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AbstractThis study presents a new method for simultaneous determination of fenoterol, procaterol, clenbuterol, terbutaline and isoprenaline drugs using thin-layer chromatography assisted by image analysis (HPTLC-IA) combined with sensitive detection modes. Separation of the selected drugs was obtained using HPTLC Silica gel 60 F254 plates and mixture of isopropyl alcohol, ethyl acetate and ammonia (20:25:1.5 v/v/v) as mobile phase. UV detection mode combined with the use of 2,2-diphenyl-1-picrylhydrazyl (DPPH•) and 2,2'-azino-bis (3-ethylbenzothiazoline)-6-sulfonic acid (ABTS+•) radicals were applied for the first time for sensible detection of the separated drugs. Grey, green, red and blue scales respectively were selected for image analysis and accurate quantification of spot area. According to the obtained result, the use of DPPH• and ABTS+• reagents revealed a higher sensitivity in detection and accurate quantification of the analyzed drugs. Lower limit of detection (LOD = 0.07 ± 0.03 µg/spot; 0.09 ± 0.03 µg/spot and 0.10 ± 0.04 µg/spot) was obtained for fenoterol, procaterol and terbutaline using the ABTS+• radical and red scale selection for image processing. With the exception of isoprenaline, the proposed HPTLC-IA method showed a good separation of fenoterol, procaterol, clenbuterol and terbutaline drugs from other compounds in urine samples. Good recovery was obtained for these drugs in spiked urine samples. [ABSTRACT FROM AUTHOR]

To evaluate the resistance of apples to bruising under transient impact loads, a pendulum device was initially employed to gather collision data. Subsequently, an image acquisition and processing system was utilised to compute the apple surface bruise area, and X-ray computed tomography technology was utilised to precisely calculate the bruise volume. Based on the CT image and bruise area image analysis, the difference between bruised area and normal tissue on the apple after collision, and effects of impact energy on apple bruising volume and bruise area has been elaborated in detail. Results analysis revealed a general increase in both bruise volume and bruise area with escalating impact energy, and there is a strong correspondence between bruise volume and bruise area across different storage days and impact energies. The relationship between bruise volume and bruise area under different storage duration were also established. On the base of bruise resistance index (BRI) model utilising impact energy and bruise volume as variables, those analysis led to the establishment of a new bruise resistance index (BRI*) model employing impact energy and bruise area as independent variables. The effectiveness of BRI* model in assessing apple bruise resistance was demonstrated by comparing the corresponding relative error for bruise thresholds of BRI and BRI* under the same condition. The relative error ≤3.3% underscores the accuracy of the new model and proves the proposed BRI* model to be an efficient and rapid tool for assessing the degree of apple bruising. • Apples damage volume calculated utilising X-ray CT was introduced. • The effect of impact energy on both bruise volume and bruise area was analysed. • A novel bruise resistance index model was established to assess apple bruise. • The model can serve as an efficient and rapid tool for assessing bruising degree. [ABSTRACT FROM AUTHOR]

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

Deep learning has been the subject of a significant amount of research interest in the development of novel algorithms for deep learning algorithms and medical image processing have proven very effective in a number of medical imaging tasks to help illness identification and diagnosis. The shortage of large-sized datasets that are also adequately annotated is a key barrier that is preventing the continued advancement of deep learning models used in medical image analysis, despite the effectiveness of these models. Over the course of the previous 5 years, a great number of research have concentrated on finding solutions to this problem. In this work, we present a complete overview of the use of deep learning techniques in a variety of medical image analysis tasks by reviewing and summarizing the current research that have been conducted in this area. In particular, we place an emphasis on the most recent developments and contributions of state-of-the-art semi-supervised and unsupervised deep learning in medical image analysis. These advancements and contributions are shortened based on various application scenarios, which include image registration, segmentation, classification and detection. In addition to this, we explore the significant technological obstacles that lie ahead and provide some potential answers for the ongoing study. [ABSTRACT FROM AUTHOR]

Purpose: Arterial spin labeling (ASL) is a widely used contrast‐free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM‐OSIPI ASL‐MRI Challenge, aiming to establish best practices for more reproducible ASL analysis. Methods: Eight teams analyzed the challenge data, which included a high‐resolution T1‐weighted anatomical image and 10 pseudo‐continuous ASL datasets simulated using a digital reference object to generate ground‐truth CBF values in normal and pathological states. We compared the accuracy of CBF quantification from each team's analysis to the ground truth across all voxels and within predefined brain regions. Reproducibility of CBF across analysis pipelines was assessed using the intra‐class correlation coefficient (ICC), limits of agreement (LOA), and replicability of generating similar CBF estimates from different processing approaches. Results: Absolute errors in CBF estimates compared to ground‐truth synthetic data ranged from 18.36 to 48.12 mL/100 g/min. Realistic motion incorporated into three datasets produced the largest absolute error and variability between teams, with the least agreement (ICC and LOA) with ground‐truth results. Fifty percent of the submissions were replicated, and one produced three times larger CBF errors (46.59 mL/100 g/min) compared to submitted results. Conclusions: Variability in CBF measurements, influenced by differences in image processing, especially to compensate for motion, highlights the significance of standardizing ASL analysis workflows. We provide a recommendation for ASL processing based on top‐performing approaches as a step toward ASL standardization. [ABSTRACT FROM AUTHOR]

The compressor is the most core component of refrigeration products, and its performance directly affects the overall performance of the product. The exhaust silencing chamber and coil are key components of a refrigeration compressor, and their working stability and lifespan determine the reliability and economy of the refrigeration compressor. This paper obtains reference data for the fluid domain of the exhaust silencing chamber through simulation analysis and image processing of the existing exhaust silencing chamber structure and inner diameter size of the coil. Then, the optimal model of the prioritized exhaust chamber is obtained through pattern recognition method. Based on the image processing method, the internal flow field of the exhaust chamber are obtained, providing a strong reference for the optimal structural design of the exhaust silencing chamber and coil of the refrigeration compression body. [ABSTRACT FROM AUTHOR]

