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Introduction: The scapholunate interosseous ligament is pivotal for wrist stability, and its impairment can result in instability and joint degeneration. This study explores the application of real-time MRI for dynamic assessment of the scapholunate joint during wrist motion with the objective of determining its diagnostic value in efficacy in contrast to static imaging modalities. Materials and Methods: Ten healthy participants underwent real-time MRI scans during wrist ab/adduction and fist-clenching maneuvers. Measurements were obtained at proximal, medial, and distal landmarks on both dynamic and static images with statistical analyses conducted to evaluate the reliability of measurements at each landmark and the concordance between dynamic measurements and established static images. Additionally, inter- and intraobserver variabilities were evaluated. Results: Measurements of the medial landmarks demonstrated the closest agreement with static images and exhibited the least scatter. Distal landmark measurements showed a similar level of agreement but with increased scatter. Proximal landmark measurements displayed substantial deviation, which was accompanied by an even greater degree of scatter. Although no significant differences were observed between the ab/adduction and fist-clenching maneuvers, both inter- and intraobserver variabilities were significant across all measurements. Conclusions: This study highlights the potential of real-time MRI in the dynamic assessment of the scapholunate joint particularly at the medial landmark. Despite promising results, challenges such as measurement variability need to be addressed. Standardization and integration with advanced image processing methods could significantly enhance the accuracy and reliability of real-time MRI, paving the way for its clinical implementation in dynamic wrist imaging studies. [ABSTRACT FROM AUTHOR]

The demand for less complex and more accurate architectures has always been a priority since the broad usage of computer vision in everyday life, like auto‐drive cars, portable applications, augmented reality systems, medical image analysis etc. There are a lot of methods that have been developed to improve the accuracy and complexity of object detection, like the generations of R‐CNNs and YOLOs. However, these methods are not the most efficient architectures, and there is always room to improve. In this study, the 5th version of YOLO is employed and the improved architecture, Inception‐YOLO, is presented. The model significantly outperforms the SOTA YOLO family. Specifically, the improvements can be summarised as follows: impressive improvement of floating point operations (FLOPs) and number of parameters, as well as improvement in accuracy compared to the models with fewer FLOPs. All our presented approaches, like the optimized inception module, proposed structures for CSP and SPPF, and the improved loss function used in this research, work together to incrementally improve detection results, accuracy, demanded memory, and FLOPs simultaneously. For a glimpse of performance, the Inception‐YOLO‐S model hits 38.7% AP with 5.9M parameters and 11.5 BFLOPs and outperforms YOLOv5‐S with 37.4% AP, 7.2M parameters, and 16.5 BFLOPs. [ABSTRACT FROM AUTHOR]

Background: Plants are known to be infected by a wide range of pathogenic microbes. To study plant diseases caused by microbes, it is imperative to be able to monitor disease symptoms and microbial colonization in a quantitative and objective manner. In contrast to more traditional measures that use manual assignments of disease categories, image processing provides a more accurate and objective quantification of plant disease symptoms. Besides monitoring disease symptoms, computational image processing provides additional information on the spatial localization of pathogenic microbes in different plant tissues. Results: Here we report on an image analysis tool called ScAnalyzer to monitor disease symptoms and bacterial spread in Arabidopsis thaliana leaves. Thereto, detached leaves are assembled in a grid and scanned, which enables automated separation of individual samples. A pixel color threshold is used to segment healthy (green) from chlorotic (yellow) leaf areas. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar manner as the leaf scans. We show that this tool is able to capture previously identified differences in susceptibility of the model plant A. thaliana to the bacterial pathogen Xanthomonas campestris pv. campestris. Moreover, we show that the ScAnalyzer pipeline provides a more detailed assessment of bacterial spread within plant leaves than previously used methods. Finally, by combining the disease symptom values with bacterial spread values from the same leaves, we show that bacterial spread precedes visual disease symptoms. Conclusion: Taken together, we present an automated script to monitor plant disease symptoms and microbial spread in A. thaliana leaves. The freely available software (https://github.com/MolPlantPathology/ScAnalyzer) has the potential to standardize the analysis of disease assays between different groups. [ABSTRACT FROM AUTHOR]

Cancer Stem Cells presumably drive tumor growth and resistance to conventional cancer treatments. From a previous computational model, we inferred that these cells are not uniformly distributed in the bulk of a tumorsphere. To confirm this result, we cultivated tumorspheres enriched in stem cells, and performed immunofluorescent detection of the stemness marker SOX2 using confocal microscopy. In this article, we present an image processing method that reconstructs the amount and location of the Cancer Stem Cells in the spheroids. Its advantage is the use of a statistical criterion to classify the cells in Stem and Differentiated, instead of setting an arbitrary threshold. Moreover, the analysis of the experimental images presented in this work agrees with the results from our computational models, thus enforcing the notion that the distribution of Cancer Stem Cells in a tumorsphere is non-homogeneous. Additionally, the method presented here provides a useful tool for analyzing any image in which different kinds of cells are stained with different markers. [ABSTRACT FROM AUTHOR]

The process of image fusion is the process of enriching an image and improving the image's quality, so as to facilitate the subsequent image processing and analysis. With the increasing importance of image fusion technology, the fusion of infrared and visible images has received extensive attention. In today's deep learning environment, deep learning is widely used in the field of image fusion. However, in some applications, it is not possible to obtain a large amount of training data. Because some special organs of snakes can receive and process infrared information and visible information, the fusion method of infrared and visible light to simulate the visual mechanism of snakes came into being. Therefore, this paper takes into account the perspective of visual bionics to achieve image fusion; such methods do not need to obtain a significant amount of training data. However, most of the fusion methods for simulating snakes face the problem of unclear details, so this paper combines this method with a pulse coupled neural network (PCNN). By studying two receptive field models of retinal nerve cells, six dual-mode cell imaging mechanisms of rattlesnakes and their mathematical models and the PCNN model, an improved fusion method of infrared and visible images was proposed. For the proposed fusion method, eleven groups of source images were used, and three non-reference image quality evaluation indexes were compared with seven other fusion methods. The experimental results show that the improved algorithm proposed in this paper is better overall than the comparison method for the three evaluation indexes. [ABSTRACT FROM AUTHOR]

