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The article contains the procedure of image acquisition, including sampling of analyzed material as well as technical solutions of hardware and preprocessing used in research. A dataset of digital images containing identified objects were obtained with help of automated mechanical system for controlling the microscope table and used to train the YOLO models. The performance of YOLOv4 as well as YOLOv8 deep learning networks was compared on the basis of automatic image analysis. YOLO constitutes a one-stage object detection model, aiming to examine the analyzed image only once. By utilizing a single neural network, the image is divided into a grid of cells, and predictions are made for bounding boxes, as well as object class probabilities for each box. This approach allows real-time detection with minimal accuracy loss. The study involved ciliated protozoa Vorticella as a test object. These organisms are found both in natural water bodies and in treatment plants that employ the activated sludge method. As a result of its distinct appearance, high abundance and sedentary lifestyle, Vorticella are good subjects for detection tasks. To ensure that the training dataset is accurate, the images were manually labeled. The performance of the models was evaluated using such metrics as accuracy, precision, and recall. The final results show the differences in metrics characterizing the obtained outputs and progress in the software over subsequent versions of the YOLO algorithm. [ABSTRACT FROM AUTHOR]

Coal gangue recognition is an important part of the coal intelligent industry, and the coal gangue recognition technology based on machine vision is the current research focus and hotspot, but there are some problems and deficiencies in the existing technology. Firstly, the influence of external factors on coal gangue image acquisition was revealed. Secondly, the common methods of coal gangue image processing were expounded and the shortcomings of each method was pointed out. Finally, the current recognition and classification technology of coal gangue based on machine learning was summarized. Based on the above research results, the further research directions of the current machine vision recognition methods for coal gangue are proposed, namely, building highquality coal gangue datasets, researching efficient image segmentation methods, selecting the most representative coal gangue features, and optimizing the deep learning network model structure. [ABSTRACT FROM AUTHOR]

The article contains the procedure of image acquisition, including sampling of analyzed material as well as technical solutions of hardware and preprocessing used in research. A dataset of digital images containing identified objects were obtained with help of automated mechanical system for controlling the microscope table and used to train the YOLO models. The performance of YOLOv4 as well as YOLOv8 deep learning networks was compared on the basis of automatic image analysis. YOLO constitutes a one-stage object detection model, aiming to examine the analyzed image only once. By utilizing a single neural network, the image is divided into a grid of cells, and predictions are made for bounding boxes, as well as object class probabilities for each box. This approach allows real-time detection with minimal accuracy loss. The study involved ciliated protozoa Vorticella as a test object. These organisms are found both in natural water bodies and in treatment plants that employ the activated sludge method. As a result of its distinct appearance, high abundance and sedentary lifestyle, Vorticella are good subjects for detection tasks. To ensure that the training dataset is accurate, the images were manually labeled. The performance of the models was evaluated using such metrics as accuracy, precision, and recall. The final results show the differences in metrics characterizing the obtained outputs and progress in the software over subsequent versions of the YOLO algorithm.

The Braille Recognition System is the process of capturing a Braille document image and turning its content into its equivalent natural language characters. The Braille Recognition System's cell transcription and Braille cell recognition are the two basic phases that follow one another. The Braille Recognition System is a technique for locating and recognizing a Braille document stored as an image, such as a jpeg, jpg, tiff, or gif image, and converting the text into a machine-readable format, such as a text file. BCR translates an image's pixel representation into its character representation. As workers at visually impaired schools and institutes, we profit from Braille recognition in a variety of ways. The Braille Recognition System contains many stages, including image acquisition, pre-processing of images, and character recognition. This review aims to examine the earlier studies on transcription and Braille cell recognition by other scholars and the comparative results and detection techniques among them. This review will look at previous work done by other researchers on Braille cell recognition and transcription, comparing previous works in this study, and will be useful and illuminating for Braille Recognition System researchers, especially newcomers.

The tenure and color of complex patterned fabrics are often irregular, making it difficult to identify surface defects. To address the above issues, a textile surface default image recognition method based on an improved Res UNet was studied. Textile images were collected and processed with gray-scale, de-noising and equalization. was used to obtain the optimal extraction network layer R'. The Res-UNet network was improved by increasing the feature extraction network layer nunser, improved ps-UNet network w^s used to identify textile surface defects to achieve accurate identification of textile surface defects. The results show that under the application of the proposed method, the identification coefficient of both plain color samples and color samples is above 0. 9. Compared with the textile defect identification method based on label embedding method and the fabric defect detection method based on dual-channel high-resolution conversion network, the identification method based on the improved Res-UNet network can identify the contour coefficient of complex color supplies better. The applicability is better, the recognition ability is stronger. [ABSTRACT FROM AUTHOR]

In the competitive landscape of steel-strip production, ensuring the high quality of steel surfaces is paramount. Traditionally, human visual inspection has been the primary method for detecting defects, but it suffers from limitations such as reliability, cost, processing time, and accuracy. Visual inspection technologies, particularly automation techniques, have been introduced to address these shortcomings. This paper conducts a thorough survey examining vision-based methodologies related to detecting and classifying surface defects on steel products. These methodologies encompass statistical, spectral, texture segmentation based methods, and machine learning-driven approaches. Furthermore, various classification algorithms, categorized into supervised, semi-supervised, and unsupervised techniques, are discussed. Additionally, the paper outlines the future direction of research focus. [ABSTRACT FROM AUTHOR]