Financial risk warning is a crucial technical issue for enterprises. Traditionally, it relied on modeling analysis of a single data type, which does not fully capture the diverse characteristics of financial risk activities. To address this, this paper introduces a multimodal deep learning framework driven by semantic analysis and image processing for intelligent warning of enterprise financial risks. Initially, natural language processing algorithms analyze textual data such as financial statements, news reports, and social media comments within the industry. Concurrently, two convolutional neural network models, M-CNN and M-RNN, extract features from images and chart data. These textual and visual feature representations are then fused to create a multimodal deep neural network framework. Extensive experimental evaluation and comparative analysis of the proposed framework were conducted. The results indicate that the financial risk rate of consumer fraud graph analysis varies significantly, displaying a fluctuating state with values ranging from 2.1% to 16.8%. Compared to other methods, the proposed approach demonstrates superior performance in financial risk warning tasks, with the risk rate increasing from 1.2% to 26.5% as it iterates from 1 to 6. [ABSTRACT FROM AUTHOR]

Biofilms, comprised of cells embedded in extracellular matrix (ECM), enable bacterial surface colonization and contribute to pathogenesis and biofouling. Yet, antibacterial surfaces are mainly evaluated for their effect on bacterial cells rather than the ECM. Here, a method is presented to separately quantify amounts and distribution of cells and ECM in Salmonella biofilms grown on electroactive poly(3,4‐ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS). Within a custom‐designed biofilm reactor, biofilm forms on PEDOT:PSS surfaces electrically addressed with a bias potential and simultaneous recording of the resulting current. The amount and distribution of cells and ECM in biofilms are analyzed using a fluorescence‐based spectroscopic mapping technique and fluorescence confocal microscopy combined with advanced image processing. The study shows that surface charge leads to upregulated ECM production, leaving the cell counts largely unaffected. An altered texture is also observed, with biofilms forming small foci or more continuous structures. Supported by mutants lacking ECM production, ECM is identified as an important target when developing antibacterial strategies. Also, a central role for biofilm distribution is highlighted that likely influences antimicrobial susceptibility in biofilms. This work provides yet a link between conductive polymer materials and bacterial metabolism and reveals for the first time a specific effect of electrochemical addressing on bacterial ECM formation. [ABSTRACT FROM AUTHOR]

This paper presents a practical method for analyzing drivers' eye movements, providing a valuable tool for understanding their behavior during driving simulations. The method, which utilizes an image processing technique, addresses the challenges when the driver's attention is on points without information about the image depth. The screen image changes or moves with the simulation. It allows us to identify the gaze position relative to the road, determining whether the glance is inside or outside. This is achieved by transforming RGB images (frames) collected by the eye-tracker video camera into a b/w image using the Canny filter. This filter can identify objects' contours by evaluating the change in color of their surfaces. A window is then applied to these new images to extract information about the gaze position in the real world. Four drivers were used as a sample for the method's testing. The findings demonstrate various driver variations and a disparity between driving in curved and rectilinear segments. The gaze is typically inside the road in curved sections, whereas in rectilinear sections, the gaze is frequently outside. [ABSTRACT FROM AUTHOR]

The airlift pump is a key part of wastewater treatment and is employed as an innovative and feasible collection tool. However, as one of the key factors in the lifting capability of airlift pumps, film thickness in the gas–liquid two-phase flow operating in pumps is still an unknown topic because it is difficult to measure. This paper proposes a visualization method for measuring film thickness and investigates the film thickness when operating under gas flow with a high rate in airlift pumps using experiments. Firstly, a simulation experiment platform was built, and the images of the film structure were acquired by a high-speed camera. Then, image-processing technology and an image distortion correction were proposed to extract the gas–liquid interface for studying the thickness of the film. The experimental results demonstrated that a large film thickness ranging from 0.15 D to 0.24 D was found in airlift pumps and that its film thickness kept a constant value, even under a high gas superficial velocity, maintaining a large output liquid flow from airlift pumps. As wastewater was carried by wastewater treatment, a larger film thickness of the annular film will benefit the high lifting rate of wastewater. The works in this paper offer valuable insights for the higher performance of working airlift pumps and wastewater treatment efficiency. [ABSTRACT FROM AUTHOR]

Biometric devices have been indicated to be vulnerable to various threats. In the present study, a novel liveness identification technique is introduced according to the noise analysis of fingerprint images. It has been shown that the textural patterns of real fingertips are different from those of spoof fingertips. In this work, Real images were successfully distinguished from fake ones with an accuracy of approximately 99.50%. The database consists of 200 fingerprints, half of which is the real database (i.e. 100 fingerprints), and the other half is the fake database. Textural features are analyzed using pyramidal mean and PDFB analysis techniques that decompose images into directional sub-bands at several scales. The Shannon entropy, approximate entropy, sample entropy, permutation entropy, fuzzy entropy, multiscale entropy, Log energy entropy, and Sure entropy were estimated for all these sub-bands and were arranged into an array. In the classification stage, an ensemble classifier based on a multiobjective genetic algorithm was employed. The results obtained revealed that the suggested technique could successfully differentiate live fingertips from fake ones. The best accuracy was obtained for the proposed ensemble technique, with an average accuracy of 99.52% compared to other classifiers. After the ensemble method, quadratic SVM achieved the best classification performance with a mean accuracy of 97.34% in discriminating real live fingerprints from fake fingerprints. In comparison to other prior techniques, the suggested method produced good classification performance in real-time fingerprint identification while remaining straightforward. Therefore, the system can be a good option for use in real biometric systems. [ABSTRACT FROM AUTHOR]

Objectives Image segmentation is one of the important subjects in the field of image processing and computer vision. Image segmentation has a wide range of applications in robot perception, autonomous driving, medical image analysis, scene understanding, video surveillance, virtual reality and augmented reality, but it also faces many challenges. For example, it is necessary to rely on the context to obtain more precise results in complex scenes. Deep learning-based image segmentation algorithms have made great progress, but they also face many problems, especially most algorithms lack the ability of combining image context, and they ignore the role and influence of the contextual information on the segmentation contour, which limits the improvement of algorithm performance. For this reason, we propose a contour-based image segmentation approach, which uses a progressive dynamic graph network to deform the contour.Methods Specifically, according to the topology of object contour, we first sample the vertices from the contour to convert them into a dynamic graph. We abandon the disorder of the graph and the uncertainty of the adjacency relationship, to construct a graph with a fixed topology. And then we perform inference learning by diffusing contextual information of the object points. We also progressively accumulates historical learning experience to update the contour graph dynamically. We adopt an end-to-end method to update the graph and predict object position, and encapsulate them into a closed-loop learning process. The proposed approach fully takes into account context and the historical learning experience. It also makes up for the shortcomings of the pixel-wise segmentation methods and traditional active contour models.Results Experiments on Cityscapes, KINS, SBD datasets show that the effectiveness and speed of the proposed approach in real-time instance segmentation. Specifically, the dynamic graph model achieves the best performances on Cityscapes and SBD datasets, which are 34.4% and 55.3% average precision（AP） respectively, and 30.5% AP on KINS dataset. It also achieves a better fitting effect on KINS dataset in terms of visualization results. Compared with deep snake model, the segmentation contours extracted by dynamic graph model fit the real boundary better and the contours are smoother. In addition, in terms of running speed, the dynamic graph model also obtains best results on three datasets, which are 3.8, 7.6 and 13.1 frame/s respectively. The proposed method has an average improvement of 0.5 frame/s on three datasets when compared with deep snake model.Conclusions Progressive dynamic graph makes better use of the topology of object contour and the characteristics of dynamic update. By disseminating context information and historical information, this method solves the problem that segmentation algorithms lack the ability of combining context. Future research can also face the following aspects: Designing a more efficient and robust network architecture, considering the objects around the target to combine the context information of scene in the image,and attempting to resolve the issue of poor convergence on non-convex objects or concave regions. [ABSTRACT FROM AUTHOR]

Numerous advancements and significant progress have been made in computer methods for medical applications, alongside technological developments. Automatic image analysis plays a crucial role in the realm of medical diagnosis and therapy. Recent breakthroughs, especially in the field of medical image processing, have enabled the automatic detection of various characteristics, alterations, diseases, and degenerative conditions using skin scans. Utilizing image processing methods, skin image analysis is instrumental in the identification and monitoring of conditions manifesting through alterations in skin structure. Notably, accurate segmentation of cancerous regions from the background remains a challenging task in the area of melanoma image analysis. The primary objective of this study is to achieve exceptional precision in delineating melanoma boundaries. Leveraging the Bat Optimization algorithm, we determine the optimal threshold for melanoma segmentation, effectively identifying the most accurate cancerous area boundaries. To evaluate the results, standard metrics such as accuracy, sensitivity, specificity, Dice coefficient, and F1 score are employed. In this study, we applied the Bat Optimization algorithm to determine the optimal threshold value for segmenting melanoma skin cancer, effectively identifying the most accurate cancerous area boundaries. For result evaluation, we employed standard metrics including accuracy, sensitivity, specificity, Dice coefficient, and F1 score, which yielded impressive values of 99.8%, 98.99%, 98.87%, 98.45%, and 98.24%, respectively. [ABSTRACT FROM AUTHOR]

In particle image velocimetry (PIV) experiments, background noise inevitably exists in the particle images when a particle image is being captured or transmitted, which blurs the particle image, reduces the information entropy of the image, and finally makes the obtained flow field inaccurate. Taking a low-quality original particle image as the research object in this research, a frequency domain processing method based on wavelet decomposition and reconstruction was applied to perform particle image pre-processing. Information entropy analysis was used to evaluate the effect of image processing. The results showed that useful high-frequency particle information representing particle image details in the original particle image was effectively extracted and enhanced, and the image background noise was significantly weakened. Then, information entropy analysis of the image revealed that compared with the unprocessed original particle image, the reconstructed particle image contained more effective details of the particles with higher information entropy. Based on reconstructed particle images, a more accurate flow field can be obtained within a lower error range. [ABSTRACT FROM AUTHOR]

(1) Background: Previously, VESsel GENeration (VESGEN) software was used to map and quantify vascular changes observed on fluorescein angiography (FA) in subjects (n = 15 eyes) with retinal pathology ranging from mild non-proliferative diabetic retinopathy (NPDR) to proliferative diabetic retinopathy (PDR). In the current study, we used VESGEN for the assessment of individuals with early-stage NPDR imaged by FA (Cohort 1) and by optical coherence tomography angiography (OCTA; Cohort 2). (2) Methods: Cohort 1 included type 2 diabetics (T2D), represented 21 eyes (ranging from no DR to moderate DR), and also included nondiabetic controls (NDC; n = 15 eyes). Cohort 2 consisted of 23 eyes from T2D subjects (including no DR subjects and moderate DR subjects) and NDC (n = 18 eyes). (3) Results: In the FA-VESGEN study, total tortuosity (Tv) of microvessels (G ≥ 6) increased in T2D with mild DR compared to the controls. In contrast, the VESGEN analysis of OCTA images showed that vessel length (characterized as density) was lower in T2D subjects before the diagnosis of DR and following the diagnosis of DR when compared to the controls. Additionally, T2D showed a significant decrease in vessel area (density). (4) Conclusions: FA elucidated the vessel morphology of small-generation microvessels to a greater degree than OCTA; however, OCTA identified changes in vessel density better than FA. VESGEN analysis can be used with both standard FA and OCTA to facilitate our understanding of early events in DR, including before the clinical diagnosis of DR. [ABSTRACT FROM AUTHOR]

