Hyperspectral Imaging Applications Research Articles

The application of hyperspectral core imaging for oil and gas

Abstract Hyperspectral core imaging as a commercial technology is fairly new to the oil and gas industry, providing a rapid and non-destructive method for mapping mineralogy of drill core and cuttings. The technology makes use of infrared spectrometers that collect imagery across the visible, near infrared, short-wave infrared and thermal (long-wave) infrared range that contains information related to a variety of mineral species, and in some cases including mineral chemistry and texture. In this paper we illustrate how the mineralogical information can be used directly for a variety of applications relevant to reservoir quality, including mineral mapping, sedimentological mapping and upscaling of micro information to the well scale. Statistical analysis allows the data to be used to segment cores into different rock types, and via calibration to quantitative mineralogical techniques, the data can be used to construct continuous curves of quantitative mineralogy, total organic content and production parameters.

A Multi-Attention Autoencoder for Hyperspectral Unmixing Based on the Extended Linear Mixing Model

Hyperspectral unmixing, which decomposes mixed pixels into the endmembers and corresponding abundances, is an important image process for the further application of hyperspectral images (HSIs). Lately, the unmixing problem has been solved using deep learning techniques, particularly autoencoders (AEs). However, the majority of them are based on the simple linear mixing model (LMM), which disregards the spectral variability of endmembers in different pixels. In this article, we present a multi-attention AE network (MAAENet) based on the extended LMM to address the issue of the spectral variability problem in real scenes. Moreover, the majority of AE networks ignore the global spatial information in HSIs and operate pixel- or patch-wise. We employ attention mechanisms to design a spatial–spectral attention (SSA) module that can deal with the band redundancy in HSIs and extract global spatial features through spectral correlation. Moreover, noticing that the mixed pixels are always present in the intersection of different materials, a novel sparse constraint based on spatial homogeneity is designed to constrain the abundance and abstract local spatial features. Ablation experiments are conducted to verify the effectiveness of the proposed AE structure, SSA module, and sparse constraint. The proposed method is compared with several state-of-the-art unmixing methods and exhibits competitiveness on both synthetic and real datasets.

Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics

Application of hyperspectral imaging (HSI) and data analysis algorithms was investigated for early and non-destructive detection of Botrytis cinerea infection. Hyperspectral images were collected from laboratory-based contaminated and non-contaminated fruits at different day intervals. The spectral wavelengths of 450 nm to 900 nm were pretreated by applying moving window smoothing (MWS), standard normal variates (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1st derivative, and Savitzky–Golay 2nd derivative algorithms. In addition, three different wavelength selection algorithms, namely; competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projection algorithm (SPA), were executed on the spectra to invoke the most informative wavelengths. The linear discriminant analysis (LDA), developed with SNV-filtered spectral data, was the most accurate classifier to differentiate the contaminated and non-contaminated kiwifruits with accuracies of 96.67% and 96.00% in the cross-validation and evaluation stages, respectively. The system was able to detect infected samples before the appearance of disease symptoms. Results also showed that the gray-mold infection significantly influenced the kiwifruits’ firmness, soluble solid content (SSC), and titratable acidity (TA) attributes. Moreover, the Savitzky–Golay 1st derivative-CARS-PLSR model obtained the highest prediction rate for kiwifruit firmness, SSC, and TA with the determination coefficient (R2) values of 0.9879, 0.9644, 0.9797, respectively, in calibration stage. The corresponding cross-validation R2 values were equal to 0.9722, 0.9317, 0.9500 for firmness, SSC, and TA, respectively. HSI and chemometric analysis demonstrated a high potential for rapid and non-destructive assessments of fungal-infected kiwifruits during storage.

Multimodal close range hyperspectral imaging combined with multiblock sequential predictive modelling for fresh produce analysis

Multimodal measurements are increasingly becoming common in the domain of spectral sensing and imaging for fresh produce. Often multiple sensors are expected to carry complementary information which allows precise estimation of responses. In this study, a novel case of multimodal hyperspectral imaging is described where two different spectral cameras working in the complementary spectral ranges were integrated into a fully standalone system for spectral imaging for fresh produce analysis. Furthermore, a comparative analysis of different multiblock predictive modelling approaches for fusing data from these two complementary spectral cameras is demonstrated. Both multiblock latent space and multiblock variable selection approaches to identify key variables of interest was examined and compared with the analysis carried out on individual data blocks. Prediction of the soluble solids content in grapes was used to demonstrate the application. The presented approach can increase the applications of multimodal hyperspectral imaging for non-destructive analysis.

