High-resolution Imaging Research Articles

Assessing the contribution of RGB VIs in improving building extraction from RGB-UAV images

Buildings are a fundamental component of the built environment, and accurate information regarding their size, location, and distribution is vital for various purposes. The ever-increasing capabilities of unmanned aerial vehicles (UAVs) have sparked an interest in exploring various techniques to delineate buildings from the very high-resolution images obtained from UAV photogrammetry. However, the limited spectral information in UAV images, particularly the number of bands, can hinder the differentiation between various materials and objects. This setback can affect the ability to distinguish between different materials and objects. To address this limitation, vegetative ındices (VIs) have been employed to enhance the spectral strength of UAV orthophotos, thereby improving building classification. The objective of this study is to evaluate the contribution of four specific VIs: the green leaf index (GLI), red-green-blue vegetation index (RGBVI), visual atmospherically resistant index (VARI), and triangular greenness index (TGI). The significance of this contribution lies in assessing the potential of each VI to enhance building classification. The approach utilized the geographic object-based image analysis (GeoBIA) approach and a random forest classifier. To achieve this aim, five datasets were created, with each dataset comprising the RGB-UAV image and a corresponding RGB VI. The experimental results on the test dataset and a post-classification assessment indicated a general improvement in the classification when the VIs were added to the RGB orthophoto.

Bacterial imaging in tumour diagnosis.

Some bacteria, such as Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium), have an inherent ability to locate solid tumours, making them a versatile platform that can be combined with other tools to improve the tumour diagnosis and treatment. In anti-cancer therapy, bacteria function by carrying drugs directly or expressing exogenous therapeutic genes. The application of bacterial imaging in tumour diagnosis, a novel and promising research area, can indeed provide dynamic and real-time monitoring in both pre-treatment assessment and post-treatment detection. Different imaging techniques, including optical technology, acoustic imaging, magnetic resonance imaging (MRI) and nuclear medicine imaging, allow us to observe and track tumour-associated bacteria. Optical imaging, including bioluminescence and fluorescence, provides high-sensitivity and high-resolution imaging. Acoustic imaging is a real-time and non-invasive imaging technique with good penetration depth and spatial resolution. MRI provides high spatial resolution and radiation-free imaging. Nuclear medicine imaging, including positron emission tomography (PET) and single photon emission computed tomography (SPECT) can provide information on the distribution and dynamics of bacterial population. Moreover, strategies of synthetic biology modification and nanomaterial engineering modification can improve the viability and localization ability of bacteria while maintaining their autonomy and vitality, thus aiding the visualization of gut bacteria. However, there are some challenges, such as the relatively low bacterial abundance and heterogeneously distribution within the tumour, the high dimensionality of spatial datasets and the limitations of imaging labeling tools. In summary, with the continuous development of imaging technology and nanotechnology, it is expected to further make in-depth study on tumour-associated bacteria and develop new bacterial imaging methods for tumour diagnosis.

Synthetic X-ray Image Generation for Non-Destructive Testing using Generative Adversarial Networks

Developing and optimizing a Machine Learning (ML) based Non-Destructive Testing (NDT) tool for automotive manufacturing can be challenging due to the need for a significant amount of annotated training data. Obtaining such an extent of real X-ray images of different defects can be challenging and expensive. Hence, generating synthetic images with adequate defects is a viable option to fill this gap. This contribution presents a novel approach that harnesses Generative Adversarial Networks (GANs) to simulate industrial X-ray images of specific parts of a car wheel (rim and spokes) with defects and their annotations. The presented method draws inspiration from techniques used in medical healthcare radiography as well as the NDT field for simulating synthetic X-ray images. We propose a novel deep neural network architecture for generating high-resolution X-ray images of size 1024 x 512 pixels with porous defects in specified locations. These images are automatically generated from edge masks of the desired structure. We conducted separate evaluations for both, the generated images and the included defects. To assess the generated image quality, we used image properties and structural similarity metrics. The generated images achieved an MSSIM index of over 0.90. We analyzed the local gray-scale profile of synthetic images to evaluate the quality of generated defects. A state-of-the-art defect detector (ISAR, developed by Fraunhofer EZRT) was used to estimate defect detection based on various IoU thresholds. In conclusion, the presented research demonstrates the feasibility of leveraging GANs to generate synthetic 2D X- ray image data, including defects for training ML-based NDT tools. This approach opens new possibilities for developing and optimizing robust NDT solutions by overcoming the challenges of obtaining real data with defects.