The field of image processing is experiencing significant advancements to support professionals in analyzing histological images obtained from biopsies. The primary objective is to enhance the process of diagnosis and prognostic evaluations. Various forms of cancer can be diagnosed by employing different segmentation techniques followed by postprocessing approaches that can identify distinct neoplastic areas. Using computer approaches facilitates a more objective and efficient study of experts. The progressive advancement of histological image analysis holds significant importance in modern medicine. This paper provides an overview of the current advances in segmentation and classification approaches for images of follicular lymphoma. This research analyzes the primary image processing techniques utilized in the various stages of preprocessing, segmentation of the region of interest, classification, and postprocessing as described in the existing literature. The study also examines the strengths and weaknesses associated with these approaches. Additionally, this study encompasses an examination of validation procedures and an exploration of prospective future research roads in the segmentation of neoplasias. Significance Statement: Critical review of histological image analysis, focusing on diagnosing follicular lymphoma.Analysis of state‐of‐the‐art image processing methodologies.Implementation of computer‐based segmentation to enhance objectivity in diagnosis.Emphasis on improving diagnostic accuracy for favorable patient outcomes.Establishes a foundation for future research in histological image analysis. [ABSTRACT FROM AUTHOR]

Sentinel-2 satellites are one of the major instruments in remote sensing (RS) technology that has revolutionized Earth observation research, as its main goal is to offer high-resolution satellite data for dynamic monitoring of Earth's surface and climate change detection amongst others. However, visual observation of Sentinel-2 satellite data has revealed that most images obtained during the winter season contain snow noise, posing a major challenge and impediment to satellite RS analysis of land surface. This singular effect hampers satellite signals from capturing important surface features within the geographical area of interest. Consequently, it leads to information loss, image processing problems due to contamination, and masking effects, all of which can reduce the accuracy of image analysis. In this study, we developed a snow-cover removal (SCR) model based on the Cycle-Consistent Adversarial Networks (CycleGANs) architecture. Data augmentation procedures were carried out to salvage the effect of the limited availability of Sentinel-2 image data. Sentinel-2 satellite images were used for model training and the development of a novel SCR model. The SCR model captures snow and other prominent features in the Sentinel-2 satellite image and then generates a new snow-free synthetic optical image that shares the same characteristics as the source satellite image. The snow-free synthetic images generated are evaluated to quantify their visual and semantic similarity with original snow-free Sentinel-2 satellite images by using different image qualitative metrics (IQMs) such as Structural Similarity Index Measure (SSIM), Universal image quality index (Q), and peak signal-to-noise ratio (PSNR). The estimated metric data shows that Q delivers more metric values, nearly 95%, than SSIM and PRSN. The methodology presented in this study could be beneficial for RS research in DL model development for environmental mapping and time series modeling. The results also confirm the DL technique's applicability in RS studies. [ABSTRACT FROM AUTHOR]

Thyroid carcinoma ranks as the 9th most prevalent global cancer, accounting for 586,202 cases and 43,636 deaths in 2020. Computerized image analysis, utilizing artificial intelligence algorithms, emerges as a potential tool for tumor evaluation. This study aims to assess and compare chromatin textural characteristics and nuclear dimensions in follicular neoplasms through gray-level co-occurrence matrix (GLCM), fractal, and morphometric analysis. A retrospective cross-sectional study involving 115 thyroid malignancies, specifically 49 papillary thyroid carcinomas with follicular morphology, was conducted from July 2021 to July 2023. Ethical approval was obtained, and histopathological examination, along with image analysis, was performed using ImageJ software. A statistically significant difference was observed in contrast (2.426 (1.774–3.412) vs 2.664 (1.963–3.610), p =.002), correlation (1.202 (1.071–1.298) vs 0.892 (0.833–0.946), p =.01), and ASM (0.071 (0.090–0.131) vs 0.044 (0.019–0.102), p =.036) between NIFTP and IFVPTC. However, morphometric parameters did not yield statistically significant differences among histological variants Computerized image analysis, though promising in subtype discrimination, requires further refinement and integration with traditional diagnostic parameters. The study suggests potential applications in scenarios where conventional histopathological assessment faces limitations due to limited tissue availability. Despite limitations such as a small sample size and a retrospective design, the findings contribute to understanding thyroid carcinoma characteristics and underscore the need for comprehensive evaluations integrating various diagnostic modalities. [ABSTRACT FROM AUTHOR]

Video processing is the most emergent area of image processing in Research and Development. This paper, we present some advanced technology interrelated to video and image processing, which will provide a super-resolution high-quality most prominent visible light video for better human visualization and background motions or gesticulations of the human brain. There are two approaches for increasing the current resolution level of an image. The first one is the improvement of spatial resolution by reducing the size of pixels through the techniques of sensor manufacturing by about 40 $ {\mathcal{M}}{\textrm{m}^2} $ M m 2 for a 0.35 $ {\mathcal{M}}\textrm{m} $ M m CMOS process and the second approach is the enhancement of chip size that also enhances the capacitance. In video processing, we mainly focus on object images, i.e. any form of signal processing in which the input is an image and the output is a photograph or a frame of video. So, it will be better to converge over techniques of digital image processing. In the proposed algorithm (MHI Simulink model), edge extraction is performed to find the precious area of MRI and CT-Scan images. Using FPGA, edge detection methods have been implemented over scanned images to get better accuracy. The accuracy of the proposed method has been compared with Prewitt and Sobel's edge detection techniques. The proposed method has given better accuracy than Prewitt and Sobel methods. Finally, this paper shows future directions for researchers to enhance the characteristics of digital image and video processing. [ABSTRACT FROM AUTHOR]