Expanding hyperspectral imaging applications to the clinical scene: non-invasive, label-free approaches for early diagnostics and precision medicine

Non-invasive, label-free hyperspectral imaging technologies are powerful tools that can provide critical insights into the spatiotemporal dynamics of evolving systems. They are already integrated into autonomous adaptive surveillance workflows for precision agriculture, remote sensing, environmental monitoring, and food analysis. However, these technologies are not yet routinely used in medicine. With these technologies applied to the biomedical sciences, we anticipate landmark advances in the physicochemical behavioral refinement of high signal-to-noise probes and in the rapid screening of patient exosome populations. To implement label-free spatiochemical imaging techniques while meeting the practical needs of clinicians, technology, workflow design and development must be tailored to the specific medical field while considering existing visualization modalities. This perspective merges basic, translational, and clinical views of label-free, non-invasive hyperspectral imaging. We explore its potential role in positively impacting cancer-related precision medicine and early diagnostics to support clinically implemented point-of-care technologies.

Stain-free mapping of polymer-blend morphologies via application of high-voltage STEM-EELS hyperspectral imaging to low-loss spectra

Polymer blends composed of multiple types of polymers are used for various industrial applications; therefore, their morphologies must be understood to predict and improve their physical properties. Herein, we propose a spectral imaging method based on scanning transmission electron microscopy (STEM) and electron energy-loss spectroscopy to map polymer morphologies with nanometric resolution as an alternative to the conventional electron staining technique. In particular, the low-loss spectra of the 5–30 eV energy-loss region were measured to minimize electron irradiation damage rather than the core-loss spectra, such as carbon K-shell absorption spectra, which require significantly longer recording times. Medium-voltage (200 kV) and high-voltage (1000 kV) STEM was used at various temperatures to compare the degrees of electron-beam damage resulting from various electron energies and sample temperatures. A multivariate curve resolution technique was used to isolate the constituent spectra and visualize their distributions by distinguishing the characteristic peaks derived from various chemical species. High-voltage STEM was more useful than medium-voltage STEM for analyzing thicker samples while suppressing ionization damage.

Spatial–Spectral Joint Hyperspectral Anomaly Detection Based on a Two-Branch 3D Convolutional Autoencoder and Spatial Filtering

Hyperspectral anomaly detection (HAD) is an important application of hyperspectral images (HSI) that can distinguish anomalies from background in an unsupervised manner. As a common unsupervised network in deep learning, autoencoders (AE) have been widely used in HAD and can highlight anomalies by reconstructing the background. This study proposed a novel spatial–spectral joint HAD method based on a two-branch 3D convolutional autoencoder and spatial filtering. We used the two-branch 3D convolutional autoencoder to fully extract the spatial–spectral joint features and spectral interband features of HSI. In addition, we used a morphological filter and a total variance curvature filter for spatial detection. Currently, most of the datasets used to validate the performance of HAD methods are airborne HSI, and there are few available satellite-borne HSI. For this reason, we constructed a dataset of satellite-borne HSI based on the GF-5 satellite for experimental validation of our anomaly detection method. The experimental results for the airborne and satellite-borne HSI demonstrated the superior performance of the proposed method compared with six state-of-the-art methods. The area under the curve (AUC) values of our proposed method on different HSI reached above 0.9, which is higher than those of the other methods.

Determination of quality and maturity of processing tomatoes using near-infrared hyperspectral imaging with interpretable machine learning methods

Processing tomato (Lycopersicon esculentum Mill.) is rich in vitamins and lycopene, which is favored by consumers. In this study, near-infrared hyperspectral imaging (HSI) technology (980–1660 nm) was used to detect the firmness, soluble solids, lycopene, and titratable acid content of processing tomatoes and to classify fruits at three maturity stages. Savitzky-Golay (SG) smoothing was used to reduce the noise of hyperspectral images. The average spectrum of the tomato fruit was extracted for model development. Random forest (RF), partial least squares (PLS), and recurrent neural network (RNN) were used to develop models for predicting the four quality attributes and identifying the maturity level. Results showed that the RNN model had a classification accuracy of 40% higher than RF and 17% higher than PLS. In the prediction of quality parameters, RNN models had the highest R2 value (>0.87), followed by PLS and RF models. Important wavelengths were identified by calculating its contribution values and were used to interpret the model. The results illustrated that near-infrared hyperspectral imaging technology combined with deep learning could effectively predict the quality and maturity of processing tomatoes. The work can provide a perspective on the application of HSI as a nondestructive testing approach for other agricultural products.

