High-definition Images Research Articles

Overview of Key Technologies for Remote Wireless Operation Platform on Water Surface

The underwater environment is complicated and full of hazards, making it tough to complete with just one piece of underwater operation equipment. Building a high-speed, low-latency wireless connection between a remote wireless operation platform on water surface and other operation platforms in order to achieve long-distance transmission of high-definition image data and control commands, as well as collaborative operations among multiple platforms, has become a development trend and focus of exploring complex and dangerous waters. This paper summarizes and elaborates on underwater communication technology, long-distance data transmission technology, multi-submersible robot collaborative operation, and information interaction technology, as well as the development status of key technologies of remote wireless operation platform on water surface. And the research direction and focus of the remote wireless operation platform on water surface are prospected.

The Khirbet Qeiyafa Ostracon: A New Collation Based on the Multispectral Images, with Translation and Commentary

This paper provides a new collation of the Khirbet Qeiyafa ostracon on the basis of the multispectral images produced by Gregory Bearman and William Christens-Barry in collaboration with a number of imaging labs in 2009. It proposes 59 letters in total, with 12 new readings, including 5 new partial reconstructions, all supported by an analysis of these detailed, high-definition images. This new collation permits a renewed attempt at translation of the ostracon, which is interpreted as a record of the summons of a defendant in a legal dispute. In the course of analysis, it is argued that if the decipherment is accepted, the language of the text appears to be an archaic, dialectal form of Hebrew.

Highly emissive dimethylamino naphthalenyl phenylethene derivatives for visualization of latent fingerprints and imaging of lysosomes

Organic fluorescent materials have been widely used in various fields including visualization of latent fingerprints and imaging of lysosomes. High-performance organic fluorescent molecules based on simple molecular structures are still limited and highly demanded for high-definition and high-contrast imaging of latent fingerprints and lysosomes. In this work, two organic fluorescent molecules (DANPAN and DANBEN) were developed based on dimethylamino naphthalenyl phenylethene structure. These two molecules have quite different fluorescent properties, and the position of cyano group significantly affects the optical properties of these dimethylamino naphthalenyl phenylethene derivatives. Compound DANPAN shows strong yellow emission in solid state, while compound DANBEN has strong fluorescent emission in organic solutions. In addition to these differences, both compounds have large Stokes shifts and positive solvatochromic properties. Further studies showed that these compounds could be used to display latent fingerprints on various substrate surfaces. The obtained fingerprint images have excellent contrast and high resolution. It is also found that these molecules could specifically stain lysosomes in living cell within short incubation time. This work provides a series of new high-performance organic fluorescent materials with diverse applications.

High-Performance Enhancement of SWIR Images

Short-wave infrared imaging technology has rapidly developed over the past decade. However, image detail and contrast enhancement techniques, which are crucial for infrared imaging systems, are rarely dedicated to SWIR imaging systems. Moreover, the existing IR image enhancement methods generally have difficulty providing real-time performance, which plays a significant role in imaging systems with high data rates. In this paper, we propose a simple and real-time SWIR image enhancement approach based on the difference of Gaussian filter and histogram remapping, and illustrate the implementation of the proposed method on FPGA. The experimental results demonstrated that our method achieves promising detail and contrast enhancement performance with a frame rate of around 50 fps for high-definition images, and that extremely high frame rates can be achieved with pipelined architecture.

High Definition Three-Dimensional Exoscope (VITOM 3D) in E.N.T. Surgery: A Systematic Review of Current Experience.

Over the last decade, technological growth has developed new devices for more precise surgery based on improved maneuverability, minimally invasive approaches, and magnification of the operating field. In this context, the exoscope has opened a new phase for more accurate and safer microsurgery, improving the perception of the volume of objects and the depth of structures for planning, targeting, and controlling fine movements. The exoscope could be used for middle ear, transcanal, transmastoid, and craniotomy procedures that require two-handed dissection, both to perform both totally VITOM-based techniques and coupled to traditional procedures with an operating microscope or endoscope. In addition, the VITOM 3D system allows the surgeon to work with high-definition images, which is essential in facial nerve surgery or submandibular salivary stone or tear surgery approaches, where magnification plays a fundamental role in surgical success and in reducing operating times. The 3D exoscope approach could also be included in traditional transoral procedures for oropharyngeal carcinoma. The exoscope may provide a relevant approach in teaching surgeons and nurses, allowing adequate training in non-oncological surgical procedures such as a tonsillectomy or lateral pharyngoplasty.