The art of screen printing largely depends on images captured from both natural and artificial sources. Obviously, with the upsurge of technology in the 21st century, images are easily captured with different kinds of cameras. Notwithstanding, creativity is propelled by a shift from normalcy from the artistic point of view. Innovative approaches to image acquisition for the conservation of textiles printing techniques, especially hand screen printing, can never be overemphasised. This study explores dabbing as an innovative image-capturing technique from found objects for textile printing with inspiration from the Rayograph technique by Man Ray. The Art studio-based research design under the Qualitative research method was employed for the study. Two approaches (Direct and Indirect dabbing) became significant. The studio exploration yielded to four articles comprising two printed fabrics for apparel purposes and two wall hangings for decorative purposes. A complement of the dabbing technique with screen printing was observed to be an innovative approach to the conservation of textile printing. It gives artists a great opportunity to utilise found objects in their surroundings that go a long way to minimize waste. The study recommends the Dabbing technique to textile design practitioners/learners as a technique worth-considering in textile design and printing. [ABSTRACT FROM AUTHOR]

Excessive yield losses are due to delayed identification of leaf diseases, resulting in lower farmer revenue. To address this, early and precise detection methods are crucial. Sunflower, a vital commercial crop in India for edible oil production, faces issues like leaf blight, downy mildew, powdery mildew, and leaf spot. Our research aims to create an algorithm that effectively identifies these diseases using hybrid feature extraction techniques. We compiled sunflower leaf images from village databases and farm visits. Initially, color images were converted to grayscale, and the noise was reduced using Gaussian filtering during image pre-processing. Disease-affected areas were segmented using edge-based approaches. Hybrid feature extraction was then employed to encompass color and texture-related attributes. The K-Nearest Neighbors (KNN) algorithm facilitated disease classification. The proposed algorithm's performance was benchmarked against SVM, RF and DT classifiers. Remarkably, our algorithm achieved exceptional accuracy: 98% for leaf blight, 97.3% for downy mildew, 95% for powdery mildew, and 96.5% for leaf spot detection. These results underscore the algorithm's effectiveness in combating plant diseases and enhancing agricultural productivity. [ABSTRACT FROM AUTHOR]

It is common to observe conventional methods for estimating soybean crop yields, making the process slow and susceptible to human error. Therefore, the objective was to develop a model based on deep learning to estimate soybean yield using digital images obtained through a smartphone. To do this, the ability of the proposed model to correctly classify pods that have different numbers of grains, count the number of pods and grains, and then estimate the soybean crop yield was analyzed. As part of the study, two types of image acquisition were performed for the same plant. Image acquisition 1 (IA1) included capturing the images of the entire plant, pods, leaves, and branches. Image acquisition 2 (IA2) included capturing the images of the pods removed from the plant and deposited in a white container. In both acquisition methods, two soybean cultivars, TMG 7063 Ipro and TMG 7363 RR, were used. In total, combining samples from both cultivars, 495 images were captured, with each image corresponding to a sample (plant) obtained through methods AI1 and AI2. With these images, the total number of pods in the entire dataset was 46,385 pods. For the training and validation of the model, the data was divided into subsets of training, validation, and testing, representing, respectively, 80, 10, and 10% of the total dataset. In general, when using the data from IA2, the model presented errors of 7.50 and 5.32% for pods and grains, respectively. These values are considerably lower than when the model used the IA1 data, where it presented errors of 34.69 and 35.25% for pod and grain counts, respectively. Therefore, the data used from IA2 provide better results to the model.

This paper introduces an online dataset focused on detecting hairiness in yarn, including loop and protruding fibers. The dataset is designed for use in assessing artificial intelligence algorithms. The dataset consists of 684 original images. Through augmentation, this number increases to 1644, with 11,037 annotations derived from videos featuring 56.4tex purple cotton yarn. The videos were captured during the winding and unwinding processes of the purple yarn coil. An image acquisition system capable of capturing high-resolution images while the yarn is in motion was used, reaching speeds of up to 4.2 m/s and producing images with a resolution of 1.6M pixels. This dataset containing 100m of purple cotton yarn images was recorded and is available for download in various formats, including, among others, YOLOv8, YOLOv5, YOLOv7, MT-YOLOv6, COCO JSON, YOLO Darknet, Pascal VOC XML, TFRecord, CreateML JSON. Within an interface developed for a designed mechatronic prototype, users can choose to gather images or videos of yarn. Various characteristics of the yarn, such us: diameter, linear mass, volume, twist orientation, twist step, number of cables, hairiness index, number of loose fibers, thin places, thick places, neps (mass parameters) and U, CV and sH (statistical parameters) can be obtained. Recently, this online yarn spinning dataset was employed to validate artificial neural network models for real-time detection of hairiness in yarns, including loop fibers and protruding fibers. The dataset presented, with its clear annotations and wide array of augmentation techniques, serves as a foundational resource for prospective studies in textile engineering, enabling progress in the analysis and comprehension of yarn analysis.

Abstract To quickly determine the blasting block degree and conduct an accurate and objective analysis of the tunnel blasting effect, this study has enhanced and improved upon the traditional genetic algorithm and Otsu algorithm. It has combined it with the marking watershed method and utilized ground digital acquisition to capture images of blasting debris. These images are then used in our custom-developed blasting analysis software to calculate the blasting block degree distribution and provide a quantitative analysis of blasting block degree. The research results show that the optimized image segmentation algorithm effectively improves the traditional threshold segmentation method on the poor effect of segmentation of the edge of the adherent block or the direct application of the watershed segmentation of the over-segmentation problem, to improve the segmentation accuracy based on the new segmentation technology is close to the traditional technology in terms of time. Through the self-developed software, the construction personnel in the project site to quickly obtain the blasting block degree histogram, block degree cumulative curve and other important indicators of the evaluation of the effect of blasting block degree, to provide data support for on-site construction, to assist in the modification of the blasting program, and to improve the efficiency of construction. This study realizes the rapid detection and block identification of blasting blocks, provides data support for the optimization of blasting parameters, and has good application and promotion value.