Microtaper leaky-mode spectrometer with picometer resolution

The wide application of optical spectroscopy makes miniaturized spectrometers with fundamental importance. The scalability, high-performance, low-cost, and small footprint are still contradicting each other and limiting the applicability of miniaturized spectrometer for practical application. Here we propose a compact spectrometer that satisfies the four advantages. The device uses a fiber taper tip to generate complex leaky mode patterns within 1 mm length. The unique correspondence between the pattern and wavelength operates effectively for hundreds of nanometers spectral range while providing a spectral resolution around ~ 1 pm. The integration of multiple taper tips enables hyperspectral imaging applications. The working range of our device can be further extended using different materials and detectors while keeping the similar architecture.

Application of UAV Push-Broom Hyperspectral Images in Water Quality Assessments for Inland Water Protection: A Case Study of Zhang Wei Xin River in Dezhou Distinct, China

A water quality parameter retrieval scheme based on the UAV push-broom hyperspectral images was designed and validated for assessing the ecological health of Zhang Wei Xin River in Dezhou distinct, China. First, a UAV carrying a push-broom hyperspectral imager that is lightweight and has a small size was used to acquire high spatial and hyperspectral resolution images. Then, the mosaicked reflectance data of the whole river were produced by a seamless image mosaicking method with high geometrical accuracy and spectral fidelity. Next, the in-field measurements of different parameters and the corresponding spectral reflectance from the mosaicked images at the sampling points were used to build the water quality parameter retrieval models for total phosphorus (TP), chlorophyll a (Chla), and total suspended solids (TSS). To validate the model, the retrieval results of the testing sampling points were compared with the measured parameters. The coefficients of determination R2 of TP, Chla, and TSS were 0.886, 0.918, and 0.968, respectively. The retrieved TP, Chla, and TSS maps showed that the water pollution of Zhang Wei Xin River is serious, the total phosphorus exceeds the standard, and the water body is in a state of eutrophication. The UAV-based hyperspectral remote sensing technique provides a cost-effective method for inland water monitoring at a local scale with high accuracy.

The Application of Hyperspectral Images in the Classification of Fresh Leaves’ Maturity for Flue-Curing Tobacco

The maturity of tobacco leaves directly affects their curing quality. However, no effective method has been developed for determining their maturity during production. Assessment of tobacco maturity for flue curing has long depended on production experience, leading to considerable variation. In this study, hyperspectral imaging combined with a novel algorithm was used to develop a classification model that could accurately determine the maturity of tobacco leaves. First, tobacco leaves of different maturity levels (unripe, under-ripe, ripe, and over-ripe) were collected. ENVI software was used to remove the hyperspectral imaging (HSI) background, and 11 groups of filtered images were obtained using Python 3.7. Finally, a full-band-based partial least-squares discriminant analysis (PLS-DA) classification model was established to identify the maturity of the tobacco leaves. In the calibration set, the model accuracy of the original spectrum was 88.57%, and the accuracy of the de-trending, multiple scattering correction (MSC), and standard normalization variable (SNV) treatments was 91.89%, 95.27%, and 92.57%, respectively. In the prediction set, the model accuracy of the de-trending, MSC, and SNV treatments was 93.85%, 96.92%, and 93.85%, respectively. The experimental results indicate that a higher model accuracy was obtained with the filtered images than with the original spectrum. Because of the higher accuracy, de-trending, MSC, and SNV treatments were selected as the candidate characteristic spectral bands, and a successive projection algorithm (SPA), competitive adaptive reweighted sampling (CASR), and particle swarm optimization (PSO) were used as the screening methods. Finally, a genetic algorithm (GA), PLS-DA, line support vector machine (LSVM), and back-propagation neural network (BPNN) classification and discrimination models were established. The combination SNV-SPA-PLS-DA model provided the best accuracy in the calibration and prediction sets (99.32% and 98.46%, respectively). Our findings highlight the efficacy of using visible/near-infrared (ViS/NIR) hyperspectral imaging for detecting the maturity of tobacco leaves, providing a theoretical basis for improving tobacco production.