Case Report: Unusual Cause of Chest Pain: A Multi-Image Assessment of a Cardiac Mass

Myxomas represent the most common benign primary cardiac tumor, they usually grow out of the interatrial septum into the left atrium with a pedunculated base. Intracardiac masses can be found incidentally on imaging studies, but symptomatology may arise secondary to the mass effect, embolization, and valvular function impairment. We present the case of a 75-year-old woman who arrived at the emergency department with atrial fibrillation and NSTEMI segment elevation myocardial infarction (NSTEMI) secondary to a highly vascularized neoplasm visible by coronary angiography and angiotomography. Scarce reports show high quality multi-imaging assessment of significantly vascularized myxomas with such atypical presentation. High-definition imaging studies played a fundamental role in the surgeon’s management of a mass with a complex neovascularization.

Correlation of Macular Sparing and Homonymous Paracentral Scotomas With MRI Lesions in Posterior Cerebral Artery Infarction.

The concepts of the representation of visual field in primary visual cortex are based on studies of war wounds and correlations with brain imaging in small cohorts. Because of the difficulty of judging brain lesion extent and the small number of studied patients, there is lingering controversy as to whether the central 15° of visual field are mapped onto the posterior 25% of primary visual cortex or onto a larger area. To improve the delineation of MRI lesion extent, we have studied only patients with posterior cerebral artery (PCA) ischemic strokes. We accrued a cohort of 92 patients with PCA strokes from an electronic medical records search between 2009 and 2020 at a single tertiary care academic institution. Patients had reliable static perimetry demonstrating homonymous hemianopias and high-definition reviewable brain imaging. We divided the primary visual cortex on the MRI T1 sagittal sequence into 8 equal segments in right and left cerebral hemispheres and located lesions according to the segments they occupied. We correlated lesion locations with 3 visual field defects (VFDs): macular-sparing homonymous quadrantanopias, macular-splitting homonymous quadrantanopias, and homonymous paracentral scotomas. Among 25 cases with macular sparing, 13 had lesion-sparing confined to the posterior 25% of visual cortex. Among 6 cases with homonymous paracentral scotomas, 2 had lesions confined to the posterior 25% of visual cortex. Macular-splitting quadrantanopia did not occur in any patients with lesions confined to the posterior 25% of visual cortex, but did occur in 3 patients with lesions confined to the posterior 50% of visual cortex. These phenomena would not be expected if the central 15° of visual field were mapped onto a region extending beyond the posterior 25% of visual cortex. In patients with PCA strokes that involved the retrogeniculate visual pathway proximal to visual cortex, the visual cortex lesions were often less extensive than predicted by the VFDs, perhaps because of widespread damage to axons before they reached their destination in visual cortex. These results support the concept that the central 15° of the visual field are represented in the posterior 25% of visual cortex. Although this study contributes a larger cohort of patients with better-defined lesion borders than in past reports, its conclusions must be tempered by the variability of patient attention during visual field testing, the subjectivity in the interpretation of the defect patterns, and the difficulty in judging MRI lesion extent even on diffusion-weighted and precontrast T1 sagittal sequences.

