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Background: Quantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions. Results: We present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, spatial alignment of images, symptom tracking, and leaf- and symptom characterization. The average accuracy of the spatial alignment of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in the number of lesions resulting from separate infection events and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR. Conclusions: The proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes. [ABSTRACT FROM AUTHOR]

The ability to detect threats quickly is crucial for survival. Primates, including humans, have been shown to identify snakes quickly and accurately due to their evolutionary history. However, it is unclear which visual features humans and primates detect as threat targets. Several studies have suggested that snake scales possess potent visual features. My previous study demonstrated that removing snake scales through digital image processing reduces attention directed toward snakes. Here, I conducted a visual search task using luminance- and contrast-adjusted photographs of snakes and salamanders in monkeys that had never seen these real reptiles and amphibians. This study demonstrates that the presence or absence of snake scales is responsible for the rapid detection of target animals. The monkeys quickly detected one snake photograph from the eight salamander photographs than vice versa. However, when the same salamanders were clothed with snake scales using image processing, the difference in detection speed between snakes and salamanders disappeared. These results are consistent with the snake-detection theory that snakes were a strong selective pressure favoring modifications in the primate visual system that allow them to detect snakes more quickly or reliably. This strongly suggests that primates' snake detection depends on the snake-scale shapes, which are both snake-specific and common to all snakes. [ABSTRACT FROM AUTHOR]

Honeycomb-structured materials are extensively utilised in both commercial and military aircraft. The occurrence of manufacturing defects and operational damage has emerged as a significant safety concern, consequently elevating the necessity for non-destructive testing (NDT) to identify flaws and damage during aircraft operation and maintenance. In addition to merely detecting defects, it is crucial to accurately characterise or classify them. In this paper, the honeycomb sandwich samples were designed and manufactured from two CFRP face-sheets and Nomex core with thick resin containing nano clay 30B mixed with ultrasonic coverage. Defects were placed into carbon sheets, resin, and core materials. An NDT technique based on infrared thermography was applied, along with PCA image processing, which automatically categorises prevalent defects found in honeycomb materials. These defects encompass debonding, adhesive issues, and fractures within the honeycomb core. Thermography results showed that defects such as delamination, core fracture, lack of adhesion, and resin inhomogeneity can be identified, especially with high accuracy using PCA image processing. [ABSTRACT FROM AUTHOR]

This is a prospective case-control study investigated the factors influencing ultra-early visual acuity and quality after Small Incision Lenticule Extraction (SMILE) surgery, with a specific focus on the morphology and distribution of "black clefts" observed in posterior lenticule laser scanning images. A total of 174 patients who underwent SMILE surgery were included, and their preoperative ocular clinical parameters, visual quality indicators, and posterior lenticule laser scanning images were analyzed. The morphological features of black clefts were calculated using image processing techniques. Univariate and multivariate logistic analyses were conducted to identify factors impacting ultra-early visual acuity recovery, while correlation analysis was used to explore the association between black cleft morphology and postoperative visual quality. The results showed that although patients experienced significant visual acuity improvement one day after surgery, 26% had poor early recovery. Postoperative visual quality parameters were significantly lower, while the Object Scatter Index (OSI) was higher compared to preoperative values. Patients with poor visual recovery had a higher number of irregular black clefts in their laser scanning images. Logistic regression analysis revealed that intraoperative morphological parameters of black clefts were independent factors influencing early postoperative visual acuity recovery. Additionally, correlation analysis demonstrated a negative correlation between black cleft morphology and postoperative visual quality parameters, and a positive correlation with OSI. In conclusion, the morphology and distribution of black clefts during SMILE surgery significantly affect ultra-early postoperative visual acuity and quality, with increased variability in black cleft area leading to higher OSI, thereby impacting visual quality recovery. [ABSTRACT FROM AUTHOR]

Foxtail millet is an important minor cereal crop rich in nutrients. Due to the small size of its seeds, there is little information on the diversity of its seed structure among germplasms, limiting the identification of genes controlling seed development and germination. This paper utilized X-ray computed tomography (CT) scanning technology and deep learning models to reveal the microstructure of foxtail millet seeds, gaining insights into their internal features, distribution, and composition. A total of 100 foxtail millet varieties were scanned with X-ray computed tomography to obtain 3D reconstruction images and slices. Pre-processing steps were adopted to improve image segmentation accuracy, including noise reduction, rotation, contrast enhancement, and brightness enhancement. The experiment revealed that traditional OpenCV image processing methods failed to achieve precise segmentation, whereas deep learning models exhibited outstanding performance in segmenting seed CT slice images. We compared UNet, PSPNet, and DeepLabV3 models, selected different backbones and optimizers based on the dataset, and continuously adjusted learning rates and maximum training epochs to train the models. Results demonstrated that VGG16-UNet achieved an accuracy of 99.19% on the foxtail millet seed CT slice image dataset, outperforming PSPNet and DeepLabV3 models. Compared to ResNet-UNet, VGG16-UNet shows an improvement of approximately 3.18% in accuracy, demonstrating superior performance in accurately segmenting the inner glume, outer glume, embryo, and endosperm under various adhesion conditions. Accurate segmentation of foxtail millet CT images enables analysis of embryo size, endosperm size, and glume thickness, which impact germination, growth, and nutrition. This study fills a gap in small grain structure research, offering insights to optimize agriculture and molecular breeding for improved yield and quality. [ABSTRACT FROM AUTHOR]

Preclinical high-field magnetic resonance imaging (MRI) systems offer a diverse array of MRI techniques, providing rich multiparametric MRI (mpMRI) platforms for studying numerous biological parameters. mpMRI platforms prove particularly indispensable when investigating tumors that exhibit profound intratumoral heterogeneity, such as breast cancer. A thoughtful comprehension of the origins of intratumoral heterogeneity is imperative for the judicious assessment of new targeted therapies and treatment interventions. Furthermore, when data from mpMRI are complemented with data from other in vivo imaging modalities, such as positron emission tomography (PET), and correlated with data from ex vivo modalities, such as matrix-assisted laser desorption imaging mass spectrometry (MALDI IMS), the in vivo parameters can be further elucidated at a molecular level and microscopic scale. Nevertheless, extracting meaningful scientific insights from such complex datasets necessitates the utilization of machine learning (ML) approaches to discern region-specific radiomic features. The development of correlative, multimodal imaging (CMI) workflows, such as one incorporating MRI, PET and MALDI IMS, is inherently challenging, given the many technological and methodological challenges related to multimodal data acquisition as well as the physiological limitations of the laboratory mice of the investigation. Standardization efforts in image acquisition and processing are required to increase the reproducibility and translatability of CMI data. To address the challenges of developing standardized CMI workflows and stimulate dialog regarding this area of need, we present a practical workflow to investigate tumor heterogeneity in breast cancer xenografts across various spatial scales. Our workflow entails simultaneous functional MRI and PET acquisitions in living mice, followed by correlation with post-imaging MALDI IMS and histologic data. Additionally, we propose data preprocessing steps for potential ML applications. We illustrate the feasibility of this workflow through two examples, showcasing its effectiveness in comparing in vivo and ex vivo images to evaluate tumor metabolism and hypoxia in mice with breast cancer xenografts. [ABSTRACT FROM AUTHOR]