Acquisition strategies for in-situ hyperspectral imaging of stained-glass windows: case studies from the Swiss National Museum

Over the last decade, hyperspectral imaging has become a popular technique for the non-invasive identification and mapping of painting materials in many typologies of artworks, thanks to the possibility of obtaining spectral information over the spatial region. A few attempts have also been made on stained-glass windows to identify the chromophore elements responsible for glass color. Hyperspectral imaging of stained glass can be complex; in most cases, stained-glass windows are an integral part of buildings, and sunlight represents the natural light source for illuminating these artifacts. While it may be considered an advantage, sunlight is not homogeneous throughout the day, and different weather conditions can affect the quality of the hyperspectral images. In addition, the presence of buildings and vegetation in the background could also modify the colors of the stained-glass windows and consequently alter the characteristic peaks of the chromophores in the spectra. This work aims to solve some of these issues and proposes different strategies to improve the results obtainable in situ. The methodology was tested on stained-glass panels displayed in the windows of the Swiss National Museum. Stained-glass panels located in windows of an internal wall were also analyzed, developing a lighting setup to account for the lack of natural light. Hyperspectral images of the selected stained glass were acquired multiple times, choosing different transmittance references for the preprocessing and exposure time to evaluate differences in the collected spectral images. The use of a diffuser sheet to mitigate the effect of external factors was also tested on some panels exposed to sunlight. Results from representative case studies will be presented to discuss the feasibility and limitations of in-situ hyperspectral imaging applications on stained glass and provide some general recommendations to consider during the acquisitions.

A Comprehensive Correction Method for Radiation Distortion of Multi-Strip Airborne Hyperspectral Images

Airborne hyperspectral imaging plays an increasingly important role in environmental monitoring. However, due to the limitations of the acquisition conditions, there are uneven radiation and chromatic aberrations in the mosaic data. Accurate preprocessing of the original data is the premise of qualitative and quantitative remote sensing. In this study, we proposed a comprehensive radiation distortion correction method that integrates radiation attenuation difference correction, topographic correction, and multi-strip images consistency adjustment (RA-TOC-CA). First, the radiation attenuation equation was constructed by combining the viewing geometry, terrain, and the elevation difference between the UAV and the ground to eliminate the radiation attenuation difference of pixels acquired at the different instantaneous field of view (IFOV). Second, an improved kernel-driven BRDF model was built combining terrain information and illumination-viewing (flight attitude and sensor IFOV) geometry to eliminate the radiation unevenness and BRDF distortion caused by topography. Third, adjusting the reflectance of multi-strip images according to the homonymous points’ reflectance of adjacent strips should be equal, eliminating the radiation differences between multiple strips. Based on multi-strip airborne hyperspectral images collected in the Shaanxi province of China, the correction results of the RA-TOC-CA method were compared with those of the SCS+C and Minnaert+SCS methods regarding various evaluation criteria. The results showed that SCS+C and Minnaert+SCS can reduce the topographic effect but cannot eliminate the reflectance difference at the edges of adjacent images, and SCS+C overcorrects the reflectance. RA-TOC-CA weakened the topographic effects and brightness gradient, which was physically stable and generalizable. Compared with previous studies, RA-TOC-CA provided a complete radiation distortion correction method for airborne hyperspectral images and had a solid theoretical basis. This study introduces an effective method for radiation distortion correction of airborne hyperspectral images and provides technical support for large-scale applications of hyperspectral images.