Endovaginal coil for pelvic high-resolution magnetic resonance imaging of cervical cancer: a preliminary parameter optimization study

Endovaginal coil for pelvic high-resolution magnetic resonance imaging of cervical cancer: a preliminary parameter optimization study

Comparative study on satellite based image encryption methods: a survey

The availability of high-resolution satellite images increases with advancements in remote sensing technology. These satellite images are used in various earth observation applications such as disaster management, military applications, weather forecasting, land use and cover, and many more. Satellite images have large volumes stored in memory devices. These satellite images are transmitted to the ground station for processing and analysis. In these cases, images are vulnerable to privacy issues. As technology advances, onboard processing of satellite images using intelligent systems processes the images faster. A model such as field programmable gate arrays (FPGA) is used in onboard processing to process satellite images. However, images are susceptible to faults induced by harsh radiation environments in space. Encryption is one of the most assured methods to provide privacy to satellite images. Hence, encryption of satellite images during processing, storage, and transmission is the present rising demand. There are various encryption methods implemented using algorithms such as advanced encryption standard (AES), homomorphic, advanced encryption standard-counter (AES-CTR), and chaotic maps. Concurrent processing and encryption of images using MapReduce with Hadoop Framework perform the task faster. The focus of this paper is a comparative study of the various encryption methods used in recent years.

Comparison between PA and PWI/TFM Techniques for Non-Destructive Testing of Carbon Steel Weld

The Total Focusing Method (TFM) technique is an advanced Non-Destructive Testing (NDT) Ultrasonic Testing (UT) technique well known for its representative images of high resolution. It was firstly developed together with Full Matrix Capture (FMC) acquisition which can be substituted later by Plane Wave Imaging (PWI) acquisition for its improved acquisition speed and gain reserve without lowering the image quality. For these advantages, the PWI/TFM technique is tending to be more and more widely implemented. This article presents some results of the related study on PWI/TFM technique. Starting by PWI/TFM reconstruction principles, mainly on the influences of beam steering settings and corresponding inspection coverage in predefined Region of Interest (ROI), it focuses on the comparison on the indication amplitudes of planar discontinuities between classical Phased Array (PA) technique and PWI/TFM technique.

Coffee bean graded based on deep net models

Coffee is a widely consumed beverage, and sorting coffee beans is a critical process that ensures high-quality graded coffee products. Coffee beans were graded into nine grades in robusta types. To automate the grading process, a deep learning-based approach was developed using a large dataset of high-resolution images and data augmentation techniques. In contrast to previous studies focusing on robusta type graded into six coffee bean grads, our research extends this framework by employing robusta type into nine grades with an outperformed accuracy. The proposed work uses four deep learning models, namely residual network 34(Resnet34), inception version 3 (Inception v3), efficient network bayesian optimization (EfficientNet-B0), and visual geometry group-16(VGG-16), where trained and evaluated for coffee bean classification into nine grades. The EfficientNet-B0 model exhibited outperformed accuracy, achieving 100% in distinguishing good and bad coffee beans, even in challenging lighting and background conditions.

Visualization of rat tendon in three dimensions using micro-Computed Tomography

Micro-computed tomography (CT) is an X-ray-based imaging modality that produces three-dimensional (3D), high-resolution images of whole-mount tissues, but is typically limited to dense tissues, such as bone. The X-rays readily pass-through tendons, rendering them transparent. Contrast-enhancing chemical stains have been explored, but their use to improve contrast in different tendon types and across developmental stages for micro-CT imaging has not been systematically evaluated. Therefore, we investigated how phosphotungstic acid (PTA) staining and tissue hydration impacts tendon contrast for micro-CT imaging. We showed that PTA staining increased X-ray absorption of tendon to enhance tissue contrast and obtain 3D micro-CT images of immature (postnatal day 21) and sexually mature (postnatal day 50) rat tendons within the tail and hindlimb. Further, we demonstrated that tissue hydration state following PTA staining significantly impacts soft tissue contrast. Using this method, we also found that tail tendon fascicles appear to cross between fascicle bundles. Ultimately, contrast-enhanced 3D micro-CT imaging will lead to better understanding of tendon structure, and relationships between the bone and soft tissues.• Simple tissue fixation and staining technique enhances soft tissue contrast for tendon visualization using micro-CT.• 3D tendon visualization in situ advances understanding of musculoskeletal tissue structure and organization.

