This review provides an overview of eXplainable AI (XAI) methods for oncological ultrasound image analysis and compares their performance evaluations. A systematic search of Medline Embase and Scopus between March 25 and April 14 2024 identified 17 studies describing 14 XAI methods, including visualization, semantics, example-based, and hybrid functions. These methods primarily provided specific, local, and post-hoc explanations. Performance evaluations focused on AI model performance, with limited assessment of explainability impact. Standardized evaluations incorporating clinical end-users are generally lacking. Enhanced XAI transparency may facilitate AI integration into clinical workflows. Future research should develop real-time methodologies and standardized quantitative evaluative metrics.

This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower procurement costs. Thereby, energy management systems optimize the operation of respective energy systems, with electrical load forecasting playing a key role. Given that prosumers may switch between communities on a daily basis, electricity demands within these groups will vary, leading to data that is non-stationary, discontinuous as well as non-identical and independently distributed. To encounter this issue, we propose a sophisticated forecasting model that applies federated learning, using informations from various distributed communities to learn domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we ultimately evaluate their effectiveness. The findings suggest that our proposed framework is capable of effectively forecast non-stationary and discontinuous time series and that it can be applied to new, unseen data through the integration of knowledge from multiple sources.

Ultrasound (US) has become a widely used imaging modality in clinical practice, characterized by its rapidly evolving technology, advantages, and unique challenges such as low imaging quality and high variability. There is a critical need to develop advanced automatic US image analysis methods to enhance diagnostic accuracy and objectivity. Vision transformer, a recent innovation in machine learning, has demonstrated significant potential in various research fields, including general image analysis and computer vision, due to its capacity to process large datasets and learn complex patterns. Its suitability for automatic US image analysis tasks, such as classification, detection, and segmentation, has been recognized. This review provides an introduction to vision transformer and discusses its applications in specific US image analysis tasks, while also addressing the open challenges and potential future trends in its application in medical US image analysis. Vision transformer has shown promise in enhancing the accuracy and efficiency of ultrasound image analysis and is expected to play an increasingly important role in the diagnosis and treatment of medical conditions using ultrasound imaging as technology progresses.

Climate change is increasing air temperatures and altering the precipitation and hydrological re-gime on a global scale. Challenges arise when assessing the impacts of climate change on the local scale for water resources management proposes, especially for low mountain headwater catch-ments that not only serve as important water towers for local communities, but also have distinct hydrological characteristics. Until now, no low flow or hydrological drought studies have been carried out on the Lauter River. This study is unique in that it compares the Lauter River, a transboundary Rhine tributary with a nearby station on the River Rhine just below its confluence at the French-German border. The Lauter catchment is a mostly natural, forested catchment, however, its water course has been influenced by past and present cultural activities. Climate change disturbances cascade through the hydrologic regime right down to the local scale. As we are expecting more low flow events, the decrease in water availability could cause conflicts be-tween different water user groups in the Lauter catchment. However, the choice of different methods for identifying low flow periods may cause confusion for local water resources manag-ers. Using flow rate time series of the Lauter River between 1956 and 2022 we compare for the first time three low flow identification methods: the variable threshold method (VT), the fixed threshold method (FT), and the Standardized Streamflow Index (SSI). Similar analyses are applied and compared to the adjacent Maxau station on the Rhine River for the same time period. This study aims at 1) interpreting the differences amongst the various low flow identification methods and 2) revealing the differences in low flow characteristics of the Lauter catchment compared to the Rhine River. It appears that the FT reacts faster to direct climate or anthropogenic impacts whereas VT is more sensitive to indirect factors such as decreasing subsurface flow which is typical for small headwater catchments, such as the Lauter where flow dynamics react faster to flow disturbances. Abnormally low flow during the early Spring in tributaries such as the Lauter can help predict low flow conditions of the Rhine River during the following half year and espe-cially for the summer. The results could facilitate Early Warning of hydrological droughts and drought management for water users in the Lauter catchment and further downstream along some of the Rhine.