Abnormal crops image data play crucial role in controlling crop diseases and pest for smart agriculture. However, current agricultural image acquisition methods suffer from low-value data. This article presents a new strategy for collect high-quality image data for abnormal crops. First, a novel agricultural Internet of Things (IoT) image acquisition system is proposed, that integrates edge intelligence, motion–static synergy, which enables both coarse and fine crop image acquisition. To enhance image acquisition efficiency and data value in the agricultural IoT, this article proposes an image acquisition method based on edge intelligence and static and motion collaboration, using banana plantations as the example object. The method comprises three phases. In the first phase, the edge server deploys the YOLO-FDAC target detection model to detect abnormal crops from the images captured by static nodes. In the second phase, the coordinate solution method of abnormal crops and the quantification method of the degree of abnormality is presented. In the third phase, based on the severity of abnormality and the ant colony optimization, a path optimization algorithm for the image acquisition robot is designed. Finally, this article evaluates the performance of each level of the proposed method by comparing it with traditional methods. The experimental results demonstrate that the proposed image acquisition strategy has high acquisition efficiency and high image data value.

Good quality of single droplet image acquisition in imaging flow cytometry (IFC) is crucial for a wide range of biological analyses. Recently, there have been significant advances in droplet microfluidic data analysis; however, acquiring blur-free single object images is still a great challenge because of the tradeoff between high flow rate and hardware setup complexity and cost. State-of-the-art hardware setups for blur-free single image acquisition are often complex, cumbersome, and not portable, limiting their suitability for point-of-care diagnostics. Moreover, motion blur and duplicate droplet image acquisition can occur with flow rate variation. To address these issues, this paper proposes a lightweight imaging pipeline for acquiring blur-free single droplet images for portable applications; this pipeline is capable of acquiring every single droplet image. While most of the existing literature focuses on complex hardware setups, utilizing high frame rate cameras that are not cost effective and complex optical solutions for droplet focusing, our pipeline utilizes minimum hardware and a lightweight algorithm for detecting, counting, and acquiring single object images from the video stream. The proposed pipeline was evaluated experimentally using videos of fast-moving droplets in which the input fluid flow rate was as high as $67.7~\mu $ L/min. The proposed pipeline achieves 100% counting accuracy on the tested videos and 2 ms, 25 ms and 10 ms processing time for each droplet on a desktop PC, single-board computer Raspberry Pi-4, and Nvidia Jetson Nano, respectively. This yields a maximum of 500, 40, and 100 blur free detected droplets per second (DPS), respectively. The Jetson Nano implementation, achieving 100 DPS with processing time of 10 ms, is faster than existing similar studies and fast enough for the target application. The results suggest that the proposed lightweight pipeline is suitable for efficient single object image acquisition in IFC on an embedded portable platform.

Abstract Objectives To observe change in economy of 9 ultrasound probe movement metrics among internal medicine trainees during a 5-day training course in cardiac point of care ultrasound (POCUS). Methods We used a novel probe tracking device to record nine features of ultrasound probe movement, while trainees and experts optimized ultrasound clips on the same volunteer patients. These features included translational movements, gyroscopic movements (titling, rocking, and rotation), smoothness, total path length, and scanning time. We determined the adjusted difference between each trainee’s movements and the mean value of the experts’ movements for each patient. We then used a mixed effects model to trend average the adjusted differences between trainees and experts throughout the 5 days of the course. Results Fifteen trainees were enrolled. Three echocardiographer technicians and the course director served as experts. Across 16 unique patients, 294 ultrasound clips were acquired. For all 9 movements, the adjusted difference between trainees and experts narrowed day-to-day (p value

To quickly determine the blasting block degree and conduct an accurate and objective analysis of the tunnel blasting effect, this study has enhanced and improved upon the traditional genetic algorithm and Otsu algorithm. It has combined it with the marking watershed method and utilized ground digital acquisition to capture images of blasting debris. These images are then used in our custom-developed blasting analysis software to calculate the blasting block degree distribution and provide a quantitative analysis of blasting block degree. The research results show that the optimized image segmentation algorithm effectively improves the traditional threshold segmentation method on the poor effect of segmentation of the edge of the adherent block or the direct application of the watershed segmentation of the over-segmentation problem, to improve the segmentation accuracy based on the new segmentation technology is close to the traditional technology in terms of time. Through the self-developed software, the construction personnel in the project site to quickly obtain the blasting block degree histogram, block degree cumulative curve and other important indicators of the evaluation of the effect of blasting block degree, to provide data support for on-site construction, to assist in the modification of the blasting program, and to improve the efficiency of construction. This study realizes the rapid detection and block identification of blasting blocks, provides data support for the optimization of blasting parameters, and has good application and promotion value. [ABSTRACT FROM AUTHOR]

Continuous high-speed (>80 km/h) tunnel-image detection introduces new challenges to defect image acquisition, including machine recognition of entry and exit from a tunnel, storage of massive image data streams, image distortion due to surface photography, and encoder errors. In this study, an automatic image acquisition system is designed, and several critical technologies are proposed for high-speed rail tunnels. The system realizes quick tunnel identification and automatic camera control and uses the proposed line-scan software-matching method to obtain accurate images. Before storage, the real-time modification of the distorted images is implemented using an error-correction algorithm. An alternative mapping storage algorithm is proposed to improve the efficiency and stability of long-term storage. The test results show that the proposed method effectively reduces photographic errors. The lateral pixel-error rate of the corrected image is 1.60%, which is 10 times lower than that of the pre-correction, and the error rate of the longitudinal image is controlled within 10% when the system is moving at a variable speed and within 1% when it is moving at a constant speed. Furthermore, experiments have proven that the alternate mapping storage algorithm improves the storage efficiency of RAID by 50% and ensures data integrity; storage is accomplished by 8 HDDs at a camera throughput of 1.52 GB/s. This study will contribute to improvements in the speed and accuracy of tunnel defect-image detection. [ABSTRACT FROM AUTHOR]