Near-infrared hyperspectral imaging combined with machine learning for physicochemical-based quality evaluation of durian pulp

This research reports on the application of near-infrared hyperspectral imaging (NIR-HSI) system for predicting the physicochemical properties; dry matter (DM), total soluble solids (TSS), and fat content (FC) of durian. Partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and 1D convolution neural network (CNN) models: custom, U-Net, and VGG19; were developed to predict DM, TSS, and FC of durian pulp. Feature wavelengths were selected using a genetic algorithm (GA) and successive projection algorithm (SPA). The selected wavelengths were then validated based on the algorithms for regression model development. GA-PLSR model was compelling to predict the DM and FC in durian pulp, which obtained the coefficient of determination for the test set (r2) and root mean square error of prediction (RMSEP) of 0.97 and 1.12% for DM and 0.86 and 0.64% for FC, respectively. The GA-PLSR model provided the best result for the TSS prediction with r2, and RMSEP of 0.90 and 1.40%, respectively, whereas the SPA-PLSR model based on only thirteen wavelengths attained fair result with the r2 and RMSEP of 0.79 and 2.03%, respectively. The above results show that the pushbroom NIR-HSI system achieved promising results for estimating DM, TSS, and FC in durian pulp. This research identified the featured wavelengths that can be used to develop a portable and reliable HSI or multispectral system to be installed at durian packaging firms for quality inspection and grading.

Hyperspectral image segmentation: a preliminary study on the Oral and Dental Spectral Image Database (ODSI-DB)

ABSTRACT Visual discrimination of clinical tissue types remains challenging, with traditional RGB imaging providing limited contrast for such tasks. Hyperspectral imaging (HSI) is a promising technology providing rich spectral information that can extend far beyond three-channel RGB imaging. Moreover, recently developed snapshot HSI cameras enable real-time imaging with significant potential for clinical applications. Despite this, the investigation into the relative performance of HSI over RGB imaging for semantic segmentation purposes has been limited, particularly in the context of medical imaging. Here we compare the performance of state-of-the-art deep learning image segmentation methods when trained on hyperspectral images, RGB images, hyperspectral pixels (minus spatial context), and RGB pixels (disregarding spatial context). To achieve this, we employ the recently released Oral and Dental Spectral Image Database (ODSI-DB), which consists of 215 manually segmented dental reflectance spectral images with 35 different classes across 30 human subjects. The recent development of snapshot HSI cameras has made real-time clinical HSI a distinct possibility, though successful application requires a comprehensive understanding of the additional information HSI offers. Our work highlights the relative importance of spectral resolution, spectral range, and spatial information to both guide the development of HSI cameras and inform future clinical HSI applications.

A capsule-vectored neural network for hyperspectral image classification

The issues of feature redundancy and insufficient labeled samples impede the widespread application of hyperspectral images. Many researchers have designed complex networks in pursuit of hyperspectral image classification, which has brought unstable factors to the networks and resulted in poor performance in the case of insufficient labeled samples. The motivation of this paper is to promote the classification accuracy by enhancing the classification approach, instead of the feature extraction phase. Differing from the existing works which focus on elaborate and complicated network designs and complex training strategies, this work is aimed at integrating a vector-neuron capsule representation with a vanilla fully convolutional network (FCN) to obtain an improved hyperspectral image classification performance in the case of insufficient labeled samples. The proposed network, which is named the capsule-vectored neural network (CVNN), consists of an encoder–decoder feature learning part and a vector-neuron capsule transformation part. In the encoder–decoder part, a down-sampling step is employed twice in the fully convolutional framework. After feature extraction by the vanilla FCN, the output is transformed into a vectorial representation with a learnable transformation matrix. All of the vector neurons have a unified dimension, and the norm of the vector neurons is utilized as the logit that is fed into the softmax function to obtain the posterior probability matrix. The experimental results confirm that CVNN can tackle deep learning modeling well in the case of limited labeled samples. The classification accuracy of CVNN is also higher than that of the other advanced classification approaches.

Rapid and Nondestructive Identification of Origin and Index Component Contents of Tiegun Yam Based on Hyperspectral Imaging and Chemometric Method