Stroke management employs a variety of diagnostic imaging modalities, image processing and analysis methods, and treatment procedures. This work categorizes methods for stroke imaging, image processing and analysis, and treatment, and provides their taxonomies illustrated by a state-of-the-art review. Imaging plays a critical role in stroke management, and the most frequently employed modalities are computed tomography (CT) and magnetic resonance (MR). CT includes unenhanced non-contrast CT as the first-line diagnosis, CT angiography, and CT perfusion. MR is the most complete method to examine stroke patients. MR angiography is useful to evaluate the severity of artery stenosis, vascular occlusion, and collateral flow. Diffusion-weighted imaging is the gold standard for evaluating ischemia. MR perfusion-weighted imaging assesses the penumbra. The stroke image processing methods are divided into non-atlas/template-based and atlas/template-based. The non-atlas/template-based methods are subdivided into intensity and contrast transformations, local segmentation-related, anatomy-guided, global density-guided, and artificial intelligence/deep learning-based. The atlas/template-based methods are subdivided into intensity templates and atlases with three atlas types: anatomy atlases, vascular atlases, and lesion-derived atlases. The treatment procedures for arterial and venous strokes include intravenous and intraarterial thrombolysis and mechanical thrombectomy. This work captures the state-of-the-art in stroke management summarized in the form of comprehensive and straightforward taxonomy diagrams. All three introduced taxonomies in diagnostic imaging, image processing and analysis, and treatment are widely illustrated and compared against other state-of-the-art classifications. [ABSTRACT FROM AUTHOR]

Due to its applications in signal analysis and image processing, the quaternionic Fourier analysis has received increasing attention. In particular, quaternionic Gabor frames (QGFs) attracted some mathematicians' interest. From the literatures, some results on QGFs are based on quaternionic Fourier orthonormal bases. But those used so-called quaternionic Fourier orthonormal bases have a gap that they are all incomplete. In this paper, we present a class of quaternionic Fourier orthonormal bases, and using them derive the corresponding Gabor orthonormal bases. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate novel, automated biomarkers, pigment epithelial detachment composition indices (PEDCI) in eyes with neovascular age-related macular degeneration (nAMD) undergoing anti-vascular endothelial growth factor (anti-VEGF) therapy through 24 months. Methods: Retrospective analysis of 37 eyes (34 patients) with PED associated with nAMD receiving as-needed anti-VEGF treatment was performed. Best-corrected visual acuity (BCVA) and optical coherence tomography images were acquired at a treatment-naïve baseline and 3-, 6-, 12-, 18-, and 24-month visits. Previously validated automated imaging biomarkers, PEDCI-S (serous), PEDCI-N (neovascular), and PEDCI-F (fibrous) within PEDs were measured. ANOVA analysis and Spearman correlation were performed. Results: Mean BCVA (in logMAR) was 0.60 ± 0.47, 0.45 ± 0.41, 0.49 ± 0.49, 0.61 ± 0.54, 0.59 ± 0.56, and 0.67 ± 0.57 at baseline, 3, 6, 12, 18, and 24 months respectively. Overall, BCVA showed minimal worsening of 0.07 ± 0.54 logMAR (p = 0.07). 13.38 ± 3.77 anti–VEGF injections were given through 24 months. PEDCI-F showed an increase of 0.116, 0.122, 0.036, and 0.006 at months 3, 6, 12, and 18 respectively and a decrease of 0.004 at month 24 (p = 0.03); PEDCI-S showed a decrease of 0.064, 0.130, 0.091, 0.092, and 0.095 at months 3, 6, 12, 18, and 24 respectively (p = 0.16); PEDCI-N showed a decrease of 0.052 at month 3 and an increase of 0.008, 0.055, 0.086, and 0.099 at months 6, 12, 18, and 24 respectively (p = 0.06). BCVA was negatively correlated with PEDCI-F (r = -0.28, p < 0.01), and positively correlated with PEDCI-N (r = 0.28, p < 0.01) and PEDCI-S (r = 0.15, p = 0.03). Conclusion: Longitudinal analysis of PEDCI supports their utility as biomarkers that characterize treatment related effects by quantifying the relative composition of PEDs. [ABSTRACT FROM AUTHOR]

This article proposes µSHD as a systematic and quantitative approach to spectra and image data in the field of MFA. The advantages of this approach lie in that it originates from the understanding of the mathematics behind AI and thus eliminates the large computation cost from DCNNs. Note that the focus of this article is only on understanding and benchmarking the role that µSHD plays in different MFA applications. Concrete routes for employing µSHD directly as the quantitative descriptor for supervised and unsupervised machine learning have been discussed. Manufacturers of MFA equipment can certainly utilize the µSHD tool to automate and improve their characterization techniques and image processing and analysis protocols. Moreover, the concept of structural hierarchy behind µSHD can help to gain deeper insights from the data from the perspective of systems. [ABSTRACT FROM AUTHOR]

Moisture damage resistance assessment in asphalt mixtures traditionally relies on the tensile strength ratio (TSR) method, which may not capture the complex behavior of asphalt under moisture ingress. This study proposes an innovative multifaceted approach for assessing moisture susceptibility, challenging the overreliance on TSR, and integrating multiple intricate aspects of asphalt behavior. Employing two dense-graded and one gap-graded polymer-modified binder mixtures with nominal maximum aggregate size (NMAS) of 13 and 19 mm, the study defines threshold values for total air voids, effective air voids, indirect tensile strength, and percentage density change. Evaluations were conducted using a device selected for its dynamic capabilities in inducing stresses and creating pore pressure under elevated temperatures. This research proposes a new zone-based approach to classify mixtures as moisture-resistant, partially moisture-resistant, or moisture-susceptible. This innovative method, grounded on maximum effective air voids and percentage density change, offers nuanced insights for more accurate moisture resistance assessments. Furthermore, the study explores the effect of effective air void size on moisture damage resistance by image processing for dense and gap-graded gradation of 13.2 NMAS, unveiling clogging behavior especially in dense-graded mixture due to smallest pore size. In conclusion, the study provides an in-depth understanding of asphalt mixture behavior under moisture ingress. By offering a novel approach that integrates multiple parameters and extends beyond the conventional TSR approach, it contributes a practical solution for mitigating moisture damage in asphalt mixtures. This research sets the stage for a potential paradigm shift in the approach to moisture susceptibility assessment, enriching the asphalt field with critical insights. [ABSTRACT FROM AUTHOR]

