I DEVELOPED A NEW HIGH THROUGHPUT DUAL CHANNEL HYPERSPECTRAL IMAGING CONFIGURATION BASED ON ACOUSTO-OPTIC TUNABLE FILTER. THE DEVELOPED IMAGING SYSTEM WAS CHARACTERIZED AND EVALUATED IN COMPARISON WITH OTHER CONVENTIONAL CONFIGURATIONS. THE NEW IMAGING SYSTEM PROVED HIGHER THROUGHPUT WITH RESPECT TO THE CURRENTLY USED CONFIGURATIONS.THE IMAGING SYSTEM WAS THEN USED TO QUANTITATIVELY ASSESS AND PRECISELY CLASSIFY SKIN ERYTHEMA INDUCED ARTIFICIALLY ON VOLUNTEERS AND NATURALLY ON SKIN CANCER PATIENTS DUE TO RADIOTHERAPY TREATMENT. / Recent cancer statistics show that 40% of Canadians might contract cancer during their life and 25% of Canadians might die due to cancer. In skin, head and neck cancers, surgery and radiation therapies are the most prevalent treatment options, while radiation therapy is the most commonly used approach. A common problem in radiation therapy is tumors behave differently against ionizing radiation. For instance, with the same dose, some tumors are fully damaged or shrunk, while others are less affected. The difference in individual tumor response to therapy is transformed into a research question: how to quantitatively assess tumor response to radiation and how to tune radiation therapy to achieve full destruction for tumor cells? Few past studies addressed the question, although no definite answer was realized. This work is a part of a project that investigates the hypothesis that radiation response of skin is correlated to individual tumor response. In the case of high correlation, the skin’s faster response to ionizing radiation can be used to modify the irradiation dose to achieve the maximum destruction of individual’s tumor. To examine the project hypothesis, radiation-induced skin redness or erythema was selected as an acute skin reaction to being objectively quantified. Hence, the overall goal of the research thesis work is to objectively assess and precisely quantify radiation-induced erythema or radiation dermatitis. Skin erythema was assessed formerly by multiple optical and non-optical modalities. The current gold standard is the visual assessment (VA). Unfortunately, VA lacks objectiveness, precise communication, and quantification. To push the limitations of VA and past techniques, hyperspectral imaging (HSI) was proposed to be used for erythema assessment. The work detailed in this thesis aims to create more confidence in HSI to be utilized toward objectively quantify skin erythema. To reach this goal, initially, a new high-throughput dual channel acousto-optic tunable filter (AOTF)-based-HSI instrument was developed for monitoring radiation dermatitis. AOTF-HSI instrument design, implementation, and full characterization are presented. Second, the developed AOTF-HSI instrument is evaluated against a liquid crystal tunable filter (LCTF) instrument. Third, to be prepared for clinical operation, the AOTF-HSI equipment was used to classify an artificially-induced erythema on healthy volunteers in an exploratory study. A robust linear discriminant analysis (LDA)-based classification method was developed for the purpose of image classification. Finally, HSI instrument and LDA classification method were utilized in a preliminary clinical study to properly monitor and precisely quantify radiation dermatitis for skin cancer patients. In the clinical study, erythema indices were computed using Dawson’s method. Least square fitting was used to fit the acquired absorbance data, and thus quantify the hemoglobin concentration change along the study duration. Moreover, LDA was used to contrast spectral and digital imaging for erythema classification. In sum, the work documented in this thesis was willfully directed to achieve an efficient, portable, user-friendly hyperspectral imaging system which has the opportunity to be a benchtop in the clinical daily procedure in the near future. / Thesis / Doctor of Philosophy (PhD)