5G-Telecommunication Allocation Network Using IoT Enabled Improved Machine Learning Technique

Recent improvements in communication technology have undergone a significant shift over the last two decades, with state-of-the-art communication equipment, standards, and protocols simplifying the lives of consumers everywhere. For more than a decade, advancements in communication technology have mostly focused on increasing the speed with which information can be delivered and retrieved from anywhere in the globe at any time of day or night, regardless of location. Four-generation (4G) communication technologies, which have already been developed and implemented, are used to offer users with seamless access to multimedia content at transmission rates of 100 megabits per second (Mbps). It is becoming more vital to create new technologies in order to meet the growing need for faster speed as well as a variety of other advanced features. 5G networks have just recently been built as a result of extensive research and development. This has resulted in the gradual replacement of existing 4G services with new 5G networks, which are capable of transmitting multimedia content such as audio-video and high definition images, among other things, at data transmission rates in the gigabyte range or higher (up to several gigabits per second). Further recent development, in addition to the Internet of Things (IoT), which was made possible by future communication technology, is the Internet of Things-based social network. Aspects of this include the ability to connect and expanding Internet connectivity to all physical devices that consumers use to access common commercial and industrial services available on the Internet. In spite of this, with the advancement of existing high-speed communication networks, the effective interaction of devices with their inputs and responses via the Internet may be made possible through 5G Internet of Things networks. This new generation of automation and communication systems has emerged as innovative platforms for the next generation of automation and communication systems to be developed further in the future. M2M data may be utilised to more efficiently distribute resources if machine learning (ML) and optimum cell clustering are applied to the situation. It is because of this heterogeneity that the ML is able to make the best use of the remaining resources of the M2M network in order to optimise efficiency. Over the last several years, the shortage of radio frequency spectrum has proven to be the most challenging hindrance to wireless communication. This has occurred from the large number of high-frequency devices that need significant amounts of bandwidth allowance. Cognitive radio networks have been designed to meet this higher demand as a result of this increased demand.

Comparison of the use of Landsat 8, Sentinel-2, and Gaofen-2 images for mapping soil pH in Dehui, northeastern China

Mapping the distribution and quantity of soil properties is important for black soil protection, management, and restoration in northeastern China. The objective of this study was to evaluate the effect of the spatial resolution on soil pH mapping using satellite images of the black soil region in northeastern China. A high spatial resolution Gaofen (GF)-2 high-definition image and multispectral images acquired by the Landsat 8 operational land imager and Sentinel-2 multi-spectral instrument were used to compare their performance in soil pH prediction. The spectral variables, including the original bands of the three satellite images and a variety of spectral indices derived from the original bands, were employed. Then, a machine learning model (quantile regression forest) was used to determine the relationships between the spectral variables and the measured soil pH, and prediction models were established to estimate the soil pH and to characterize the spatial pattern of the soil pH. The results revealed that the soil pH prediction model based on the GF-2 image had a slightly higher prediction accuracy than the models constructed using the Landsat 8 and Sentinel-2 images. The prediction models for Landsat 8, Sentinel-2, and GF-2 had root mean square errors of 0.34, 0.39, and 0.31, respectively. The use of remote sensing images with a high spatial resolution may not substantially increase the prediction accuracy of soil pH mapping compared with the results derived from medium-resolution images.

A real-time monitoring system for automatic morphology analysis of yeast cultivation in a jar fermenter.

The monitoring of microbial cultivation in real time and controlling their cultivation aid in increasing the production yield of useful material in a jar fermenter. Common sensors such as dissolved oxygen (DO) and pH can easily provide general-purpose indexes but do not reveal the physiological states of microbes because of the complexity of measuring them in culture conditions. It is well known from microscopic observations that the microbial morphology changes in response to the intracellular state or extracellular environment. Recently, studies have focused on rapid and quantitative image analysis techniques using machine learning or deep learning for gleaning insights into the morphological, physiological or gene expression information in microbes. During image analysis, it is necessary to retrieve high-definition images to analyze the microbial morphology in detail. In this study, we have developed a microfluidic device with a high-speed camera for the microscopic observation of yeast, and have constructed a system capable of generating their morphological information in real-time and at high definition. This system was connected to a jar fermenter, which enabled the automatic sampling for monitoring the cultivation. We successfully acquired high-definition images of over 10,000 yeast cells in about 2.2s during ethanol fermentation automatically for over 168h. We recorded 33,600 captures containing over 1,680,000 cell images. By analyzing these images, the morphological changes of yeast cells through ethanol fermentation could be captured, suggesting the expansion of the application of this system in controlling microbial fermentation using the morphological information generated. KEY POINTS: • Enables real-time visualization of microbes in a jar fermenter using microscopy. • Microfluidic device for acquiring high-definition images. • Generates a large amount of image data by using a high-speed camera.