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, 'what you put is what you get' (WYPIWYG) - that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research. [ABSTRACT FROM AUTHOR]

This research introduces quantum adaptive machine learning (QAML) as an innovative solution to enhance the processing efficiency of machine learning in medical image classification, focusing on the diagnosis of brain tumors. QAML leverages quantum algorithms to address challenges associated with large and high-dimensional medical images, offering advantages such as accelerated processing rates, reduced model complexity with heightened accuracy, enhanced precision in handling intricate data relationships, and increased resistance to noise—critical in medical image analysis. The study details the implementation of QAML through a hybrid quantum–classical neural network, employing parameterized quantum circuits for image processing. Comparative experiments with traditional machine learning models demonstrate QAML's faster convergence and comparable accuracy. The research also explores the impact of adaptive optimization strategies on QAML performance, indicating promising results. Overall, QAML, with its quantum convolutional and pooling layers, emerges as a promising and efficient solution for medical image classification, marking significant progress in the integration of quantum-enhanced machine learning in healthcare applications. [ABSTRACT FROM AUTHOR]

Table tennis players need to have tactical awareness, that is, to choose the most appropriate tactical strategy to deal with the opponent's situation and the game situation. But in practice, many athletes tend to neglect the training of tactical awareness, resulting in a disconnect between training and competition. In order to solve this problem, this paper introduces a spatial dynamic image planning method based on wearable optoelectronic devices, and applies it to the tactical simulation of table tennis match. By analyzing the characteristics and demands of table tennis competition, an optical sensing system is designed and integrated into wearable devices. The system uses optical sensors to capture the position and movement trajectory of ping-pong balls in real time, and carries out real-time image processing and analysis based on this information. In the process of tactical simulation, the system can predict the opponent's tactical intention according to the position and trajectory of the ping-pong ball, and give corresponding coping strategies. The experimental results show that the system achieves good results in both accuracy and real-time performance. The application of tactical optimization model in table tennis competition will help to improve the competitive level of athletes and promote the development of table tennis. [ABSTRACT FROM AUTHOR]

Optical processing technology can improve the accuracy and efficiency of image processing. Visual packaging art design simulation is to quickly generate realistic packaging effects in the early stage of packaging design, so as to make better design decisions. Traditional simulation methods often require a lot of time and human resources, and the results lack realism and detail. Machine vision technology based on optical processing can make full use of image processing algorithms and computer vision technology to achieve efficient and accurate packaging design simulation. In this paper, a machine vision technology framework based on image optical processing is proposed. In the image acquisition stage, high-resolution cameras are used to acquire real packaging images, and in the optical processing stage, algorithms are used to enhance, reduce noise and correct images to improve image quality and clarity. In the image analysis stage, machine learning algorithms are used to extract features and detect and identify packaging elements, providing data support for subsequent simulation rendering. The results show that the machine vision technology based on image optical processing can generate high quality and realistic simulation effect of packaging design. Compared with traditional methods, this technique has achieved significant improvements in terms of time cost and design quality. [ABSTRACT FROM AUTHOR]

Artificial intelligence has played a significant role in the expansion of the agriculture industry in recent times by evaluating data and making recommendations for better production. An automated method for determining significant information in seed quality analysis is the peanut maturity analysis in image processing through sensory images. The majority of the time, changes in picture intensity result in feature independence and precise maturity level determination. Therefore, agricultural precision in identifying essential features is low. To address this issue, we suggest employing a Cross-Layer Multi-Perception Neural Network (CLMPNN) for hyperspectral sensory image feature observation in order to determine the optimal assessment of peanut maturity in agriculture. The sensing unit first determines the angular cascade projection's (ACP) structural dependencies for the peanut pod structure. With the aid of color-intensive saturation, the entity projection of pod growth is found using the Slicing Fragment Segmentation (SFS) technique. This generates the various entity variations by integrating relational maturity and non-maturity findings with spectral values. Next, cross-layer multi-perception neural networks are trained with hyperspectral values optimized by LSTM to distinguish between mature and immature pods. In comparison to the other system, this one does exceptionally well in precision agriculture, with a 98.6 well recall rate, a 97.3% classification accuracy, and a 98.9% production accuracy. [ABSTRACT FROM AUTHOR]

This study presents a methodology to prevent the overdesign of electric dispensers for dental impression materials by analyzing the necessary load and determining the appropriate pressurization speed and drive motor capacity. We derived an equation to calculate the required torque and rotational speed of the motor based on the extrusion load and the speed of the impression material. A specialized load measurement system was developed to measure the load necessary to extrude the impression material. Through experiments and image processing, we measured the radius of curvature of the trajectory of the impression material and correlated it with the pressurization speed. Techniques such as position coordinate plotting, curve fitting, and circle fitting were employed to determine the pressurization speed that aligns with the manufacturer's recommended curvature radius. These findings led to a substantial decrease in the necessary motor torque and rotational speed compared with the current standards. This research provides a systematic approach to sizing drive motors using extrusion load and pressurization speed, aiming to reduce overdesign, power consumption, and the weight and size of the motor and battery, thereby contributing to the development of more efficient and compact dental impression material dispensers. [ABSTRACT FROM AUTHOR]

