HyperVein: A Hyperspectral Image Dataset for Human Vein Detection

HyperSpectral Imaging (HSI) plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. HyperSpectral (HS) image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper presents a HS image dataset encompassing left- and right-hand images captured from 100 subjects with varying skin tones. The dataset was annotated using anatomical data to represent vein and non-vein areas within the images. This dataset is utilised to explore the effectiveness of dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward’s Linkage Strategy using Mutual Information (WaLuMI) for vein detection. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with the Support Vector Machine (SVM) binary classification were determined using the Training dataset. The performance of the three dimensionality reduction-based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection

NEAR INFRARED IMAGING FOR SUBCUTANEOUS VEINS LOCALIZATION USING WEARABLE OPTICAL IMAGING DEVICE

Intravenous (IV) catheterization is a basic need for medical treatment. Skilled trained medical practices such as doctor, nurse or even paramedic need to learn as this their basic knowledge. To perform this treatment, they need to locate veins they can get by visual or feel it with their fingers. This technique has its own downside as sometimes different patient have differ skin tone and deepness of their veins. Sometimes patients also get scars or even some of them have thick hair. To attempt venipuncture, sometimes they need to repeat it two or three times if it does not succeed. This is due to non-visibility to locate patient’s veins. This may result severe pain to the patient and leave a bigger impact to their health. These inaccurate catheter insertions need to be overcome with a device that can help medical practitioners to locate veins from patients easily and fast for venipuncture process. Today’s technology give human to look through human’s body but non of them have a capability to locate and display subcutaneous veins structure right in front of their eyes for a user to perform iv catheterization process. In this project, near infrared (NIR) imaging technique will be choose as it has several advantages in compared to the other techniqu

QUEST Hierarchy for Hyperspectral Face Recognition

Face recognition is an attractive biometric due to the ease in which photographs of the human face can be acquired and processed. The non-intrusive ability of many surveillance systems permits face recognition applications to be used in a myriad of environments. Despite decades of impressive research in this area, face recognition still struggles with variations in illumination, pose and expression not to mention the larger challenge of willful circumvention. The integration of supporting contextual information in a fusion hierarchy known as QUalia Exploitation of Sensor Technology (QUEST) is a novel approach for hyperspectral face recognition that results in performance advantages and a robustness not seen in leading face recognition methodologies. This research demonstrates a method for the exploitation of hyperspectral imagery and the intelligent processing of contextual layers of spatial, spectral, and temporal information. This approach illustrates the benefit of integrating spatial and spectral domains of imagery for the automatic extraction and integration of novel soft features (biometric). The establishment of the QUEST methodology for face recognition results in an engineering advantage in both performance and efficiency compared to leading and classical face recognition techniques. An interactive environment for the testing and expansion of this recognition framework is also provided