With the widespread application of digital images, image processing technology plays an important role in fields such as computer vision and image analysis. Based on the orthogonal matching pursuit algorithm, an image processing method is proposed. In the process, sparse representation and reconstruction algorithm are used for image compressed sensing to complete image sampling operation. Afterwards, the theory of overcomplete sparse representation is introduced to optimize sparse representation, and an overcomplete dictionary is used to remove Gaussian noise, achieving the goal of image processing. The experimental results indicate that the research method do not show significant deficiencies in signal reconstruction when testing reconstructed signals under sparsity of 8; When testing the calculation time, the calculation time of the research method is about 0.212 s when the sparsity is 5 in the Lenna; In the error test, the mean square difference of the research method in the Lenna is stable at about 14.6; When conducting application analysis, the variance eigenvalues of the research method remained below 9.4. This indicates that the research method has good performance and can effectively process images, providing new technical support for image processing. [ABSTRACT FROM AUTHOR]

The characterisation of edges in medical images is critical for disease diagnosis. However, existing systems are still deficient in this task. Traditionally, integer-based derivative operators are employed due to their efficiency in time complexity but lack the ability to track nonlocal and non-singular edge maps. This study proposes a new mask based on Atangana-Beleanu fractional operator. This operator has the same complexity as the state-of-the-art integer-order derivative mask known as the Canny edge detector but has the added advantage to characterise more efficiently nonlocal and nonsingular edge maps. Performance evaluation of the proposed mask reveals an enhanced performance in the context of robustness to noise and quality edge extraction, a significant contribution to literature. The metric for the study is the signal-to-noise ratio, and the structural similarity index and appropriate mask observed is a mask of dimension greater than five. [ABSTRACT FROM AUTHOR]

Agriculture plays a crucial role in fostering sustainable growth through the integration of various technological advancements such as image processing, artificial intelligence, deep learning, and the Internet of Things (IoT). The global population is increasing on a daily basis. The increasing demand within the agriculture industry has necessitated the collective enhancement of plant cultivation and field productivity. This paper emphasizes the significance of effectively managing the crop during its initial growth phase as well as during the harvesting era. Image processing and artificial neural networks are employed as distinct methodologies for detecting illnesses on leaves. When capturing images using drones, the resulting images undergo a process of segmentation and transformation, resulting in the identification of three distinct vectors that represent diseases. These vectors include colour, texture, and morphology. This paper reviews on various disease classification strategies that can be utilized for the detection of plant diseases. [ABSTRACT FROM AUTHOR]

Due to its high rates of both mortality and morbidity, cancer stands as the primary cause of death globally. The examination of histopathological images plays a crucial role in the early prediction of cancer, relying on manual practices for each individual affected. However, this phase is prone to errors, time-consuming, and doesn't facilitate early-stage decision-making for pathologists. Despite significant advancements in computer-aided image processing, the analysis of histopathological morphology remains challenging due to the intricate nature of these images. Moreover, limited annotations restrict sample analysis. This work focuses on developing efficient deep-learning methods to analyze histopathological images by considering both global and local features. It involves the analysis of image patches and features to address issues related to image annotations. A unique task-specific slicing model, integrated with the convolutional network model (ts-CNN) is proposed. This framework aims to conduct patch-level feature examination and combine multiple samples to achieve the necessary outcomes for classification. The envisioned model aims to rectify inefficiencies present in various existing approaches. Using MATLAB 2020a, the proposed model was employed to classify cancer-based histopathological images, demonstrating superior predictive performance. It assisted pathologists in early cancer prediction with an accuracy of 98.2 % and aided in decision-making processes, offering promising results. [ABSTRACT FROM AUTHOR]

Recently, quaternionic Fourier analysis has received increasing attention due to its applications in signal analysis and image processing. This paper addresses quaternionic generalized phase retrieval (QGPR) problem in quaternion Euclidean spaces ℍM$$ {\mathrm{\mathbb{H}}}^M $$. We introduce the concept of QGPR which aims to reconstruct a signal f$$ f $$ in ℍM$$ {\mathrm{\mathbb{H}}}^M $$ from the quadratic measurements {fFlf∗}l=0N−1$$ {\left\{f{F}_l{f}^{\ast}\right\}}_{l=0}^{N-1} $$, where each Fl$$ {F}_l $$ is an M×M$$ M\times M $$ self‐adjoint quaternion matrix. We characterize QGPR sequences in terms of their real Jacobian matrices, prove that the set of QGPR sequences is an open set in some sense, and present some phaselift‐based sufficient conditions on QGPR which gives a method to construct QGPR sequences. [ABSTRACT FROM AUTHOR]

The appearance of dried fruit clearly influences the consumer's perception of the quality of the product but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online. This paper describes a method that combines several simple strategies to assess a suitable surrogate for the elusive quality using imaging, combined with multivariate statistics and machine learning. With such a convenient tool, this study also shows how one can vary the pretreatments and drying conditions to optimize the resultant product quality. Specifically, an image batch processing method was developed to extract color (hue, saturation, and value) and morphological (area, perimeter, and compactness) features. The accuracy of this method was verified using data from a case study experiment on the pretreatment of hot-air-dried kiwifruit slices. Based on the extracted image features, partial least squares and random forest models were developed to satisfactorily predict the moisture ratio (MR) during drying process. The MR of kiwifruit slices during drying could be accurately predicted from changes in appearance without using any weighing device. This study also explored determining the optimal drying strategy based on appearance quality using principal component analysis. Optimal drying was achieved at 60 °C with 4 mm thick slices under ultrasonic pretreatment. For the 70 °C, 6 mm sample groups, citric acid showed decent performance. [ABSTRACT FROM AUTHOR]

Image segmentation and identification are crucial to modern medical image processing techniques. This research provides a novel and effective method for identifying and segmenting liver tumors from public CT images. Our approach leverages the hybrid ResUNet model, a combination of both the ResNet and UNet models developed by the Monai and PyTorch frameworks. The ResNet deep dense network architecture is implemented on public CT scans using the MSD Task03 Liver dataset. The novelty of our method lies in several key aspects. First, we introduce innovative enhancements to the ResUNet architecture, optimizing its performance, especially for liver tumor segmentation tasks. Additionally, by harassing the capabilities of Monai, we streamline the implementation process, eliminating the need for manual script writing and enabling faster, more efficient model development and optimization. The process of preparing images for analysis by a deep neural network involves several steps: data augmentation, a Hounsfield windowing unit, and image normalization. ResUNet network performance is measured by using the DC metric Dice coefficient. This approach, which utilizes residual connections, has proven to be more reliable than other existing techniques. This approach achieved DC values of 0.98% for detecting liver tumors and 0.87% for segmentation. Both qualitative and quantitative evaluations show promising results regarding model precision and accuracy. The implications of this research are that it could be used to increase the precision and accuracy of liver tumor detection and liver segmentation, reflecting the potential of the proposed method. This could help in the early diagnosis and treatment of liver cancer, which can ultimately improve patient prognosis. [ABSTRACT FROM AUTHOR]

Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers' approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications. [ABSTRACT FROM AUTHOR]

Introduction: During the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities. Methods: Advances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image. Results: The performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset. Discussion: Furthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this research was to evaluate and select the best architecture among native convolutional neural network (CNN), MobileNetV2, ResNet50V2, and EfficientNetB0 for predicting the endpoint of the high shear wet granulation process, with accuracy as the main evaluation metric. Methods: The dataset was captured from an industrial camera using static image analysis and was manually labeled as “NOT READY” and “READY” according to the traditional endpoint method based on the mixer’s ampere point in the granulator. The dataset contained a total of 180 images, which were split between training and validation sets. Native CNN and TensorFlow Keras application programming interface (API) were utilized with MobileNetV2, EfficientNetB0, and ResNet50V2 as base feature encoders. Hyperparameters, such as final Fully Connected (FC) layer width, dropout rate, and learning rate, were optimized for binary classification using Keras hyper tuning. Results: The best was the native CNN, it was also the fastest among the three other models, taking only 20-30 ms per step for inference during runtime, though it requires 9000 ms time for training, the longest time among the models. It achieved an accuracy of 98%, and a validation accuracy of 97%. Conclusion: The system was able to determine when a wet granulation process has reached its endpoint based on live images from a camera after being trained on previously labeled data. The native CNN was the best model, offering the fastest runtime performance and the highest accuracy. [ABSTRACT FROM AUTHOR]

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

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

Preventing fluid penetration poses a challenging reliability concern in the context of power electronics, which is usually caused by unforeseen microfractures along the sealing joints. A better and more reliable product design heavily depends on the understanding of the dynamic wetting processes happening inside these complex microfractures, i.e. microchannels. A novel automated image processing procedure is proposed in this work for analyzing the moving interface and the dynamic contact angle in microchannels. In particular, the developed method is advantageous for experiments involving non-transparent samples, where extracting the fluid interface geometry poses a significant challenge. The developed method is validated with theoretical values and manual measurements and exhibits high accuracy. The implementation is made publicly available. The developed method is validated and applied to experimental investigations of forced wetting with two working fluids (water and 50 wt% glycerin/water mixture) in four distinct microchannels characterized by different dimensions and curvature. The comparison between the experimental results and molecular kinetic theory (MKT) reveals that the dynamic wetting behavior can be described well by MKT, even in highly curved microchannels. The dynamic wetting behavior shows a strong dependency on the channel geometry and curvature. [ABSTRACT FROM AUTHOR]

With the continuous progress of science and technology, image processing techniques have been used increasingly in recent years. Image processing plays an indispensable role in the fields of computer vision, artificial intelligence, pattern recognition, and related fields. Improvements in basic algorithms and the development of new algorithms have resulted in considerable innovation and progress. This paper is devoted to finding new game applications in a branch of image processing. It introduces an analysis model proposed by the author and discusses the relationship between roughness in the frequency domain and visual image interpretation. By using the concept of roughness, we separated the image features into meaningful information and residual information and analysed the image in the frequency domain. The results were compared with those of traditional image processing methods. The starting point is the visual identification of a feature based on human interpretation. The image information was separated into meaningful features and the residual component to reduce the redundancy of the model. This allowed for a sparse representation of the feature information in the image. By analysing the meaningful features and residual components of an image separately, we established a relationship between the results and the original images. Parameters such as texture, morphology, and the degree of blurring were considered and we developed a parameter called "frequency roughness". The algorithm incorporates the concepts of frequency and roughness and the roughness is determined in the frequency domain. The frequency roughness algorithm successfully separated the rough features in the frequency domain and calculated the residual value in an image. This model provided more accurate image processing results than comparable methods. This paper includes an analysis and game applications of the proposed model for de-blurring, image enhancement, recognition, and other image processing tasks. Some game applications were successful, whereas others require further investigation. [ABSTRACT FROM AUTHOR]

