Search

In this retrospective case series on neovascular age-related macular degeneration (nAMD), we aimed to improve Choroidal Neovascularization (CNV) visualization in Optical Coherence Tomography Angiography (OCTA) scans by addressing segmentation errors. Out of 198 eyes, 73 OCTA scans required manual segmentation correction. We compared uncorrected scans to those with minimal (2 corrections), moderate (10 corrections), and detailed (50 corrections) efforts targeting falsely segmented Bruch's Membrane (BM). Results showed that 55% of corrected OCTAs exhibited improved quality after manual correction. Notably, minimal correction (2 scans) already led to significant improvements, with additional corrections (10 or 50) not further enhancing expert grading. Reduced background noise and improved CNV identification were observed, with the most substantial improvement after two corrections compared to baseline uncorrected images. In conclusion, our approach of correcting segmentation errors effectively enhances image quality in OCTA scans of nAMD. This study demonstrates the efficacy of the method, with 55% of resegmented OCTA images exhibiting enhanced quality, leading to a notable increase in the proportion of high-quality images from 63 to 83%.

Light microscopy methods have continued to advance allowing for unprecedented analysis of various cell types in tissues including the brain. Although the functional state of some cell types such as microglia can be determined by morphometric analysis, techniques to perform robust, quick, and accurate measurements have not kept pace with the amount of imaging data that can now be generated. Most of these image segmentation tools are further burdened by an inability to assess structures in three-dimensions. Despite the rise of machine learning techniques, the nature of some biological structures prevents the training of several current day implementations. Here we present PrestoCell, a novel use of persistence-based clustering to segment cells in light microscopy images, as a customized Python-based tool that leverages the free multidimensional image viewer Napari. In evaluating and comparing PrestoCell to several existing tools, including 3DMorph, Omipose, and Imaris, we demonstrate that PrestoCell produces image segmentations that rival these solutions. In particular, our use of cell nuclei information resulted in the ability to correctly segment individual cells that were interacting with one another to increase accuracy. These benefits are in addition to the simplified graphically based user refinement of cell masks that does not require expensive commercial software licenses. We further demonstrate that PrestoCell can complete image segmentation in large samples from light sheet microscopy, allowing quantitative analysis of these large datasets. As an open-source program that leverages freely available visualization software, with minimum computer requirements, we believe that PrestoCell can significantly increase the ability of users without data or computer science expertise to perform complex image analysis.

SignificanceX-ray-induced acoustic computed tomography (XACT) offers a promising approach to biomedical imaging, leveraging X-ray absorption contrast. It overcomes the shortages of traditional X-ray, allowing for more advanced medical imaging.AimThe review focuses on the significance and draws onto the potential applications of XACT to demonstrate it as an innovative imaging technique.ApproachThis review navigates the expanding landscape of XACT imaging within the biomedical sphere. Integral topics addressed encompass the refinement of imaging systems and the advancement in image reconstruction algorithms. The review particularly emphasizes XACT's significant biomedical applications.ResultsKey uses, such as breast imaging, bone density maps for osteoporosis, and X-ray molecular imaging, are highlighted to demonstrate the capability of XACT. A unique niche for XACT imaging is its application in in vivo dosimetry during radiotherapy, which has been validated on patients.ConclusionsBecause of its unique property, XACT has great potential in biomedicine and non-destructive testing. We conclude by casting light on potential future avenues in this promising domain.

In recent years, convolutional neural networks have significantly advanced the field of computer vision by automatically extracting features from image data. CNNs enable the modeling of complex and abstract image features using learnable filters, eliminating the need for manual feature extraction. However, combining feature maps obtained from CNNs with different approaches can lead to more complex and interpretable inferences, thereby enhancing model performance and generalizability. In this study, we propose a new method called RGB angle rotation to effectively obtain feature maps from RGB images. Our method rotates color channels at different angles and uses the angle information between channels to generate new feature maps. We then investigate the effects of integrating models trained with these feature maps into an ensemble architecture. Experimental results on the CIFAR-10 dataset show that using the proposed method in the ensemble model results in performance increases of 9.10 and 8.42% for the B and R channels, respectively, compared to the original model, while the effect of the G channel is very limited. For the CIFAR-100 dataset, the proposed method resulted in a 17.09% improvement in ensemble model performance for the R channel, a 5.06% increase for the B channel, and no significant improvement for the G channel compared to the original model. Additionally, we compared our method with traditional feature extraction methods like scale-invariant feature transform and local binary pattern and observed higher performance. In conclusion, it has been observed that the proposed RGB angle rotation method significantly impacts model performance. [ABSTRACT FROM AUTHOR]

In this study, we developed a residue number system (RNS), a numeral system consisting of an arbitrary number of pairwise coprime integers representing a specific integer by its value. We discovered that this system reduced the runtime or training speed of image processing. In addition, we observed that the characteristic operations of the core functions of the decentralized congruential system derived from the proposed system reduced the overall execution time or training speed of image processing. Both the speed of calculation and the properties of the congruential system ensured the accuracy and security of information after distributed processing. Overall, this approach enabled the use of different algorithms on microdevices while ensuring confidentiality. In summary, we developed a system capable of increasing the operation speed of image processing by 50% through core function precomputation, with a modified image data input, a modified core function, and a reverse core function. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Cameras cannot capture the same colors as those seen by the human eye because the eye and the cameras' sensors differ in their spectral sensitivity. To obtain a plausible approximation of perceived colors, the camera's Image Signal Processor (ISP) employs a color correction step. However, even advanced color correction methods cannot solve this underdetermined problem, and visible color inaccuracies are always present. Here, we explore an approach in which we can capture accurate colors with a regular camera by optimizing the spectral composition of the illuminant and capturing one or more exposures. We jointly optimize for the signal‐to‐noise ratio and for the color accuracy irrespective of the spectral composition of the scene. One or more images captured under controlled multispectral illuminants are then converted into a color‐accurate image as seen under the standard illuminant of D65. Our optimization allows us to reduce the color error by 20–60% (in terms of CIEDE 2000), depending on the number of exposures and camera type. The method can be used in applications in which illumination can be controlled, and high colour accuracy is required, such as product photography or with a multispectral camera flash. The code is available at https://github.com/gfxdisp/multispectral_color_correction. [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]

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]

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]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Estimation of the actual size of an object from its digital image can help to automate processes and increase productivity. One example of object-size measurement is evaluation of the pavement condition by estimating crack length. As input images taken by different cameras are often inconsistent, a robust method of automatically measuring objects is necessary. Here, the crack size has been estimated by combining the proposed 'asphalt crack length extractor' (ACLE) method and an object-size conversion method called 'pixel to actual size conversion engine' (PACE). In ACLE, image segmentation is performed to detect the object and extract its pixel-wise length from the image. In PACE, the pixel-wise measurements are converted to metric units. Several experiments were conducted to evaluate the proposed methods. The combined PACE/ACLE module showed an average error of less than 3%. Thus, it is proposed for use in crack length determination and condition assessment of asphalt pavements. [ABSTRACT FROM AUTHOR]

This paper describes a method for integrating multiple dense point clouds using a shared landmark to generate a single real-scale integrated result for photogrammetry. It is difficult to integrate high-density point clouds reconstructed by photogrammetry because the scale differs with each photogrammetry. To solve this problem, this study places a QR code of known sizes, which is a shared landmark, in the reconstruction target environment and divides the reconstruction target environment based on the position of the QR code that is placed. Then, photogrammetry is performed for each divided environment to obtain each high-density point cloud. Finally, we propose a method of scaling each high-density point cloud based on the size of the QR code and aligning each high-density point cloud as a single high-point cloud by partial-to-partial registration. To verify the effectiveness of the method, this paper compares the results obtained by applying all images to photogrammetry with those obtained by the proposed method in terms of accuracy and computation time. In this verification, ideal images generated by simulation and images obtained in real environments are applied to photogrammetry. We clarify the relationship between the number of divided environments, the accuracy of the reconstruction result, and the computation time required for the reconstruction. [ABSTRACT FROM AUTHOR]

Breast cancer occurs when cells or tissues of the breast grow abnormally. On a global scale, Globally, cancer is a common and deadly type of disease that most affects women. These studies provide a new paradigm for skillful deep recognition of preprocessed images using deep neural networks. At this stage, the paper presents an efficient BreastNet model for breast cancer detection containing 48 layers, applied to three datasets namely breast ultrasound images (BUST), which includes 780 images classified into benign, malignant, normal and invasive ductal carcinoma (IDC) and BreakHis_v1_400X. Breast cancer images are processed using different feature extraction vectors. Medical image processing is a crucial step in adjusting the brightness and contrast of images. Gamma correction can help preserve detail in the darkest and brightest areas of an image. In addition to its combination of complex-frequency features, Geronimo-Hardin-Massopust (GHM) multiwave conversion is a type of multiwave conversion that offers many advantages for image processing tasks. By capturing low-frequency (soft areas) and high-frequency (edges and textures) components of an image more accurately, Discrete Wavelet Transform (DWT) processing can also In order to represent an image more efficiently, it is necessary to reduce the amount of data required while retaining important visual details. The proposed model, BreastNet, improved the classification accuracy performed on the proposed dataset by 95.88%, 93.30%, and 91.16%. The proposed model has a better execution rate than AlexNet, VGG16, Googlenet, Resnet18, and Resnet50 models. [ABSTRACT FROM AUTHOR]

In autonomous driving, capturing high-quality images with visual sensors in adverse weather conditions presents a significant challenge for object detection. This paper introduces a candid and effective preprocessing method called Contrast Enhancement through Mutual Entropy (CEME) to improve the visual quality of images. Unlike previous methods such as traditional image processing, image restoration, and deep learning techniques, CEME enhances image quality using simple filtering operations. CEME works by adjusting gray levels appropriately through the calculation of mutual entropy between adjacent gray levels in each plane of a color image. Experimental simulations were conducted on various images taken in weather conditions like snow, fog, sand, and rain. To evaluate performance, this study used two natural image quality assessment metrics: Novel Blind Image Quality Assessment (NBIQA) and Natural Image Quality Evaluator (NIQE). The proposed method achieved an average NBIQA for sandy, snowy, rainy, and foggy images of 28.1576, 35.7233, 29.8796, and 36.1944 respectively. [ABSTRACT FROM AUTHOR]

Protecting data transmission, which plays a capital importance for data confidentiality, has recently been recognized as significant attention. Several research works using the data hiding paradigm have tried to apply steganography in digital images to protect the secret bits of confidential data against unwanted access throughout the public network commonly used. Nevertheless, the existing steganography algorithm faces a common challenge of a significant distortion of the image transmitted through the public network after concealing the secret bits in its pixels. To address this steganography problem in digital images, this article presents a new algorithm to optimize embeddable pixels used to yield a quality stego image with fewer distortions. The proposed method transforms the secret bits into a simplified format by changing the base system and mathematical sorting to reduce the magnitude, making the payload size significantly minimal. The proposed method is experimentally tested using 256 x 256 grayscale images from the Signal and Image Processing Institute (SIPI) database as cover images and random bits generated from Lorem Ipsum text as secret data. The method's performance is compared to the recent Difference Expansion (DE) method, showing superior results with an average Peak Signal-to-Noise Ratio (PSNR) of 85 dB and a Structural Similarity Index Measure (SSIM) of 0.99, indicating higher stego image quality. [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]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) 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.)

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]

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]

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]

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]

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]

In order to improve defects detection efficiency in polyethylene (PE) gas pipelines and decrease leakage or other pipelines abnormalities in operation, this research proposed an improved YOLO(You Only Look Once) v5 detection model. First, the collected pipeline defect images were processed in grey scale, which improved the computational efficiency of the computer; then, Gamma transform and double filtering algorithms were applied respectively for image enhancement and noise reduction filtering of defects, which enhanced image quality and reduced image noise. Finally, the improved Sobel algorithm was applied to detect defective image edges and the defects in the image were segmented by adaptive threshold segmentation method to obtain binary images. The obtained binary images were employed to train the improved YOLO v5 detection model. The obtained experimental results showed that, compared with the original algorithm, the improved detection algorithm had better detection efficiency and higher robustness as well as higher recognition for common defects, improved YOLOv5 mAP and recall were 97.18% and 98.03%, respectively, the mAP has increased by 1.33% and the recall has increased by 3.83%, which can achieve the detection and identification of defect types of effects in PE gas pipes. [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]