Investigation of aluminium Whipple Shield response to hypervelocity impacts close to ballistic limit between 2.5 and 5 km/s

For impact velocities larger than a threshold velocity of 3 km/s, aluminium Whipple Shields present an enhanced protection capability with respect to monolithic protections with the same areal density. In particular, in the range between 3 and 7 km/s the projectile partially fragments and melts after impacting the bumper; modelling this transition might be complex due to the high number of parameters affecting the collision. In particular, limited data is available in literature with a systematic evaluation of such parameters.In this context, a campaign of 22 experiments was performed to assess the response of aluminium Whipple Shields to normal impacts of aluminium projectiles in the transition range up to 5 km/s. In the tests, the projectile diameter was fixed at 2.9 mm and the bumper thickness and impact velocity were systematically varied respectively at 1, 1.5, and 2 mm and between 2.6 and 5 km/s. Collected data included high-velocity videos of the debris cloud generated by the impacts at both low resolution (for all shots) and high resolution (for 19 out of 22 tests); in addition, for 10 experiments high-resolution images of both the bumper and the wall were acquired after the tests.In this paper, the experimental campaign is described and the main collected results are presented; in particular, the influence of the different impact parameters is discussed. Experimental results are finally compared with numerical simulations conducted with a smoothed particle hydrodynamics method available in IMPETUS Solver.

Lung Cancer Detection

Abstract: This paper presents a comprehensive approach to lung cancer detection utilizing state-of-the-art machine learning techniques, specifically Convolutional Neural Networks (CNNs). Using CNN[1] the model is trained and it can detect whether the given lung cancer cell image contains cancerous cells or not. The first component of the proposed approach involves high-resolution medical imaging, such as computed tomography (CT) scans, to capture detailed anatomical information about the lungs. Image processing algorithms are applied to enhance the quality of the images and extract relevant features. Additionally, innovative three-dimensional reconstruction techniques are employed to visualize the lung tissue at a microscopic level, facilitating the identification of subtle abnormalities.

Enhancing Agricultural Sustainability: Automated Crop Disease Detection through Image Processing Techniques

Abstract: The increasing demand for sustainable and high- yielding agricultural practices has driven the need for advanced technologies in crop disease detection. This paper presents a novel approach to automated crop disease detection utilizing stateof-the-art image processing techniques. In the image acquisition phase, high-resolution images of crops are captured using modern imaging devices. These images are then subjected to preprocessing techniques, including resizing, normalization, and filtering, to enhance their quality. Feature extraction follows, where relevant information is derived from the preprocessed images using advanced image processing algorithms, such as texture analysis and color-based feature extraction. The heart of the proposed system lies in the classification phase, where a machine learning or deep learning model is trained on a curated dataset comprising labeled examples of healthy and diseased crops. The integration of this automated crop disease detection system with existing agricultural frameworks provides real-time or periodic monitoring capabilities. This integration empowers farmers with timely and precise information, enabling early intervention and targeted treatment strategies to minimize crop losses. This research contributes to the ongoing efforts in sustainable agriculture by providing a reliable and efficient solution for early crop disease detection.

Application and challenge of pathomics technique in the management of hepatocellular carcinoma

The incidence and mortality rate of hepatocellular carcinoma rank among the top of all cancer types,seriously threatening the life and health of human beings. In recent years,the rapid development of artificial intelligence and the deepening of the concept of precision medicine have led to a boom in interdisciplinary research. Pathomics,as an emerging omics technology driven by artificial intelligence,can mine massive information from high-resolution whole slide images,and shows broad application prospects in the diagnosis,treatment and prognosis assessment of hepatocellular carcinoma. However, pathomics research in hepatocellular carcinoma is still in its infancy, and its research patterns and clinical applications still face several controversies and challenges, including data security, ethics, and "black box" issues. Future research should focus on conducting prospective studies, integrating multimodal data, improving computational technologies, and establishing professional standards to promote the high-quality development of pathomics technology in both clinical and basic research of hepatocellular carcinoma.