Purpose Acquisition and validation methods Data format and usage notes Potential applications Evaluating deformable image registration (DIR) algorithms is vital for enhancing algorithm performance and gaining clinical acceptance. However, there is a notable lack of dependable DIR benchmark datasets for assessing DIR performance except for lung images. To address this gap, we aim to introduce our comprehensive liver computed tomography (CT) DIR landmark dataset library. This dataset is designed for efficient and quantitative evaluation of various DIR methods for liver CTs, paving the way for more accurate and reliable image registration techniques.Forty CT liver image pairs were acquired from several publicly available image archives and authors’ institutions under institutional review board (IRB) approval. The images were processed with a semi‐automatic procedure to generate landmark pairs: (1) for each case, liver vessels were automatically segmented on one image; (2) landmarks were automatically detected at vessel bifurcations; (3) corresponding landmarks in the second image were placed using two deformable image registration methods to avoid algorithm‐specific biases; (4) a comprehensive validation process based on quantitative evaluation and manual assessment was applied to reject outliers and ensure the landmarks’ positional accuracy. This workflow resulted in an average of ∼56 landmark pairs per image pair, comprising a total of 2220 landmarks for 40 cases. The general landmarking accuracy of this procedure was evaluated using digital phantoms and manual landmark placement. The landmark pair target registration errors (TRE) on digital phantoms were 0.37 ± 0.26 and 0.55 ± 0.34 mm respectively for the two selected DIR algorithms used in our workflow, with 97% of landmark pairs having TREs below 1.5 mm. The distances from the calculated landmarks to the averaged manual placement were 1.27 ± 0.79 mm.All data, including image files and landmark information, are publicly available at Zenodo (https://zenodo.org/records/13738577). Instructions for using our data can be found on our GitHub page at https://github.com/deshanyang/Liver‐DIR‐QA.The landmark dataset generated in this work is the first collection of large‐scale liver CT DIR landmarks prepared on real patient images. This dataset can provide researchers with a dense set of ground truth benchmarks for the quantitative evaluation of DIR algorithms within the liver. [ABSTRACT FROM AUTHOR]

ABSTRACT Fruit colour diversity within different ripening stages confers ornamental value for pepper plants. Using images can be helpful in analysing the fruit colour‐related genetic diversity and enable selecting accessions for ornamental purposes by avoiding subjectiveness. This study aimed to evaluate the phenotypic diversity among accessions of ornamental pepper using image processing techniques to identify fruit colours. Photos from 40 fruits from each of 12 accessions were captured by a camera from a smartphone. Separate channels and colour indices were extracted from the images to analyse the accessions using both dendrogram and principal components analysis. Dendrogram analysis allowed separating accessions with predominance of dark fruits from those whose fruits showed lighter and more vivid colours, with a cophenetic correlation coefficient of 0.811. The VARI and NGRDI vegetation indices were efficient in discriminating fruits with a predominance of green colour, and the BGI could be used to discriminate accessions with predominantly reddish‐brown fruits. The proposed method can be used in small‐scale breeding programs for accurately assessing the development of varieties regarding their fruit colour diversity. [ABSTRACT FROM AUTHOR]

Droplet digital PCR (ddPCR) is recognized as a high-precision method for nucleic acid quantification, extensively utilized in biomedical research and clinical diagnostics. This technique employs microfluidic technology to partition the nucleic acid-containing reaction mixture into discrete droplets for amplification, achieving absolute quantification by identifying and enumerating the number of fluorescent droplets. The accuracy of droplet quantification is pivotal to the success of the assay. However, current image-processing tools are operationally complex, and commercial instruments are costly. Moreover, the designed algorithms exhibit a need for enhanced accuracy and are often restricted to use by trained personnel with specific microscopy equipment. In response to these challenges, we introduce an automated device (A-MMD), capable of detecting fluorescent droplets in ddPCR images captured by multiple microscopes. The device integrates three distinct algorithms tailored for the image processing of Laser Scanning Confocal Microscopy (LSCM), inverted microscopy, and self-assembled microscopy. Experimental validation using λ DNA demonstrated a 100.00% identification rate for positive droplets across all three image types, and the average identification rates for total droplets being 99.27% for LSCM, 98.96% for inverted microscopy, and 99.08% for self-assembled microscopy. Furthermore, the A-MMD is equipped with a user-friendly interface (UI) that streamlines the operational process, enabling non-specialists to efficiently perform droplet detection tasks. Our device not only has good environmental adaptability and identification accuracy, but also significantly reduces costs and operational complexity. It offers an economical, efficient, and user-friendly solution for ddPCR image analysis, thereby further propelling the advancement and application of nucleic acid detection technology. [ABSTRACT FROM AUTHOR]

The grading of products is important in many ways. One of the important activities after harvesting agricultural products is product grading based on shape and color dimensions. This activity in the agricultural transformation industries, Bas Controller, improves various processes on fruits and vegetables with the same dimensions, which improves the storage conditions of the product, creates added value for the farmer, and gives the consumer the power to choose. The main focus of this study is the application of image processing in the field of identification and classification of fruits. It is an application that has received much less attention than other applications of image processing. The proposed systems presented in this article, are software solutions based on image processing techniques, including histogram matching techniques, for detection, Sable edge detection algorithms, Private edge and Kenny edge, Otsu threshold limit, and clustering method It is an automatic mean and classification of different degrees of fruit. In addition, it has been mentioned more about the examination and description of product grading and clustering methods, that by using the proposed application hardware and its connection with the software, a big step can be taken in product quality grading. This method can be used in product classification and packaging. The accuracy rate for peaches, lemons, apples, and tomatoes is 94.58%, 88.23%, 70%, and 93.33%, respectively. The best accuracy for all 20 sample levels is for peach fruit. [ABSTRACT FROM AUTHOR]

The paper focuses on the seasonal oil accumulation in traditional and super-high density (SHD) olive plantations and its modelling employing image-based linear models. For these purposes, at 7-10-day intervals, fruit samples (cultivar Arbequina, Fasola, Frantoio, Koroneiki, Leccino, Maiatica) were pictured and images segmented to extract the Red (R), Green (G), and Blue (B) mean pixel values which were re-arranged in 35 RGB-derived colorimetric indexes (CIs). After imaging, the samples were crushed and oil concentration was determined (NIR). The analysis of the correlation between oil and CIs revealed a differential hysteretic behavior depending on the covariates (CI and cultivar). The hysteresis area (Hyst) was then quantified and used to rank the CIs under the hypothesis that CIs with the maximum or minimum Hyst had the highest correlation coefficient and were the most suitable predictors within a general linear model. The results show that the predictors selected according to Hyst-based criteria had high accuracy as determined using a Global Performance Indicator (GPI) accounting for various performance metrics (R 2, RSME, MAE). The use of a general linear model here presented is a new computational option integrating current methods mostly based on artificial neural networks. RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the transition of the olive sector towards a digital agriculture domain. [ABSTRACT FROM AUTHOR]

Background: Somatostatin receptor (SSR) targeting radiotracer 68Ga-DOTATATE is used for Positron Emission Tomography (PET)/Computed Tomography (CT) imaging to assess patients with Pheochromocytoma and paraganglioma (PPGL), rare types of Neuroendocrine tumor (NET) which can metastasize thereby becoming difficult to quantify. The goal of this study is to develop an artificial intelligence (AI) model for automated lesion segmentation on whole-body 3D DOTATATE-PET/CT and to automate the tumor burden calculation. 132 68Ga-DOTATATE PET/CT scans from 38 patients with metastatic and inoperable PPGL, were split into 70, and 62 scans, from 20, and 18 patients for training, and test sets, respectively. The training set was further divided into patient-stratified 5 folds for cross-validation. 3D-full resolution nnUNet configuration was trained with 5-fold cross-validation. The model's detection performance was evaluated at both scan and lesion levels for the PPGL test set and two other clinical cohorts with NET (n = 9) and olfactory neuroblastoma (ONB, n = 5). Additionally, quantitative statistical analysis of PET parameters including SUVmax, total lesion uptake (TLU), and total tumor volume (TTV), was conducted. Results: The nnUNet AI model achieved an average 5-fold validation dice similarity coefficient of 0.84 at the scan level. The model achieved dice similarity coefficients (DSC) of 0.88, 0.6, and 0.67 at the scan level, the sensitivity of 86%, 61.13%, and 61.64%, and a positive predictive value of 89%, 74%, and 86.54% at the lesion level for the PPGL test, NET and ONB cohorts, respectively. For PPGL cohorts, smaller lesions with low uptake were missed by the AI model (p < 0.001). Anatomical region-based failure analysis showed most of the false negative and false positive lesions within the liver for all the cohorts, mainly due to the high physiologic liver background activity and image noise on 68Ga- DOTATATE PET scans. Conclusions: The developed deep learning-based AI model showed reliable performance for automated segmentation of metastatic PPGL lesions on whole-body 68Ga-DOTATATE-PET/CT images, which may be beneficial for tumor burden estimation for objective evaluation during therapy follow-up. https://www.clinicaltrials.gov/study/NCT03206060, https://www.clinicaltrials.gov/study/NCT04086485, https://www.clinicaltrials.gov/study/NCT05012098. [ABSTRACT FROM AUTHOR]

Ferroelectric photoelectric devices show promising applications in artificial vision systems. However, this type of devices used in artificial vision systems exhibits low photoresponsivity and this limited cyclability of photocurrents, which hinders the progress of artificial vision systems. In this study, an artificial vision system is constructed with high photoresponsivity, fast photoresponse speed, and high read/write speed based on the Pd/PbZr0.4Ti0.6O3/La0.3Sr0.7MnO3/LaAlO3 ferroelectric photomemristor. The high optical response (18.86 nanoamperes) and read/write speeds (50 nanoseconds) of the device significantly improve the robustness and speed of the neuromorphic visual system while reducing power consumption for recognition. The artificial vision system developed in this study provides excellent real‐time image processing capabilities, including edge detection and classification with 100% accuracy for musical key patterns, and it is expected to offer great potential for the development of high‐performance self‐powered artificial vision systems. [ABSTRACT FROM AUTHOR]

Jujube is susceptible to biotic and abiotic adversity stresses resulting in abnormal phenotypic defects. Therefore, abnormal phenotype fruits should be removed during postharvest sorting to increase added value. An improved maximum horizontal diameter linear regression (MHD‐LR) method for size grading of jujube prior to detection of abnormal phenotypic defects was developed. The accuracy of the MHD‐LR model is 95%, with an error of only 0.95 mm. In addition, a method for detecting abnormal phenotypic defects in jujube was established. It can effectively and accurately classify seven kinds of jujube phenotypes (regular, irregular, wrinkled, moldy, hole‐broken, skin‐broken, and scarred). The data augmentation method based on linear interpolation can effectively expand the dataset with a variance of only 0.0006. Support vector machine‐decision tree (SVMDT), logistic regression, back propagation neural network, and long short‐term memory network models were established to classify jujube samples with different phenotypes, with accuracies of 99.57%, 99.00%, 99.14%, and 99.29%, respectively. The results showed that the SVMDT model had higher accuracy and explainability. This research is expected to provide a new method to improve the precise classification of abnormal phenotypic defects in postharvest jujube. [ABSTRACT FROM AUTHOR]

The residue number system is widely used in cryptography, digital signal processing, image processing systems, and other areas where high-performance computing is required. One of the main tools used in the residue number system is the Akushsky core function. However, its use is limited due to the existence of so-called critical cores. This study aims to develop Akushsky core functions that effectively eliminate the occurrence of critical cores, thereby enhancing their applicability in real-world scenarios. We introduce a fundamental approach to critical core detection that reduces the average time for critical core detection by 99.48% compared to the brute force algorithm. The results of our analysis indicate not only a substantial improvement in the speed of core detection but also an enhancement in the overall performance of systems utilizing the Akushsky core function. Our findings provide important insights into optimizing residue number systems and encourage further exploration into advanced computational techniques within this domain. [ABSTRACT FROM AUTHOR]

The rise of complex high-rise buildings has made building management increasingly challenging, especially the nighttime supervision of university laboratories. Idle occupation increases the risk of accidents and undermines campus sustainability. Effective occupancy detection is essential for optimizing campus building safety and energy efficiency. Environmental sensors for occupancy detection offer limited coverage and are costly, making them unsuitable for campuses. Surveillance cameras, as part of campus infrastructure, provide wide coverage. On this basis, we designed a detection algorithm that uses light brightness to assess nighttime building use. Experimental results showed that the algorithm achieves an average accuracy of 98.67%, enabling large-scale nighttime occupancy detection without the need for installing additional sensors, significantly improving the efficiency of campus building management. In addition, to address the limitations of indoor space representation in geographic information system (GIS) management models, this paper developed a comprehensive 3D GIS model based on a "building–floor–room" hierarchical structure, utilizing oblique photogrammetry and laser scanning technology. This study combined the detection results with real-world 3D data for visualization, providing a new perspective for the 3D spatiotemporal refinement of complex high-rise buildings, and providing a reference framework for the detection and analysis of other types of building environments. [ABSTRACT FROM AUTHOR]

To address the issues of image blurring and color distortion in hazy conditions, an image restoration method based on a parametric analytical modulation transfer function model is proposed under turbid atmospheric conditions. A source database is established using a numerical radiative transfer method based on discrete ordinate. Through multivariate nonlinear fitting and linear interpolation, the quantitative relationships among critical spatial frequency, turbid atmospheric MTF, and key atmospheric optical parameters—such as optical thickness, single scattering albedo, and asymmetry factor—are examined. A fast and efficient parametric analytical MTF model for turbid atmospheres is developed and applied to restore images affected by fog. The results demonstrate that, within the applicable range of the model, the model's maximum mean relative error and the root mean square error are 7.16% and 0.0454, respectively. The computational speed is nearly a thousand times faster than that of the numerical radiative transfer method, achieving high accuracy and ease of application. Images restored using this model exhibit enhanced clarity and quality, effectively compensating for the degradation in image quality caused by turbid atmospheres. This approach represents a novel solution to the challenges of image processing in complex atmospheric environments. [ABSTRACT FROM AUTHOR]

Anomaly detection in hyperspectral imaging is crucial for remote sensing, driving the development of numerous algorithms. However, systematic studies reveal a dichotomy where algorithms generally excel at either detecting anomalies in specific datasets or generalizing across heterogeneous datasets (i.e., lack adaptability). A key source of this dichotomy may center on the singular and like biases frequently employed by existing algorithms. Current research lacks experimentation into how integrating insights from diverse biases might counteract problems in singularly biased approaches. Addressing this gap, we propose stacking-based ensemble learning for hyperspectral anomaly detection (SELHAD). SELHAD introduces the integration of hyperspectral anomaly detection algorithms with diverse biases (e.g., Gaussian, density, partition) into a singular ensemble learning model and learns the factor to which each bias should contribute so anomaly detection performance is optimized. Additionally, it introduces bootstrapping strategies into hyperspectral anomaly detection algorithms to further increase robustness. We focused on five representative algorithms embodying common biases in hyperspectral anomaly detection and demonstrated how they result in the previously highlighted dichotomy. Subsequently, we demonstrated how SELHAD learns the interplay between these biases, enabling their collaborative utilization. In doing so, SELHAD transcends the limitations inherent in individual biases, thereby alleviating the dichotomy and advancing toward more adaptable solutions. [ABSTRACT FROM AUTHOR]