Ships and other maritime objects are often unable to endure the harsh and dynamic sea environment. Collecting real-time data and detecting these objects using various sensors such as RADARs, Synthetic Aperture RADARs, and mounted RADARs present significant challenges due to numerous influencing factors. To address this issue, our research aims to develop an Internet of Things (IoT)-based multi-scale and multi-scene ship identification system. This system leverages a multi-scale neural network integrated with a high-response convolutional neural network (CNN)-based Kalman filter architecture. To construct this model, we selected various ship categories and initially employed a base CNN model to develop a new model with different convolutional layers. Our approach utilizes mixed methods for tracking and detecting objects, with a focus on small ships. The dataset is processed through multiple neural network layers, and we implemented the Kalman filter to estimate and predict the ships' positions. Additionally, using the YOLOv3 model, we achieved improved accuracy and reduced error rates through mathematical optimization. Our method utilizes a dataset of 5,604 samples and incorporates a hybrid approach with YOLOv3. Our model demonstrates significant improvements for both medium-sized and small ships. The proposed work provides both qualitative and quantitative advancements. Our model exceeded the best results from parallel experiments by 3.9% and 1.2% in terms of Average Precision (AP). Furthermore, YOLOv3 achieved a performance score of 97.34% across various metric parameters, while our proposed approach attained the highest scores of 97.8% and 94.87%, respectively. [ABSTRACT FROM AUTHOR]

Sandstorms pose a substantial hurdle to image clearness, affecting visibility, detail representation, and color quality. In this study, a novel algorithm named multistep color reparation (MCR) is constructed, particularly for the enhancement of images impacted by sandstorms. MCR includes a thorough procedure comprising five distinct phases. The first phase focuses on enhancing color indemnity by effectively compensating the missing color information, assuring that the enhanced images appear with better true-to-life colors. Next, a statistics-based intensity redistribution is employed as a second phase to emphasize tonality, ensuring better perceptual quality. The third phase applies an optimized intuitionistic fuzzy model, leveraging the fuzzy model's undiscovered abilities to remove the dust veil. Following this, a color deviation correction phase is executed to rectify the emerging color shifts and inaccuracies, applying corrective measures to enliven accurate color representation. Finally, a fast visibility restoration approach is implemented to improve the visibility and provide better saturation. To evaluate the efficacy of MCR, thorough experiments were performed comparing it with eight state-of-the-art algorithms. Quality assessments were carried out using six high-level image evaluation metrics alongside the processing times. The findings exhibited that MCR outperforms all competing algorithms, and it efficaciously addressed the challenges posed by sandstorm-induced degradation, presenting substantial enhancements in perceptual fidelity and quality. [ABSTRACT FROM AUTHOR]

In recent decades, the advancement of deep learning algorithms and their effectiveness in saliency detection has garnered significant attention in research. Among these methods, U Network ( U-Net ) is widely used in computer vision and image processing. However, most previous deep learning-based saliency detection methods have focused on the accuracy of salient regions, often overlooking the quality of boundaries, especially fine boundaries. To address this gap, we developed a method to detect boundaries effectively. This method comprises two modules: prediction and residual refinement, based on U-Net structure. The refinement module improves the mask predicted by the prediction module. Additionally, to boost the refinement of the saliency map, a channel attention module is integrated. This module has a significant impact on our proposed method. The channel attention module is implemented in the refinement module, aiding our network in obtaining a more accurate estimation by focusing on the crucial and informative regions of the image. To evaluate the developed method, five well-known saliency detection datasets are employed. The proposed method consistently outperforms the baseline method across all five datasets, demonstrating improved performance. [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]