Revolutionizing astronomy: Technological advancements and future horizons

This comprehensive article explores the transformative impact of technological innovations in the field of astronomical physics. It delves into key advancements in precision optics, spectroscopy, high-resolution imaging, radio and infrared technologies, space-based observatories, and the exciting prospects that lie ahead. The discussion covers the role of emerging technologies, international collaboration, and ethical considerations in shaping the future of astronomical research. By examining these facets, this article highlights the pivotal synergy between technology and astronomy, driving profound discoveries and enriching our understanding of the cosmos.

Comparison of the monitoring of surface deformations in open-pit mines with Sentinel-1A and TerraSAR-X satellite radar data.

In case necessary precautions are not taken in surface mines, serious accidents and loss of life may occur, particularly due to large mass displacements. It is extremely important to identify the early warning signs of these displacements and take the necessary precautions. In this study, free medium-resolution satellite radar images from the European Space Agency's (ESA) C-band Sentinel-1A satellite and commercial high-resolution satellite radar images (SAR, Synthetic Aperture Radar) from the Deutsches Zentrum für Luft- und Raumfahrt's (DLR) X-band TerraSAR-X satellite were obtained, and it was attempted to reveal the traceability and adequacy of monitoring of deformations and possible mass displacements in the dump site of an open-pit coal mine. The compatibility of the results obtained from the satellite radar data with two devices of Global Positioning System (GPS) which were installed in the field was evaluated. Furthermore, the velocity results in the Line Of Sight (LOS) direction and vertical deformation velocity results obtained with all three approaches (GPS/Sentinel-1A, GPS/TerraSAR-X, and Sentinel-1A/TerraSAR-X) were compared. It was observed that the results were statistically equal and the directions of movement were similar/compatible. The result of this study showed that deformations at mine sites can be monitored with sufficient accuracy for early warning with free Sentinel-1A satellite data, although the TerraSAR-X satellite offers a higher resolution.

Diagnostic potential of TSH to HDL cholesterol ratio in vulnerable carotid plaque identification

ObjectiveThis study aimed to investigate the predictive value of the thyroid-stimulating hormone to high-density lipoprotein cholesterol ratio (THR) in identifying specific vulnerable carotid artery plaques.MethodsIn this retrospective analysis, we included 76 patients with carotid plaques who met the criteria for admission to Zhejiang Hospital from July 2019 to June 2021. High-resolution magnetic resonance imaging (HRMRI) and the MRI-PlaqueView vascular plaque imaging diagnostic system were utilized to analyze carotid artery images for the identification of specific plaque components, including the lipid core (LC), fibrous cap (FC), and intraplaque hemorrhage (IPH), and recording of the area percentage of LC and IPH, as well as the thickness of FC. Patients were categorized into stable plaque and vulnerable plaque groups based on diagnostic criteria for vulnerable plaques derived from imaging. Plaques were categorized based on meeting one of the following consensus criteria for vulnerability: lipid core area over 40% of total plaque area, fibrous cap thickness less than 65 um, or the presence of intraplaque hemorrhage. Plaques meeting the above criteria were designated as the LC-associated vulnerable plaque group, the IPH-associated group, and the FC-associated group. Multivariate logistic regression was employed to analyze the factors influencing carotid vulnerable plaques and specific vulnerable plaque components. Receiver operating characteristic (ROC) curves were used to assess the predictive value of serological indices for vulnerable carotid plaques.ResultsWe found that THR (OR = 1.976; 95% CI = 1.094–3.570; p = 0.024) and TSH (OR = 1.939, 95% CI = 1.122–3.350, p = 0.018) contributed to the formation of vulnerable carotid plaques. THR exhibited an area under the curve (AUC) of 0.704 (95% CI = 0.588–0.803) (p = 0.003), and the AUC for TSH was 0.681 (95% CI = 0.564–0.783) (p = 0.008). THR was identified as an independent predictor of LC-associated vulnerable plaques (OR = 2.117, 95% CI = 1.064–4.212, p = 0.033), yielding an AUC of 0.815. THR also demonstrated diagnostic efficacy for LC-associated vulnerable plaques.ConclusionThis study substantiated that THR and TSH have predictive value for identifying vulnerable carotid plaques, with THR proving to be a more effective diagnostic indicator than TSH. THR also exhibited predictive value and specificity in the context of LC-associated vulnerable plaques. These findings suggest that THR may be a promising clinical indicator, outperforming TSH in detecting specific vulnerable carotid plaques.