Remote-sensing estimation of potato above-ground biomass based on spectral and spatial features extracted from high-definition digital camera images

Above-ground biomass (AGB) is a significant phenotypic index for evaluating photosynthesis capacity, healthy growth, and estimating crop yield. Accurately monitoring the AGB helps improve agricultural fertilization management and optimize planting patterns. Numerous studies have confirmed that canopy spectrum saturation causes optical vegetation indices (VIs) to underestimate the AGB of crops at multiple growth periods. To solve this problem, the present research used a remote sensing method to obtain RGB images of potato tuber formation-, tuber growth-, and starch accumulation- periods by a high-definition digital camera sensor on the unmanned aerial vehicle (UAV). From the ultrahigh spatial resolution RGB images, we then extracted RGB-VIs, textures (based on the gray level co-occurrence matrix, GLCM), and crop height (Hdsm) and analyzed the correlation between the three image features and the potato AGB for single and multiple growth periods. Finally, we estimated potato AGB at multiple growth periods based on (1) RGB-VIs, (2) RGB-VIs + GLCM-based textures, (3) RGB-VIs + Hdsm, and (4) RGB-VIs + GLCM-based textures + Hdsm by applying multiple stepwise regression (MSR) and extreme learning machine (ELM). The results showed that (i) unlike the texture features of wheat and maize that increased with growth period, the texture features and crop height of the potato canopy both increased first and then decreased with the growth period. (ii) The potato AGB was poorly estimated when using RGB-VIs, CLCM-based textures, or Hdsm individually; (iii) combining GLCM-based textures, Hdsm, and RGB-VIs solved the problem of underestimating the high AGB values of potato samples by the RGB-VIs model alone. Therefore, combining GLCM-based textures, Hdsm, and RGB-VIs obtained from UAV digital images could enhance the accuracy of potato AGB estimation under high coverage.

Abnormal Target Detection Method in Hyperspectral Remote Sensing Image Based on Convolution Neural Network.

Abnormal target detection in hyperspectral remote sensing image is one of the hotspots in image research. The image noise generated in the detection process will lead to the decline of the quality of hyperspectral remote sensing image. In view of this, this paper proposes an abnormal target detection method of hyperspectral remote sensing image based on the convolution neural network. Firstly, the deep residual learning network model has been used to remove the noise in hyperspectral remote sensing image. Secondly, the spatial and spectral features of hyperspectral remote sensing images were used to optimize the clustering dictionary, and then the image segmentation containing target information is completed. Finally, the image was input into the deep convolution neural network with a dual classifier, and the network detects the abnormal target in the image. The test results of this algorithm show that the structural similarity of the denoised image is higher than 0.86, which shows that this method has good noise reduction performance, image details will not damage, segmentation effect is good, and it can obtain high-definition target image information and accurately detect abnormal targets in the image.

A novel DeepML framework for multi‐classification of breast cancer based on transfer learning

AbstractIn the automated diagnosis of breast cancer (BC), microscopic images based on multi‐classification play a prominent role. Multi‐classification of BC means to differentiate among the sub‐categories of BC (papillary carcinoma, ductal carcinoma, fibroadenoma, etc.). However, unpretentious contrasts in various sub‐categories of BC occur due to the wide fluctuation of 1) excessive coherency of malignant cells, 2) high definition image appearance, and 3) excessive heterogeneity in color distribution, which makes the task more crucial. Therefore, the automated sub‐category discrimination using microscopic images has great medical diagnostic significance yet has not much explored. Thus, the present paper proposes a framework based on machine learning (ML) and deep learning (DL) to multi‐classify BC cells into 8 sub‐categories. These 8 sub‐categories comprise four kinds that delineate benigncy, and the other four portray malignancy. More appropriately, both the ML and DL models with the concept of transfer learning have been proposed as DeepML framework to achieve multi‐classification of BC using histopathological images. The DeepML framework has achieved distinguished performance (approx. 98% & 89% average accuracy for 90–10% and 80–20% train‐test split, respectively) on a wide scale dataset, which intimate the quality of the proposed framework among existing approaches.

