themselves, but moderately accurate when therapist’s grades were compared to each. A follow-up study with real patients and fewer but more extensively trained raters was proposed to demonstrate the grading accuracy of the CEA-RT scale. As an alternatively to VA, spectroscopy has the ability to monitor erythema by measuring the change in concentration of hemoglobin (Hb) within the vessels of the skin. These changes in Hb concentration are linked to the degree of erythema. This thesis also investigated the use of hyperspectral imaging (HSI) and diffuse reflectance spectroscopy (DRS) as potential technological alternatives for evaluating erythema. In a second study, Erythema was artificially induced in 3 volunteers who participated in a pilot study designed to assess the ability of an experimental HSI camera to detect skin changes. The data extracted from the hyperspectral images was found to effectively yield spectral profiles and Dawson’s erythema indices (EI) in agreement with the erythema grades assigned by the gold standard therefore showing HSI to be a viable alternative of assessing erythema. Finally, a third study compared DRS measurements to VA using the CEA-RT scale. The DRS system was previously used to measure in vivo erythema but did not compare spectral measurements to an accepted standard. Ten patient volunteers received daily DRS and VA evaluations for a period of 2 to 4 weeks. The results demonstrated that the Dawson’s EI calculated from the spectral data correlated well with the gold standard (VA grades) and that DRS is able to detect changes in the skin throughout the course of radiation treatments. / Thesis / Master of Science (MSc)

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