The valuable saffron metabolites have encouraged researchers to study their laboratory production. In this study, two experiments have been designed to optimize saffron corm callogenesis in-vitro conditions and assessed the application of image analysis to address some characteristics of calli without cell destruction. Effect of physiological age of the corm, different types of media, and plant growth regulators (PGRs) with different concentrations as independent variables were investigated on cell growth index, the redness intensity (a*value) of callus and the percentage of black spot as dependent variables. Results showed that the mature corm-derived explants on Gamborg (B5) medium create the best callogenesis and production of red-colored cells with the least black spot. It was also revealed that naphthalene acetic acid (NAA), (2 mg/L) in combination with 6-Benzylaminopurine (BA) (1 mg/L) increased the growth index to 1.14. In contrast, the combination of indole-3-acetic acid (IAA), (2 mg/L) or indole-3-butyric acid (IBA), (10 mg/L) with kinetin (KIN), (1 mg/L) was the most effective PGRs for the production of red cells. The HPLC results confirmed that the red color of the calli was related to crocin presence. It is concluded that although the cultivation of saffron mature corms in the B5 medium is the most suitable option for biomass growth and crocin production, the choice of PGRs varies depending on the purpose of the culture (biomass or crocin production). Moreover, image analysis can be considered as a promising, alternative, un-destructive, and fast method for estimating the cell growth index and crocin content. [ABSTRACT FROM AUTHOR]

The current study is focused on a novel methodology that removes the artifacts after automated segmentation of micrographs using some efficient image processing techniques from three dual-phase (DP) steel microstructures with varying martensite morphologies (islands, blocky and banded, and fine and fibrous martensite). Microstructures of DP steel consisting of martensite and ferrite phases were binarized by the Otsu thresholding algorithm, where the threshold value is automatically selected from the gray-level histogram. Subsequently, the artifact is removed by different modes, such as horizontally, vertically, and diagonal traverse manners, separately and in the combinations of all possible traverse manners, through AND operation. Optical micrographs were chosen for image analysis since the low-cost optical microscope is easily portable and effortlessly usable over a large area. The results have been validated with AxioVision commercial metallographic image analyzing software. The proposed scheme has successfully produced artifact-free binarized images of DP steel microstructures. Artifact-free microstructures provide the best quantification result, like the volume percent of the phases. [ABSTRACT FROM AUTHOR]

Due to the high abrasiveness and anisotropic nature of composites, along with the need to machine different materials at the same time, drilling multi-materials is a difficult task, and usually results in material damage, such as uncut fibres and delamination, hindering hole functionality and reliability. Image processing and analysis algorithms can be developed to effectively assess such damage, allowing for the calculation of delamination factors essential to the quality control of hole inspection in composite materials. In this study, a digital image processing and analysis algorithm was developed in Python to perform the delamination evaluation of drilled holes on a carbon fibre reinforced polymer (CFRP) and aluminium (Al) multi-material. This algorithm was designed to overcome several limitations often found in other algorithms developed with similar purposes, which frequently lead to user mistakes and incorrect results. The new algorithm is easy to use and, without requiring manual pre-editing of the input images, is fully automatic, provides more complete and reliable results (such as the delamination factor), and is a free-of-charge software. For example, the delamination factors of two drilled holes were calculated using the new algorithm and one previously developed in Matlab. Using the previous Matlab algorithm, the delamination factors of the two holes were 1.380 and 2.563, respectively, and using the new Python algorithm, the results were equal to 3.957 and 3.383, respectively. The Python results were more trustworthy, as the first hole had a higher delamination area, so its factor should be higher than that of the second one. [ABSTRACT FROM AUTHOR]

This study presents a responsive analysis of the role of artificial intelligence (AI) in risk management, contrasting traditional approaches with those augmented by AI and highlighting the challenges and opportunities that emerge. AI, intense learning methodologies such as convolutional neural networks (CNNs), have been identified as pivotal in extracting meaningful insights from image data, a form of analysis that holds significant potential in identifying and managing risks across various industries. The research methodology involves a strategic selection and processing of images for analysis and introduces three case studies that serve as benchmarks for evaluation. These case studies showcase the application of AI, in place of image processing capabilities, to identify hazards, evaluate risks, and suggest control measures. The comparative evaluation focuses on the accuracy, relevance, and practicality of the AI-generated findings alongside the system's response time and comprehensive understanding of the context. Results reveal that AI can significantly enhance risk assessment processes, offering rapid and detailed insights. However, the study also recognises the intrinsic limitations of AI in contextual interpretation, advocating for a synergy between technological and domain-specific expertise. The conclusion underscores the transformative potential of AI in risk management, supporting continued research to further integrate AI effectively into risk assessment frameworks. [ABSTRACT FROM AUTHOR]

Pre-flight inspection and maintenance are essential prerequisites for aviation safety. This study focused on developing a real-time monitoring system designed to assess the condition of composite material structures on the exterior of aircraft. Implementing such a system can reduce operational costs, enhance flight safety, and increase aircraft availability. This study aims to detect defects in aircraft fuselages manufactured from composite materials by applying image visual recognition technology. This study integrated a drone and an infrared camera for real-time image transmission to ground stations. MATLAB image analysis software (MATLAB 2020b) was used to analyze infrared (IR) images and detect structural defects in the aircraft's appearance. This methodology was based on the inspection of damaged engine cowlings. The developed approach compares composite material conditions with known defects before and after repair, considering mechanical performance, defect size, and strength. Simultaneously, tests were conducted on various composite material panels with unknown defects, yielding favorable results. This study underscores an integrated system offering rapid detection, real-time feedback, and analysis, effectively reducing time, and potential hazards associated with high-altitude operations. Furthermore, it addresses blind spots in aircraft inspections, contributing to effective flight safety maintenance. [ABSTRACT FROM AUTHOR]