Feasibility of Transabdominal Real-time CDFI and HDFI Techniques for Fetal Pulmonary Vein Display in the First Trimester.

This study aimed to assess the feasibility and usefulness of transabdominal color Doppler flow imaging (CDFI) technology and the high-definition flow imaging (HDFI) technique in detecting fetal pulmonary veins (PVs) in the first trimester (11-13+6 weeks). From December 2018 to October 2019, 328 pregnant women with 328 normal singleton fetuses (crown-rump length: 45-84 mm) who had undergone CDFI and HDFI scans for fetal heart and vessel examination were enrolled in this study. The cases were divided into three groups according to the gestational age: group A, 11+0 -11+6 weeks; group B, 12+0 -12+6 weeks; and group C, 13+0 -13+6 weeks. Baseline sonograms and CDFI and HDFI images were analyzed by two senior radiologists independently and blindly. The abilities of CDFI and HDFI to display PVs were compared. Successful PV display rates via CDFI and HDFI were 2.3% and 68.2% (P<0.01), 22.4% and 82.4% (P<0.01), 41.5% and 91.2% (P<0.01) for group A, group B, and group C, respectively. The total successful display rates for the two methods were 28.9% (CDFI) and 84.8% (HDFI) (P<0.01). The HDFI technique is more valuable than CDFI for detecting PVs in early pregnancy (11-13+6 weeks). HDFI can detect at least one PV in all cases and may be used to detect pulmonary venous anomalies early.

OTCS: An Online Target Close-Up Shooting Method Based on the UAV Image System

Unmanned aerial vehicles (UAV) equipped with intelligent gimbaled cameras can take images and identify specific targets from them in real-time. However, the targets in the images are generally small and difficult to be seen. This paper proposes an Online Target Close-up Shooting (OTCS) method to solve the problem of taking high-definition (HD) close-up images of the targets. Firstly, we build an online target close-up shooting model, which uses deep learning (DL) algorithm to identify the targets from the images taken online. Then, an intelligent control algorithm is presented to control the gimbaled camera to take a close-up shot of the target, which considered the target being fixed on the ground statically and moving at an ununiform speed. Finally, we build a UAV-based prototype system and conduct a series of experiments to verify our proposal. Experimental results show that our proposed method is feasible and outperforms traditional methods in effectiveness.

D-4 | Increased Anatomical and Device Detail Using a High Definition (Hi-Def) Imaging System: Elevating Precision in Complex Interventions

A new generation of flat panel detectors (FPD) is able to perform high definition (Hi-Def) imaging with a resolution of 76-microns. This allows unparalleled real-time visualization of anatomy and devices during interventional procedures.

Drone Laying Honey Bee Workers in Queen Monitoring Cages.

Techniques to monitor honey bee (Apis mellifera) egg production in cages allow researchers to study how different environmental factors contribute to reproduction. However, although the conditions required to facilitate queen egg production in a laboratory setting have been established, limited work has addressed the requirements for stimulating and monitoring worker egg laying. Here, we documented that drone laying workers will lay eggs in Queen Monitoring Cages (QMC), specialized cages designed to facilitate queen egg laying under controlled conditions. Egg production and worker mortality were compared between QMCs containing queens and those containing drone laying workers. High-definition images of the last abdominal segments of living first-instar larvae hatched from worker laid eggs and those putatively laid by queens were qualitatively compared to identify candidate characteristics to determine their sex.