Objectives: To observe change in economy of 9 ultrasound probe movement metrics among internal medicine trainees during a 5-day training course in cardiac point of care ultrasound (POCUS). Methods: We used a novel probe tracking device to record nine features of ultrasound probe movement, while trainees and experts optimized ultrasound clips on the same volunteer patients. These features included translational movements, gyroscopic movements (titling, rocking, and rotation), smoothness, total path length, and scanning time. We determined the adjusted difference between each trainee's movements and the mean value of the experts' movements for each patient. We then used a mixed effects model to trend average the adjusted differences between trainees and experts throughout the 5 days of the course. Results: Fifteen trainees were enrolled. Three echocardiographer technicians and the course director served as experts. Across 16 unique patients, 294 ultrasound clips were acquired. For all 9 movements, the adjusted difference between trainees and experts narrowed day-to-day (p value < 0.05), suggesting ongoing improvement during training. By the last day of the course, there were no statistically significant differences between trainees and experts in translational movement, gyroscopic movement, smoothness, or total path length; yet on average trainees took 28 s (95% CI [14.7–40.3] seconds) more to acquire a clip. Conclusions: We detected improved ultrasound probe motion economy among internal medicine trainees during a 5-day training course in cardiac POCUS using an inexpensive probe tracking device. Objectively quantifying probe motion economy may help assess a trainee's level of proficiency in this skill and individualize their POCUS training. [ABSTRACT FROM AUTHOR]

The completion degree of sports training can not reach the corresponding standard, and the training effect will be greatly weakened. In order to improve the effect of sports training, the evaluation method of sports training completion degree based on deep residual network is studied. The image collector based on ARM is used to collect the action images of athletes in sports training, and the collected action images are preprocessed based on spatial scale filtering and regression factors. Construct a depth residual network, learn the implicit relationship between athletes' state and the dynamic change process of sports training actions through off-line training, and train the model; In the online application process, the preprocessed action images will be input into the trained evaluation model to evaluate the athletes' sports training action completion in real time. At the same time, residual shrinkage unit and attention mechanism are used to optimize the depth residual network, which improves the training efficiency and evaluation performance of the network. The experimental results show that this method has good evaluation performance under the condition of setting parameters, and can effectively improve the effect of physical training. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools has resulted in a rapid increase of DL models and subsequent peer‐reviewed publications. However, the rate of deployment in clinical settings is low. Therefore, this review attempts to bring together the ideas from data collection to deployment in the clinic, building on the guidelines and principles that accreditation agencies have espoused. We introduce the need for and the role of DL to deliver accessible MRI. This is followed by a brief review of DL examples in the context of neuropathologies. Based on these studies and others, we collate the prerequisites to develop and deploy DL models for brain MRI. We then delve into the guiding principles to develop good machine learning practices in the context of neuroimaging, with a focus on explainability. A checklist based on the United States Food and Drug Administration's good machine learning practices is provided as a summary of these guidelines. Finally, we review the current challenges and future opportunities in DL for brain MRI. [ABSTRACT FROM AUTHOR]

In the context of the artificial intelligence era, the application of artificial intelligence technology in modern brand design is increasingly widespread and is profoundly changing the design creation in this field. The purpose of this paper is to propose an intelligent image acquisition and automated brand design technology that utilizes AI technology to assist with brand design. The image acquisition and retrieval process of the online CBIR system is established, the average precision mean is used as the evaluation index of image retrieval, and the BoF model is constructed to realize RootSIFT feature extraction and image acquisition. Meanwhile, MultiStyleLogo GAN, a multi-style generative adversarial network, is designed to intelligently automate random output and generate brand design solutions through input sketches and style semantic descriptions to improve designers’ design efficiency. This paper utilizes technology to redesign the brand of A green plum wine in a province in China. Except for the pattern and font indicators, the average value of the newly designed brand image in each evaluation index of aesthetic design is greater than 4. The average values of the image aesthetics, cultural connotation, and emotional resonance indicators in the evaluation of brand narrative reach 4.25, 4.24, and 4.15, respectively, which achieve a high consumer satisfaction level.

The 3D real scene model of high-steep rock slope established based on UAV image provides convenience for non-contact identification and interpretation of rock mass structures. The quality of 3D models directly influences the interpretability of rock mass structures. However, quantitative studies on the relationship between these two aspects are scarce. Therefore, this study investigates the influence of indicators such as photography distance (D), model brightness (l), angle between photography optical axis and the discontinuity to be interpreted (θd), and texture distortion area ratio (tdp), among others, on the interpretability of the 3D models. Furthermore, it delves into the relationships among these indicators and their practical application effects in complex high-steep rock slopes. On this foundation, a novel multi-indicator quality evaluation method for 3D models constructed using UAV photogrammetry is introduced for the first time. This method, based on a composite index model and incorporating empirical approaches, classifies the quality of 3D models into five levels. Guided by the quality evaluation results and taking into account the terrain development and geometric characteristics of discontinuities, a technical framework supporting the acquisition of high-quality 3D models is proposed, known as multi-angle nap-of-the-object image acquisition (MNIA). The research indicates that the 3D models established based on MNIA exhibit significantly reduced texture distortion, leading to a substantial improvement in recognizability, with a goodness rate of 79.98%, far surpassing that of oblique photogrammetry (41.18%). This study provides crucial guidance for obtaining high-quality UAV image in complex terrain environments and holds significant engineering application value in the fine identification and interpretation of rock mass structures on high-steep rock slopes.

