Snapshot Hyperspectral Imaging for Complete Fundus Oximetry

In this work, a snapshot hyperspectral imager capable of tuning its average spectral resolution from 22.7 nm to 13.9 nm in a single integrated form is presented. The principle of this system will enable future snapshot systems to dynamically adapt to a wide range of imaging situations. Additionally, the system overcomes datacube size limitations imposed by detector array size limits. The work done in this thesis also advances oximetry of the retina using data collected by the Image Mapping spectrometer (IMS), a snapshot spectrometer. Hyperspectral images of the retina are acquired, and oximetry of individual vessels in four diseased eyes is presented. Further, oximetry of the entire fundus is performed using a novel algorithm with data collected with the IMS. We present oxyhemoglobin concentration maps of the eye and demonstrate oxygen sensitivity of the maps by comparing normal and diseased eyes. The aim of this work is to advance the general capabilities of snapshot hyperspectral imagers and to advance the integration of retinal oximetry into the standard ophthalmology instrument repertoire

Assessing and Enabling Independent Component Analysis As A Hyperspectral Unmixing Approach

As a result of its capacity for material discrimination, hyperspectral imaging has been utilized for applications ranging from mining to agriculture to planetary exploration. One of the most common methods of exploiting hyperspectral images is spectral unmixing, which is used to discriminate and locate the various types of materials that are present in the scene. When this processing is done without the aid of a reference library of material spectra, the problem is called blind or unsupervised spectral unmixing. Independent component analysis (ICA) is a blind source separation approach that operates by finding outputs, called independent components, that are statistically independent. ICA has been applied to the unsupervised spectral unmixing problem, producing intriguing, if somewhat unsatisfying results. This dissatisfaction stems from the fact that independent components are subject to a scale ambiguity which must be resolved before they can be used effectively in the context of the spectral unmixing problem. In this dissertation, ICA is explored as a spectral unmixing approach. Various processing steps that are common in many ICA algorithms are examined to assess their impact on spectral unmixing results. Synthetically-generated but physically-realistic data are used to allow the assessment to be quantitative rather than qualitative only. Additionally, two algorithms, class-based abundance rescaling (CBAR) and extended class-based abundance rescaling (CBAR-X), are introduced to enable accurate rescaling of independent components. Experimental results demonstrate the improved rescaling accuracy provided by the CBAR and CBAR-X algorithms, as well as the general viability of ICA as a spectral unmixing approach

Imaging ductal carcinoma using a hyperspectral imaging system

Hyperspectral Imaging (HSI) is a non-invasive optical imaging modality that shows the potential to aid pathologists in breast cancer diagnoses cases. In this study, breast cancer tissues from different patients were imaged by a hyperspectral system to detect spectral differences between normal and breast cancer tissues, as well as early and late stages of breast cancer. If the spectral differences in these tissue types can be measured, automated systems can be developed to help the pathologist identify suspect biopsy samples, which will improve sample throughput and assist in making critical treatment decisions. Tissue samples from ten different patients were provided by the WVU Pathology Department. The samples from each patient included both normal and ductal carcinoma tissue, both stained and unstained. These cells were imaged using a snapshot HSI system, and the spectral reflectances were evaluated to see if there was a measurable spectral difference between the various cell types. Analysis of the spectral reflectance values indicated that wavelengths near 550nm show the best differentiation between tissue types. This information was used to train image processing algorithms using supervised and unsupervised data. K-Means and Support Vector Machine (SVM) approaches were applied to the hyperspectral data cubes, and successfully detected spectral tissue differences with sensitivity of 85.45%, and specificity of 94.64% with TNR of 95.8%, and FPR of 4.2%. These results were verified by ground truth marking of the tissue samples by a pathologist. This interdisciplinary work will build a bridge between pathology and hyperspectral optical diagnostic imaging in order to reduce time and workload on the pathologist, which can lead to benefit of lead reducing time, and increasing the accuracy of diagnoses

Analytical and mathematical methods for revealing hidden details in ancient manuscripts and paintings: A review

In this work, a critical review of the current nondestructive probing and image analysis approaches is presented, to revealing otherwise invisible or hardly discernible details in manuscripts and paintings relevant to cultural heritage and archaeology. Multispectral imaging, X-ray fluorescence, Laser-Induced Breakdown Spectroscopy, Raman spectroscopy and Thermography are considered, as techniques for acquiring images and spectral image sets; statistical methods for the analysis of these images are then discussed, including blind separation and false colour techniques. Several case studies are presented, with particular attention dedicated to the approaches that appear most promising for future applications. Some of the techniques described herein are likely to replace, in the near future, classical digital photography in the study of ancient manuscripts and paintings

Neuronal and Hemodynamic Functional Connectivity in the Awake Mouse

Resting State functional Magnetic Resonance Imaging (rs-fMRI) has revealed brain-wide correlation patterns throughout the human brain, interpreted as Functional Connectivity. Dynamic Functional Connectivity (DFC) has recently expanded on this technique via sliding window correlation analysis, revealing moment-to-moment changes in functional connectivity across an imaging session. However, the meaning of these transitions in terms of neural activity and behavior are not well understood.In this work, I utilized Dynamic Functional Connectivity analytical techniques in conjunction with Wide Field Optical Mapping (WFOM) in the awake, freely behaving mouse. I hypothesized that neural and hemodynamic activity observed with WFOM would exhibit similar transitions between functional connectivity states as reported by fMRI DFC studies. I also explored whether changes in functional connectivity would correspond to changes in behavior. Simultaneous neural and hemodynamic activity was collected using WFOM from five freely behaving head-fixed Thy1-jRGECO1a mice. Behavioral metrics of movement, whisking and pupillometry were acquired simultaneously. Raw neuroimaging data were dimensionally reduced to representative time courses across the dorsal surface of the cortex for each subject utilizing a semi-supervised clustering technique. Functional Connectivity analysis revealed rich spatiotemporal structures within neural and hemodynamic activity, which were consistent across imaging sessions and subjects. I observed broad changes in Functional Connectivity metrics during rest, locomotion, and transitional epochs between the two by directly comparing windows captured during these epochs. It was also observed that Functional Connectivity metrics immediately following locomotion offset could be distinguished from periods of sustained rest. Similar to human fMRI studies, a distinct increase in bilateral connectivity of anterior lateral prefrontal cortex was observed, which became significantly less synchronized with posterior brain regions during sustained periods of rest. I next used an unsupervised clustering technique on the same data to test if these properties could be observed in an indirect manner. This approach has been previously used in numerous human fMRI studies, and contextualized this work to human fMRI studies. A sliding window was used to calculate moment-to-moment Functional Connectivity maps across each imaging session. These dynamic correlation maps were clustered into multiple states, which could then be used to calculate the most representative state for any given epoch. Unsupervised clustering revealed strikingly similar dynamic states to our previous observations. These dynamic states also exhibited independent distributions of behavioral activity both in neural and hemodynamic models, leading us to conclude that there is not only a meaningful link between Functional Connectivity in neural and hemodynamic activity, but that behavioral shifts largely drive these changes. My findings provide strong evidence that Dynamic Functional Connectivity has neural origins, and hemodynamic responses are able to depict correlation patterns that tracks rapid changes in behavior and internal brain states such as the level of arousal or alertness. Future studies are necessary to further investigate this speculation, but this offers an excellent framework to better understand the rich, dynamic properties of brain activity

Image Analysis and Machine Learning in Agricultural Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed. Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability. With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research. Advisor: Gary L. Hei

Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter