Hyperspectral Imaging System Research Articles

A birefringent spectral demultiplexer enables fast hyper-spectral imaging of protoporphyrin IX during neurosurgery

Hyperspectral imaging and spectral analysis quantifies fluorophore concentration during fluorescence-guided surgery1–6. However, acquisition of the multiple wavelengths required to implement these methods can be time-consuming and hinder surgical workflow. To this end, a snapshot hyperspectral imaging system capable of acquiring 64 channels of spectral data simultaneously was developed for rapid hyperspectral imaging during neurosurgery. The system uses a birefringent spectral demultiplexer to split incoming light and redirect wavelengths to different sections of a large format microscope sensor. Its configuration achieves high optical throughput, accepts unpolarized input light and exceeds channel count of prior image-replicating imaging spectrometers by 4-fold. Tissue-simulating phantoms consisting of serial dilutions of the fluorescent agent characterize system linearity and sensitivity, and comparisons to performance of a liquid crystal tunable filter based hyperspectral imaging device are favorable. The new instrument showed comparable, if not improved, sensitivity at low fluorophore concentrations; yet, acquired wide-field images at more than 70-fold increase in frame rate. Image data acquired in the operating room during human brain tumor resection confirm these findings. The new device is an important advance in achieving real-time quantitative imaging of fluorophore concentration for guiding surgery.

Spectral-Spatial Attention Rotation-Invariant Classification Network for Airborne Hyperspectral Images

An airborne hyperspectral imaging system is typically equipped on an aircraft or unmanned aerial vehicle (UAV) to capture ground scenes from an overlooking perspective. Due to the rotation of the aircraft or UAV, the same region of land cover may be imaged from different viewing angles. While humans can accurately recognize the same objects from different viewing angles, classification methods based on spectral-spatial features for airborne hyperspectral images exhibit significant errors. The existing methods primarily involve incorporating image or feature rotation angles into the network to improve its accuracy in classifying rotated images. However, these methods introduce additional parameters that need to be manually determined, which may not be optimal for all applications. This paper presents a spectral-spatial attention rotation-invariant classification network for the airborne hyperspectral image to address this issue. The proposed method does not require the introduction of additional rotation angle parameters. There are three modules in the proposed framework: the band selection module, the local spatial feature enhancement module, and the lightweight feature enhancement module. The band selection module suppresses redundant spectral channels, while the local spatial feature enhancement module generates a multi-angle parallel feature encoding network to improve the discrimination of the center pixel. The multi-angle parallel feature encoding network also learns the position relationship between each pixel, thus maintaining rotation invariance. The lightweight feature enhancement module is the last layer of the framework, which enhances important features and suppresses insignificance features. At the same time, a dynamically weighted cross-entropy loss is utilized as the loss function. This loss function adjusts the model’s sensitivity for samples with different categories according to the output in the training epoch. The proposed method is evaluated on five airborne hyperspectral image datasets covering urban and agricultural regions. Compared with other state-of-the-art classification algorithms, the method achieves the best classification accuracy and is capable of effectively extracting rotation-invariant features for urban and rural areas.

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 core scanner: An effective mineral mapping tool for apatite in the Upper Zone, northern limb, Bushveld Complex

The technological advances in efficient, rapid, and non-destructive hyperspectral core logging systems for systematic mineral mapping have led to the discovery and exploitation of new mineral deposits Hyperspectral imaging in the long-wave infrared range has been recently used successfully to identify various phosphate-bearing minerals (monazite, xenotime, and britholite), with limited work on apatite associated with mafic-ultramafic layered intrusions. In this study we investigate the effectiveness of a hyperspectral imaging (HSI) system with long-wave infrared (LWIR) bandwidthsto identify apatite in the Upper Zone of the Bushveld Complex. The accuracy of the HSI results was validated by mineralogical and geochemical data. The two apatite-enriched zones detected by HSI suggesting widespread development of apatite throughout the uppermost 600 m of the Upper Zone. The lower apatite-enriched zone is approximately 40 m thick, while the upper apatite-enriched zone is about 23 m thick, consistent with previous thickness determinations by traditional logging and analytical methods. Spectral mixing observed in the response of apatite is ascribed either to the common association of apatite and olivine in these rocks, or to differences between the spatial resolution of the hyperspectral image and the size of apatite grains. The VNIR-SWIR wavelength region did not show prominent spectral features of apatite. Nonetheless, HSI in the LWIR range is effective in mapping apatite and should therefore be considered as an exploration tool. This research advances our knowledge of the reflectance spectroscopy of REE-bearing minerals, which makes it easier to detect, identify, and quantify REE-bearing silicate minerals by HSI.

Universal high spatial resolution hyperspectral imaging using hybrid-resolution image fusion

By fusing a low spatial resolution hyperspectral image (LR-HSI) and a high spatial resolution RGB image (HR-RGB), hybrid-resolution hyperspectral imaging has been a popular framework for acquiring high spatial resolution hyperspectral images (HR-HSIs). Existing fusion methods always employ a known spectral response function (SRF) of the RGB camera to reconstruct the HR-HSI. The SRF is often limited or unavailable in practice, restricting the performance of existing methods. To address this problem, we propose a color space transfer-based fusion strategy that obtains HR-HSIs based on a hybrid resolution hyperspectral imaging system without measuring the SRF. Specifically, using a clustered-based backpropagation neural network, the HR-RGB is mapped into the CIE XYZ color space, and the HR-XYZ is obtained. In the CIE XYZ color space, its SRF is known; thus, the the SRF measurement is successfully skipped. To efficiently fuse the HR-XYZ and the LR-HSI, we propose a polynomial fusion model that estimates the ratio matrix between the target HR-HSI and the upsampled LR-HSI. Finally, the target HR-HSI is reconstructed by combining the ratio matrix and the unsampled LR-HSI. Experimental results on two public data sets and our real-world data sets show that the proposed method outperforms five state-of-the-art fusion methods.

Malignant versus normal breast tissue: Optical differentiation exploiting hyperspectral imaging system

Breast malignancy is a critical problem that severely affects women’s health globally with a high-frequency rate, necessitating fast, effective, and early diagnostic methods. The present study aims to measure the breast tissue’s optical properties by capturing the spectral signatures from malignant and normal breast tissue for therapeutic and diagnostic applications. The optical imaging system incorporates a hyperspectral (HS) camera to capture the spectral signatures for both the malignant and normal breast tissues within 400 ~ 1000 nm. The system was subdivided into two exploratory (reflection/transmission) to measure the tissue’s diffuse reflectance (Rd) and light transmission (Tr), respectively. The study involved 30 breast tissue (normal/tumor) samples from 30 females in the age range of 46 ~ 72 years, who were optically inspected in the visible and near-infrared (VIS-NIR) spectra. Then, the inverse adding doubling (IAD) method for breast tissue characterization and descriptive analysis (T-test) was exploited to verify the significant difference between the various types of breast tissues and select the optimum wavelength. Finally, comparing the study outcome with the histopathological examination to evaluate the system’s effectiveness by calculation (sensitivity, specificity, and accuracy). The average outcome values demonstrated that the optimal spectral bands distinguishing between the normal and the tumor tissues regarding the reflectance approach were 600 ~ 680 nm and 750 ~ 960 nm at the VIS and NIR spectrum, respectively. Then, for the transmission technique, the optimal spectral bands were 560 ~ 590 nm and 760 ~ 810 nm at the VIS and NIR spectra, respectively. Later, the T-test and the IAD verified that the highest Rd values for discrimination were 600 ~ 640 nm and 800 ~ 840 nm at the VIS and NIR spectra, respectively. On the other side, the highest Tr values were 600 ~ 640 nm and 760 ~ 800 nm at the VIS and NIR spectra, respectively. The investigation’s average reading accuracy, sensitivity, and specificity were 85%, 81.88%, and 88.8%, respectively. The experimental trials revealed that the system could identify the optimal wavelength for therapeutic and diagnostic applications through the light interaction behavior of the breast tissue’s optical properties.

Emerging technology for intraoperative margin assessment and post-operative tissue diagnosis for breast-conserving surgery

IntroductionTissue-preserving surgery is utilized progressively in cancer therapy, where a clear surgical margin is critical to avoid cancer recurrence, specifically in breast cancer (BC) surgery. The Intraoperative pathologic approaches that rely on tissue segmenting and staining have been recognized as the ground truth for BC diagnosis. Nevertheless, these methods are constrained by its complication and timewasting for tissue preparation. ObjectiveWe present a non-invasive optical imaging system incorporating a hyperspectral (HS) camera to discriminate between cancerous and non-cancerous tissues in ex-vivo breast specimens, which could be an intraoperative diagnostic technique to aid surgeons during surgery and later a valuable tool to assist pathologists. MethodsWe have established a hyperspectral Imaging (HSI) system comprising a push-broom HS camera at wavelength 380∼1050 nm with source light 390∼980 nm. We have measured the investigated samples' diffuse reflectance (Rd), fixed on slides from 30 distinct patients incorporating mutually normal and ductal carcinoma tissue. The samples were divided into two groups, stained tissues during the surgery (control group) and unstained samples (test group), both captured with the HSI system in the visible and near-infrared (VIS-NIR) range. Then, to address the problem of the spectral nonuniformity of the illumination device and the influence of the dark current, the radiance data were normalized to yield the radiance of the specimen and neutralize the intensity effect to focus on the spectral reflectance shift for each tissue. The selection of the threshold window from the measured Rd is carried out by exploiting the statistical analysis by calculating each region's mean and standard deviation. Afterward, we selected the optimum spectral images from the HS data cube to apply a custom K-means algorithm and contour delineation to identify the regular districts from the BC regions. ResultsWe noticed that the measured spectral Rd for the malignant tissues of the investigated case studies versus the reference source light varies regarding the cancer stage, as sometimes the Rd is higher for the tumor or vice versa for the normal tissue. Later, from the analysis of the whole samples, we found that the most appropriate wavelength for the BC tissues was 447 nm, which was highly reflected versus the normal tissue. However, the most convenient one for the normal tissue was at 545 nm with high reflection versus the BC tissue. Finally, we implement a moving average filter for noise reduction and a custom K-means clustering algorithm on the selected two spectral images (447, 551 nm) to identify the various regions and effectively-identified spectral tissue variations with a sensitivity of 98.95%, and specificity of 98.44%. A pathologist later confirmed these outcomes as the ground truth for the tissue sample investigations. ConclusionsThe proposed system could help the surgeon and the pathologist identify the cancerous tissue margins from the non-cancerous tissue with a non-invasive, rapid, and minimum time method achieving high sensitivity up to 98.95%.

Automated Polarized Hyperspectral Imaging (PHSI) for ex-vivo and in-vivo Tissue Assessment.

Polarized light interactions with biological tissues can reveal information regarding tissue structure, while spectral characteristics are closely related to tissue composition. An integration of both modalities in a compact system could better assist tissue assessment. This study aims to develop a polarized hyperspectral imaging (PHSI) system that fulfills both linearly and circularly polarized hyperspectral imaging for in vivo and ex vivo applications. The system is comprised of a white LED, two linear polarizers, two liquid crystal variable retarders (LCVRs), and a hyperspectral snapshot camera. The system was calibrated to compute the full Stokes polarimetry. For tissue differentiation, fresh ex vivo mouse tissue specimens from kidney, liver, spleen, muscle, lung, and salivary gland of mice were imaged. The spectra of three features, named degree of polarization (DOP), degree of linear polarization (DOLP), and degree of circular polarization (DOCP), were generated. A k-nearest neighbor (k-NN) classifier was trained with multi-class spectra and 5-fold cross validation. It was found that DOP better differentiates tissue with an average accuracy of 0.87. Additionally, support vector machine (SVM) classifiers were trained to differentiate between each two of the organs, and it was determined that DOLP better identified kidney, liver, and spleen, whereas DOCP better identified muscle and lung tissues. Then, the setup was employed to image in vivo human fingers with and without a blood occlusion to qualitatively estimate oxygen saturation. Preliminary results demonstrate that both DOLP and DOCP reveal a distinction of oxygen saturation states. These results demonstrate the feasibility of the PHSI system for distinguishing between optical properties of tissues, which has the potential to reveal disease-related information for diverse medical applications.

Rapid shipboard measurement of net-collected marine microplastic polymer types using near-infrared hyperspectral imaging

Isolation and detection of microplastics (MP) in marine samples is extremely cost- and labor-intensive, limiting the speed and amount of data that can be collected. In the current work, we describe rapid measurement of net-collected MPs (net mesh size 300 µm) using a benchtop near-infrared hyperspectral imaging system during a research expedition to the subtropical North Atlantic gyre. Suspected plastic particles were identified microscopically and mounted on a black adhesive background. Particles were imaged with a Specim FX17 near-infrared linescan camera and a motorized stage. A particle mapping procedure was built on existing edge-finding algorithms and a polymer identification method developed using spectra from virgin polymer reference materials. This preliminary work focused on polyethylene, polypropylene, and polystyrene as they are less dense than seawater and therefore likely to be found floating in the open ocean. A total of 27 net tows sampled 2534 suspected MP particles that were imaged and analyzed at sea. Approximately 77.1% of particles were identified as polyethylene, followed by polypropylene (9.2%). A small fraction of polystyrene was detected only at one station. Approximately 13.6% of particles were either other plastic polymers or were natural materials visually misidentified as plastics. Particle size distributions for PE and PP particles with a length greater than 1 mm followed an approximate power law relationship with abundance. This method allowed at-sea, near real-time identification of MP polymer types and particle dimensions, and shows great promise for rapid field measurements of microplastics in net-collected samples.

Simultaneous Dual-Band Hyperspectral Stimulated Raman Scattering Microscopy with Femtosecond Optical Parametric Oscillators.

Stimulated Raman scattering (SRS) microscopy is a label-free quantitative optical technique for imaging molecular distributions in cells and tissues by probing their intrinsic vibrational frequencies. Despite its usefulness, existing SRS imaging techniques have limited spectral coverage due to either a wavelength tuning constraint or narrow spectral bandwidth. High-wavenumber SRS imaging is commonly used to map lipid and protein distribution in biological cells and visualize cell morphology. However, to detect small molecules or Raman tags, imaging in the fingerprint region or "silent" region, respectively, is often required. For many applications, it is desirable to collect SRS images in two Raman spectral regions simultaneously for visualizing the distribution of specific molecules in cellular compartments or providing accurate ratiometric analysis. In this work, we present an SRS microscopy system using three beams generated by a femtosecond oscillator to acquire hyperspectral SRS image stacks in two arbitrary vibrational frequency bands, between 650-3280 cm-1, simultaneously. We demonstrate potential biomedical applications of the system in investigating fatty acid metabolism, cellular drug uptake and accumulation, and lipid unsaturation level in tissues. We also show that the dual-band hyperspectral SRS imaging system can be adapted for the broadband fingerprint region hyperspectral imaging (1100-1800 cm-1) by simply adding a modulator.

Estimation of optical properties of turbid media using spatially resolved diffuse reflectance combined with LSTM-attention network.

The accurate estimation of the optical properties of turbid media by using a spatially resolved (SR) technique remains a challenging task due to measurement errors in the acquired spatially resolved diffuse reflectance (SRDR) and challenges in inversion model implementation. In this study, what we believe to be a novel data-driven model based on a long short-term memory network and attention mechanism (LSTM-attention network) combined with SRDR is proposed for the accurate estimation of the optical properties of turbid media. The proposed LSTM-attention network divides the SRDR profile into multiple consecutive and partially overlaps sub-intervals by using the sliding window technique, and uses the divided sub-intervals as the input of the LSTM modules. It then introduces an attention mechanism to evaluate the output of each module automatically and form a score coefficient, finally obtaining an accurate estimation of the optical properties. The proposed LSTM-attention network is trained with Monte Carlo (MC) simulation data to overcome the difficulty in preparing training (reference) samples with known optical properties. Experimental results of the MC simulation data showed that the mean relative error (MRE) with 5.59% for the absorption coefficient [with the mean absolute error (MAE) of 0.04 cm-1, coefficient of determination (R2) of 0.9982, and root mean square error (RMSE) of 0.058 cm-1] and 1.18% for the reduced scattering coefficient (with an MAE of 0.208 cm-1, R2 of 0.9996, and RMSE of 0.237 cm-1), which were significantly better than those of the three comparative models. The SRDR profiles of 36 liquid phantoms, collected using a hyperspectral imaging system that covered a wavelength range of 530-900 nm, were used to test the performance of the proposed model further. The results showed that the LSTM-attention model achieved the best performance (with the MRE of 14.89%, MAE of 0.022 cm-1, R2 of 0.9603, and RMSE of 0.026 cm-1 for the absorption coefficient; and the MRE of 9.76%, MAE of 0.732 cm-1, R2 of 0.9701, and RMSE of 1.470 cm-1for the reduced scattering coefficient). Therefore, SRDR combined with the LSTM-attention model provides an effective method for improving the estimation accuracy of the optical properties of turbid media.

Geographical origin differentiation and quality determination of saffron using a portable Hyperspectral imaging system

A portable Hyperspectral Imaging (HSI) system (400–1000 nm) coupled with chemometrics techniques was developed to identify the geographical origin and to determine the Crocins content of saffron rapidly and non-destructively. Reflectance spectra of 160 saffron samples from two geographical origins were captured and preprocessed using the Savitzky–Golay (SG) algorithm. PCA as an unsupervised clustering and PLS-DA, RBF, and MLP as supervised techniques were applied for the classification and geographical origin differentiation of the samples. Afterward, the sample’s Crocins content was measured and their associations with their spectra were modeled using PLSR, RBF, and MLP models. The PCA results indicated that saffron samples were originally distributed in two different clusters. PLS-DA model with a spectral range from 400 to 1000 nm achieved the best clustering performance (Sensitivity and Specificity: 100 %). The RBF model showed good predictive capabilities in the saffron Crocins content prediction and quality evaluation (R2cal > 0.94 and R2pred > 0.83).

Hyperspectral Imaging Combined with Chemometrics Analysis for Monitoring the Textural Properties of Modified Casing Sausages with Differentiated Additions of Orange Extracts

The textural properties (hardness, springiness, gumminess, and adhesion) of 16-day stored sausages with different additions of orange extracts to the modified casing solution were estimated by response surface methodology (RSM) and a hyperspectral imaging system in the spectral range of 390-1100 nm. To improve the model performance, normalization, 1st derivative, 2nd derivative, standard normal variate (SNV), and multiplicative scatter correction (MSC) were applied for spectral pre-treatments. The raw, pretreated spectral data and textural attributes were fit to the partial least squares regression model. The RSM results show that the highest R2 value achieved at adhesion (77.57%) derived from a second-order polynomial model, and the interactive effects of soy lecithin and orange extracts on adhesion were significant (p < 0.05). The adhesion of the PLSR model developed from reflectance after SNV pretreatment possessed a higher calibration coefficient of determination (0.8744) than raw data (0.8591). The selected ten important wavelengths for gumminess and adhesion can simplify the model and can be used for convenient industrial applications.

UAV-Hyperspectral Imaging to Estimate Species Distribution in Salt Marshes: A Case Study in the Cadiz Bay (SW Spain)

Salt marshes are one of the most productive ecosystems and provide numerous ecosystem services. However, they are seriously threatened by human activities and sea level rise. One of the main characteristics of this environment is the distribution of specialized plant species. The environmental conditions governing the distribution of this vegetation, as well as its variation over time and space, still need to be better understood. In this way, these ecosystems will be managed and protected more effectively. Low-altitude remote sensing techniques are excellent for rapidly assessing salt marsh vegetation coverage. By applying a high-resolution hyperspectral imaging system onboard a UAV (UAV-HS), this study aims to differentiate between plant species and determine their distribution in salt marshes, using the salt marshes of Cadiz Bay as a case study. Hyperspectral processing techniques were used to find the purest spectral signature of each species. Continuum removal and second derivative transformations of the original spectral signatures highlight species-specific spectral absorption features. Using these methods, it is possible to differentiate salt marsh plant species with adequate precision. The elevation range occupied by these species was also estimated. Two species of Sarcocornia spp. were identified on the Cadiz Bay salt marsh, along with a class for Sporobolus maritimus. An additional class represents the transition areas from low to medium marsh with different proportions of Sarcocornia spp. and S. maritimus. S. maritimus can be successfully distinguished from soil containing microphytobenthos. The final species distribution map has up to 96% accuracy, with 43.5% of the area occupied by medium marsh species (i.e., Sarcocornia spp.) in the 2.30–2.80 m elevation range, a 29% transitional zone covering in 1.91–2.78 m, and 25% covered by S. maritims (1.22–2.35 m). Basing a method to assess the vulnerability of the marsh to SLR scenarios on the relationship between elevation and species distribution would allow prioritizing areas for rehabilitation. UAV-HS techniques have the advantage of being easily customizable and easy to execute (e.g., following extreme events or taking regular measurements). The UAV-HS data is expected to improve our understanding of coastal ecosystem responses, as well as increase our capacity to detect small changes in plant species distribution through monitoring.

Fragments under the Lens: A Case Study of Multispectral versus Hyperspectral Imaging for Manuscript Recovery

This article seeks to clarify the varied utilities of multispectral imaging (MSI) and hyperspectral imaging (HSI) for the purposes of fragment recovery and analysis. The two technologies are discussed in detail, with the aim of explaining their functionality and required methodology to a humanities-oriented audience. For purposes of comparison, two medieval manuscript fragments—one a palimpsest, the other damaged by abrasion and staining—were imaged using both MSI and HSI systems. The data sets were then compared using several metrics and the results outlined. MSI was found to have significantly better spatial resolution (the amount of fine detail that the system is capable of capturing), while HSI had vastly better spectral resolution (the number of wavelengths discerned by the system). The MSI system also displayed a superior signal-to-noise ratio (SNR) and edge response, meaning that images were clearer and sharper. MSI images enabled the identification, transcription, and approximate dating of the palimpsested fragment, but the less visually clear HSI data set failed to fully do so. However, the superior spectral resolution of the HSI system allowed for the noninvasive and nondestructive identification of inks and pigments and enabled our team to differentiate between even those that appear to be identical to the naked eye. In this case, the red pigment used on the palimpsest was identified from the hyperspectral data but could not be from the multispectral. Our conclusion is that HSI systems can offer valuable information about material composition and history and may shed light on provenance. Neither system is universally superior; the choice of which one to employ depends upon what questions a scholar seeks to ask of the object.

Multi-Sensor Image Feature Fusion via Subspace-Based Approach Using $\ell _{1}$-Gradient Regularization

Image fusion is a technique of combining two or more images into a single image which is more informative from an interpretation point of view. With the rapid development of different synthetic aperture radar sensing satellites capturing information from the earth by measuring energy in different portions of the electromagnetic spectrum (narrow/wide-band), complementary information about the area captured by different satellites is available (e.g. high-resolution spectral and RGB images). However, the estimation of the full-resolution image may not be necessary for inference approaches, including the pixel-based classification. Instead, it is desirable to extract the relevant information embedded in the available data to improve the inference capabilities. This work proposes a computational framework to estimate features with high-spatial-resolution and appropriate spectral content by combining information from a multi-sensor system. The considered multi-sensor setup is a hyperspectral imaging system with a complementary RGB sensor. The proposed framework first extracts spatial features from the RGB image using morphological profiles. Then, the fusion model assumes that the extracted features, and the hyperspectral measurements, lie in different subspaces matrices. In addition, this work developed a joint optimization scheme to solve the feature fusion problem by integrating the alternating direction method of multipliers with the block coordinate descent method. The alternating optimization method estimates the spatial features in the fusion model by penalizing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> -norm of the spatial gradient magnitudes. The quality of extracted features is measured in terms of supervised pixel-based classification methods. Extensive simulations show that the proposed approach outperforms other state-of-the-art methods in terms of classification accuracy.

Bio-inspired Compact, High-resolution Snapshot Hyperspectral Imaging System with 3D Printed Glass Lightguide Array.

To address the major challenges to obtain high spatial resolution in snapshot hyperspectral imaging, 3D printed glass lightguide array has been developed to sample the intermediate image in high spatial resolution and redistribute the pixels in the output end to achieve high spectral resolution. Curved 3D printed lightguide array can significantly simplify the snapshot hyperspectral imaging system, achieve better imaging performance, and reduce the system complexity and cost. We have developed two-photon polymerization process to print glass lightguide array, and demonstrated the system performance with biological samples. This new snapshot technology will catalyze new hyperspectral imaging system development and open doors for new applications from UV to IR.