We introduce HumanBrainAtlas, an initiative to construct a highly detailed, open-access atlas of the living human brain that combines high-resolution in vivo MR imaging and detailed segmentations previously possible only in histological preparations. Here, we present and evaluate the first step of this initiative: a comprehensive dataset of two healthy male volunteers reconstructed to a 0.25 mm isotropic resolution for T1w, T2w, and DWI contrasts. Multiple high-resolution acquisitions were collected for each contrast and each participant, followed by averaging using symmetric group-wise normalisation (Advanced Normalisation Tools). The resulting image quality permits structural parcellations rivalling histology-based atlases, while maintaining the advantages of in vivo MRI. For example, components of the thalamus, hypothalamus, and hippocampus are often impossible to identify using standard MRI protocols—can be identified within the present data. Our data are virtually distortion free, fully 3D, and compatible with the existing in vivo Neuroimaging analysis tools. The dataset is suitable for teaching and is publicly available via our website (hba.neura.edu.au), which also provides data processing scripts. Instead of focusing on coordinates in an averaged brain space, our approach focuses on providing an example segmentation at great detail in the high-quality individual brain. This serves as an illustration on what features contrasts and relations can be used to interpret MRI datasets, in research, clinical, and education settings. [ABSTRACT FROM AUTHOR]

Chest radiography is the standard and most affordable way to diagnose, analyze, and examine different thoracic and chest diseases. Typically, the radiograph is examined by an expert radiologist or physician to decide about a particular anomaly, if exists. Moreover, computer-aided methods are used to assist radiologists and make the analysis process accurate, fast, and more automated. A tremendous improvement in automatic chest pathologies detection and analysis can be observed with the emergence of deep learning. The survey aims to review, technically evaluate, and synthesize the different computer-aided chest pathologies detection systems. The state-of-the-art of single and multi-pathologies detection systems, which are published in the last five years, are thoroughly discussed. The taxonomy of image acquisition, dataset preprocessing, feature extraction, and deep learning models are presented. The mathematical concepts related to feature extraction model architectures are discussed. Moreover, the different articles are compared based on their contributions, datasets, methods used, and the results achieved. The article ends with the main findings, current trends, challenges, and future recommendations. [ABSTRACT FROM AUTHOR]

In March 2022, the first ISMRM Workshop on Low‐Field MRI was held virtually. The goals of this workshop were to discuss recent low field MRI technology including hardware and software developments, novel methodology, new contrast mechanisms, as well as the clinical translation and dissemination of these systems. The virtual Workshop was attended by 368 registrants from 24 countries, and included 34 invited talks, 100 abstract presentations, 2 panel discussions, and 2 live scanner demonstrations. Here, we report on the scientific content of the Workshop and identify the key themes that emerged. The subject matter of the Workshop reflected the ongoing developments of low‐field MRI as an accessible imaging modality that may expand the usage of MRI through cost reduction, portability, and ease of installation. Many talks in this Workshop addressed the use of computational power, efficient acquisitions, and contemporary hardware to overcome the SNR limitations associated with low field strength. Participants discussed the selection of appropriate clinical applications that leverage the unique capabilities of low‐field MRI within traditional radiology practices, other point‐of‐care settings, and the broader community. The notion of "image quality" versus "information content" was also discussed, as images from low‐field portable systems that are purpose‐built for clinical decision‐making may not replicate the current standard of clinical imaging. Speakers also described technical challenges and infrastructure challenges related to portability and widespread dissemination, and speculated about future directions for the field to improve the technology and establish clinical value. [ABSTRACT FROM AUTHOR]

Summary: Clinical photography is essential in dermatology. However, a comprehensive literature review of photography in dermatology is lacking. This scoping review aims to summarize the literature regarding photography practices in dermatology, photography of skin of color, patient preferences, and medical‐legal considerations. A search was conducted utilizing Embase, MEDLINE, PubMed, and Evidence Based Medicine databases in accordance with the PRISMA extension for Scoping Reviews. In total, 33 studies were summarized. Clinical photography is commonly used in biopsy site marking, assessment, diagnosis, disease monitoring, evaluation of treatment response, medical education, research, seeking advice from colleagues, and teledermatology. Although dermatologic photography remains devoid of skin of color representation, photographic considerations for darker skin are available. Most patients support medical photography, with a preference for clinical photographs to be taken by their own physicians, and for use of clinic/hospital‐owned cameras over personal devices. Pertinent medical‐legal issues include concerns around privacy, personal device use, and documentation of consent. Photography in dermatology is continuously evolving with broader applications. Improved practices and innovations will benefit individuals of various skin tones. Management of consent and privacy must be upheld to sustain the increasing ease of image capture and sharing. [ABSTRACT FROM AUTHOR]

Copyright of Journal der Deutschen Dermatologischen Gesellschaft is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A heterogeneous Image acquisition and processing system is presented based on commercially available components. Due to the addition of a field programmable array which is configured by the central processing unit, the system is very flexible in terms of which image sensor is used and what type and level of image processing is accomplished in hardware. The ease of use of the system is improved by a library of intellectual property cores that enable the gate array to be interfaced to other processing cores. Pipelining of the pixel data stream allows insertion of several image processing operations as frames are captured. The system is compact and can easily be integrated into the camera housing without having to introduce forced cooling of the camera. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Microfluidics, also known as lab-on-a-chip or micro total analysis systems, can precisely regulate and manipulate micro-sized fluids. They have great potential in biology, chemistry, and medicine, as well as other fields of science. By definition, microfluidic devices operate with small-volume samples and small reactant quantities, which renders them both efficient and affordable. However, such small objects have very demanding requirements for the utilized optical detection system. Due to the specifics of those devices, monitoring the results of experiments is carried out with commercial inverted optical microscopes. Unfortunately, that type of optical device is still expensive. In this article, we present a truly functional, inexpensive, standalone, three-dimensionally printed, and inverted microscope, including the design, engineering, and manufacturing process and some of the experiments that have been conducted with it. Finally, we summarize the advantages of this three-dimensionally printed microscope (including the total fabrication costs) and the future improvements that will be introduced to it.