Pulses are a major source of human protein intake nowadays and will continue to be so because of their high protein content. Pulse crops are members of the family Leguminosae. The five major pulse crops grown in Canada are chick peas, green peas, lentils, pinto bean and kidney beans. Over the past 20 years, Canada has emerged as the world’s largest exporter of lentils and one of world’s top five exporters of beans. These contribute more than $2 billion income to the Canadian economy. The major causes of fungal infection in these pulses are Aspergillus flavus and Penicillium commune. Early stages of fungal infections in pulses are not detectable with human eyes. Near infrared (NIR) hyperspectral imaging system is an advanced technique widely used for detection of insect infestation and fungal infection in cereal grains and oil seeds. A typical NIR instrument captures images across the electromagnetic spectrum at evenly spaced wavelengths from 700 to 2500 nm (a system at the University of Manitoba captures images in the 960 nm to 1700 nm range). From the captured images, the spatial relationships for different spectra in the neighborhood can be found allowing more elaborate spectral-spatial methods for a more accurate classification of the images. The primary objective of this study was to assess the feasibility of the NIR hyperspectral system to identify fungal infections in pulses. Hyperspectral images of healthy and fungal infected chick peas, green peas, lentils, pinto bean and kidney beans were acquired and features (statistical and histogram) were used to develop classification models to identify fungal infection caused by Aspergillus flavus and Penicillium commune. Images of healthy and fungal infected kernels were acquired at 2 week intervals (0, 2, 4, 6, 8 and 10 weeks from artificial inoculation). Six-way (healthy vs the five different stages of infection) and two-way (healthy vs every stage of infection) models were developed and classifications were done using linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) classifiers. The LDA classifier identified with 90-94% accuracy while using the six-way model, and with 98-100% accuracy when using the two-way models for all five types of pulses and for both types of fungal infections. The QDA classifier also showed promising results as it identified 85-90% while using the six-way model and 96-100% when using the two-way models. Hence, hyperspectral imaging is a promising and non-destructive method for the rapid detection of fungal infections in pulses, which cannot be detected using human eyes. / October 2015

ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV / Compressive Sensing (CS) is a set of techniques that can faithfully acquire a signal from sub- Nyquist measurements, provided the class of signals have certain broadly-applicable properties. Reconstruction (or exploitation) of the signal from these sub-Nyquist measurements requires a forward model - knowledge of how the system maps signals to measurements. In high-dimensional CS systems, determination of this forward model via direct measurement of the system response to the complete set of impulse functions is impractical. In this paper, we will discuss the development of a parameterized forward model for the Adaptive, Feature-Specific Spectral Imaging Classifier (AFSSI-C), an experimental compressive spectral image classifier. This parameterized forward model drastically reduces the number of calibration measurements.

<p dir="ltr">Advancements in machine learning are narrowing the gap in visual capabilities between machines and healthcare professionals, resulting in a transformation of the way we understand and address health challenges. Despite these advances, underlying limitations persist in addressing real-world problems, particularly in the precise capture of biological and physiological information. This is primarily because traditional trichromatic cameras fall short of representing reflectance spectra due to their limited spectral information. To overcome these limitations, hyperspectral imaging has emerged as a powerful tool for biomedical applications. By collecting a wealth of information at different wavelengths, hyperspectral imaging provides a comprehensive view of electromagnetic spectra, allowing non-invasive clinical analysis for accurate diagnostics. Snapshot hyperspectral imaging, in particular, is a competitive alternative to traditional cameras as it can capture a hyperspectral image in a single shot without the need for scanning individual wavelengths. Here, we introduce a computational snapshot hyperspectral imaging method, achieved through the integration of a machine learning approach with a streamlined optical system. We design an explainable machine learning algorithm by incorporating optical and biological knowledge into the algorithm. Therefore, the algorithm can reconstruct hyperspectral images with high spectralspatial resolution comparable to those of scientific spectrometers, despite the use of sparse information captured from the optical system. To demonstrate its versatility in biomedical applications, we extract hemodynamic parameters of peripheral microcirculation from embryonic model systems, tissue phantom samples, and human conjunctivas. Furthermore, we validate high accuracy of the results using conventional hyperspectral imaging and functional near-infrared spectroscopy. This learning-powered imaging method, characterized by high resolution and simplified hardware requirements, has the potential to offer solutions for various biomedical challenges by surpassing the constraints of conventional cameras and hyperspectral imaging.</p>