Nanoscale Adhesion and Material Transfer at 2D MoS2-MoS2 Interfaces Elucidated by In Situ Transmission Electron Microscopy and Atomistic Simulations.

Low-dimensional materials, such as MoS2, hold promise for use in a host of emerging applications, including flexible, wearable sensors due to their unique electrical, thermal, optical, mechanical, and tribological properties. The implementation of such devices requires an understanding of adhesive phenomena at the interfaces between these materials. Here, we describe combined nanoscale in situ transmission electron microscopy (TEM)/atomic force microscopy (AFM) experiments and simulations measuring the work of adhesion (Wadh) between self-mated contacts of ultrathin nominally amorphous and nanocrystalline MoS2 films deposited on Si scanning probe tips. A customized TEM/AFM nanoindenter permitted high-resolution imaging and force measurements in situ. The Wadh values for nanocrystalline and nominally amorphous MoS2 were 604 ± 323 mJ/m2 and 932 ± 647 mJ/m2, respectively, significantly higher than previously reported values for mechanically exfoliated MoS2 single crystals. Closely matched molecular dynamics (MD) simulations show that these high values can be explained by bonding between the opposing surfaces at defects such as grain boundaries. Simulations show that as grain size decreases, the number of bonds formed, the Wadh and its variability all increase, further supporting that interfacial covalent bond formation causes high adhesion. In some cases, sliding between delaminated MoS2 flakes during separation is observed, which further increases the Wadh and the range of adhesive interaction. These results indicate that for low adhesion, the MoS2 grains should be large relative to the contact area to limit the opportunity for bonding, whereas small grains may be beneficial, where high adhesion is needed to prevent device delamination in flexible systems.

Neural adaptations to congenital deafness: enhanced tactile discrimination through cross-modal neural plasticity - an fMRI study.

This study explores the compensatory neural mechanisms associated with congenital deafness through anexamination of tactile discrimination abilities using high-resolution functional magnetic resonance imaging (fMRI). To analyze the neural substrates underlying tactile processing in congenitally deaf individuals and compare themwith hearing controls. Our participant pool included thirty-five congenitally deaf individuals and thirty-five hearing controls. All participantsengaged in tactile discrimination tasks involving the identification of common objects by touch. We utilized ananalytical suite comprising voxel-based statistics, functional connectivity multivariate/voxel pattern analysis (fc-MVPA), and seed-based connectivity analysis to examine neural activity. Our findings revealed pronounced neural activity in congenitally deaf participants within regions typically associatedwith auditory processing, including the bilateral superior temporal gyrus, right middle temporal gyrus, and rightrolandic operculum. Additionally, unique activation and connectivity patterns were observed in the right insula andbilateral supramarginal gyrus, indicating a strategic reorganization of neural pathways for tactile informationprocessing. Behaviorally, both groups demonstrated high accuracy in the tactile tasks, exceeding 90%. However, thedeaf participants outperformed their hearing counterparts in reaction times, showcasing significantly enhancedefficiency in tactile information processing. These insights into the brain's adaptability to sensory loss through compensatory neural reorganization highlight theintricate mechanisms by which tactile discrimination is enhanced in the absence of auditory input. Understandingthese adaptations can help develop strategies to harness the brain's plasticity to improve sensory processing inindividuals with sensory impairments, ultimately enhancing their quality of life through improved tactile perceptionand sensory integration.

Single-Domain Antibody-Gold Nanoparticle Bioconjugates as Immunosensors for the Detection of Hantaviruses.