In the competitive landscape of steel-strip production, ensuring the high quality of steel surfaces is paramount. Traditionally, human visual inspection has been the primary method for detecting defects, but it suffers from limitations such as reliability, cost, processing time, and accuracy. Visual inspection technologies, particularly automation techniques, have been introduced to address these shortcomings. This paper conducts a thorough survey examining vision-based methodologies related to detecting and classifying surface defects on steel products. These methodologies encompass statistical, spectral, texture segmentation based methods, and machine learning-driven approaches. Furthermore, various classification algorithms, categorized into supervised, semi-supervised, and unsupervised techniques, are discussed. Additionally, the paper outlines the future direction of research focus.

Vision plays a crucial role in the ability of compound-eyed insects to perceive the characteristics of their surroundings. Compound-eyed insects (such as the honeybee) can change the optical flow input of the visual system by autonomously controlling their behavior, and this is referred to as visual–motor coordination (VMC). To analyze an insect's VMC mechanism in dynamic scenes, we developed a platform for studying insects that actively shape the optic flow of visual stimuli by adapting their flight behavior. Image-processing technology was applied to detect the posture and direction of insects' movement, and automatic control technology provided dynamic scene stimulation and automatic acquisition of perceptual insect behavior. In addition, a virtual mapping technique was used to reconstruct the visual cues of insects for VMC analysis in a dynamic obstacle scene. A simulation experiment at different target speeds of 1–12 m/s was performed to verify the applicability and accuracy of the platform. Our findings showed that the maximum detection speed was 8 m/s, and triggers were 95% accurate. The outdoor experiments showed that flight speed in the longitudinal axis of honeybees was more stable when facing dynamic barriers than static barriers after analyzing the change in geometric optic flow. Finally, several experiments showed that the platform can automatically and efficiently monitor honeybees' perception behavior, and can be applied to study most insects and their VMC. [ABSTRACT FROM AUTHOR]

An image acquisition device for asphalt pavement was developed based on industrial camera and shading box. The improved segmentation method of texture structure for pavement image based on rectangular partition and OTSU was proposed. The influence of external factors on acquisition effect of pavement texture image was also studied. The results show that the image acquisition device can regulate the acquisition height and illumination conditions, and avoid the information error caused by external factors to the original image. The improved threshold segmentation algorithm effectively segments the texture structure from asphalt mortar and aggregate particles. The acquisition height has a significant influence on the calculation of macroscopic structural width of asphalt pavement. After the illumination condition reaches a certain intensity, the influence of illumination intensity on macroscopic structure width of pavement image can be ignored. When evaluating the aggregate distribution uniformity based on pavement images, the recommended height range of image acquisition is 50-70 cm, and the power of LED light is set to be larger than 5 W. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: Optimizing three‐dimensional (3D) k‐space sampling trajectories is important for efficient MRI yet presents a challenging computational problem. This work proposes a generalized framework for optimizing 3D non‐Cartesian sampling patterns via data‐driven optimization. Methods: We built a differentiable simulation model to enable gradient‐based methods for sampling trajectory optimization. The algorithm can simultaneously optimize multiple properties of sampling patterns, including image quality, hardware constraints (maximum slew rate and gradient strength), reduced peripheral nerve stimulation (PNS), and parameter‐weighted contrast. The proposed method can either optimize the gradient waveform (spline‐based freeform optimization) or optimize properties of given sampling trajectories (such as the rotation angle of radial trajectories). Notably, the method can optimize sampling trajectories synergistically with either model‐based or learning‐based reconstruction methods. We proposed several strategies to alleviate the severe nonconvexity and huge computation demand posed by the large scale. The corresponding code is available as an open‐source toolbox. Results: We applied the optimized trajectory to multiple applications including structural and functional imaging. In the simulation studies, the image quality of a 3D kooshball trajectory was improved from 0.29 to 0.22 (NRMSE) with Stochastic optimization framework for 3D NOn‐Cartesian samPling trajectorY (SNOPY) optimization. In the prospective studies, by optimizing the rotation angles of a stack‐of‐stars (SOS) trajectory, SNOPY reduced the NRMSE of reconstructed images from 1.19 to 0.97 compared to the best empirical method (RSOS‐GR). Optimizing the gradient waveform of a rotational EPI trajectory improved participants' rating of the PNS from "strong" to "mild." Conclusion: SNOPY provides an efficient data‐driven and optimization‐based method to tailor non‐Cartesian sampling trajectories. [ABSTRACT FROM AUTHOR]

The fingerprint is a widely adopted biometric trait in forensic and civil applications. Fingerprint biometric systems have been investigated using contact prints and latent and contactless images which range from low to high resolution. While the imaging techniques are advancing with sensor variations, the input fingerprint images also vary. A general fingerprint recognition pipeline consists of a sensor module to acquire images, followed by feature representation, matching and decision modules. In the sensor module, the image quality of the biometric traits significantly affects the biometric system's accuracy and performance. Imaging modality, such as contact and contactless, plays a key role in poor image quality, and therefore, paying attention to imaging modality is important to obtain better performance. Further, underlying physical principles and the working of the sensor can lead to their own forms of distortions during acquisition. There are certain challenges in each module of the fingerprint recognition pipeline, particularly sensors, image acquisition and feature representation. Present reviews in fingerprint systems only analyze the imaging techniques in fingerprint sensing that have existed for a decade. However, the latest emerging trends and recent advances in fingerprint sensing, image acquisition and their challenges have been left behind. Since the present reviews are either obsolete or restricted to a particular subset of the fingerprint systems, this work comprehensively analyzes the state of the art in the field of contact-based, contactless 2D and 3D fingerprint systems and their challenges in the aspects of sensors, image acquisition and interoperability. It outlines the open issues and challenges encountered in fingerprint systems, such as fingerprint performance, environmental factors, acceptability and interoperability, and alternate directions are proposed for a better fingerprint system. [ABSTRACT FROM AUTHOR]