Hyperspectral (HS) imaging combines spectral measurement of a pixel with 2D imaging technology. It is capable to provide a series of images containing both spectral and spatial information, and has been widely used in medical domain. However, most researches on medical HS imaging are regarding ex-vivo biopsy or skin and oral mucosa. The study on HS imaging regarding in-vivo disease lags far behind. In this thesis, we developed a novel flexible HS endoscope system. It is capable to obtain a series of HS images in vivo in a non-contact way among the wavelength range of 405 – 665 nm. After a lot of time-consuming modifying and debugging work, this new system has high stability and convenience to be applied in clinic now. We evaluated this system in clinic. First, we got ethics approval for clinical trials. Then, we obtained HS images regarding gastrointestinal (GI) diseases inside patients using this system. As far as we know, this type of in-vivo image data has not been reported in previous literatures. Thus using these HS images, we built a database for GI mucosa. Next, we analyzed some typical HS images tentatively. The method of Recursive Divergence is implemented to extract valuable and diagnostic information from these HS images. The results prove the effect and applicability of this new HS endoscope system, which has shown the great potential to be used as a platform and guidance for further medical studies. To further apply the analysis results in clinic, we propose a novel Adaptive Narrow-Band Imaging (ANBI) method based on band selection of HS images of a specific type of disease. It is expected that the new technique has higher accuracy, sensitivity, and specificity compared to conventional Narrow-Band Imaging (NBI) technique. In this thesis, we also discuss the future direction of the system improvement. Especially, to improve light intensity and signal-noise-ratio of HS images in wide-field view, we propose a new imaging method using broad- and overlapped-band filters. Although this method only performs greatly on the foundation of accurate image registration, we hope to apply it in our system in the future.

ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV / The AFSSI-C is a spectral imager that generates spectral classification directly, in fewer measurements than are required by traditional systems that measure the spectral datacube (which is later interpreted to make material classification). By utilizing adaptive features to constantly update conditional probabilities for the different hypotheses, the AFSSI-C avoids the overhead of directly measuring every element in the spectral datacube. The system architecture, feature design methodology, simulation results, and preliminary experimental results are given.

No / The purpose of the research was to examine the practical feasibility of developing a hyperspectral camera from a Zeiss fundus camera and to illustrate its use in imaging diabetic retinopathy and glaucoma patients. The original light source of the camera was replaced with an external lamp filtered by a fast tunable liquid-crystal filter. The filtered light was then brought into the camera through an optical fiber. The original film camera was replaced by a digital camera. Images were obtained in normals and patients (primary open angle glaucoma, diabetic retinopathy) recruited at the Manchester Royal Eye Hospital. A series of eight images were captured across 495- to 720-nm wavelengths, and recording time was less than 1.6s. The light level at the cornea was below the ANSI limits, and patients judged the measurement to be very comfortable. Images were of high quality and were used to generate a pixel-to-pixel oxygenation map of the optic nerve head. Frame alignment is necessary for frame-to-frame comparison but can be achieved through simple methods. We have developed a hyperspectral camera with high spatial and spectral resolution across the whole visible spectrum that can be adapted from a standard fundus camera. The hyperspectral technique allows wavelength-specific visualization of retinal lesions that may be subvisible using a white light source camera. This hyperspectral technique may facilitate localization of retinal and disc pathology and consequently facilitate the diagnosis and management of retinal disease.

Electromagnetic radiation reflectance increases dramatically around 700 nm for vegetation. This increase in reflectance is known as the vegetation red edge. The NDVI (Normalized Difference Vegetation index) is an imaging technique for quantifying red edge contrast for the identification of vegetation. This imaging technique relies on reflectance values for radiation with wavelength equal to 680 nm and 830 nm. The imaging systems required to obtain this precise reflectance data are commonly space-based; limiting the use of this technique due to satellite availability and cost. This thesis presents a robust and inexpensive new terrestrial-based method for identifying the vegetation red edge. This new technique does not rely on precise wavelengths or narrow wavelength bands and instead applies the NDVI to the visible and NIR (near infrared) spectrums in toto. The measurement of vegetation fluorescence has also been explored, as it is indirectly related to the efficiency of photochemistry and heat dissipation and provides a relative method for determining vegetation health. The imaging methods presented in this thesis represent a unique solution for the real time monitoring of vegetation growth and senesces and the determination of qualitative vegetation health. A single, inexpensive system capable of field and greenhouse deployment has been developed. This system allows for the early detection of variations in plant growth and status, which will aid production of high quality horticultural crops. / Master of Science