Images taken on rainy days have rain streaks of varying degrees of intensity, which seriously affect the visibility of the background scene. Aiming at the above problems, we propose a rain mark removal algorithm based on the combination of dual-attention mechanism U-Net and multi-convolution. First, we add a double attention mechanism to the encoder of U-Net. It can give different weights to the rain mark features that need to be extracted in different channels and spaces so that sufficient rain mark features can be obtained. With different dilation factors, we can obtain rain mark characteristics of different depths. Secondly, the multi-convolutional channel integrates the characteristics of rain streaks and prepares sufficient rain mark information for the task of clearing rain streaks. By introducing a cyclic rain streaks detection and removal mechanism into the network architecture, it can achieve gradual removal of rain streaks. Even in the case of heavy rain, our algorithm can get good results. Finally, we tested on both synthetic and real datasets to obtain subjective results and objective evaluations. Experimental results show that for the rainy day image de-rain task with different intensities of rain streaks, our algorithm is more robust. Moreover, the ability of our algorithm to remove rain streaks is better than that of the other five different classical algorithms. The de-raining images produced by our algorithm are visually sharper, and its visibility enhancements are effective for computer vision applications (Google Vision API). [ABSTRACT FROM AUTHOR]

The volumetric and hydrological responses of clayey soils subjected to drying-wetting (D-W) cycles are of paramount importance for the integrity of geoenvironmental infrastructures. The study aimed to investigate the cracking behavior of clayey soils under D-W cycles by using advanced 2D imaging and 3D scanning techniques to capture the initiation and propagation of desiccation cracks within a soil specimen. The temporal variation in the soil water content and the corresponding 2D digital photography and 3D morphology of cracks were simultaneously monitored, and the cracking characteristics were interpreted. It was found that the time-dependent evaporation process was independent of the D-W cycles. Both 2D and 3D characterization showed the cracking hysteresis phenomenon in the unsaturated soil, which indicates the dependency of the crack opening and closure on the degree of saturation. D-W cycles led to the formation of subcracks and the increase in the total crack length, reflecting the soil degradation. Additionally, it was demonstrated that the 3D characterization exhibited the advantage of capturing the volumetric change and the subtle change in the macroporosity of the cracked soil over the 2D visualization. The current study provides a perspective of combining 2D and 3D characterization for interpreting the volumetric change of cracked soils and enhancing the understanding of the hydromechanical responses and the soil-atmosphere interactions. [ABSTRACT FROM AUTHOR]

The automatic extraction of bottom sediment annotations in large-scale raster nautical charts has limitations, including an imprecise semantic information description and low efficiency. To overcome them, we propose a convolutional neural network (CNN)-based method for the automatic extraction of bottom sediment annotations in raster nautical charts, using image processing techniques to improve it. First, an adaptive chart partitioning model that considers element completeness is constructed. Second, a principle for the unique identification of elements based on spatial conflicts is designed. Finally, a model for accurately extracting semantic information for bottom sediment annotations is established. To evaluate the effectiveness of the proposed method, we implemented a model based on the PyTorch framework and used the PIL library to analyze the results. We also conducted comparative experiments on multiple CNN models to recommend the selection of such models in the proposed method by comparing their classification and recognition performance. The experimental results indicate that (1) the proposed model can achieve high-precision extraction of bottom sediment annotations in raster nautical charts. (2) Furthermore, the proposed model generally has high recognition accuracy and semantic completeness, with better recognition precision than traditional pattern recognition methods. [ABSTRACT FROM AUTHOR]

Cryogenic electron microscopy (cryoEM) is a rapidly growing structural biology modality that has been successful in revealing molecular details of biological systems. However, unlike established biophysical and analytical techniques with calibration standards, cryoEM has lacked comprehensive biological test samples. Here, a cryoEM calibration sample consisting of a mixture of compatible macromolecules is introduced that can not only be used for resolution optimization, but also provides multiple reference points for evaluating instrument performance, data quality and image‐processing workflows in a single experiment. This combined test specimen provides researchers with a reference point for validating their cryoEM pipeline, benchmarking their methodologies and testing new algorithms. [ABSTRACT FROM AUTHOR]

Background: Medical imaging segmentation is the use of image processing techniques to expand specific structures or areas in medical images. This technique is used to separate and display different textures or shapes in an image. The aim of this study is to develop a deep learning-based method to perform maxillary sinus segmentation using cone beam computed tomography (CBCT) images. The proposed segmentation method aims to provide better image guidance to surgeons and specialists by determining the boundaries of the maxillary sinus cavities. In this way, more accurate diagnoses can be made and surgical interventions can be performed more successfully. Methods: In the study, axial CBCT images of 100 patients (200 maxillary sinuses) were used. These images were marked to identify the maxillary sinus walls. The marked regions are masked for use in the maxillary sinus segmentation model. U-Net, one of the deep learning methods, was used for segmentation. The training process was carried out for 10 epochs and 100 iterations per epoch. The epoch and iteration numbers in which the model showed maximum success were determined using the early stopping method. Results: After the segmentation operations performed with the U-Net model trained using CBCT images, both visual and numerical results were obtained. In order to measure the performance of the U-Net model, IoU (Intersection over Union) and F1 Score metrics were used. As a result of the tests of the model, the IoU value was found to be 0.9275 and the F1 Score value was 0.9784. Conclusion: The U-Net model has shown high success in maxillary sinus segmentation. In this way, fast and highly accurate evaluations are possible, saving time by reducing the workload of clinicians and eliminating subjective errors. [ABSTRACT FROM AUTHOR]

Studies have shown that the morphologies of galaxies are substantially transformed following coalescence after a merger, but post-mergers are notoriously difficult to identify, especially in imaging that is shallow or low resolution. We train convolutional neural networks (CNNs) to identify simulated post-merger galaxies in a range of image qualities, modelled after five real surveys: the Sloan Digital Sky Survey (SDSS), the Dark Energy Camera Legacy Survey (DECaLS), the Canada–France Imaging Survey (CFIS), the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), and the Legacy Survey of Space and Time (LSST). Holding constant all variables other than imaging quality, we present the performance of the CNNs on reserved test set data for each image quality. The success of CNNs on a given data set is found to be sensitive to both imaging depth and resolution. We find that post-merger recovery generally increases with depth, but that limiting 5 |$\sigma$| point-source depths in excess of |$\sim 25$| mag, similar to what is achieved in CFIS, are only marginally beneficial. Finally, we present the results of a cross-survey inference experiment, and find that CNNs trained on a given image quality can sometimes be applied to different imaging data to good effect. The work presented here therefore represents a useful reference for the application of CNNs for merger searches in both current and future imaging surveys. [ABSTRACT FROM AUTHOR]