Tiegun yam is a typical food and medicine agricultural product, which has the effects of nourishing the kidney and benefitting the lungs. The quality and price of Tiegun yam are affected by its origin, and counterfeiting and adulteration are common. Therefore, it is necessary to establish a method to identify the origin and index component contents of Tiegun yam. Hyperspectral imaging combined with chemometrics was used, for the first time, to explore and implement the identification of origin and index component contents of Tiegun yam. The origin identification models were established by partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF) using full wavelength and feature wavelength. Compared with other models, MSC-PLS-DA is the best model, and the accuracy of the training set and prediction set is 100% and 98.40%. Partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR) models were used to predict the contents of starch, polysaccharide, and protein in Tiegun yam powder. The optimal residual predictive deviation (RPD) values of starch, polysaccharide, and protein prediction models selected in this study were 5.21, 3.21, and 2.94, respectively. The characteristic wavelength extracted by the successive projections algorithm (SPA) method can achieve similar results as the full-wavelength model. These results confirmed the application of hyperspectral imaging (HSI) in the identification of the origin and the rapid nondestructive prediction of starch, polysaccharide, and protein contents of Tiegun yam powder. Therefore, the HSI combined with the chemometric method was available for conveniently and accurately determining the origin and index component contents of Tiegun yam, which can expect to be an attractive alternative method for identifying the origin of other food.

Fluorescence Spectral Imaging Based on Computational Spectral Sensing

As a powerful optical tool, spectrometers are widely used in materials analysis and light-source characterization. Although commercial spectrometers based on dispersive diffraction or Fourier transform offer high resolution and broad dynamic range, spatial information of the sample is lost, limiting its application in snapshot hyperspectral imaging. Herein, we propose a computational spectrometer based on the tailored point spread function of the imaging system that uses disordered microstructures fabricated by low-cost photolithography. The spectrometer achieves a high quantum efficiency (${Q}_{\mathrm{avg}}\phantom{\rule{0.2em}{0ex}}\ensuremath{\sim}\phantom{\rule{0.2em}{0ex}}60\mathrm{%}$) and ultracompact footprint as low as $24.5\ifmmode\times\else\texttimes\fi{}24.5\phantom{\rule{0.2em}{0ex}}\ensuremath{\mu}{\mathrm{m}}^{2}$, exhibits outstanding performance in weak-light hyperspectral imaging (e.g., Raman or fluorescence spectroscopy) in the microscopic system. Apart from the advancements mentioned above, our spectrometer offers good compatibility with commercial microscopes, implying it is a promising candidate for spectral imaging in chemistry, biology, and others.

The Application of Hyperspectral Imaging to the Measurement of Pressure Injury Area.

Wound size measurement is an important indicator of wound healing. Nurses measure wound size in terms of length × width in wound healing assessment, but it is easy to overestimate the extent of the wound due to irregularities around it. Using hyperspectral imaging (HIS) to measure the area of a pressure injury could provide more accurate data than manual measurement, ensure that the same tool is used for standardized assessment of wounds, and reduce the measurement time. This study was a pilot cross-sectional study, and a total of 30 patients with coccyx sacral pressure injuries were recruited to the rehabilitation ward after approval by the human subjects research committee. We used hyperspectral images to collect pressure injury images and machine learning (k-means) to automatically classify wound areas in combination with the length × width rule (LW rule) and image morphology algorithm for wound judgment and area calculation. The results calculated from the data were compared with the calculations made by the nursing staff using the length × width rule. The use of hyperspectral images, machine learning, the length × width rule (LW rule), and an image morphology algorithm to calculate the wound area yielded more accurate measurements than did nurses, effectively reduced the chance of human error, reduced the measurement time, and produced real-time data. HIS can be used by nursing staff to assess wounds with a standardized approach so as to ensure that proper wound care can be provided.

Clinical applications of non-invasive multi and hyperspectral imaging of cell and tissue autofluorescence beyond oncology.

Hyperspectral and multispectral imaging of cell and tissue autofluorescence employs fluorescence imaging, without exogenous fluorophores, across multiple excitation/emission combinations (spectral channels). This produces an image stack where each pixel (matched by location) contains unique information about the sample's spectral properties. Analysis of this data enables access to a rich, molecularly specific data set from a broad range of cell-native fluorophores (autofluorophores) directly reflective of biochemical status, without use of fixation or stains. This non-invasive, non-destructive technology has great potential to spare the collection of biopsies from sensitive regions. As both staining and biopsy may be impossible, or undesirable, depending on the context, this technology great diagnostic potential for clinical decision making. The main research focus has been on the identification of neoplastic tissues. However, advances have been made in diverse applications-including ophthalmology, cardiovascular health, neurology, infection, assisted reproduction technology and organ transplantation.