Translational Functional Imaging in Surgery Enabled by Deep Learning

Many clinical applications currently rely on several imaging modalities such as Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), Computed Tomography (CT), etc. All such modalities provide valuable patient data to the clinical staff to aid clinical decision-making and patient care. Despite the undeniable success of such modalities, most of them are limited to preoperative scans and focus on morphology analysis, e.g. tumor segmentation, radiation treatment planning, anomaly detection, etc. Even though the assessment of different functional properties such as perfusion is crucial in many surgical procedures, it remains highly challenging via simple visual inspection. Functional imaging techniques such as Spectral Imaging (SI) link the unique optical properties of different tissue types with metabolism changes, blood flow, chemical composition, etc. As such, SI is capable of providing much richer information that can improve patient treatment and care. In particular, perfusion assessment with functional imaging has become more relevant due to its involvement in the treatment and development of several diseases such as cardiovascular diseases. Current clinical practice relies on Indocyanine Green (ICG) injection to assess perfusion. Unfortunately, this method can only be used once per surgery and has been shown to trigger deadly complications in some patients (e.g. anaphylactic shock). This thesis addressed common roadblocks in the path to translating optical functional imaging modalities to clinical practice. The main challenges that were tackled are related to a) the slow recording and processing speed that SI devices suffer from, b) the errors introduced in functional parameter estimations under changing illumination conditions, c) the lack of medical data, and d) the high tissue inter-patient heterogeneity that is commonly overlooked. This framework follows a natural path to translation that starts with hardware optimization. To overcome the limitation that the lack of labeled clinical data and current slow SI devices impose, a domain- and task-specific band selection component was introduced. The implementation of such component resulted in a reduction of the amount of data needed to monitor perfusion. Moreover, this method leverages large amounts of synthetic data, which paired with unlabeled in vivo data is capable of generating highly accurate simulations of a wide range of domains. This approach was validated in vivo in a head and neck rat model, and showed higher oxygenation contrast between normal and cancerous tissue, in comparison to a baseline using all available bands. The need for translation to open surgical procedures was met by the implementation of an automatic light source estimation component. This method extracts specular reflections from low exposure spectral images, and processes them to obtain an estimate of the light source spectrum that generated such reflections. The benefits of light source estimation were demonstrated in silico, in ex vivo pig liver, and in vivo human lips, where the oxygenation estimation error was reduced when utilizing the correct light source estimated with this method. These experiments also showed that the performance of the approach proposed in this thesis surpass the performance of other baseline approaches. Video-rate functional property estimation was achieved by two main components: a regression and an Out-of-Distribution (OoD) component. At the core of both components is a compact SI camera that is paired with state-of-the-art deep learning models to achieve real time functional estimations. The first of such components features a deep learning model based on a Convolutional Neural Network (CNN) architecture that was trained on highly accurate physics-based simulations of light-tissue interactions. By doing this, the challenge of lack of in vivo labeled data was overcome. This approach was validated in the task of perfusion monitoring in pig brain and in a clinical study involving human skin. It was shown that this approach is capable of monitoring subtle perfusion changes in human skin in an arm clamping experiment. Even more, this approach was capable of monitoring Spreading Depolarizations (SDs) (deoxygenation waves) in the surface of a pig brain. Even though this method is well suited for perfusion monitoring in domains that are well represented with the physics-based simulations on which it was trained, its performance cannot be guaranteed for outlier domains. To handle outlier domains, the task of ischemia monitoring was rephrased as an OoD detection task. This new functional estimation component comprises an ensemble of Invertible Neural Networks (INNs) that only requires perfused tissue data from individual patients to detect ischemic tissue as outliers. The first ever clinical study involving a video-rate capable SI camera in laparoscopic partial nephrectomy was designed to validate this approach. Such study revealed particularly high inter-patient tissue heterogeneity under the presence of pathologies (cancer). Moreover, it demonstrated that this personalized approach is now capable of monitoring ischemia at video-rate with SI during laparoscopic surgery. In conclusion, this thesis addressed challenges related to slow image recording and processing during surgery. It also proposed a method for light source estimation to facilitate translation to open surgical procedures. Moreover, the methodology proposed in this thesis was validated in a wide range of domains: in silico, rat head and neck, pig liver and brain, and human skin and kidney. In particular, the first clinical trial with spectral imaging in minimally invasive surgery demonstrated that video-rate ischemia monitoring is now possible with deep learning

The 1990 Johnson Space Center bibliography of scientific and technical papers

Abstracts are presented of scientific and technical papers written and/or presented by L. B. Johnson Space Center (JSC) authors, including civil servants, contractors, and grantees, during the calendar year of 1990. Citations include conference and symposium presentations, papers published in proceedings or other collective works, seminars, and workshop results, NASA formal report series (including contractually required final reports), and articles published in professional journals

Aerospace Medicine and Biology, a continuing bibliography with indexes

This bibliography lists 365 reports, articles and other documents introduced into the NASA scientific and technical information system in October 1984

Human Skin Modelling and Rendering

Creating realistic-looking skin is one of the holy grails of computer graphics and is still an active area of research. The problem is challenging due to the inherent complexity of skin and its variations, not only across individuals but also spatially and temporally among one. Skin appearance and reflectance vary spatially in one individual depending on its location on the human body, but also vary temporally with the aging process and the body state. Emotions, health, physical activity, and cosmetics for example can all affect the appearance of skin. The spatially varying reflectance of skin is due to many parameters, such as skin micro- and meso-geometry, thickness, oiliness, and pigmentation. It is therefore a daunting task to derive a model that will include all these parameters to produce realistic-looking skin. The problem is also compounded by the fact that we are very well accustomed to the appearance of skin and especially sensitive to facial appearances and expressions. Skin modelling and rendering is crucial for many applications such as games, virtual reality, films, and the beauty industry, to name a few. Realistic-looking skin improves the believability and realism of applications. The complexity of skin makes the topic of skin modelling and rendering for computer graphics a very difficult, but highly stimulating one. Skin deformations and biomechanics is a vast topic that we will not address in this dissertation. We rather focus our attention on skin optics and present a simple model for the reflectance of human skin along with a system to support skin modelling and rendering

Endoscopic Fluorescence Imaging:Spectral Optimization and in vivo Characterization of Positive Sites by Magnifying Vascular Imaging