Practical Application Conventional methods for evaluating of fish freshness based on physiological and biochemical methods are often destructive, complicated, and costly. This study aimed to predict the freshness of large yellow croaker which was sampled every second day in 9 consecutive days at 4°C, using computer vision technology combined with pupil color parameters and different machine learning algorithms (back propagation neural network, BPNN; radial basis function neural network; support vector regression; and random forest regression, RFR). In the process of model building, the RFR model provided the most accurate prediction for the value of total volatile basic nitrogen (TVB‐N), with the R‐square of the test set (Rp2$R^{2}_p $) of 0.993. The BPNN model exhibited the best fit for predicting the value of thiobarbituric acid (TBA), with Rp2$R^{2}_p $ of 0.959. Additionally, the RFR model was the most effective in forecasting total viable count (TVC), with Rp2$R^{2}_p $ of 0.935. After validation, the root mean square error values of the RFR model for predicting TVB‐N value, TBA value, and TVC value were the lowest, which were 0.764, 0.067, and 0.219, respectively. It demonstrated the applicability and good predictive performance of the RFR model for predicting biochemical and microbiological indicators. These findings also demonstrated that monitoring the changes in pupil color could successfully predict the freshness of chilled fish.Application Scenario: Quality inspectors detect changes in the freshness of large yellow croaker in real time from the beginning of distribution to the selling site. [ABSTRACT FROM AUTHOR]

Three‐dimensional visualisation of mesh reconstruction of the medical images is commonly used for various clinical applications including pre / post‐surgical planning. Such meshes are conventionally generated by extracting the surface from volumetric segmentation masks. Therefore, they have inherent limitations of staircase artefacts due to their anisotropic voxel dimensions. The time‐consuming process for manual refinement to remove artefacts and/or the isolated regions further adds to these limitations. Methods for directly generating meshes from volumetric data by template deformation are often limited to simple topological structures, and methods that use implicit functions for continuous surfaces, do not achieve the level of mesh reconstruction accuracy when compared to segmentation‐based methods. In this study, we address these limitations by combining the implicit function representation with a multi‐level deep learning architecture. We introduce a novel multi‐level local feature sampling component which leverages the spatial features for the implicit function regression to enhance the segmentation result. We further introduce a shape boundary estimator that accelerates the explicit mesh reconstruction by minimising the number of the signed distance queries during model inference. The result is a multi‐level deep learning network that directly regresses the implicit function from medical image volumes to a continuous surface model, which can be used for mesh reconstruction from arbitrary high volume resolution to minimise staircase artefacts. We evaluated our method using pelvic computed tomography (CT) dataset from two public sources with varying z‐axis resolutions. We show that our method minimised the staircase artefacts while achieving comparable results in surface accuracy when compared to the state‐of‐the‐art segmentation algorithms. Furthermore, our method was 9 times faster in volume reconstruction than comparable implicit shape representation networks. [ABSTRACT FROM AUTHOR]

AbstractIn high-dose radiological environments, where precision and safety are of utmost importance, the ability to acquire accurate and clear visual information is of paramount importance for ensuring safety and reliability in critical industrial processes. However, these environments inherently introduce significant challenges due to the adverse effects of radiation on imaging equipment. As a consequence, inspection videos captured within such high-radiation environments often contain a significant amount of noise. This noise substantially complicates the task of identifying and assessing potential defects in vital components. It also diverts attention and resources toward investigating false positives created by noise, leading to inefficiencies, and for industrial processes on the critical path, this can further prolong the outage. Addressing this noise is essential not only for precision, but also for ensuring safety, reliability, and efficiency in critical industrial processes.In this paper, we present a custom-designed filter utilizing a priori information about camera position and trajectory to remove the noise from the inspection videos, making the defects easier to manually identify. As the camera movement is in one direction at a constant speed, the proposed approach uses this temporal and spatial information to accurately remove the noise. This approach applies to a subset of visual inspection problems throughout the nuclear industry, as well as many other industries where there is knowledge available about the camera speed and direction of travel. The proposed approach is compared with three accepted/well-known approaches, median filtering, bilateral filtering, and fast nonlocal means denoising, and an additional state-of-the-art deep learning model is also used for comparison. It was found that the proposed approach produces the most accurate video denoising in terms of visual quality and the retainment of the defect features throughout the videos tested. [ABSTRACT FROM AUTHOR]