The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance tracking. Traditional manual PCI assessment methods are limited by labor intensity, subjectivity, and susceptibility to human error. Addressing these challenges, this paper presents a novel, end-to-end automated method for PCI calculation, integrating deep learning and image processing technologies. The first stage employs a deep learning algorithm for accurate detection of pavement cracks, followed by the application of a segmentation-based skeleton algorithm in image processing to estimate crack width precisely. This integrated approach enhances the assessment process, providing a more comprehensive evaluation of pavement integrity. The validation results demonstrate a 95% accuracy in crack detection and 90% accuracy in crack width estimation. Leveraging these results, the automated PCI rating is achieved, aligned with standards, showcasing significant improvements in the efficiency and reliability of PCI evaluations. This method offers advancements in pavement maintenance strategies and potential applications in broader road infrastructure management. [ABSTRACT FROM AUTHOR]

Radiological image analysis using machine learning has been extensively applied to enhance biopsy diagnosis accuracy and assist radiologists with precise cures. With improvements in the medical industry and its technology, computer-aided diagnosis (CAD) systems have been essential in detecting early cancer signs in patients that could not be observed physically, exclusive of introducing errors. CAD is a detection system that combines artificially intelligent techniques with image processing applications thru computer vision. Several manual procedures are reported in state of the art for cancer diagnosis. Still, they are costly, time-consuming and diagnose cancer in late stages such as CT scans, radiography, and MRI scan. In this research, numerous state-of-the-art approaches on multi-organs detection using clinical practices are evaluated, such as cancer, neurological, psychiatric, cardiovascular and abdominal imaging. Additionally, numerous sound approaches are clustered together and their results are assessed and compared on benchmark datasets. Standard metrics such as accuracy, sensitivity, specificity and false-positive rate are employed to check the validity of the current models reported in the literature. Finally, existing issues are highlighted and possible directions for future work are also suggested. [ABSTRACT FROM AUTHOR]

• The presence of frost on the evaporator coil degrades the cooling performance. • K-means image segmentation-based frost detection and thickness estimation is proposed. • Frost thickness was estimated using image analysis with an error of 13.69%. • Results were compared with conventional sensors and existing image processing methods. • The method can serve as a reliable feedback source for efficient defrosting. Frost accumulation is a common problem when moisture in the air condenses and freezes on surfaces like heat exchange tubes of refrigeration units. Frost accumulation negatively impacts heat exchange by disrupting the process, reducing system efficiency, and causing operational issues. Therefore, defrosting is mandatory to maintain the rated performance; however, modern automatic defrosting systems rely on sophisticated sensors for frost quantification. These sensors are susceptible to degraded performance with the passage of time under varying environmental conditions. To this end, we introduce a robust and generic image processing-based solution that relies on building a data-driven regression-based model for frost detection and thickness estimation. We evaluated the effectiveness of the proposed method on a newly collected dataset with encouraging performance in terms of a low error margin of 13.69% when compared to conventional capacitive and photoelectric sensors-based frost thickness estimation with error margins of 15.17% and 17.5%, respectively. Similarly, other image processing-based methods, such as Global thresholding, Adaptive mean, and Adaptive gaussian thresholding for segmentation, were compared with the proposed method. Deviations in the error margins were found to be 19.94%, 28.96%, and 27.85%, respectively. These findings highlight the appropriateness of employing K-means for estimating frost thickness. [ABSTRACT FROM AUTHOR]

Image processing is an important area to bring out the best in an image for human interpretation. This process has been widely used in various fields such as machine vision, remote sensing image analysis, medical diagnosis, image restoration, image pattern recognition, and video processing. During the acquisition and transmission of digital images, several random signals, known as noise, can affect image quality. To reduce or to remove efficiently the noise in the acquired or transmitted images, various image denoising techniques can be applied. The performance of denoising methods increases progressively when the noise parameters are taken into account as input parameters. Traditional denoising approaches adopt some assumptions to model noise, such noise is known as purely additive or multiplicative, pixel-independent, and channel-invariant. Usually, these assumptions limit the denoising effect due to inaccurate estimation of noise parameters in these algorithm models. However, the real noise model is signal-dependent and even device-dependent. In this paper, a new denoising method called signal-dependant noise-reducing anisotropic diffusion is developed, which is a version of the speckle reducing anisotropic diffusion (SRAD) filter. It differs from the standard SRAD filter approach by the insertion of a suitable noise parameters estimation framework. The new filter is designed to handle a variety of images corrupted by several common types of signal-dependent noises that are produced by charge-coupled device sensors. As well as it offers great potential for denoising with preserving textures and fine details. Extensive experiments demonstrate a significant increase in the image denoising performance in terms of SNR and RMSE. Qualitative (visual) results underline the efficacy of the proposed algorithm for filtering mixed signal-dependent noise. [ABSTRACT FROM AUTHOR]

As microscopy diversifies and becomes ever more complex, the problem of quantification of microscopy images has emerged as a major roadblock for many researchers. All researchers must face certain challenges in turning microscopy images into answers, independent of their scientific question and the images they have generated. Challenges may arise at many stages throughout the analysis process, including handling of the image files, image pre‐processing, object finding, or measurement, and statistical analysis. While the exact solution required for each obstacle will be problem‐specific, by keeping analysis in mind, optimizing data quality, understanding tools and tradeoffs, breaking workflows and data sets into chunks, talking to experts, and thoroughly documenting what has been done, analysts at any experience level can learn to overcome these challenges and create better and easier image analyses. [ABSTRACT FROM AUTHOR]

With technological developments, the resolution of the display systems is increasing, causing image data to increase. Several technologies are aiding in optimizing these data during transmission and storage while maintaining quality. Still, the internal data transmission for accessing the memory sub‐system during reading and writing cycles consumes significant power while requiring large memory for storage, which is inefficient, especially for portable displays. Hence, this work investigates the feasibility of reducing data through approximation for images in different formats and resolutions for human and computer vision based applications. Different image quality assessment metrics are utilized for performance evaluations with optimum image quality assessment selection according to application requirements. In addition, the feasibility analysis is conducted for three different applications as examples of computer vision. The experimental analysis highlights theoretical and industrial importance by showing that image data can be reduced from 25.0% to 62.5%, while satisfying requirements for HVS‐based and computer vision‐based applications. [ABSTRACT FROM AUTHOR]

Deep Self-Attention Network (Transformer) is an encoder–decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with the inductive bias of convolutional neural network (CNN), Transformer has been gradually applied to medical image processing, including kidney image processing. It has become a hot research topic in recent years. To further explore new ideas and directions in the field of renal image processing, this paper outlines the characteristics of the Transformer network model and summarizes the application of the Transformer-based model in renal image segmentation, classification, detection, electronic medical records, and decision-making systems, and compared with CNN-based renal image processing algorithm, analyzing the advantages and disadvantages of this technique in renal image processing. In addition, this paper gives an outlook on the development trend of Transformer in renal image processing, which provides a valuable reference for a lot of renal image analysis. [ABSTRACT FROM AUTHOR]

Artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. Likewise, initial applications have been explored in rheumatology. Deep learning might not easily surpass the accuracy of classic techniques when performing classification or regression on low-dimensional numerical data. With images as input, however, deep learning has become so successful that it has already outperformed the majority of conventional image-processing techniques developed during the past 50 years. As with any new imaging technology, rheumatologists and radiologists need to consider adapting their arsenal of diagnostic, prognostic and monitoring tools, and even their clinical role and collaborations. This adaptation requires a basic understanding of the technical background of deep learning, to efficiently utilize its benefits but also to recognize its drawbacks and pitfalls, as blindly relying on deep learning might be at odds with its capabilities. To facilitate such an understanding, it is necessary to provide an overview of deep-learning techniques for automatic image analysis in detecting, quantifying, predicting and monitoring rheumatic diseases, and of currently published deep-learning applications in radiological imaging for rheumatology, with critical assessment of possible limitations, errors and confounders, and conceivable consequences for rheumatologists and radiologists in clinical practice. Deep learning is a powerful technique with great potential for the analysis and interpretation of rheumatological images. To successfully use deep learning, rheumatologists should understand the tasks involved in image processing and the potential confounders and limitations that can affect the analysis of clinical data. Key points: The number of research studies on deep learning in rheumatological imaging has grown rapidly during the past 5 years, but they mainly consist of pilot studies that require external validation. Confounding factors and errors in deep-learning methods need to be ruled out before deep learning can be applied in clinical practice, for which the intended use should be strictly defined. Deep-learning techniques, together with mapping to explain their reasoning, will enable hypothesis-free image analysis and could identify new imaging biomarkers. Deep learning might assist rheumatologists and radiologists in interpreting rheumatological images, increasing their diagnostic, prognostic and monitoring accuracy, and decreasing workloads and costs. [ABSTRACT FROM AUTHOR]

With the popularity and development of football matches, the demand for real-time monitoring and analysis of football matches is increasing. There are some limitations in the traditional soccer real-time monitoring method, which can not get the real-time information of soccer match accurately and efficiently. The aim of this study is to use optical imaging equipment and virtual reality technology to realize the real-time monitoring of football. In this paper, optical imaging equipment is used for real-time image acquisition of football matches, and virtual reality technology is used for image processing and analysis. Through the accurate extraction and analysis of the position and track of the players, the field and the ball in the football match, the real-time monitoring of the football match is realized. The experimental results show that the real-time monitoring method based on optical imaging equipment detection and virtual reality technology sequence can effectively realize the accurate monitoring of football match state. Through this method, the position, speed and other information of each element in the football match can be obtained in real time, and real-time analysis and visual display can be carried out. [ABSTRACT FROM AUTHOR]

Lung diseases are one of the most common diseases around the world. The risk of these diseases are more in under-developed and developing countries, where millions of people are battling with poverty and living in polluted air. Chest X-Ray images are helpful screening tool for lung disease detection. However, disease diagnosis requires expert medical professionals. Furthermore, in developing and under-developed nations, the doctor-to-patient ratio is comparatively poor. Deep learning algorithms have recently demonstrated promise in the analysis of medical images and the discovery of patterns. In this current work, we have proposed a model MLDC (Multi-Lung Disease Classification) to detect common lung diseases. It introduces a MLDC feature extraction model with two different new classifiers, considering ANN (an artificial neural network) and QC (a quantum classifier). In this proposed model, tests are performed on the LDD (Lung Disease Dataset), which includes COVID-19, pneumonia, tuberculosis, and a healthy person's lung from chest X-ray images. Our proposed model achieves an accuracy of 95.6% for MLDC-ANN and 97.5% for MLDC-QC at a lower computational cost. [ABSTRACT FROM AUTHOR]

Edge detection is one of the most fundamental and critical operations in image analysis and computer vision. This paper proposes an adaptive edge detection method that combines multi-scale closest neighbor operator with grid partition technique (MSCNOGP). The multi-scale closest neighbor operator can be used to remove both noisy data and small area textures, while the grid partition technique can improve the precision of the edges. By utilizing the concepts of both twin edge pixel and grid divergence, the resulting edges can be further improved. Compared with prevailing traditional methods, the MSCNOGP method achieves both the best precision and almost the best visual effect, where both line segment fitting and ellipse fitting are applied for testing different edge detection methods. The performance on the F-measure score of the MSCNOGP method is much better than those of prevailing traditional methods. [ABSTRACT FROM AUTHOR]

This research delves into the unexplored territory of using CT image processing techniques to assess capillary flow in freshly felled Scotch pine logs during the drying process. Leveraging a high-resolution CT scanner, the study captures intricate details of the wood's internal structure, which were then quantified using image processing algorithms. The findings confirm the efficacy of CT image processing in accurately determining capillary flow in wood. This opens up new opportunities for non-destructive testing methods and contributes to a deeper understanding of wood properties. The implications of these findings are farreaching, offering substantial advancements in quality control and processing optimization in the wood industry. [ABSTRACT FROM AUTHOR]