Different kinds of techniques are evaluated and analyzed for various classification models for the detection of diseases of citrus fruits. This paper aims to systematically review the papers that focus on the prediction, detection, and classification of citrus fruit diseases that have employed machine learning, deep learning, and statistical techniques. Additionally, this paper explores the present state of the art of the concept of image acquisition, digital image processing, feature extraction, and classification approaches, and each one is discussed separately. A total of 78 papers are selected after applying primary selection criteria, inclusion/exclusion criteria, and quality assessment criteria. We observe that the following are widely used in the selected studies: hyperspectral imaging systems for the image acquisition process, thresholding for image processing, support vector machine (SVM) models as machine learning (ML) models, convolutional neural network (CNN) architectures as deep learning models, principal component analysis (PCA) as a statistical model, and classification accuracy as evaluation parameters. Moreover, the color feature is the most popularly used feature for the RGB color space. From the review studies that performed comparative analyses, we find that the best techniques that outperformed other techniques in their respective categories are as follows: SVM among the ML methods, ANN among the neural network networks, CNN among the deep learning methods, and linear discriminant analysis (LDA) among the statistical techniques.This study concludes with meta-analysis, limitations, and future research directions. [ABSTRACT FROM AUTHOR]

Microfluidics, also known as lab-on-a-chip or micro total analysis systems, can precisely regulate and manipulate micro-sized fluids. They have great potential in biology, chemistry, and medicine, as well as other fields of science. By definition, microfluidic devices operate with small-volume samples and small reactant quantities, which renders them both efficient and affordable. However, such small objects have very demanding requirements for the utilized optical detection system. Due to the specifics of those devices, monitoring the results of experiments is carried out with commercial inverted optical microscopes. Unfortunately, that type of optical device is still expensive. In this article, we present a truly functional, inexpensive, standalone, three-dimensionally printed, and inverted microscope, including the design, engineering, and manufacturing process and some of the experiments that have been conducted with it. Finally, we summarize the advantages of this three-dimensionally printed microscope (including the total fabrication costs) and the future improvements that will be introduced to it. [ABSTRACT FROM AUTHOR]

Parkinson's Disease (PD) is a progressive neurological disorder that develops through several stages, including suggestive symptoms, stage 1, stage 2, and stage 3. Identifying the severity of PD and predicting its progression can be challenging due to the limitations of medical settings. Consequently, there is a need for novel techniques to facilitate early-stage classification of PD, potentially addressing the financial and time constraints associated with determining disease stages. Recent research has focused on employing supervised and unsupervised machine learning algorithms for the reliable diagnosis of PD, stage identification, and trajectory prediction using clinical and preclinical data. The diagnosis of PD may be improved by incorporating various techniques, including deep learning and machine learning approaches. This study proposes a Deep Convolutional Neural Networks (DCNN) approach based on MRI data to enhance the accuracy of PD classification. The paper presents several key contributions. Initially, a DCNN model was developed to classify Parkinson's Disease. Subsequently, the main objective was to identify the network topology that yielded optimal accuracy and recall. To achieve this, various network structures were examined, and several topological variants were determined. The proposed model was trained using features from the Parkinson's Progression Markers Initiative (PPMI) database, achieving an accuracy of 95% for the PPMI data. The results obtained through applying the proposed model on the PPMI datasets demonstrate its superior performance compared to traditional methods. [ABSTRACT FROM AUTHOR]

The introduction of the first whole-body CT scanner in 1974 marked the beginning of cross-sectional spine imaging. In the last decades, the technological advancement, increasing availability and clinical success of CT led to a rapidly growing number of CT examinations, also of the spine. After initially being primarily used for trauma evaluation, new indications continued to emerge, such as assessment of vertebral fractures or degenerative spine disease, preoperative and postoperative evaluation, or CT-guided interventions at the spine; however, improvements in patient management and clinical outcomes come along with higher radiation exposure, which increases the risk for secondary malignancies. Therefore, technical developments in CT acquisition and reconstruction must always include efforts to reduce the radiation dose. But how exactly can the dose be reduced? What amount of dose reduction can be achieved without compromising the clinical value of spinal CT examinations and what can be expected from the rising stars in CT technology: artificial intelligence and photon counting CT? In this article, we try to answer these questions by systematically reviewing dose reduction techniques with respect to the major clinical indications of spinal CT. Furthermore, we take a concise look on the dose reduction potential of future developments in CT hardware and software. [ABSTRACT FROM AUTHOR]

Underwater imaging has been present for many decades due to its relevance in vision and navigation systems. In recent years, advances in robotics have led to the availability of autonomous or unmanned underwater vehicles (AUVs, UUVs). Despite the rapid development of new studies and promising algorithms in this field, there is currently a lack of research toward standardized, general-approach proposals. This issue has been stated in the literature as a limiting factor to be addressed in the future. The key starting point of this work is to identify a synergistic effect between professional photography and scientific fields by analyzing image acquisition issues. Subsequently, we discuss underwater image enhancement and quality assessment, image mosaicking and algorithmic concerns as the last processing step. In this line, statistics about 120 AUV articles fro recent decades have been analyzed, with a special focus on state-of-the-art papers from recent years. Therefore, the aim of this paper is to identify critical issues in autonomous underwater vehicles encompassing the entire process, starting from optical issues in image sensing and ending with some issues related to algorithmic processing. In addition, a global underwater workflow is proposed, extracting future requirements, outcome effects and new perspectives in this context. [ABSTRACT FROM AUTHOR]