Compressive sensing (CS) is a new sampling theory which allows reconstructing signals using sub-Nyquist measurements. It states that a signal can be recovered exactly from randomly undersampled data points if the signal exhibits sparsity in some transform domain (wavelet, Fourier, etc). Instead of measuring it uniformly in a local scheme, signal is correlated with a series of sensing waveforms. These waveforms are so called sensing matrix or measurement matrix. Every measurement is a linear combination of randomly picked signal components. By applying a nonlinear convex optimization algorithm, the original can be recovered. Therefore, signal acquisition and compression are realized simultaneously and the amount of information to be processed is considerably reduced. Due to its unique sensing and reconstruction mechanism, CS creates a new situation in signal acquisition hardware design as well as software development, to handle the increasing pressure on imaging sensors for sensing modalities beyond visible (ultraviolet, infrared, terahertz etc.) and algorithms to accommodate demands for higher-dimensional datasets (hyperspectral or video data cubes). The combination of CS with traditional optical imaging extends the capabilities and also improves the performance of existing equipments and systems. Our research work is focused on the direct application of compressive sensing for imaging in both 2D and 3D cases, such as infrared imaging, hyperspectral imaging and sum frequency generation microscopy. Data acquisition and compression are combined into one step. The computational complexity is passed to the receiving end, which always contains sufficient computer processing power. The sensing stage requirement is pushed to the simplest and cheapest level. In short, simple optical engine structure, robust measuring method and high speed acquisition make compressive sensing-based imaging system a strong competitor to the traditional one. These applications have and will benefit our lives in a deeper and wider way.

Wheat grain quality is defined by several parameters, of which insect and fungal damage and sprouting are considered important degrading factors. At present, Canadian wheat is inspected and graded manually by Canadian Grain Commission (CGC) inspectors at grain handling facilities or in the CGC laboratories. Visual inspection methods are time consuming, less efficient, subjective, and require experienced personnel. Therefore, an alternative, rapid, objective, accurate, and cost effective technique is needed for grain quality monitoring in real-time which can potentially assist or replace the manual inspection process. Insect-damaged wheat samples by the species of rice weevil (Sitophilus oryzae), lesser grain borer (Rhyzopertha dominica), rusty grain beetle (Cryptolestes ferrugineus), and red flour beetle (Tribolium castaneum); fungal-damaged wheat samples by the species of storage fungi namely Penicillium spp., Aspergillus glaucus, and Aspergillus niger; and artificially sprouted wheat kernels were obtained from the Cereal Research Centre (CRC), Agriculture and Agri-Food Canada, Winnipeg, Canada. Field damaged sprouted (midge-damaged) wheat kernels were procured from five growing locations across western Canada. Healthy and damaged wheat kernels were imaged using a long-wave near-infrared (LWNIR) and a short-wave near-infrared (SWNIR) hypersprctral imaging systems and an area scan color camera. The acquired images were stored for processing, feature extraction, and algorithm development. The LWNIR classified 85-100% healthy and insect-damaged, 95-100% healthy and fungal-infected, and 85-100% healthy and sprouted/midge-damaged kernels. The SWNIR classified 92.7-100%, 96-100% and 93.3-98.7% insect, fungal, and midge-damaged kernels, respectively (up to 28% false positive error). Color imaging correctly classified 93.7-99.3%, 98-100% and 94-99.7% insect, fungal, and midge-damaged kernels, respectively (up to 26% false positive error). Combined the SWNIR features with top color image features correctly classified 91-100%, 99-100% and 95-99.3% insect, fungal, and midge- damaged kernels, respectively with only less than 4% false positive error.