Rapid, accurate, and fully automated estimation of both length and width of asphalt pavement cracks, essential for achieving a proactive asset management, presents a significant challenge, primarily due to limitations in the effectiveness of automatic image segmentation and the accuracy of crack width and length estimation algorithms. To address this challenge, this paper introduces the Branch Growing (BG) algorithm, specifically designed for crack length estimation in asphalt pavements, along with an optimized OrthoBoundary algorithm tailored for crack width estimation. Leveraging four widely adopted deep learning models for asphalt pavement crack segmentation, four distinct sets of image segmentation results have been produced. Subsequently, a comprehensive evaluation has been conducted to assess the effectiveness of both crack dimensions estimation algorithms. The findings demonstrate that the integration of the BG algorithm, the optimized OrthoBoundary algorithm, and the fully convolutional network with the HRNet backbone achieve a prediction accuracy of 80.21% for crack length estimation and 84.32% for average width estimation. Moreover, the image processing speed, at a resolution of 3024 × 3024, can be maintained at approximately 5 s, with average width estimation observed to be up to 9.1‐fold faster than the unoptimized OrthoBoundary algorithm. These results signify advancements in automated crack quantification methodologies, with implications for enhancing civil infrastructure maintenance practices. [ABSTRACT FROM AUTHOR]

When interpreting marine very high-resolution (VHR) single-channel seismic reflection data, the signal in the water column is generally considered as noise and is often eliminated by a water-mute application to focus on geological information under the seafloor. Alternatively, the signal in the water column can be used to study ocean currents or gas/fluid emissions. To provide images of the sedimentary formations and tectonic structures beneath the seafloor in shallow water regions, such as continental shelves and lakes, marine seismic reflection profiles are often acquired using a single-channel streamer and sparker-type source, providing VHR data, with limited penetration depth. To exploit the full potential of these single-channel data, we propose a simple algorithm, called REWARE (Recovery of Water-column Acoustic Reflectors). This algorithm allows to extract further geological information from the water-column data using open-source codes (Seismic Un*x), adding the coherent signal from the previous shots, recorded in the water column, to the previous traces. The record length becomes longer while maintaining a very high trace-to-trace consistency. To demonstrate its efficiency, we present two examples of the REWARE processing in two different geological contexts: the East Sardinia shelf (Italy) and the North Evia Gulf (Greece). This method provides deeper images than with original data for seismic data acquired across steep slopes, such as canyons or continental shelf breaks. Thus, depending on the seafloor geometry and subseafloor structures, it is possible to image or map sediment layers and tectonic structures at depth, keeping a very high structural resolution. [ABSTRACT FROM AUTHOR]

Background: Retinal prostheses offer hope for individuals with degenerative retinal diseases by stimulating the remaining retinal cells to partially restore their vision. This review delves into the current advancements in retinal prosthesis technology, with a special emphasis on the pivotal role that image processing and machine learning techniques play in this evolution. Methods: We provide a comprehensive analysis of the existing implantable devices and optogenetic strategies, delineating their advantages, limitations, and challenges in addressing complex visual tasks. The review extends to various image processing algorithms and deep learning architectures that have been implemented to enhance the functionality of retinal prosthetic devices. We also illustrate the testing results by demonstrating the clinical trials or using Simulated Prosthetic Vision (SPV) through phosphene simulations, which is a critical aspect of simulating visual perception for retinal prosthesis users. Results: Our review highlights the significant progress in retinal prosthesis technology, particularly its capacity to augment visual perception among the visually impaired. It discusses the integration between image processing and deep learning, illustrating their impact on individual interactions and navigations within the environment through applying clinical trials and also illustrating the limitations of some techniques to be used with current devices, as some approaches only use simulation even on sighted‐normal individuals or rely on qualitative analysis, where some consider realistic perception models and others do not. Conclusion: This interdisciplinary field holds promise for the future of retinal prostheses, with the potential to significantly enhance the quality of life for individuals with retinal prostheses. Future research directions should pivot towards optimizing phosphene simulations for SPV approaches, considering the distorted and confusing nature of phosphene perception, thereby enriching the visual perception provided by these prosthetic devices. This endeavor will not only improve navigational independence but also facilitate a more immersive interaction with the environment. [ABSTRACT FROM AUTHOR]

Medical image copyright protection is becoming increasingly relevant as medical images are used more frequently in medical networks and institutions. The traditional embedded watermarking system is inappropriate for medical images since it degrades the original images' quality. Furthermore, medical-colored image watermarking options are constrained since most medical watermarking systems are built for gray-scale images. This paper proposes a zero-watermarking scheme for medical color image copyright protection based on a chaotic system and Resnet50, which is a convolutional neural network method. The network Resnet50 is used to extract features from the color medical image, and then a logistic Gaussian map is used to scramble these features and scramble the binary image. Finally, an exclusive OR operation is performed (scrambled binary image, scrambled features for the medical color image) to form a zero watermarking. The experimental result proves that our scheme is effective and robust to geometric and common image processing attacks. The BER values of the extracted watermarks are below 0.0039, and the NCC values are above 0.9942, while the average PSNR values of the attacked images are 29.0056 dB. Also, it is superior to other zero-watermark schemes for medical images in terms of robustness to conventional image processing and geometric attacks. Furthermore, the experimental results show that the Resnet50 model outperforms other models in terms of reducing the mean squared errors of the features between the attacked and original image. [ABSTRACT FROM AUTHOR]

The heat exchange tubes in the steam generator are susceptible to vibration caused by fluid flow, which can lead to damage to both the tubes and their support structures due to collisions. To enhance the predictive accuracy and cost effectiveness of fluid-elastic instability mitigation, multiple models have been created to circumvent its occurrence. In this research, a model has been developed to predict fluid-elastic instability in tube arrays by integrating Hassan's time-domain-solving model with a parameter acquisition method using computational fluid dynamics (CFD) simulations. By utilizing CFD methods, a comprehensive set of tube-in-channel model parameters were acquired. This method eliminates the requirement of empirical parameters obtained through experiments. The acquired parameters were integrated into the time-domain, tube-in-channel model. This model predicts fluid-elastic stability for a single flexible tube or a bundle of seven tubes within a rigid tube array, accounting for fluid forces in the lift direction. The stability map accurately represents the stiffness effect of flow-induced vibration, agreeing with experimental results and highlighting that the model may effectively utilize parameters obtained from CFD simulations. The combination of the time-domain-solving model and the CFD-based parameter acquisition method has been shown to produce a reliable model. [ABSTRACT FROM AUTHOR]

Particle shape and grain-scale deformability play key roles in controlling the response of granular material; however, in the literature, the effects of these parameters have generally been evaluated separately. In this context, this study attempts to investigate the coupled effects of these parameters on the repose angle of the sand–rubber mixture using the discrete-element method. For this purpose, the realistic shapes of three different sands and two different rubbers were reflected in the simulations. The grain-scale deformability of rubber particles was modeled by connecting the ball-cluster structure with the linear parallel bond contact model. In addition, to clearly distinguish the role of deformability in the response, the rubber particles were simulated also using a rigid clump model. The parameters of the model were validated using different tests for grain and sample scales. A series of numerical analyses were conducted, and repose angles were determined using image processing techniques. The variation of repose angle was correlated with porosity in the sample scale. The contribution of interparticle friction and interlocking mechanism on the repose angle was revealed using the coordination number and anisotropy coefficient of the contacts in the microscale. The results show that the angle of repose increases with increasing irregularity of the particles. Even though the interlocking mechanism is weakened by the addition of soft rubber, there is an increase in the repose angle of the mixtures thanks to the superior interparticle friction properties of the rubber particles. The results indicate that a high repose angle can be constituted by controlling the particle shape and deformability parameters. [ABSTRACT FROM AUTHOR]