Carotid atherosclerotic lesions are a major cause of cerebrovascular disease (CVD). Identification and quantification of carotid plaques are important for categorizing the vulnerability of plaques for rupture and assessing the impact of treatments. The irregularity of plaque surface is associated with previous plaque rupture and plays an important role in the risk of stroke. Thus, the aim of this study is to develop and validate novel vulnerability biomarkers from three-dimensional ultrasound (3DUS) images by analyzing the surface morphological characterization of carotid plaque using fractal geometry features. 3D box-counting and 3D blanket are the two types of 3D fractal dimension that were employed to describe the smoothness of plaques. This fractal dimension analysis tool was used to evaluate the effect of atorvastatin using 3DUS carotid images, which were acquired from 6 patients treated with atorvastatin with 80 mg daily and 5 patients with placebo. The Student's T Test results showed that those two fractal features were effective for detecting the statin-related changes in carotid atherosclerosis with p<0.0068 and p<0.015 respectively, suggesting that 3D fractal dimension measurements can be used effectively to analyze the surface characteristics of carotid plaques, especially for evaluating the impact of the treatment.

Cervical cancer remains a major public health problem. Despite the screening programs, cervical cancer is the second most commonly diagnosed cancer. Most of the cases occur in less developed countries, and it is the third leading cause of cancer death among females. In 2000–2007, the five-year relative survival rate for European women diagnosed with cervical cancer was 62%.[1] Standard treatment for locally advanced cervical cancer reveals external beam radiotherapy (EBRT) with concurrent chemotherapy followed by image-guided adaptive brachytherapy (IGABT) boost [2, 3]. In the last years, the Gynaecological (GYN) working group composed of Group Européen de Curiethérapie-European Society for Therapeutic Radiology and Oncology (GEC-ESTRO) has developed new recommendations for the treatment of cervical cancer based on IGABT. The treatment plan includes 3-dimensional (3D) imaging based on magnetic resonance imaging (MRI) or computed tomography (CT), adaptive to the target volume and organ at risk [4–6]. The brachytherapy insertion is made under anaesthesia. At the time of the application insertion, the ultrasound is used to guide the dilatation of the uterine canal and assess the depth of the uterine cavity. In this paper, we review the data available in international literature about the use of ultrasound (US) in brachytherapy treatment of cervical cancer.

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.

Cystic echinococcosis (CE), caused by the larval stage of Echinococcus granulosus, is significantly underreported in Mongolia due to geographical remoteness, lack of early diagnostics, and poor clinical management. This study aimed to provide a more accurate estimate of CE in Mongolia by comparing data of surgical (reported) and diagnosed (unreported) cases, and assessing the challenges faced by rural doctors in disease management and surveillance. We collected data on surgical cases hospitalized between 2006-2016 and newly diagnosed CE cases in 2016 from eight provinces. Using a quasi-Poisson regression model of the collected data, we used extrapolation to estimate the number of diagnosed cases for the entire country. Additionally, fourty health professionals from all 21 provinces rated local clinical management for CE through a questionnaire. Results reveal that surgical cases (2.2 per year) represent only one-eighth of diagnosed cases (15.9 per year). The laboratory facilities, disease reporting, and cyst classification usage scored below 2. These results highlight the significant underreporting of CE in Mongolia and urges human and animal health experts, along with policymakers, to invest into combating CE, particularly in remote provincial areas. It also emphasizes the need for standard clinical management involving cyst classification according to WHO-IWGE, seamless integration of CE reporting and monitoring mechanisms, which can significantly contribute to the national and global burden estimation of CE.

Ultrasound (US) scans of the inferior vena cava (IVC) provide useful information on the volume status of a patient. However, their investigation is user-dependent and prone to measurement errors. An important technical problem is the objective difficulty in studying a very compliant blood vessel like IVC, which makes large respirophasic movements and shows a complicated three dimensional geometry. Using bi-dimensional (2D) B-mode views either in long or short axis has improved the characterization of IVC dynamics compared to a measurement along a single direction (M-mode). However, specific movements of the IVC can challenge also the information provided by these 2D sections. Thus, these two orthogonal views, provided by an US system in X-plane, are here integrated by an innovative method. It is tested on simulated videos of the IVC performing complicated movements, that are compensated by the new method, overcoming the biased measurements provided by 2D scans. The method is then applied on example experimental data.