Concrete surface cracks serve as early indicators of potential structural threats. Visual inspection, a commonly used and versatile concrete condition assessment technique, is employed to assess concrete degradation by observing signs of damage on the surface level. However, the method tends to be qualitative and needs to be more comprehensive in providing accurate information regarding the extent of damage and its evolution, notwithstanding its time-consuming and environment-sensitive nature. As such, the integration of image analysis techniques with artificial intelligence (AI) has been increasingly proven efficient as a tool to capture damage signs on concrete surfaces. However, to improve the performance of automated crack detection, it is imperative to intensively train a machine learning model, and questions remain regarding the required image quality and image collection methodology needed to ensure the model's accuracy and reliability in damage quantitative analysis. This study aims to establish a procedure for image acquisition and processing through the application of an image-based measurement approach to explore the capabilities of concrete surface damage diagnosis. Digitizing crack intensity measurements were found to be feasible; however, larger datasets are required. Due to the anisotropic behavior of the damage, the model's ability to capture crack directionality was developed, presenting no statistically significant differences between the observed and predicted values used in this study with correlation coefficients of 0.79 and 0.82. [ABSTRACT FROM AUTHOR]

To develop a fast and effective image deblurring method, the blind recovery of motion‐blurred images based on DeblurGAN(GAN, Generative Adversarial Networks) is researched.Firstly, the number of residual modules in the DeblurGAN network is changed, and an attempt is made to optimize the network structure to achieve better results in the blind recovery of motion‐blurred images. Secondly, an image deblurring method based on the improved DeblurGAN network is proposed.This paper makes corresponding adjustments to the generator and discriminator networks to change their input and output size to 512 × 512 while keeping the overall structure of the network unchanged, and the experimental results show that the quality of the restored images has been greatly improved. In addition, the images were divided into four classes of images,to achieve improved restoration of blurred images with specific content. Experimental results on the benchmark GoPro dataset validate that the improved DeblurGAN achieves more than 1.5 dB improvement than DeblurGAN in terms of peak signal‐to‐noise ratio (PSNR) as trained utilizing the same amount of data, and structural similarity assessment (SSIM) evaluation means and maximum values increased between 0.2 and 0.3. The improved DeblurGAN is more effective in terms of both blur removal and detail recovery. [ABSTRACT FROM AUTHOR]

Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, enabling the automated, reproducible, and accurate analysis of biological images. Here, we provide an overview of the history and principles of machine learning and deep learning in the context of bioimage analysis. We discuss the essential steps of the bioimage analysis workflow, emphasizing how machine learning and deep learning have improved preprocessing, segmentation, feature extraction, object tracking, and classification. We provide examples that showcase the application of machine learning and deep learning in bioimage analysis. We examine user‐friendly software and tools that enable biologists to leverage these techniques without extensive computational expertise. This review is a resource for researchers seeking to incorporate machine learning and deep learning in their bioimage analysis workflows and enhance their research in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

Motivation BISCAP is a state-of-the-art tool for automatically characterizing biofilm images obtained from Optical Coherence Tomography. Limited availability of other software tools is reported in the field. BISCAP's first version processes 2D images only. Processing 3D images is a problem of greater scientific relevance since it deals with the entire structure of biofilms instead of their 2D slices. Results Building on the image-processing principles and algorithms proposed earlier for 2D images, these were adapted to the 3D case, and a more general implementation of BISCAP was developed. The primary goal concerns the extension of the initial methodology to incorporate the depth axis in 3D images; multiple improvements were also made to boost computational performance. The calculation of structural properties and visual outputs was extended to offer new insights into the 3D structure of biofilms. BISCAP was tested using 3D images of biofilms with different morphologies, consistently delivering accurate characterizations of 3D structures in a few minutes using standard laptop machines. Low user dependency is required for image analysis. Availability and implementation BISCAP is available from https://github.com/diogonarciso/BISCAP. All images used in the tutorials and the validation examples are available from https://web.fe.up.pt/∼fgm/biscap3d. [ABSTRACT FROM AUTHOR]

• Postoperative prognosis based on the expression level of ACTH for Cushing's disease with an accuracy of 0.83. • A computationally efficient single-linkage clustering algorithm for image analysis. • Novel medical image registration and classification methods on Cushing's disease for unsupervised image registration and supervised image classification. Due to the abnormal secretion of adreno-cortico-tropic-hormone (ACTH) by tumors, Cushing's disease leads to hypercortisonemia, a precursor to a series of metabolic disorders and serious complications. Cushing's disease has high recurrence rate, short recurrence time and undiscovered recurrence reason after surgical resection. Qualitative or quantitative automatic image analysis of histology images can potentially in providing insights into Cushing's disease, but still no software has been available to the best of our knowledge. In this study, we propose a quantitative image analysis-based pipeline CRCS, which aims to explore the relationship between the expression level of ACTH in normal cell tissues adjacent to tumor cells and the postoperative prognosis of patients. CRCS mainly consists of image-level clustering, cluster-level multi-modal image registration, patch-level image classification and pixel-level image segmentation on the whole slide imaging (WSI). On both image registration and classification tasks, our method CRCS achieves state-of-the-art performance compared to recently published methods on our collected benchmark dataset. In addition, CRCS achieves an accuracy of 0.83 for postoperative prognosis of 12 cases. CRCS demonstrates great potential for instrumenting automatic diagnosis and treatment for Cushing's disease. [ABSTRACT FROM AUTHOR]

The term out-of-stock (OOS) describes a problem that occurs when shoppers come to a store and the product they are seeking is not present on its designated shelf. Missing products generate huge sales losses and may lead to a declining reputation or the loss of loyal customers. In this paper, we propose a novel deep-learning (DL)-based OOS-detection method that utilizes a two-stage training process and a post-processing technique designed for the removal of inaccurate detections. To develop the method, we utilized an OOS detection dataset that contains a commonly used fully empty OOS class and a novel class that represents the frontal OOS. We present a new image augmentation procedure in which some existing OOS instances are enlarged by duplicating and mirroring themselves over nearby products. An object-detection model is first pre-trained using only augmented shelf images and, then, fine-tuned on the original data. During the inference, the detected OOS instances are post-processed based on their aspect ratio. In particular, the detected instances are discarded if their aspect ratio is higher than the maximum or lower than the minimum instance aspect ratio found in the dataset. The experimental results showed that the proposed method outperforms the existing DL-based OOS-detection methods and detects fully empty and frontal OOS instances with 86.3% and 83.7% of the average precision, respectively. [ABSTRACT FROM AUTHOR]