Ultrasound high-definition microvasculature imaging with novel quantitative biomarkers improves breast cancer detection accuracy

ObjectivesTo overcome the limitations of power Doppler in imaging angiogenesis, we sought to develop and investigate new quantitative biomarkers of a contrast-free ultrasound microvasculature imaging technique for differentiation of benign from malignant pathologies of breast lesion.MethodsIn this prospective study, a new high-definition microvasculature imaging (HDMI) was tested on 521 patients with 527 ultrasound-identified suspicious breast masses indicated for biopsy. Four new morphological features of tumor microvessels, microvessel fractal dimension (mvFD), Murray’s deviation (MD), bifurcation angle (BA), and spatial vascularity pattern (SVP) as well as initial biomarkers were extracted and analyzed, and the results correlated with pathology. Multivariable logistic regression analysis was used to study the performance of different prediction models, initial biomarkers, new biomarkers, and combined new and initial biomarkers in differentiating benign from malignant lesions.ResultsThe new HDMI biomarkers, mvFD, BA, MD, and SVP, were statistically significantly different in malignant and benign lesions, regardless of tumor size. Sensitivity and specificity of the new biomarkers in lesions > 20 mm were 95.6% and 100%, respectively. Combining the new and initial biomarkers together showed an AUC, sensitivity, and specificity of 97% (95% CI: 95–98%), 93.8%, and 89.2%, respectively, for all lesions regardless of mass size. The classification was further improved by adding the Breast Imaging Reporting and Data System (BI-RADS) score to the prediction model, showing an AUC, sensitivity, and specificity of 97% (95% CI: 95–98%), 93.8%, and 89.2%, respectively.ConclusionThe addition of new quantitative HDMI biomarkers significantly improved the accuracy in breast lesion characterization when used as a complementary imaging tool to the conventional ultrasound.Key Points• Novel quantitative biomarkers extracted from tumor microvessel images increase the sensitivity and specificity in discriminating malignant from benign breast masses.• New HDMI biomarkers Murray’s deviation, bifurcation angles, microvessel fractal dimension, and spatial vascularity pattern outperformed the initial biomarkers.• The addition of BI-RADS scores based on US descriptors to the multivariable analysis using all biomarkers remarkably increased the sensitivity, specificity, and AUC in all size groups.

Multispectral vineyard segmentation: A deep learning comparison study

Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural sector, including vineyards which are a relevant crop in the Mediterranean region. In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods. Camera data have been collected on vineyards using an Unmanned Aerial System (UAS) equipped with a dual imaging sensor payload, namely a high-definition RGB camera and a five-band multispectral and thermal camera. Extensive experiments using deep-segmentation networks and unsupervised methods have been performed on multimodal datasets representing four distinct vineyards located in the central region of Portugal. The reported results indicate that SegNet, U-Net, and ModSegNet have equivalent overall performance in vine segmentation. The results also show that multimodality slightly improves the performance of vine segmentation, but the NIR spectrum alone generally is sufficient on most of the datasets. Furthermore, results suggest that high-definition RGB images produce equivalent or higher performance than any lower resolution multispectral band combination. Lastly, Deep Learning (DL) networks have higher overall performance than classical methods. The code and dataset are publicly available on https://github.com/Cybonic/DL_vineyard_segmentation_study.git.

Endoscopic Surveillance in Inflammatory Bowel Diseases: Selecting a Suitable Technology.

In the treat-to-target era, endoscopy has become the backbone of the assessment of remission, defined as mucosal healing, in inflammatory bowel disease (IBD) patients. Current recommendations indicate that endoscopic procedures should be performed with high-definition white-light endoscopy (HD-WLE), as it guarantees the best possible visualization of the mucosa. With respect to endoscopic surveillance, the preventive strategy for dysplasia and colorectal cancer (CRC) in long-standing IBD, is the use of dye-chromoendoscopy (DCE), which enhances the mucosal pattern of the colonic walls. DCE has been established as the gold standard for dysplasia detection and is at present incorporated in all international guidelines. Over the past years, novel technologies, such as high-definition endoscopic imaging, and optical and digital enhancement tools have revolutionized the quality and level of fine details of vascular and mucosal patterns. These endoscopic images have the ambition to reflect histological changes for suspected neoplastic lesions and inflammation or healing and are emerging as potential alternatives to DCE. Indeed, the comparison of DCE with high-definition imaging is an open issue that deserves further investigation. We aimed to examine and summarize the technical aspects and the current evidence on endoscopic technologies with a specific focus on the surveillance in IBD patients.