Medical image segmentation is a critical step in various healthcare applications, aiding in diagnosis, treatment planning, and disease monitoring. In this study, we investigate the efficacy of a segmentation approach based on the 2D wavelet transform. Leveraging a dataset comprising 10 diverse medical images, we evaluate the performance of our segmentation method using three key metrics: accuracy, precision, and recall. Our findings demonstrate that the proposed approach enhances segmentation accuracy, offering promising results compared to existing methods. By harnessing the multi-resolution feature extraction capabilities of the 2D wavelet transform, our method achieves improved delineation of medical image structures, paving the way for more accurate and efficient healthcare interventions.

Enclosed public spaces are the hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of Particulate Matter Sensors has been developed and named as Dynamic Aerosol Transport for Indoor Ventilation or DATIV system. This system can use multiple Particulate Matter Sensors (PMS) simultaneously and can be remotely controlled using a Raspberry Pie based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or Smartphone with corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

Lignocellulosic biomass, an abundant and renewable resource, serves as a crucial raw material for the production of biofuels, biochemicals, and various value-added products. This paper aims to develop and optimize a mild alkaline treatment of sawdust assisted by ultrasound, along with enzymatic hydrolysis of the pretreated material. The sonochemical pretreatment with alkali emerged as the optimal approach to enhance the susceptibility of cellulose to subsequent enzymatic hydrolysis, thereby increasing the yield of reducing sugars. A comparative study was performed using various ultrasonic applicators (horn and bath) and conventional assisted alkaline pretreatment. The ultrasonic assisted pretreatment revealed a higher delignification as 68% (horn) and 57% (bath) comparing with conventional pretreatment. Processes were optimized using a statistical analysis based on a 23 factorial design. The ratio between sawdust and alkanine solution (RSL = 0.5–1.5 g/100 mL), US amplitude (A = 20–60%), and working temperature (t = 30–50 °C) were selected as process factors. The optimal operating conditions to maximize the reducing sugar yield (138.15 mg GE /gsubstrate) were found as following: solid/liquid ratio RSL,opt = 1.25 g/100 mL, US amplitude Aopt = 60%, and and pretreatment temperature topt = 50 °C. The overall outcomes clearly confirmed the intensification of delignification by ultrasound assisted alkaline pretreatment

For 80 years high-intensity focused ultrasound (HIFU) has been a significant area for medical research. It is a non-invasive procedure which causes cell death in a highly localised area at a defined distance from the transducer. Since 2000, the clinical indications have broadened considerable as the technology has improved. This review looks back at the history of HIFU and the current clinical indications.

Underwater imaging presents unique challenges, notably color distortions and reduced contrast due to light attenuation and scattering. Most underwater image enhancement methods, first use linear transformations for color compensation and then enhance the image. We observed that linear transformation for color compensation is not suitable for certain images. For such images, a non-linear mapping is a better choice. Our paper introduces a unique underwater image restoration approach leveraging a streamlined convolutional neural network (CNN) for dynamic weight learning for linear and non-linear mapping. In the first phase, a classifier is applied that classifies the input images into Type-I or Type-II. In the second phase, depending on the classification, the Deep Line Model (DLM) for Type-I images or the Deep Curve Model (DCM) for Type-II images. For mapping an input image to an output image, DML creatively combines color compensation and contrast adjustment in a single step and uses deep lines for transformation, whereas DCM employs higher-order curves. Both models utilize lightweight neural networks that learn per-pixel dynamic weights based on the input image’s characteristics. Comprehensive evaluations on benchmark datasets using metrics like peak signal-to-noise ratio(PSNR) and root mean squares error (RMSE) affirm our method’s effectiveness in accurately restoring underwater images, outperforming existing techniques.