The liver moves with respiratory motion. Respiratory motion causes image artifacts as MRI is a motionsensitive imaging modality; thus, MRI scan speed improvement has been an important technical development target for liver MRI for years. Recent pulse sequence and image reconstruction technology advancement has realized a fast liver MRI acquisition method. Such new technologies allow us to obtain liver MRI in a shorter time, particularly, within breath-holding time. Other benefits of new the technology and the higher spatial resolution liver MRI within a given scan time are improved slice coverage and smaller pixel size. In this review, MRI pulse sequence and reconstruction technologies to accelerate scan speed for Tx- and T2-weighted liver MRI will be discussed. Technologies that reduce scan time while keeping image contrast, SNR and image spatial resolution are needed for fast MRI acquisition. We will discuss the progress of MRI acquisition methods, the enabling technology, established applications, current trends, and the future outlook. [ABSTRACT FROM AUTHOR]

In order to pay more attention to the quality of construction concrete and accurately judge whether concrete material meets the standard, a nondestructive testing algorithm of building concrete material defects based on machine learning is proposed. Through the ray tracing algorithm of Snell's theorem, the shortest path between two random punctuation marks of building concrete is calculated. The original coordinate system and grid size were set, the trend and length of the line in the grid were calculated, and the coordinates between the grid corner points and the transmitting probe were calculated so as to obtain the position of the intermediate refractive points of the two probes. Finally, the vector dot product of the local defects is obtained by the optimal hyperplane calculation of the binary classification in the support vector machine. Experimental results show that the proposed method has the advantages of high precision. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Medical Imaging & Radiation Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: The patient's position may affect the bone mineral density (BMD) measurements; however, the extent of this effect is undefined. This CT image-based simulation study quantified changes in BMD induced by hip flexion, adduction, and rotations to recommend appropriate patient positioning when acquiring dual-energy x-ray absorptiometry images. Purpose: Several studies have analyzed the effect of hip rotation on the measurement of bone mineral density (BMD) of the proximal femur by dual-energy x-ray absorptiometry (DXA). However, as the effects of hip flexion and abduction on BMD measurements remain uncertain, a computational simulation study using CT images was performed in this study. Methods: Hip CT images of 120 patients (33 men and 87 women; mean age, 82.1 ± 9.4 years) were used for analysis. Digitally reconstructed radiographs of the proximal femur region were generated from CT images to calculate the BMD of the proximal femur region. BMD at the neutral position was quantified, and the percent changes in BMD when hip internal rotation was altered from −30° to 15°, when hip flexion was altered from 0° to 30°, and when hip abduction was altered from –15° to 30° were quantified. Analyses were automatically performed with a 1° increment in each direction using computer programming. Results: The alteration of hip angles in each direction affected BMD measurements, with the largest changes found for hip flexion (maximum change of 17.7% at 30° flexion) and the smallest changes found for hip rotation (maximum change of 2.2% at 15° internal rotation). The BMD measurements increased by 0.34% for each 1° of hip abduction, and the maximum change was 12.3% at 30° abduction. Conclusion: This simulation study quantified the amount of BMD change induced by altering the hip position. Based on these results, we recommend that patients be positioned carefully when acquiring DXA images. [ABSTRACT FROM AUTHOR]

In this paper, a computer vision-based cashew nut grading system has been designed and implemented for classifying different grades of cashew nuts using combined features and machine learning approaches. The important task in the cashew nut grading system is to classify the whole and split down cashew nuts. Since these cashew nuts look very similar from the top view, it is a challenging task to classify the whole cashew nut and split down cashew nuts. Hence, a single-view image of cashew nut has been captured by placing a camera with a distance of 17 cm (from the right side of the conveyor belt). The captured red, blue and green images are normalized and converted into hue, saturation and value color space. S channel from HSV image is used for segmentation process using Otsu threshold technique. The total numbers of features extracted are 275 and the features are texture (180), color (90), and shape (5). The constrained optimization-based feature selection method is used and 30 features are selected for further process. The Support Vector Machine (SVM) classifier is used for the classification, and the results obtained from different kernel functions are computed and compared. The 8-layer convolutional neural network (CNN) has been developed in this work for classification and to analyze the performance and accuracy. The accuracy of different machine learning classifiers like SVM 1-1, SVM 1-All and CNN model is also evaluated and compared. The overall accuracy obtained by SVM 1-All with kernel function radial basis for classification is 98.93%. [ABSTRACT FROM AUTHOR]

Magnetic resonance image (MRI) is extensively adapted in clinical diagnosis due to its improved representation of changes with soft tissue. The current innovations in compressive sensing (CS) showed that it is viable to precisely reconstruct MRI with K-space data with reduced scanning duration and it takes more time for computation and it is complex to capture the fine details. The CS is capable to minimize the imaging time and it uses compressibility for reconstructing the images. In this paper, a novel deep-learning method is devised for reconstructing images using a set of MRI. Here, MRI acquisition and MRIsub sampling is performed for reducing the size of images to perform improved analysis. Here, a convolution layer is utilized for imitating the process of compressed sampling that can learn the sampling matrix to prevent complex designs. In addition, a convolution layer is utilized to execute the initial reconstruction. In addition, Deep Convolutional Neural Network (Deep CNN) is adapted for image reconstruction. The training of Deep CNN is performed using proposed Fractional Sailfish Optimizer (FSO) algorithm. The proposed FSO is designed by incorporating fractional concepts in Sail fish optimization for tuning the optimal weights of Deep CNN. The proposed FSO-based CSNet outperformed other methods with minimal MSE of 1.877 and maximal PSNR of 45.396 dB. [ABSTRACT FROM AUTHOR]