The digital image method of monitoring structural displacement is receiving more attention today, especially in non-contact structure health monitoring. Some obvious advantages of this method, such as economy and convenience, were shown while it was used to monitor the deformation of the bridge structure during the service period. The image processing technology was used to extract structural deformation feature information from surveillance video images containing structural displacement in order to realize a new non-contact online monitoring method in this paper. The influence of different imaging distances and angles on the conversion coefficient (η) that converts the pixel coordinates to the actual displacement was first studied experimentally. Then, the measuring and tracking of bridge structural displacement based on surveillance video images was investigated by laboratory-scale experiments under idealized conditions. The results showed that the video imaging accuracy can be affected by changes in the relative position of the imaging device and measured structure, which is embodied in the change in η (actual size of individual pixel) on the structured image. The increase in distance between the measured structure and the monitoring equipment will have a significant effect on the change in the η value. The value of η varies linearly with the change in shooting distance. The value of η will be affected by the changes in shooting angle. The millimeter-level online monitoring of the structure displacement can be realized using images based on surveillance video images. The feasibility of measuring and tracking structural displacement based on surveillance video images was confirmed by a laboratory-scale experiment. [ABSTRACT FROM AUTHOR]

Partial differential equations have recently garnered substantial attention as an image processing framework due to their extensibility, the ability to rigorously engineer and analyse the governing dynamics as well as the ease of implementation using numerical methods. This paper explores a novel approach to image trinarization with a concrete real-world application of classifying regions of sperm images used in the automatic analysis of sperm morphology. The proposed methodology engineers a diffusion equation with non-linear source term, exhibiting three steady-states. The model is implemented as an image processor using a standard finite difference method to illustrate the efficacy of the proposed approach. The performance of the proposed approach is benchmarked against standard image clustering/segmentation methods and shown to be highly effective. • A novel approach to image trinarization using a dynamic PDE based framework. • Data driven Medical Imaging application (Sperm Morphology). • Provably stable method. [ABSTRACT FROM AUTHOR]

Simple Summary: In the current study, we proposed a system that discriminates the state of an animal by combining two types of features: behavioral information, which determines the kind of movement performed by the animal, and internal information, which reflects the animal's internal state. The proposed system measures biological information from camera images and extracts features to discriminate states using machine learning. In addition, a neural network was used to increase the accuracy of detecting the target, and an anomaly detection method was used to perform discrimination. In the experiments performed, we prepared video images containing routine behaviors and generated a model that can detect non-routine behaviors using the proposed system. We also prepared video images including non-routine behavior in which the hamster was stimulated by clapping to generate an abnormal sound. In the video, the behavior changed significantly before and after the stimulus presentation, and the extracted feature values also changed according to the behavior. As a result of detection, the system discriminated the behavior as non-routine behavior after the stimulation. In conclusion, the results supported the possibility of using a pet monitoring system to immediately inform the owner when the animal is under load. Managing the risk of injury or illness is an important consideration when keeping pets. This risk can be minimized if pets are monitored on a regular basis, but this can be difficult and time-consuming. However, because only the external behavior of the animal can be observed and the internal condition cannot be assessed, the animal's state can easily be misjudged. Additionally, although some systems use heartbeat measurement to determine a state of tension, or use rest to assess the internal state, because an increase in heart rate can also occur as a result of exercise, it is desirable to use this measurement in combination with behavioral information. In the current study, we proposed a monitoring system for animals using video image analysis. The proposed system first extracts features related to behavioral information and the animal's internal state via mask R-CNN using video images taken from the top of the cage. These features are used to detect typical daily activities and anomalous activities. This method produces an alert when the hamster behaves in an unusual way. In our experiment, the daily behavior of a hamster was measured and analyzed using the proposed system. The results showed that the features of the hamster's behavior were successfully detected. When loud sounds were presented from outside the cage, the system was able to discriminate between the behavioral and internal changes of the hamster. In future research, we plan to improve the accuracy of the measurement of small movements and develop a more accurate system. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has revolutionized medical imaging procedures, specifically with regard to image segmentation, reconstruction, interpretation, and research. 3D Slicer, an open-source medical image analysis platform, has become a valuable tool in medical imaging education due to its integration of various AI applications. Through its open-source architecture, students can gain practical experience with diverse medical images and the latest AI technology, reinforcing their understanding of anatomy and imaging technology while fostering independent learning and clinical reasoning skills. The implementation of this platform improves instruction quality and nurtures skilled professionals who can meet the demands of clinical practice, research institutions, and technology innovation enterprises. AI algorithms' application in medical image processing have facilitated their translation from the lab to practical clinical applications and education. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) in diagnosing diabetic retinopathy, a major contributor to global vision impairment, is becoming increasingly pronounced. Notably, the detection of vision-threatening diabetic retinopathy (VTDR) has been significantly fortified through automated techniques. Traditionally, the reliance on manual analysis of retinal images, albeit slow and error-prone, constituted the conventional approach. Addressing this, our study introduces a novel methodology that amplifies the robustness and precision of the detection process. This is complemented by the groundbreaking Hierarchical Block Attention (HBA) and HBA-U-Net architecture, which notably propel attention mechanisms in image segmentation. This innovative model refines image processing without imposing excessive computational demands by honing in on individual pixel intricacies, spatial relationships, and channel-specific attention. Building upon this innovation, our proposed method employs a multi-stage strategy encompassing data pre-processing, feature extraction via a hybrid CNN-SVD model, and classification employing an amalgamation of Improved Support Vector Machine-Radial Basis Function (ISVM-RBF), DT, and KNN techniques. Rigorously tested on the IDRiD dataset classified into five severity tiers, the hybrid model yields remarkable performance, achieving a 99.18% accuracy, 98.15% sensitivity, and 100% specificity in VTDR detection, thus surpassing existing methods. These results underscore a more potent avenue for diagnosing and addressing this crucial ocular condition while underscoring AI's transformative potential in medical care, particularly in ophthalmology. [ABSTRACT FROM AUTHOR]