Design and Validation of a Custom-Made Hyperspectral Microscope Imaging System for Biomedical Applications

Hyperspectral microscope imaging (HMI) is an emerging modality that integrates spatial information collected by standard laboratory microscopy and the spectral-based contrast obtained by hyperspectral imaging and may be instrumental in establishing novel quantitative diagnostic methodologies, particularly in histopathology. Further expansion of HMI capabilities hinges upon the modularity and versatility of systems and their proper standardization. In this report, we describe the design, calibration, characterization, and validation of the custom-made laboratory HMI system based on a Zeiss Axiotron fully motorized microscope and a custom-developed Czerny-Turner-type monochromator. For these important steps, we rely on a previously designed calibration protocol. Validation of the system demonstrates a performance comparable to classic spectrometry laboratory systems. We further demonstrate validation against a laboratory hyperspectral imaging system for macroscopic samples, enabling future comparison of spectral imaging results across length scales. An example of the utility of our custom-made HMI system on a standard hematoxylin and eosin-stained histology slide is also shown.

Spectral Characterization and Identification of Natural and Regenerated Leather Based on Hyperspectral Imaging System

Currently, the methods to identify leather materials have limitations, and identifying natural leather types is also relatively complex. In this research, the microstructures of four types of mammalian leathers (cattle leather, pig leather, sheep leather, and deer leather), three kinds of reptilian leathers (crocodile leather, lizard leather, and snake leather) and regenerated leather were characterized by scanning electron microscopy. The spectral curves (from 900 to 1700 nm) of these leather samples were extracted using a hyperspectral imaging system, and their spectral characteristics were analyzed. A method of leather identification by the hyperspectral imaging system combined with chemometrics was established. The results showed that the spectral curves of natural and regenerated leather differed in the number, position, and depth of the characteristic peaks, enabling the classification of regenerated leather by comparative analysis with the naked eye. The first-order derivative processing–principal component analysis–discriminant analysis model achieved a 98% correct classification rate, confirming the hyperspectral imaging system’s feasibility in the leather material identification field. We believe that his research is beneficial for the leather industry to understand the classifications scientifically, in order to protect consumer rights and further develop the leather testing industry.

Microcirculatory tissue oxygenation correlates with kidney function after transcatheter aortic valve implantation-Results from a prospective observational study.

Kidney dysfunction is common in patients with aortic stenosis (AS) and correction of the aortic valve by transcatheter aortic valve implantation (TAVI) often affects kidney function. This may be due to microcirculatory changes. We evaluated skin microcirculation with a hyperspectral imaging (HSI) system, and compared tissue oxygenation (StO2), near-infrared perfusion index (NIR), tissue hemoglobin index (THI) and tissue water index (TWI) in 40 patients undergoing TAVI versus 20 control patients. HSI parameters were measured before TAVI (t1), directly after TAVI (t2), and on postinterventional day 3 (t3). The primary outcome was the correlation of tissue oxygenation (StO2) to the creatinine level after TAVI. We performed 116 HSI image recordings in patients undergoing TAVI for the treatment of severe aortic stenosis and 20 HSI image recordings in control patients. Patients with AS had a lower THI at the palm (p = 0.034) and a higher TWI at the fingertips (p = 0.003) in comparison to control patients. TAVI led to an increase of TWI, but had no uniform enduring effect on StO2 and THI. Tissue oxygenation StO2 at both measurement sites correlated negatively with creatinine levels after TAVI at t2 (palm: ρ = -0.415; p = 0.009; fingertip: ρ = -0.519; p < 0.001) and t3 (palm: ρ = -0.427; p = 0.008; fingertip: ρ = -0.398; p = 0.013). Patients with higher THI at t3 reported higher physical capacity and general health scores 120 days after TAVI. HSI is a promising technique for periinterventional monitoring of tissue oxygenation and microcirculatory perfusion quality, which are related to kidney function, physical capacity, and clinical outcomes after TAVI. https://drks.de/search/de/trial, identifier DRKS00024765.