Image Data Processing Research Articles

Utilizing Electromagnetic Radiation in Remote Sensing for Vegetation Health Analysis Using NDVI Approach with Sentinel-2 Imagery

The process of utilizing electromagnetic radiation from the radiometric values (Near-Infrared/NIR) and RED in the Normalized Difference Vegetation Index (NDVI) on Sentinel-2 imagery involves measuring light reflection at specific wavelengths reflected by vegetation. NDVI is computed by taking the difference between NIR and RED reflectance, then dividing it by their sum. In-depth analysis of electromagnetic wave interaction and plant spectral characteristics, such as NIR light reflection in healthy leaves, enables mapping vegetation health levels. The research aims to process image data and create a model visualizing vegetation health in the Sentinel-2 research area. Across the total area of 601,971,281.26 m2, 30.37% is healthy vegetation, 12.52% is moderate, and 53.33% is low or unhealthy. The least healthy area has an NDVI of -0.2006, while the healthiest area has a value of 0.6043.

Artificial intelligence applications in disease diagnosis and treatment: recent progress and outlook

The use of computers and other technologies to replicate human-like intelligent behaviour and critical thinking is known as artificial intelligence (AI).The development of AI-assisted applications and big data research has accelerated as a result of the rapid advancements in computing power, sensor technology, and platform accessibility that have accompanied advances in artificial intelligence. AI models and algorithms for planning and diagnosing endodontic procedures. The search engine evaluated information on artificial intelligence (AI) and its function in the field of endodontics, and it also incorporated databases like Google Scholar, PubMed, and Science Direct with the search criterion of original research articles published in English. Online appointment scheduling, online check-in at medical facilities, digitization of medical records, reminder calls for follow-up appointments and immunisation dates for children and pregnant women, as well as drug dosage algorithms and adverse effect warnings when prescribing multidrug combinations, are just a few of the tasks that already use artificial intelligence. Data from the review supported the conclusion that AI can play a significant role in endodontics, including the identification of apical lesions, classification and numbering of teeth, detection of dental caries, periodontitis, and periapical disease, diagnosis of various dental problems, aiding dentists in making referrals, and helping them develop more precise treatment plans for dental disorders. Although artificial intelligence (AI) has the potential to drastically alter how medicine is practised in ways that were previously unthinkable, many of its practical applications are still in their infancy and need additional research and development. Over the past ten years, artificial intelligence in ophthalmology has grown significantly and will continue to do so as imaging techniques and data processing algorithms improve.

Deteksi Penyakit Pada Daun Tanaman Ubi Jalar Menggunakan Metode Convolutional Neural Network

Sweet potatoes are the world's third most important root crop and the fourth most popular staple food in developing countries, including Indonesia. Some diseases commonly found in sweet potato leaves are early blight (identified by leaves containing batataezim) and late blight (characterized by leaves that have chlorosis). These two diseases have different symptoms and require different treatments, but a slow identification process can lead to additional costs for plant care. This research will classify image data of sweet potato diseases using the Convolutional Neural Network (CNN) method. CNN is a derivative of the Multilayer Perceptron (MLP) designed to process image data with high network depth and is often used for classification tasks. The research uses a total of 750 images divided into 3 classes: images of healthy leaves, images of leaves with chlorosis, and images of leaves containing batataezim. Each leaf class will be labeled with 250 image data, and the labeled data will be further divided into training and testing sets. From these sets, prediction data will be obtained from the testing process during the CNN model training. The training accuracy resulted in a value of 98.17%, while the testing accuracy reached 98.67%. Additionally, the resulting loss values are remarkably low, at 0.04% for training and 0.03% for testing. The research findings will provide insights into the CNN method's ability to detect diseases in sweet potato plants, potentially impacting agricultural supervision, plant disease identification, and enabling more precise decisions regarding plant care actions.

Applications of visual perception techniques using neural networks in autonomous driving

The perception system and the decision system are important components of a complete autonomous driving vehicle. The perception system can help the decision system to obtain the necessary information of external environment and vehicle status. The traditional perception system mainly relies on the on-board radar. But in recent years, vision-based perception techniques have become a hot research topic. Meanwhile, thanks to the excellent performance of neural networks in processing image data, the processing algorithms for visual perception images have also made great progress. Visual perception techniques can not only acquire more information, but also is more cost effective and easier to install. This paper provides an overview of the more mature and promising visual perception techniques, including their principles and data processing algorithms, in terms of acquiring 2D image data and 3D depth information. For acquiring 2D image data, this paper introduces the principle of event camera and reviews the current progress on the event camera. Regarding the acquisition of 3D depth information, three techniques are introduced, namely binocular stereo-vision, time of flight (TOF), and structured light. Their performance when combined with neural networks for autonomous driving applications is also reviewed. Finally, this paper lists the current dilemmas faced by the above 2D and 3D imaging techniques and the possible solutions.

The effects of physical activity on white matter microstructure in children with overweight or obesity: The ActiveBrains randomized clinical trial

BackgroundEmerging research supports the idea that physical activity benefits brain development. However, the body of evidence focused on understanding the effects of physical activity on white matter microstructure during childhood is still in its infancy, and further well-designed randomized clinical trials are needed. AimThis study aimed: (i) to investigate the effects of a 20-week physical activity intervention on global white matter microstructure in children with overweight or obesity, and (ii) to explore whether the effect of physical activity on white matter microstructure is global or restricted to a particular set of white matter bundles. MethodsIn total, 109 children aged 8 to 11 years with overweight or obesity were randomized and allocated to either the physical activity program or the control group. Data were collected from November 2014 to June 2016, with diffusion tensor imaging (DTI) data processing and analyses conducted between June 2017 and November 2021. Images were pre-processed using the Functional Magnetic Resonance Imaging (MRI) of the Brain´s Software Library (FSL) and white matter properties were explored by probabilistic fiber tractography and tract-based spatial statistics (TBSS). ResultsIntention-to-treat analyses were performed for all children who completed the pre-test and post-test DTI assessment, with good quality DTI data (N = 89). Of them, 83 children (10.06±1.11 years, 39 % girls, intervention group=44) met the per-protocol criteria (attended at least 70 % of the recommended sessions). Our probabilistic fiber tractography analysis did not show any effects in terms of global and tract-specific fractional anisotropy (FA) and mean diffusivity (MD) in the per-protocol or intention-to-treat analyses. Additionally, we did not observe any effects on the voxel-wise DTI parameters (i.e., FA and MD) using the most restricted TBSS approach (i.e., per protocol analyses and p-corrected image with a statistical threshold of p < 0.05). In the intention-to-treat analysis, we found that our physical activity program had a borderline effect (p-corrected image with a statistical threshold of p < 0.1) on 7 different clusters, including a cluster in the corpus callosum. ConclusionWe conclude that a 20-week physical activity intervention was not enough to induce changes in global and tract-specific white matter during childhood. The effects of physical activity on white matter microstructure could be restricted to local changes in several white matter tracts (e.g., the body of the corpus callosum). However, our results were not significant, and more interventions are needed to determine whether and how physical activity affects white matter microstructure during childhood.

Neuroimaging of new and novel Alzheimer’s Disease (AD) mice from the MODEL AD Consortium

AbstractBackgroundThe ability of preclinical neuroimaging in rodent models of Alzheimer’s Disease and Related Dementia (ADRD) to assess the progression of disease is increasingly providing novel insights into AD, like human AD imaging. Preclinical studies have the advantage of being able to directly correlate findings to the observed neuropathology. Recently, University of California Irvine and Indiana University School of Medicine along with Jackson Laboratories were awarded funding to design and phenotype relevant mouse models of late onset AD (LOAD), forming the MODEL AD consortium (https://www.model‐ad.org/). Imaging cores from these will utilize existing state of the art translationally relevant medical imaging methodologies as well as ascertain feasibility of emerging imaging methods for increased diagnostic sensitivity.MethodsTwo primary imaging modalities will be utilized: high‐field MRI (9.4T and 17.6T) and PET/CT. Deep MRI phenotyping are based on the accepted human‐AD standards like ADNI (Structural T2W, FLAIR, diffusion, perfusion (PWI), susceptibility, and functional MRI and MR spectroscopy). Novel histological vessel painting combined with PWI will assess vascular modifications. PET imaging will evaluate brain metabolism (18F‐FDG), regional perfusion (64Cu‐PTSM), amyloid (18F‐AV45), tau (18F‐AV1451), neuroinflammation (18F‐CPPC and 18F‐GSK1482160), and astrogliosis (18F‐FEBU) markers in new mouse models.ResultsMODEL AD consortia will utilize platform mice (Table 1) across their lifespans and effects after being fed a high fat diet. Known and emerging GWAS risk variants for AD will be added to these platform mice to better replicate human AD. Neuroimaging data will be presented from PET/CT and MRI, focusing on exemplars from relevant AD mouse models including platform mice. A long‐term goal of the MODEL‐AD Imaging cores is to co‐register MRI, PET/CT and vascular measures to concordant histopathological and spatial RNA data. The cores have developed state‐of‐the‐art analytical pipelines to process imaging data to support spatial and temporal localization of ADRD.ConclusionsThe MODEL AD consortium (UCI/IUSM/Jax) will develop new and novel AD mouse models and the imaging cores will deeply phenotype these models using existing and emerging neuroimaging methods and analytic approaches, using MRI and PET/CT. All data will be made available to AD researchers via the Sage Knowledge Network (https://adknowledgeportal.synapse.org/).

Neuroimaging of new and novel Alzheimer’s Disease (AD) mice from the MODEL AD Consortium

AbstractBackgroundThe ability of preclinical neuroimaging in rodent models of Alzheimer’s Disease and Related Dementia (ADRD) to assess the progression of disease is increasingly providing novel insights into AD, like human AD imaging. Preclinical studies have the advantage of being able to directly correlate findings to the observed neuropathology. Recently, University of California Irvine and Indiana University School of Medicine along with Jackson Laboratories were awarded funding to design and phenotype relevant mouse models of late onset AD (LOAD), forming the MODEL AD consortium (https://www.model‐ad.org/). Imaging cores from these will utilize existing state of the art translationally relevant medical imaging methodologies as well as ascertain feasibility of emerging imaging methods for increased diagnostic sensitivity.MethodTwo primary imaging modalities will be utilized: high‐field MRI (9.4T and 17.6T) and PET/CT. Deep MRI phenotyping are based on the accepted human‐AD standards like ADNI (Structural T2W, FLAIR, diffusion, perfusion (PWI), susceptibility, and functional MRI and MR spectroscopy). Novel histological vessel painting combined with PWI will assess vascular modifications. PET imaging will evaluate brain metabolism (18F‐FDG), regional perfusion (64Cu‐PTSM), amyloid (18F‐AV45), tau (18F‐AV1451), neuroinflammation (18F‐CPPC and 18F‐GSK1482160), and astrogliosis (18F‐FEBU) markers in new mouse models.ResultMODEL AD consortia will utilize platform mice (Table 1) across their lifespans and effects after being fed a high fat diet. Known and emerging GWAS risk variants for AD will be added to these platform mice to better replicate human AD. Neuroimaging data will be presented from PET/CT and MRI, focusing on exemplars from relevant AD mouse models including platform mice. A long‐term goal of the MODEL‐AD Imaging cores is to co‐register MRI, PET/CT and vascular measures to concordant histopathological and spatial RNA data. The cores have developed state‐of‐the‐art analytical pipelines to process imaging data to support spatial and temporal localization of ADRD.ConclusionThe MODEL AD consortium (UCI/IUSM/Jax) will develop new and novel AD mouse models and the imaging cores will deeply phenotype these models using existing and emerging neuroimaging methods and analytic approaches, using MRI and PET/CT. All data will be made available to AD researchers via the Sage Knowledge Network (https://adknowledgeportal.synapse.org/).

Radiogenomics uncovers an interplay between angiogenesis and clinical outcomes in bladder cancer.

Precision medicine has become a promising clinical treatment strategy for various cancers, including bladder cancer, where angiogenesis plays a critical role in cancer progression. However, the relationship between angiogenesis, immune cell infiltration, clinical outcomes, chemotherapy, and targeted therapy remains unclear. We conducted a comprehensive evaluation of angiogenesis-related genes (ARGs) to identify their association with immune cell infiltration, transcription patterns, and clinical outcomes in bladder cancer. An ARG score was constructed to identify angiogenic subgroups in each sample and we evaluated their predictive performance for overall survival rate and treatment response. In addition, we optimized existing clinical detection protocols by performing image data processing. Our study revealed the genomic-level mutant landscape and expression patterns of ARGs in bladder cancer specimens. Using analysis, we identified three molecular subgroups where ARG mutations correlated with patients' pathological features, clinical outcomes, and immune cell infiltration. To facilitate clinical applicability, we constructed a precise nomogram based on the ARG score, which significantly correlated with stem cell index and drug sensitivity. Finally, we proposed the radiogenomics model, which combines the precision of genomics with the convenience of radiomics. Our study sheds light on the prognostic characteristics of ARGs in bladder cancer and provides insights into the tumor environment's characteristics to explore more effective immunotherapy strategies. The findings have significant implications for the development of personalized treatment approaches in bladder cancer and pave the way for future studies in this field.

306 Shinyanimalcv: Interactive Web Application for Object Detection and Three-Dimensional Visualization of Animals Using Computer Vision

Abstract Precision livestock farming applies integrated sensors and information technology to provide individualized animal care in a timely manner. A computer vision system, a non-intrusive and cost-efficient sensor technology, may be useful for monitoring animals to support farm management decisions. While the advancement in camera sensors provides a great opportunity for producers to improve animal health and welfare sustainably, the limited availability of user-friendly image data processing software tools substantially hinders the implementation of computer vision in livestock production systems. The objective of this study was to develop ShinyAnimalCV, which is a Shiny-based interactive animal computer vision web application. This software tool offers a user-friendly graphical user interface for object detection and three-dimensional visualization. The object detection module employs the Mask-RCNN to precisely segment the masks (regions) of the focal animals from two-dimensional images. A Python-based computer vision library OpenCV was used to draw minimum bounding boxes covering the segmented masks for object detection. The three-dimensional visualization module takes the depth map file captured from a top-view three-dimensional camera as an input, which contains the numerical distances between the camera and the objects (animals and background). The depth map file was first converted to a heatmap image, followed by identifying and segmenting the animal from the background using the Mask-RCNN model. The object detection module returns detection results, including the detected animal’s location, class/type, and morphological traits (e.g., body length and width). The visualization module generates a segmented mask to extract the height of the animal from the depth map file, which can be used to interactively visualize the three-dimensional surface of the animal and estimate its morphological traits, including length, width, height, and volume. By integrating these two modules into R Shiny, we deployed ShinyAnimalCV on a cloud server with pre-trained Mask-RCNN models using pig and cattle data to allow users to upload custom data and perform object detection and three-dimensional surface visualization. The features extracted by ShinyAnimalCV are expected to be useful for performing animal identification, feed intake monitoring, body weight predictions, and body condition score estimations. We conclude that the newly developed ShinyAnimalCV could facilitate the application of computer vision in the animal science community.

File Monitoring System based on Hadoop Framework for Image Data Processing

File Monitoring System based on Hadoop Framework for Image Data Processing

MATLAB-assisted visual ratiometric fluorescence sensing of tetracycline based on antenna effect

Tetracycline (TC) is a widely used antibiotic that accumulates in animal foods, posing a threat to human health and even causing environmental pollution. This research aimed to establish a rapid method for the detection of TC. To this end, sodium alginate-protected gold nanoclusters (SA-AuNCs) were synthesized, which combined with europium ions (Eu3+) to construct a ratiometric-sensing platform for the detection of TC. When added TC, Eu3+ interacted with TC could result in enhanced fluorescence at 620 nm, which was used as a response signal, while the fluorescence intensity of SA-AuNCs remained unchanged, acting as a reference signal. Accordingly, a linear relationship was established in a wide range of TC (0–60 μM) and the detection limit (LOD) was 22.07 nM. The recoveries of the spiked samples ranged from 94.71 % to 109.73 %, implying that the as-proposed method could be successfully applied to detect TC in real samples. Furthermore, color information was extracted from the fluorescence images taken with a smartphone and analyzed by MATLAB, in which a visual mini program could be designed for calculating the channel values of red (R), green (G), and blue (B) of the generated fluorescence images. Utilizing the image processing functions and data processing capabilities of MATLAB, the obtained R/G values were selected for the semi-quantitative detection of TC in real samples without any time-consuming pretreatment and expensive instruments, demonstrating the great potential prospects of integrating MATLAB algorithm with SA-AuNCs-based ratiometric fluorescence probe into one ideal system for the development of rapid food assay.

Instant processing of large-scale image data with FACT, a real-time cell segmentation and tracking algorithm

Quantifying cellular characteristics from a large heterogeneous population is essential to identify rare, disease-driving cells. A recent development in the combination of high-throughput screening microscopy with single-cell profiling provides an unprecedented opportunity to decipher disease-driving phenotypes. Accurately and instantly processing large amounts of image data, however, remains a technical challenge when an analysis output is required minutes after data acquisition. Here, we present fast and accurate real-time cell tracking (FACT). FACT can segment ∼20,000 cells in an average of 2.5s (1.9-93.5 times faster than the state of the art). It can export quantifiable features minutes after data acquisition (independent of the number of acquired image frames) with an average of 90%-96% precision. We apply FACT to identify directionally migrating glioblastoma cells with 96% precision and irregular cell lineages from a 24h movie with an average F1 score of 0.91.

Applications of transformers in computer vision

Recently, research based on transformers has become a hot topic. Owing to their ability to capture long-range dependencies, transformers have been rapidly adopted in the field of computer vision for processing image and video data. Despite their widespread adoption, the application of transformer in computer vision such as semantic segmentation, image generation and image repair are still lacking. To address this gap, this paper provides a thorough review and summary of the latest research findings on the applications of transformers in these areas, with a focus on the mechanism of transformers and using ViT (Vision Transformer) as an example. The paper further highlights recent or popular discoveries of transformers in medical scenarios, image generation, and image inpainting. Based on the research, this work also provides insights on future developments and expectations.

One-stop multimodal medical imaging intelligent checkup system

Given the increasing demand for daily health checks and the shortage of related multimodal and multifunctional imaging equipment, a new multifunction and multimodal biomedical tomography system for health examination in which combines five agents is proposed. The main methods are, first, that the architecture framework is based on magneto-acoustic tomography with magnetic induction, integrated four other species of imaging agents, for seamlessly fusing of functional with anatomical information to reconstruct multimodality images. And the image data acquisition and processing is consisted of upper and lower computer which form a virtual instrument: the lower computer is made up by five intelligent agents while all of them were an embedded system severally. Linked with the WIFI networks, the upper computer process the image data and reconstruct the multimodal image. Thirdly, the image fusion is implemented by the system automatically itself and without other post image registrations and software. Most prominent feature of the system is the spatial resolution of images was 0.24 mm, and the imaging speed gets 50 frames per second that meet real time imaging requirements. The phantom system promised a new type noninvasive biomedical imaging multimodality and one-stop health examination system to realize one-stop-shop omni-tomography as well as holographic imaging service. Furthermore, the system and its methods can promote the construction as well as realization of smart medicine and smart hospitals.

Trustworthy in silico cell labeling via ensemble-based image translation

Artificial intelligence (AI) image translation has been a valuable tool for processing image data in biological and medical research. To apply such a tool in mission-critical applications, including drug screening, toxicity study, and clinical diagnostics, it is essential to ensure that the AI prediction is trustworthy. Here, we demonstrate that an ensemble learning method can quantify the uncertainty of AI image translation. We tested the uncertainty evaluation using experimentally acquired images of mesenchymal stromal cells. We find that the ensemble method reports a prediction standard deviation that correlates with the prediction error, estimating the prediction uncertainty. We show that this uncertainty is in agreement with the prediction error and Pearson correlation coefficient. We further show that the ensemble method can detect out-of-distribution input images by reporting increased uncertainty. Altogether, these results suggest that the ensemble-estimated uncertainty can be a useful indicator for identifying erroneous AI image translations.

Streamlined structure determination by cryo-electron tomography and subtomogram averaging using TomoBEAR

Structures of macromolecules in their native state provide unique unambiguous insights into their functions. Cryo-electron tomography combined with subtomogram averaging demonstrated the power to solve such structures in situ at resolutions in the range of 3 Angstrom for some macromolecules. In order to be applicable to the structural determination of the majority of macromolecules observable in cells in limited amounts, processing of tomographic data has to be performed in a high-throughput manner. Here we present TomoBEAR—a modular configurable workflow engine for streamlined processing of cryo-electron tomographic data for subtomogram averaging. TomoBEAR combines commonly used cryo-EM packages with reasonable presets to provide a transparent (“white box”) approach for data management and processing. We demonstrate applications of TomoBEAR to two data sets of purified macromolecular targets, to an ion channel RyR1 in a membrane, and the tomograms of plasma FIB-milled lamellae and demonstrate the ability to produce high-resolution structures. TomoBEAR speeds up data processing, minimizes human interventions, and will help accelerate the adoption of in situ structural biology by cryo-ET. The source code and the documentation are freely available.

Computer vision-based image data processing of digital media video images with ultra-clear picture quality

Abstract To improve the clarity of video images and reduce subtle content omission, this paper transforms video images into data supported by the system for recognition through computer vision techniques, quantifies the gray pixel levels, and obtains a two-dimensional array image. The direction constraint is added to the constraints to construct the DOG video image pyramid and search the extreme value points of the target image to get the projection space of the unmixing matrix of the image. The median value of the window is selected as the filter output, the image edges are sharpened, and the image details are enhanced by combining with the inverse Fourier transform to complete the video image data processing. The results show that the average processing vector time is 4.74µs, the image data processing time is short, and the image picture quality is high definition.

DRAGONS—A Quick Overview

Data Reduction for Astronomy from Gemini Observatory North and South (DRAGONS) is a platform for the reduction and processing of astronomical data. The Python-based, open-source package includes infrastructure for automation and algorithms for the processing of imaging and spectroscopic data, up to the analysis-ready stage. DRAGONS currently focuses on the reduction of Gemini data, although it allows for support of data from other instruments and telescopes through third-party extensions. Its latest release (v3.1) enables automated reduction of all currently active Gemini imaging facility instruments, as well as optical longslit spectroscopic data, acquired with GMOS.

Research on computer network information image data processing based on machine learning

Abstract In this paper, we aim to progress the efficiency and accuracy of information processing and security detection in computer networks by introducing convolutional neural networks in machine learning algorithms that are capable of multi-scaling from the channel attention module and spatial attention module in extracting image information. Global maximum pooling and global averaging are done for the feature maps generated by both modules to get the clearest feature maps by dimensionality reduction. The loss function is used to calculate the feature maps to reduce the data loss generated during data extraction and finally complete the image data processing. To verify the effectiveness of the proposed platform, network images containing different amounts of data are input into the platform, and the accuracy and loss of data extraction are obtained. The results show that the data extraction accuracy of the reduced platform is up to 100%, which is 6% higher than other platforms. The number of data losses in other platforms is more than twice of this paper, while the number of losses in this paper can be controlled within 5. It can be seen that convolutional neural network in machine learning improves the accuracy of data extraction from computer network information images and reduces the loss in data extraction.

Research Opportunities and Challenges in Image Processing

The processing of image data for storage, transmission, and representation for autonomous machine perception are the two primary application fields that have contributed to the growing interest in digital image processing technologies. The development of improved pictorial information for human interpretation is the other primary application field that has contributed to this growing interest. The purpose of this paper is to describe the scope of image processing, examine the numerous phases and techniques involved in a typical image processing, and provide examples of applications of image processing tools and procedures in cutting-edge fields of study.