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

Due to deep learning's success in many medical applications, it is regarded as a significant technique for use in the area of dentistry, where several panoramic X-ray images are frequently used to assess oral health and identify disorders that damage the teeth and bones (e.g., cavities). In order to give diagnostic information for the management of dental problems and diseases, automatic segmentation is a crucial role in medical image processing and analysis. There are just a few datasets for panoramic radiograph images now accessible, according to the state of the art. For this endeavor we present a real dataset composed of 107 panoramic x-ray image were collected from two dental clinics and annotated. A Convolutional Neural Network (CNN) was trained using the annotated data for identification and instance segmentation. which enables the development of deep learning-based automatic tooth detection systems to segment teeth and assess oral status. Mask-RCNN is a deep CNN model for object detection, object localization, and object instance segmentation of medical images. In this paper, we demonstrate that Mask-RCNN can be used to perform highly effective and efficient automatic segmentation and identification of the tooth from panoramic radiographs images. The performance of the implemented networks is 90% of mean average precision (mAP), 63% of F1-sores and 96% of precision. [ABSTRACT FROM AUTHOR]

In the field of cytogenetics, chromosome image analysis plays a critical role in the diagnosis of various genetic disorders and cancers. As the gold standard, chromosome image analysis focuses on metaphase images, as all chromosomes become distinctly visible during this phase of cell mitosis. However, not all the detected metaphases are suitable for karyotyping due to chromosome stickiness. Therefore, cytogenetics selects the analyzable metaphase images by exploring all the samples. Since this task is very time-consuming, efforts have been made to automate the analyzable metaphase selection step of karyotyping. Nevertheless, these studies have drawbacks, such as utilizing hand-crafted features, requiring expert-provided candidate metaphase cell regions for classification, and demanding 100X magnified microscopic images. In this study, a novel two-staged deep-learning based workflow is presented for automatic detection of analyzable metaphase cells. The proposed model operates on 10X magnified images while omitting traditional image processing steps that are used in state-of-the-art methods for determining candidate metaphases. In the first stage, Faster R-CNN method with Inception V2 architecture is used to directly identify analyzable metaphases in specimen slide images. The second stage involves analyzable metaphase image verification based on VGG19 model. To assess the success of the proposed method, we also evaluate two state-of-the-art techniques: YOLOv8 and EfficientNet algorithms. The results obtained with different combination options achieved true positive rates up to 98.5% while reducing false positive rates to 0.0001. This study is the first to introduce an end-to-end deep learning-based approach for analyzable metaphase detection. Comparing it with existing literature, it becomes evident that the proposed approach achieves an optimal balance between true positive rates and false positive rates, maintains efficient processing speed, and eliminates the need for high-magnification image acquisition. All these factors play imperative roles in the clinical usage of the system. [ABSTRACT FROM AUTHOR]

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

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

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

Medical image processing has been used in medical image analysis for many years and has achieved great success. However, one challenge is that medical image processing algorithms ineffectively utilize multi-modality characteristics to further extract features. To address this issue, we propose SSGNet based on UNet, which comprises a selective multi-scale receptive field (SMRF) module, a selective kernel self-attention (SKSA) module, and a skip connection attention module (SCAM). The SMRF and SKSA modules have the same function but work in different modality groups. SMRF functions in the T1 and T1ce modality groups, while SKSA is implemented in the T2 and FLAIR modality groups. Their main tasks are to reduce the image size by half, further extract fused features within the groups, and prevent information loss during downsampling. The SCAM uses high-level features to guide the selection of low-level features in skip connections. To improve performance, SSGNet also utilizes deep supervision. Multiple experiments were conducted to evaluate the effectiveness of our model on the BraTS2018 dataset. SSGNet achieved Dice coefficient scores for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) of 91.04, 86.64, and 81.11, respectively. The results show that the proposed model achieved state-of-the-art performance compared with more than twelve benchmarks. [ABSTRACT FROM AUTHOR]

Purpose: Glioblastoma is the most common type of primary brain malignancy and has a poor prognosis. The standard treatment strategy is based on maximal safe surgical resection followed by radiotherapy and chemotherapy. Surgical resection can be optimized by using 5-delta-aminolevulinic acid (5-ALA)-induced fluorescence, which is the current mainstay. Although 5-ALA-induced fluorescence has gained general acceptance, it is also limited by inter-observer variability and non-standardized fluorescence parameters. We present a new software for processing images analysis to better recognize the tumor infiltration margins using an intraoperative immediate safety map of 5-ALA-induced fluorescence. We tested this in a brainmodel using a commercial surgical exoscope. Methods: A dedicated software GLIOVIS (ACQuF-II, Advanced Colorimetrybased Quantification of Fluorescence) was designed for processing analysis of images taken on the Intraoperative Orbital Camera Olympus Orbeye (IOC) to determine the relative quantification of Protoporphyrin IX (5-ALA metabolite) fluorescence. The software allows to superpose the new fluorescence intensity map and the safety margins over the original images. The software was tested on gel-based brain models. Results: Two surrogate models were developed: PpIX agarose gel-integrated in gelatin-based brain model at different scales (1:25 and 1:1). The images taken with the IOC were then processed using GLIOVIS. The intensity map and safety margins could be obtained for all available models. Conclusions: GLIOVIS for 5-ALA-guided surgery image processing was validated on various gelatin-based brain models. Different levels of fluorescence could be qualitatively digitalized using this technique. These results need to be further confirmed and corroborated in vivo and validated clinically in order to define a new standard of care for glioblastoma resection. [ABSTRACT FROM AUTHOR]

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

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

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

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