The current pathologic system classifies structural deformation caused by hepatic fibrosis semi-quantitatively, which may lead to a disagreement among pathologists. We measured hepatic fibrosis quantitatively using collagen proportionate area (CPA) in compensated cirrhotic patients and assessed its impact on predicting the development of liver decompensation. From January 2010 to June 2018, we assessed 101 patients who went through liver biopsy and received diagnosis as compensated cirrhosis with digital image analysis of CPA. Clinical and laboratory data were collected at the baseline and at the time of the last follow-up or progression to liver decompensation (LD). The mean age was 50.8 ± 10.5 years, and the most common etiology of liver disease was chronic hepatitis B (48.5%), followed by alcoholic hepatitis (18.8%). The mean CPA was 16.91 ± 9.60%. The mean CPA values were different in patients with and without LD development (21.8 ± 11.1 vs. 15.2 ± 8.5). During the median follow-up of 60.0 months, 26 out of 101 patients experienced LD. Older age (hazard ratio [HR],1.069; p = 0.015), prolonged international normalized ratio (HR, 6.449; p = 0.019) and higher CPA (HR, 1.049; p = 0.040) were independent predictors of liver decompensation on multivariate cox-regression analysis. When patients were divided according to the optimal CPA threshold (26.8%), higher CPA predicted LD better than lower CPA. (Log-rank test: p < 0.001) CPA could be a useful quantitative prognostic value for patients with compensated cirrhosis. [ABSTRACT FROM AUTHOR]

Mixing time is an important parameter for quantifying the mixing efficiency of a bioreactor system, essential for successful bioprocess development in various branches of the bioengineering sector (e.g., biopharma, biorefineries, food industry and bioreactor design). The colourimetric method is one of the ways of obtaining valuable quantitative data about the mixing process and the liquid flow inside a vessel. This review consists of a catalogue and a discussion of previously published data in which the colourimetric method has been utilised; a critical comparison between the colourimetric and other mixing time measurement methods; an explanation of practical considerations regarding the bioreactor setup and the choice of reagents; a practical guide for requirements for the acquisition of high-quality images of the mixing process; a thorough discussion of aspects connected to the computer image processing of the video material. This review is intended to thoroughly present the versatility of the colourimetric method for mixing time measurement in miscellaneous bioreactor systems, i.e., in classical tanks and modern single-use (disposable) plastic film-based bag-like containers, and to facilitate the implementation of the colourimetric method in new research setups by providing complete and valuable recommendations about each step of the methodology. [ABSTRACT FROM AUTHOR]

Communication is the exchange of ideas and information between individuals, societies, or locales. Language serves as the primary medium for communication, with writing representing a pivotal tool within linguistic frameworks. The art of Arabic calligraphy, which has its origins in the Arabic script and was historically employed for conveying religious messages associated with Islam, embodies a rich source of knowledge pertaining to Turkish-Islamic art and civilization. These artistic artifacts are integral components of numerous historical sites throughout Turkey. Interpreting the written content within these works holds significant importance for both local inhabitants and tourists, as it enhances the understanding of the historical context and preserves the essence of these locations. It is very difficult to convey the meaning of millions of artifacts to people with a manual process. For this reason, to make this process automatic, the problems of identifying the styles of the writings in the works and recognizing the letters in the writings as a step in the transition from image to text in the literature have been studied. In this study, as a technique that has not been tried in other studies, Arabic calligraphy style determination and letter classification were performed by transfer learning, and an F1 score of over 79% was obtained. [ABSTRACT FROM AUTHOR]

In the Malvaceae family, dynamic solar tracking by leaves is actuated by the deformation of the pulvinus, a thickened region at the leaf blade–petiole junction. While the internal structure is believed to play a crucial role in this process, experimental verification has been challenging due to technical limitations. To address this gap, we developed a semi-automated workflow, which integrates data analysis and image processing to simultaneously analyze the shape and internal structure of a Malvaceae pulvinus using X-ray microtomography. Firstly, we found that kenaf (Hibiscus cannabinus L.), a Malvaceae species with curved pulvini, exhibited solar-tracking leaf movement and selected it as a model system. We employed diffusible iodine-based contrast-enhanced computed tomography to visualize the internal structure of the kenaf pulvinus. Analysis of the pulvini's shape revealed variations in pulvinus morphology, yet plausible prediction of the centerline was accomplished using polar polynomial regression. Upon slicing the pulvini perpendicular to the centerline, we observed distinct gray value gradients along the proximo-distal and adaxial-abaxial axes, challenging threshold-based tissue segmentation. This workflow successfully generated three modified 3D images and derived quantitative parameters. Using these quantitative parameters, we conducted network analysis and found the linkage between the size-normalized cortex cross-sectional area and curvature. Polynomial least absolute shrinkage and selection operator (LASSO) regression revealed the relationship between the size-normalized cortex cross-sectional area and curvature commonly in all three tested samples. This workflow enables simultaneous analysis of the shape and internal structure, significantly improving the reproducibility of Malvaceae leaf pulvinus characterization. [ABSTRACT FROM AUTHOR]

We present estimated images of a hypothetical satellite system in the presence of Seidel aberrations. We exemplified the effects of defocus, spherical, coma, astigmatism, field curvature, and distortion aberrations. Our analysis methods allow the investigation of aberration effects individually and in any combined form. Thereby, we introduce a potentially rich analysis dimension for the imaging chain, image processing, and image quality analyses. For a number of degrees of aberrations, we provide image estimates, show and quantify how individual (field‐dependent) aberrations would introduce degradations on the satellite images. We got correlation coefficient values for defocus, spherical, coma, astigmatism, field curvature, and distortion aberrations as 0.69, 0.71, 0.66, 0.79, 0.76, and 0.60, respectively. Thereby, we identfied the significance order of Seidel aberrations for this system by using image simulations and image similarity measures. [ABSTRACT FROM AUTHOR]