Hantavirus, a zoonotic pathogen, causes severe syndromes like hemorrhagic fever with renal syndrome (HFRS), sometimes fatal in humans. Considering the importance of detecting the hantavirus antigen, the construction of an immunosensor is essential. The structural and functional characteristics of camelid nanobodies (VHHs) encourage their application in the areas of nanobiotechnology, therapeutics, diagnostics, and basic research. Therefore, this study aimed to standardize stable bioconjugates using gold nanoparticles (AuNPs) and VHHs, in order to develop immunobiosensors for the diagnosis of hantavirus infection. Immobilized metal affinity chromatography (IMAC) was performed to obtain purified recombinant anti-hantavirus nucleocapsid nanobodies (anti-prNΔ85 VHH), while AuNPs were synthesized for bioconjugation. UV-visible spectrophotometry and transmission electron microscopy (TEM) analysis were employed to characterize AuNPs. The bioconjugation stability parameters (VHH-AuNPs), analyzed by spectrophotometry, showed that the ideal pH value and VHH concentration were obtained at 7.4 and 50 μg/mL, respectively, after addition of 1 M NaCl, which induces AuNP aggregation. TEM performed before and after bioconjugation showed uniform, homogeneous, well-dispersed, and spherical AuNPs with an average diameter of ~ 14 ± 0.57 nm. Furthermore, high-resolution images revealed a thin white halo on the surface of the AuNPs, indicating the coating of the AuNPs with protein. A biosensor simulation test (dot blot-like [DB-like]) was performed in stationary phase to verify the binding and detection limits of the recombinant nucleocapsid protein from the Araucária hantavirus strain (prN∆85). Using AuNPs/VHH bioconjugates, a specific interaction was detected between 5 and 10 min of reaction in a dose-dependent manner. It was observed that this test was sensitive enough to detect prNΔ85 at concentrations up to 25 ng/μL. Considering that nanostructured biological systems such as antibodies conjugated with AuNPs are useful tools for the development of chemical and biological sensors, the stability of the bioconjugate indicates proficiency in detecting antigens. The experimental results obtained will be used in a future immunospot assay or lateral flow immunochromatography analysis for hantavirus detection.

Super-resolution reconstruction of single image for latent features

AbstractSingle-image super-resolution (SISR) typically focuses on restoring various degraded low-resolution (LR) images to a single high-resolution (HR) image. However, during SISR tasks, it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features. This challenge can lead to issues such as model collapse, lack of rich details and texture features in the reconstructed HR images, and excessive time consumption for model sampling. To address these problems, this paper proposes a Latent Feature-oriented Diffusion Probability Model (LDDPM). First, we designed a conditional encoder capable of effectively encoding LR images, reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images. We then employed a normalized flow and multimodal adversarial training, learning from complex multimodal distributions, to model the denoising distribution. Doing so boosts the generative modeling capabilities within a minimal number of sampling steps. Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics, providing a fresh perspective for tackling SISR tasks.

Experimental and theoretical studies on cobalt ruthenium phosphate structure optimization towards supercapacitor application

Recent studies have given indications that nanoarchitectured binary transition metal phosphates can act as potential supercapacitor (SC) electrode materials due to their exceptional energy storage capacity and remarkable electrochemical stability. However, the highly amorphous nature or broad X-ray diffraction peaks of metal phosphates put a limit on the structural optimization of these materials. We demonstrate a combined experimental and theoretical study leading to an optimized cobalt-ruthenium phosphate (CRP) structure for high-performance SC application. The material structure was optimized by detailed density functional theory (DFT) calculations, including the Bader charge analysis, DOS, and PDOS, revealed the lattice and volume expansion, reduced band gap, and improved conductivity of the cobalt phosphate lattice by including ruthenium in the cobalt phosphate. These predictions are verified with experimental band gap and high-resolution transmission electron microscope plane image indexing. Further, the CRP was developed by potentiostatic deposition onto the carbon cloth substrate. The flexible CRP electrode exhibited a high specific capacitance of 1258 F g−1 (943.5C g−1) in the aqueous 2 M KOH electrolyte. The asymmetric SC device (CRP//activated carbon) has exhibited an excellent specific energy of ∼59 Wh kg−1 at a specific power of ∼751 W kg−1.