We show that convolution neural networks (CNNs) trained to find strong gravitational lens systems are biased towards systems with larger Einstein radii and large concentrated sources. This selection function is key to fully realizing the potential of the large samples of strong gravitational lens systems that will be found in upcoming wide-field surveys. In this paper, we use a CNN and three training data sets to quantify the network selection function and its implication for the many scientific applications of strong gravitational lensing. We use CNNs with similar architecture as is commonly found in the literature. The networks preferentially select systems with larger Einstein radii and larger sources with more concentrated source-light distributions. Increasing the detection significance threshold to 12 |$\sigma$| from 8 |$\sigma$| results in 50 per cent of the selected strong lens systems having Einstein radii |$\theta _\mathrm{E}$| |$\ge$| 1.04 arcsec from |$\theta _\mathrm{E}$| |$\ge$| 0.879 arcsec, source radii |$R_S$| |$\ge$| 0.194 arcsec from |$R_S$| |$\ge$| 0.178 arcsec, and source Sérsic indices |$n_{\mathrm{Sc}}^{\mathrm{S}}$| |$\ge$| 2.62 from |$n_{\mathrm{Sc}}^{\mathrm{S}}$| |$\ge$| 2.55. The model trained to find lensed quasars shows a stronger preference for higher lens ellipticities than those trained to find lensed galaxies. The selection function is independent of the slope of the power law of the mass profiles, hence measurements of this quantity will be unaffected. The lens finder selection function reinforces that of the lensing cross-section, and thus we expect our findings to be a general result for all galaxy–galaxy and galaxy–quasar lens finding neural networks. [ABSTRACT FROM AUTHOR]

By applying vision sensing to realize online intelligent control on the pipeline welding process, the welding efficiency and qualification rate of pipeline girth butt joint during on-site construction of long-distance oil and gas pipelines can be effectively improved. During the pipeline external welding process, based on the designed combined line structured lights vision sensor (C-LSLVS), a partitioned time–frequency-space (PTFS) image processing algorithm is proposed to extract the laser centerline for the deformed laser lines image of double-V composite groove. The algorithm first utilizes information in the time domain to remove random interference from the image, and then, based on Radon transform (RT) and discrete Fourier transform (DFT), other stationary interferences can be eliminated and laser line characteristics can be enhanced by back-projection reconstruction in the frequency domain and space domain, so the stable and accurate extraction of laser centerline can be achieved under different situations. Subsequently, the theoretical analysis and error calculation of the pipeline groove sizes and related parameters solution model based on the local plane fitting method are carried out, and high-precision calculations of the parameters are completed. Finally, an intelligent pipeline welding system based on the C-LSLVS is constructed. In the intelligent welding experiment, the maximum detection error of the pipeline welding groove sizes did not exceed 0.20 mm, and the weld seam tracking deviation did not exceed 0.25 mm. The welding results show that the system has important application value for the development of pipeline intelligent welding. [ABSTRACT FROM AUTHOR]

Visible imaging is a fast, cheap, and accurate technique in the assessment of food quality and safety. The technique was used in the present research to detect sea foam adulterant levels in black and red peppers. The fraud levels included 0, 5, 15, 30, and 50%. Sample preparation, image acquisition and preprocessing, and feature engineering (feature extraction, selection, and classification) were the conducted steps in the present research. The efficient features were classified using artificial neural networks and support vector machine methods. The classifiers were evaluated using the specificity, sensitivity, precision, and accuracy metrics. The artificial neural networks had better results than the support vector machine method for the classification of different adulterant levels in black pepper with the metrics' values of 98.89, 95.67, 95.56, and 98.22%, respectively. Reversely, the support vector machine method had higher metrics' values (99.46, 98.00, 97.78, and 99.11%, respectively) for red pepper. The results showed the ability of visible imaging and machine learning methods to detect fraud levels in black and red pepper. [ABSTRACT FROM AUTHOR]

The exponential growth of data from sensors and internet‐connected devices has challenged traditional electronic computing, particularly in processing speed and power efficiency. Optical information processing, with its inherent high speed and parallelism, offers a promising solution. Moreover, optical processing is vital for developing all‐optical networks, where all‐optical imaging is essential. In this paper, a novel all‐optical imaging technique that leverages in situ anion exchange reactions between perovskite nanocrystals to create an array of pixels with tunable emission wavelengths is proposed. This method enables the conversion of UV light, whether transmitted or reflected by an object, into the visible spectrum, thereby establishing a direct correlation between spectral and spatial information. By employing wavelength division multiplexing (WDM), the approach reduces the dimensionality of information acquisition from two dimensions to one, enhancing imaging speed. This innovation paves the way for significant advancements in all‐optical imaging and image processing, offering new possibilities for faster and more efficient imaging technologies. [ABSTRACT FROM AUTHOR]