In this paper, the approximate parallel prefix adders with minimizing hardware and timing complexities are proposed. Moreover, an approximate multiplier based on these adders is designed. The approximate structures include two approximate Sklansky adders, one approximate Ladner-Fischer adder, and one approximate Kogge-Stone adder. The proposed adders are free from carry rippling. The main strategy for approximate design is primarily based on rearranging and deleting sub-blocks and secondary reducing the critical path delay and area in the adders. In this case, we have a trade-off between accuracy, delay, and area. The proposed approximate multiplier has a serial structure that is designed based on using one approximate parallel prefix adder. The proposed approximate adders and multiplier are compared from hardware and accuracy point of view such as gate count, delay, area delay product, error rate, mean error distance, mean relative error distance, and normalized error distance. The efficacy of proposed structures in image processing applications such as image smoothing (low-pass filter) and image multiplication is performed using MATLAB. The results show the proposed approximate structures are comparable in terms of area, delay, PSNR, and mean structural similarity index metric parameters with other works. [ABSTRACT FROM AUTHOR]

Infrared thermal imaging presents a promising avenue for detecting physiological phenomena such as hot flushes in animals, presenting a non-invasive and efficient approach for monitoring health and behavior. However, thermal images often suffer from various types of noise, which can impede the accuracy of analysis. In this study, the efficacy of different noise filtering algorithms is investigated utilizing filtering algorithms as preprocessing steps to enhance U-Net convolutional neural network (CNN) performance in processing animal skin infrared image sets for hot flush recognition. This study compares the performance of four commonly used filtering methods: mean, median, Gaussian, and bilateral filters. The impact of each filtering technique on noise reduction and preservation of features critical for hot flush detection is evaluated in a machine learning hot flush detection algorithm. The optimal filtering for skin thermal imaging was a median filter which significantly improved the U-Net CNN's ability to accurately identify hot flush patterns, achieving an Intersection over Union score of 92.6% compared to 90.4% without filters. This research contributes to the advancement of thermal image processing methodologies for animal health monitoring applications, providing valuable insights for researchers and practitioners in the field of veterinary medicine and animal behavior studies utilizing autonomous thermal image segmentation. [ABSTRACT FROM AUTHOR]

Operational modal analysis (OMA) is required to maintain large-scale and necessary civil infrastructures. The non-contact method of OMA using digital image correlation and point tracking algorithms requires a speckle pattern placed on the structure. Alternatively, advanced computer vision methods like optical flow and phase-based video motion magnification (PBVMM) techniques are used to measure modal parameters. Despite the importance of PBVMM, the users should know the range of frequencies in which the natural structure frequency lies. A methodology based on an unsupervised machine learning technique is developed to extract the modal parameters blindly from its recorded digital video. The proposed methodology uses complex steerable pyramids and an unsupervised machine learning technique, also known as principal component analysis, and analytical mode decomposition with a random decrement technique to blindly extract the modal parameters of a structure. This study validated the proposed methodology using a multi-degree of freedom (DOF) numerical model. The results are compared with theoretical and estimated values and are in good agreement. Furthermore, it is implemented on a laboratory benchmark SDOF, MDOF, and real-time videos of the London Millennium and Tacoma Narrows bridges for blindly extracting the modal frequencies and damping ratios. [ABSTRACT FROM AUTHOR]

Fabricating ultra-large aspect ratio microcylindrical electrode array efficiently, quickly, and reliably while maintaining high quality and low cost presents a continuous challenge. In this paper, a new method is proposed to produce ultra-large aspect ratio microcylindrical electrode array by taking advantage of the peculiar phenomenon that the front end of micro-prismatic electrode array becomes rounded after being worn in electric discharge machining (EDM). Specifically, micro-hole array with rounded outlets is machined on the workpiece using worn micro-prismatic electrode array, and then the micro-prismatic electrode array is converted into micro-cylindrical electrode array by reverse EDM. First, the simulation and analysis of the electric field distribution of the micro-prismatic electrode is carried out to reveal the mechanism of morphology evolution. Then, various micro-prism electrode arrays with different cross-sectional shapes were produced using the low-speed wire EDM technique. Next, the effects of cross-sectional shape, substrate material, peak current, and pulse width on electrode wear are studied. And the process conditions for rapid wear of the electrode front section into a circle and good wear consistency of the electrode array were optimized. The mathematical model of the front-end wear of the micro-prismatic electrode is also established. Following this, the development of electrode morphology was presented, transitioning from micro-prism electrode array to micro-cylindrical electrode array. The process of substrate shape change from square to circular holes was also discussed. The dimensional consistency of the micro-circular hole array was evaluated using image processing technology. Finally, a microcylindrical electrode array, measuring approximately 230 μm in diameter and having an aspect ratio of 18.15, has been successfully prepared and evaluated. This research has the potential to overcome the technological bottleneck of achieving high precision machining of micro-cylindrical electrode array with large aspect ratios. [ABSTRACT FROM AUTHOR]

Electron tomography of frozen, hydrated samples allows structure determination of macromolecular complexes that are embedded in complex environments. Provided that the target complexes may be localised in noisy, three‐dimensional tomographic reconstructions, averaging images of multiple instances of these molecules can lead to structures with sufficient resolution for de novo atomic modelling. Although many research groups have contributed image processing tools for these tasks, a lack of standardisation and interoperability represents a barrier for newcomers to the field. Here, we present an image processing pipeline for electron tomography data in RELION‐5, with functionality ranging from the import of unprocessed movies to the automated building of atomic models in the final maps. Our explicit definition of metadata items that describe the steps of our pipeline has been designed for interoperability with other software tools and provides a framework for further standardisation. [ABSTRACT FROM AUTHOR]

Recent advances in artificial intelligence have led to deepfake images, enabling users to replace a real face with a genuine one. deepfake images have recently been used to malign public figures, politicians, and even average citizens. deepfake but realistic images have been used to stir political dissatisfaction, blackmail, propagate false news, and even carry out bogus terrorist attacks. Thus, identifying real images from fakes has got more challenging. To avoid these issues, this study employs transfer learning and data augmentation technique to classify deepfake images. For experimentation, 190,335 RGB-resolution deepfake and real images and image augmentation methods are used to prepare the dataset. The experiments use the deep learning models: convolutional neural network (CNN), Inception V3, visual geometry group (VGG19), and VGG16 with a transfer learning approach. Essential evaluation metrics (accuracy, precision, recall, F1-score, confusion matrix, and AUC-ROC curve score) are used to test the efficacy of the proposed approach. Results revealed that the proposed approach achieves an accuracy, recall, F1-score and AUC-ROC score of 90% and 91% precision, with our fine-tuned VGG16 model outperforming other DL models in recognizing real and deepfakes. [ABSTRACT FROM AUTHOR]

The article discusses the comparison between two radiomics research quality scoring tools, METRICS and RQS, highlighting their unique features and differences. METRICS, endorsed by leading imaging societies, aims to standardize the methodological quality assessment of radiomics research, while RQS has been widely adopted in the research community despite its limitations. The article presents hypothetical scenarios to compare the two tools, emphasizing the distinct focus of each on different aspects of the radiomic pipeline. Researchers are advised to consider the unique features of each tool based on the insights provided in the analysis. [Extracted from the article]