Since several decades, the physicians are able to access hollow organs with endoscopic methods, which serve both as diagnostic and surgical means in a wide range of disciplines of the modern medicine (e.g. urology, pneumology, gastroenterology). Unfortunately, white light (WL) endoscopy displays a limited sensitivity to early pre-cancerous lesions. Hence, several endoscopic methods based on fluorescence imaging have been developed to overcome this limitation. Both endogenous and exogenously-induced fluorescence have been investigated, leading to commercial products. Indeed, autofluorescence bronchoscopy, as well as porphyrin-based fluorescence cystoscopy, are now on the market. As a matter of fact, fluorescence-based endoscopic detection methods show very high sensitivity to pre-cancerous lesions, which are often overlooked in WL endoscopy, but they still lack specificity mainly due to the high false-positive rate. Although most of these false positives can easily be rejected under WL observation, tissue abnormalities such as inflammations, hyperplasia, and metaplasia are more difficult to identify, often resulting in supplementary biopsies. Therefore, the purpose of this thesis is to study novel, fast, and convenient method to characterize fluorescence positive spots in situ during fluorescence endoscopy and, more generally, to optimize the existing endoscopic setup. In this thesis, several clinical evaluations were conducted either in the tracheo-bronchial tree and the urinary bladder. In the urinary bladder, fluorescence imaging for detection of non-muscle invasive bladder cancer is based on the selective production and accumulation of fluorescing porphyrins, mainly protoporphyrin IX (PpIX), in cancerous tissues after the instillation of Hexvix® during one hour. In this thesis, we adapted a rigid cystoscope to perform high magnification (HM) cystoscopy in order to discriminate false from true fluorescence positive findings. Both white light and fluorescence modes are possible with the magnification cystoscope, allowing observation of the bladder wall with magnification ranging between 30× – for standard observation – and 650×. The optical zooming setup allows adjusting the magnification continuously in situ. In the high magnification regime, the smallest diameter of the field of view is 600 microns and the resolution is 2.5 microns, when in contact with the bladder wall. With this HM cystoscope, we characterized the superficial vascularization of the fluorescing sites in WL (370–700 nm) reflectance imaging in order to discriminate cancerous from non-cancerous tissues. This procedure allowed us to establish a classification based on observed vascular patterns. 72 patients subject to Hexvix® f luorescence cystoscopy were included in the study. Comparison of HM cystoscopy classification with histopathology results confirmed 32/33 (97%) cancerous biopsies, and rejected 17/20 (85%) non-cancerous lesions. No vascular alteration could be observed on the only positive lesion that was negative in HM mode, probably because this sarcomatoid carcinoma was not originating in the bladder mucosa. We established with this study that a magnification ranging between 80× and 100× is an optimal tradeoff to perform both macroscopic PDD and HM reflectance imaging. In order to make this approach more quantitative, different algorithms of image processing (vessel segmentation and skeletonisation, global information extraction) were also implemented in this thesis. In order to better visualize the vessels, we improved their contrast with respect to the background. Since hemoglobin is a very strong absorber, we targeted the two hemoglobin absorption peaks by placing appropriate bandpass filters (blue 405±50 nm, green 550±50 nm) in the light source. HM cystoscopy was then performed sequentially with WL, blue and green illumination. The two latter showed higher vessel-to-background contrast, identifying different layers of vascularization due to the light penetration depth. During fluorescence cystoscopy, we often observed that the images are somehow "blurred" by a greenish screen between endoscope tip and bladder mucosa. Since this effect is enhanced by the urine production, it is more visible with flexible scopes (lower flushing capabilities) and imaging systems that collect only autofluorescence as background. Indeed, when the bladder is not flushed regularly, greenish flows coming out of the ureters can easily be observed. For this reason, it is supposed that some fluorophores contained in the urine are excited by the photodetection excitation light, and appear greenish on the screen. This effect may impair the visualization of the bladder mucosa, and thus cancerous lesions, and lowers sensitivity of the fluorescence cystoscopy. In this thesis, we identified the main metabolites responsible for the liquid fluorescence, and optimized the spectral design accordingly. In the tracheo-bronchial tree, the fluorescence contrast is based on the sharp autofluorescence (AF) decrease on early cancerous lesions in the green spectral region (around 500 nm) and a relatively less important decrease in the red spectral region (> 600 nm) when excited with blue-violet light (around 410 nm). It has been shown over the last years, that this contrast may be attributed to a combined effect of epithelium thickening and higher concentration of hemoglobin in the tissues underneath the (pre-)cancerous lesions. In this thesis, we contributed to the definition of the input design of several new prototypes, that were subsequently tested in the clinical environment. We first showed that narrow-band excitation in the blue-violet could increase the tumor-to-normal spectral contrast in the green spectral region. Then, we quantified the intra- and inter-patient variations in the AF intensities in order to optimize the spectral response of the endoscopic fluorescence imaging system. For this purpose, we developed an endoscopic reference to be placed close to the bronchial mucosa during bronchoscopy. Finally, we evaluated a novel AF bronchoscope with blue-backscattered light on 144 patients. This new device showed increased sensitivity for pre-neoplastic lesions. Similar to what we observed in the bladder, it is likely that developing new imaging capabilities (including vascular imaging) will facilitate discriminating true from false positive in AF bronchoscopy. Here, we demonstrated that this magnification allowed us to resolve vessels with a diameter of about 30 µm. This resolution is likely to be sufficient to identify Shibuya's vascular criteria (loops, meshes, dotted vessels) on AF positive lesions. This criteria allow him to recognize pre-cancerous lesions, and thus can potentially decrease the false-positive rate with our AF imaging system. This magnification was also showed to be better for routine bronchoscopy, since it delivers sharper and more structured images to the operator