Given the significant role of asphaltic pavement in surface transportation systems, even minor enhancements in asphalt materials hold the potential for cost savings and reduced environmental impacts. The healing properties of asphalt have drawn interest for sustainability, yet much remains unknown about the causes and influencing factors. To leverage self‐healing for extending pavement life, understanding the mechanisms and conditions that stimulate microcrack healing is essential. This study aims to investigate the dynamic progress of microcrack closure in asphalt mastics. The time‐series evolution of crack closure led by self‐healing is tracked by neutron computed tomography, with image processing facilitating volumetric analysis over a 7 h period. The study examines the healing process in mastic samples with three different sand filler contents (10, 20, and 30%). Results show that for all the investigated samples, the healing rate declines exponentially over time, with 100% recovery not achieved after 7 h at room temperature. Filler content‐influenced healing speed and 20% sand demonstrate the best performance. Meanwhile, the healing performance varies within the 10% and 30% groups, depending on initial crack size. Additionally, the study finds that initial crack size influences healing behavior in the samples. [ABSTRACT FROM AUTHOR]

This study proposes the Pairwise Attention Enhancement (PAE) model to address the limitations of the Vision Transformer (ViT). While the ViT effectively models global relationships between image patches, it encounters challenges in medical image analysis where fine-grained local features are crucial. Although the ViT excels at capturing global interactions within the entire image, it may potentially underperform due to its inadequate representation of local features such as color, texture, and edges. The proposed PAE model enhances local features by calculating cosine similarity between the attention maps of training and reference images and integrating attention maps in regions with high similarity. This approach complements the ViT's global capture capability, allowing for a more accurate reflection of subtle visual differences. Experiments using Clock Drawing Test data demonstrated that the PAE model achieved a precision of 0.9383, recall of 0.8916, F1-Score of 0.9133, and accuracy of 92.69%, showing a 12% improvement over API-Net and a 1% improvement over the ViT. This study suggests that the PAE model can enhance performance in computer vision fields where local features are crucial by overcoming the limitations of the ViT. [ABSTRACT FROM AUTHOR]

Additive manufacturing encompasses technologies that produce three-dimensional computer-aided design (CAD) models through a layer-by-layer production process. Compared to traditional manufacturing methods, additive manufacturing technologies offer significant advantages in producing intricate components with minimal energy consumption, reduced raw material waste, and shortened production timelines. AM methods based on shielded gas welding have recently piqued the interest of researchers due to their high efficiency and cost-effectiveness in manufacturing critical components. However, one of the most formidable challenges in additive manufacturing methods based on shielded gas welding lies in the irregularity of weld bead height at different points, compromising the precision of components produced using these techniques. In this current research, we aimed to achieve uniform weld heights along the welding path by considering the most influential parameters on weld bead geometry and conducting experimental tests. Input parameters of the process, including nozzle angle, welding speed, wire speed, and voltage, were considered. Simultaneously, image processing and wavelet transform were employed to assess the uniformity of weld bead height. These parameters were applied to produce intricate parts after identifying optimal parameters that yielded the smoothest weld lines. According to the results, the appropriate bead for manufacturing the part was extracted. The results show that the smoothest bead line is achieved in 27 V as the highest level of voltage, at a 90° nozzle position and the maximum wire feed rate. Parts manufactured using this method across different layers exhibited no distortions, and the repeatability of production substantiated the high reliability of this approach for component manufacturing. [ABSTRACT FROM AUTHOR]

Waterborne illnesses lead to millions of fatalities worldwide each year, particularly in developing nations. In this paper, we introduce a comprehensive system designed for the autonomous early detection of viral outbreaks transmitted through water to ensure sustainable access to healthy water resources, especially in remote areas. The system utilizes an autonomous water quality monitoring setup consisting of an airborne water sample collector, an autonomous sample processor, and an artificial intelligence-aided microscopic detector for risk assessment. The proposed system replaces the time-consuming conventional monitoring protocol by automating sample collection, sample processing, and pathogen detection. Furthermore, it provides a safer processing method against the spillage of contaminated liquids and potential resultant aerosols during the heat fixation of specimens. A morphological image processing technique of light microscopic images is used to segment images, assisting in selecting a unified appropriate input segment size based on individual blob areas of different bacterial cultures. The dataset included harmful pathogenic bacteria (A. baumanii, E. coli, and P. aeruginosa) and harmless ones found in drinking water and wastewater (E. faecium, L. paracasei, and Micrococcus spp.). The segmented labeled dataset was used to train deep convolutional neural networks to automatically detect pathogens in microscopic images. To minimize prediction error, Bayesian optimization was applied to tune the hyperparameters of the networks' architecture and training settings. Different convolutional networks were tested in accordance with different required output labels. The neural network used to classify bacterial cultures as harmful or harmless achieved an accuracy of 99.7%. The neural network used to identify the specific types of bacteria achieved a cumulative accuracy of 93.65%. [ABSTRACT FROM AUTHOR]

This paper explores an application of the sigmoidal function—the complementary integral Gaussian error function (erfc(.))—in two-dimensional (2D) uniform and nonuniform filter bank synthesis. The complementary integral Gaussian error function graph represents a smooth low-pass filter magnitude response. A parameter changes the function slope and increases the magnitude response selectivity. The theory is applied to 2D band-pass filter banks. Exact expressions for the magnitude response parameters are determined. As a result, 2D uniform and nonuniform filter banks with very high selectivity and exact shapes are obtained. Three synthesis examples of 2D filter banks with circular and fan-shaped magnitude responses are provided. The theoretical exposition is supplemented with two examples of image analysis using 2D uniform and nonuniform filter banks. A procedure to reduce the computations in image analysis is proposed. A comparison of filter synthesis between Parks–McLellan's 2D filters and the erfc(.) demonstrates the significantly shorter calculation time of the proposed method. [ABSTRACT FROM AUTHOR]

Currently, brain iron content represents a new neuromarker for understanding the physiopathological mechanisms leading to Parkinson's disease (PD). In vivo quantification of biological iron is possible by reconstructing magnetic susceptibility maps obtained using quantitative susceptibility mapping (QSM). Applying QSM is challenging, as up to now, no standardization of acquisition protocols and phase image processing has emerged from referenced studies. Our objectives were to compare the accuracy and the sensitivity of 10 QSM pipelines built from algorithms from the literature, applied on phantoms data and on brain data. Two phantoms, with known magnetic susceptibility ranges, were created from several solutions of gadolinium chelate. Twenty healthy volunteers from two age groups were included. Phantoms and brain data were acquired at 1.5 and 3 T, respectively. Susceptibility‐weighted images were obtained using a 3D multigradient‐recalled‐echo sequence. For brain data, 3D anatomical T1‐ and T2‐weighted images were also acquired to segment the deep gray nuclei of interest. Concerning in vitro data, the linear dependence of magnetic susceptibility versus gadolinium concentration and deviations from the theoretically expected values were calculated. For brain data, the accuracy and sensitivity of the QSM pipelines were evaluated in comparison with results from the literature and regarding the expected magnetic susceptibility increase with age, respectively. A nonparametric Mann–Whitney U‐test was used to compare the magnetic susceptibility quantification in deep gray nuclei between the two age groups. Our methodology enabled quantifying magnetic susceptibility in human brain and the results were consistent with those from the literature. Statistically significant differences were obtained between the two age groups in all cerebral regions of interest. Our results show the importance of optimizing QSM pipelines according to the application and the targeted magnetic susceptibility range, to achieve accurate quantification. We were able to define the optimal QSM pipeline for future applications on patients with PD. [ABSTRACT FROM AUTHOR]

The goal of this work was to develop a general blueprint for a semi-automated image processing tool (SIPT) to measure small, complex features of stent prototypes that can replace the current gold standard of manual measurements. The stents were designed using CAD software and manufactured via laser cutting. Stent prototypes were imaged using a Keyence microscope in top and side view orientations. The SIPT algorithm was developed in MATLAB to extract and measure 4 dimensions of the stent (inner and outer diameter, spring bend outer radius, spring bend width). The same dimensions were also manually measured by an experienced metrology technician as a gold standard comparison. We successfully made over 5000 unique measurements across the 4 key dimensions of 15 stents using the SIPT algorithm. Compared to the gold standard manual method, SIPT reduced measurement time by nearly 90% and increased the total number of measurements captured by over 2300%. The two one-sided test and Bland–Altman analysis demonstrated that SIPT achieved equivalency against the manual method of measurement for all 4 dimensions. In summary, we found that our SIPT software could be used to replace manual measurements and provided substantial time savings with consistent accuracy. Overall, this paper presents a generalizable workflow to isolate and measure critical features of stent prototypes that we believe will provide a valuable, cost-effective tool to other medical device designers seeking to rapidly iterate on unique stent designs or other manufactured parts with small and complex structures. [ABSTRACT FROM AUTHOR]

With the intelligent development of magnetic particle inspection, the quality of magnetic indications formed at cracks is closely related to the accuracy of magnetic particle inspection image analysis results. The concentration of magnetic suspension is a key process parameter affecting the quality of magnetic indication formation. Hence, this study presents an online detection method based on machine vision for measuring magnetic suspension concentration. The method initially enhances the contrast of images of the pear-shaped measuring tube containing magnetic suspension and then extracts scale lines through feature analysis and morphological processing. A method for extracting the magnetic particle sedimentation area of magnetic suspension based on a dual-threshold segmentation algorithm is proposed. The contour filtering algorithm and pixel calibration method are used to obtain the magnetic particle concentration of the non-estimation and estimation areas based on scale line extraction, ultimately forming an online accurate detection method for magnetic suspension concentration values. Experiments were conducted to validate the method against different concentrations, turbidity levels, tilting angles of the pear-shaped measuring tube, and ambient brightness. The results show that the error in magnetic suspension concentration detection based on this method is within 5%. This has certain reference value for the stable control of magnetic suspension concentration and for enhancing the reliability of intelligent decision-making results in magnetic particle inspection. [ABSTRACT FROM AUTHOR]

Breast cancer is widespread across the globe. It's the primary cause of death in cancer fatalities. According to the Palestinian Ministry of Health annual report, it ranked as the third reported death of all reported cancer deaths in the West Bank. Mammogram screening is the most common technique to diagnose breast abnormalities, but there is a challenge in the lack of skilled experts able to accurately interpret mammograms. Machine learning plays an important role in medical image processing particularly in early detection when the treatment is less expensive and available. In this paper we proposed different convolutional neural network (CNN) models to detect breast abnormalities with promising results. Six CNN models were used in this research on a unique (first-hand) dataset collected from the Palestinian Ministry of Health. The models are VGG16, VGG19, DenseNet121, ResNet50, Xception, and EfficientNetB7. Consequently, DenseNet121 outperformed other models with 0.83 and 0.85 for testing accuracy and area under curve (AUC) respectively. As a future work, the outperformed model can be combined with other patient data like genetic information, medical history, and lifestyle factors to evaluate the risk of developing specific diseases. This would increase the survival rate and enable proactive measures. [ABSTRACT FROM AUTHOR]

A novel image-based long-range tracking continuously scanning laser Doppler vibrometer (CSLDV) is developed to measure vibration of a rotating structure with a complex background. The image-based long-range tracking CSLDV can track and scan the rotating structure by processing its images captured by a camera in the image-based long-range tracking CSLDV. A novel image-processing method with a background averaging technique is developed to extract the rotating structure from the background in captured images and determine its location. A one-dimensional scan scheme is used to scan along a straight line along the rotating structure. An improved demodulation method is used to process measurements of the image-based long-range tracking CSLDV to estimate modal parameters of the rotating structure, including damped natural frequencies and undamped mode shapes. Experiments of tracking and scanning a rotating blade are conducted to validate vibration measurement of a rotating blade using the image-based long-range tracking CSLDV. Measured responses of the rotating blade are processed to estimate its speeds and modal parameters. The first three damped natural frequencies of out-of-plane vibration of the rotating blade and the first three undamped mode shapes of the rotating blade along its middle line are successfully estimated and compared with results from the finite element model of the corresponding stationary blade. [ABSTRACT FROM AUTHOR]

In this study, the authors explore an approach to detect expiration dates of food products using a live feed stream and the integration with Large Language Models in order to improve accessibility for visually impaired people. The main objective is to enhance their capacity to engage in common tasks like grocery shopping autonomously. The novelty of this research lies in employing Meta LLAMA 2, a large language model, and experimenting with both traditional and a new OCR solution to find the expiration date using image processing. This approach offers audio information about whether the product has expired or when it will expire, helping in shopping and product recognition for visually challenged customers. The proposed solution consists of optical character recognition, mainly the EasyOCR library, fine-tuned on cropped images containing only the expiration dates and a validation phase that filters and checks the extracted data. [ABSTRACT FROM AUTHOR]

This research paper introduces a novel approach in the realm of digital preservation and inheritance of Intangible Cultural Heritage (ICH) through a Customized 3D Convolutional Neural Network (CNN). The core of this study lies in the development of an advanced image processing algorithm tailored to accurately recognize, categorize, and archive diverse forms of ICH, which include traditional performances, ceremonies, oral traditions, and crafts. Utilizing a volumetric 3D CNN, this paper demonstrates how complex ICH elements can be effectively captured and analyzed, overcoming the limitations of traditional 2D image processing methods. The network is trained on a comprehensive dataset of ICH imagery, ensuring sensitivity to the subtle nuances and dynamic nature of these cultural expressions. This paper highlights the algorithm's capability in not only safeguarding the visual aspects of ICH but also in providing an interactive, digital medium for education and cultural dissemination. The proposed method shows significant promise in aiding the efforts of cultural preservationists and educators, offering a technologically advanced pathway for the protection and inheritance of the world's rich, yet vulnerable, cultural heritage. This study sets a precedent in the interdisciplinary field of cultural heritage conservation, digital technology, and artificial intelligence, providing a scalable and effective solution for global ICH preservation initiatives. [ABSTRACT FROM AUTHOR]

In this work, we introduce a novel framework, GAN-based Image Processing (GAN-IP), to design and manipulate digital experimental settings that effectively combine computer vision and image processing technology. Using the skills of Generative Adversarial Networks (GANs), GAN-IP creates and complements virtual scenes and affords rich, adaptive surroundings for analysing and creating computationally smart and perceptive algorithms. GAN-IP solves the pressing troubles of variability and absence of data through synthesising realistic pictures and scenarios, enabling simulations of the diverse environments in which computer vision structures have to function. Our approach improves the fidelity and sort of digital scenes and introduces a way to mechanically alter and evoke a pleasant image, enabling more accurate and powerful computer vision and prescient models. Through extensive experimentation, GAN-IP demonstrates remarkable improvements in the performance of computer vision tasks, including object detection, segmentation, and recognition in complicated virtual environments. This research lays the foundation for future studies in this field and provides an adaptive tool for researchers and practitioners to simulate and test superior computer imaginative and prescient image processing technologies. [ABSTRACT FROM AUTHOR]