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

The paper describes the new method for image compression based on coordinated group signal transformation. The algorithm is a type of difference coding. Coordinated processing significantly simplifies the difference signal conversion scheme using a single group codec for all signals. The described method considers color channels as correlated signals of a multi-channel communication system. The specifics of the processed data required modification of the coordinated group processing algorithm. First, we changed how to calculate the difference signals to prevent data loss. Secondly, the group codec was supplemented with a neural network to improve the quality of reconstructed images. We considered the following types of neural networks: fully connected, recurrent, convolution, and convolution in the Fourier space. Based on the simulation results, it is best to use fully connected neural networks if the goal is to minimize processing delay time. These networks have a response time of 13 ms. On the other hand, if the priority is to improve quality in cases where delays are not critical, then convolution neural networks in the Fourier space should be used. These networks have a response time of 140 ms and are of significant interest.

Color image binarization plays a pivotal role in image preprocessing work, significantly impacting subsequent tasks, particularly in text recognition. This paper concentrates on Document Image Binarization (DIB), aiming to separate a image into foreground (text) and background (non-text content). Through a thorough analysis of conventional and deep learning-based approaches, we conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields pre- and post-network training to underscore the Transformer model's advantages. Subsequently, we introduce a lightweight model based on the U-Net structure, enhanced with the Mobile ViT module to better capture global information features in document images. Given its adeptness at learning both local and global features, our proposed model exhibits superior performance on two standard datasets (DIBCO2012 and DIBCO2017) compared to state-of-the-art methods. Notably, our proposed DIB method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing. Moreover, its parameter count is less than a quarter of the HIP'23 model, which achives best results on three datasets(DIBCO2012, DIBCO2017 and DIBCO2018). Finally, two sets of ablation experiments were conducted to verify the effectiveness of the proposed binarization model.

Background: Bariatric surgery (BS) has a significant impact on body composition (BC) and con-sequently may affect established sarcopenic obesity (SO) in candidate patients. The aim of this study is to assess the utility of muscle ultrasound (MUS) of rectus femoris thickness (RFT) for the evalua-tion of BC and skeletal muscle function in patients undergoing BS compared to bioimpedance analysis (BIA), dual-energy X-ray absorptiometry (DEXA) and dynamometry. On the other hand, we aim to demonstrate how MUS of RFT correlates better with the quality of life (Qol) in this population, likely because it enables the detection of regional quadriceps muscle sarcopenia, in comparison to the other mentioned methods. Methods: This is a prospective pilot study including 77 subjects (64.9% female, mean age: 53.2 ± 8.67 years) who underwent BS. Handgrip strength was measured using a dynamometer, fat-free mass index (iFFM) was assessed by BIA, appendicular muscle index (AMI) was calculated using DEXA and RFT was measured by MUS. Moreover, Homeostasis Model Assessment of Insulin Resintance (HOMA-IR) was calculated. All these measurements were conducted 1 month prior to BS and at the 12-month follow-up. QoL was as-sessed using the Moorehead-Ardelt questionnaire. Results: The mean BMI decreased by 12.95 ± 3.56 kg/m2 (p=0.001). Firstly, we observed positive correlation pre-surgery between HOMA and: RFT (r=0.27, p=0.02), iFFM (r=0.36, p=0.001), AMI (r=0.31, p=0.01) and dynamometer (r=0.26, p=0.02). In addition, we found correlation between RFT and iFFM (pre-surgical: r=0.31, p=0.01; post-surgical: r=0.25, p=0.05) and between RFT and lower extremities AMI post-surgical (r=0.27, p=0.04). Secondly, we observed significant reductions in iFFM, AMI and RFT (p=0.001), but not with dynamometer (p=0.94). Finally, a tendency to a positive correlation between QoL question-naire and RFT post-surgery (r=0.23, p=0.079) was observed. Conclusions: Our results suggest that RFT measured by MUS for the evaluation of SO and the follow-up after BS of these patients. Moreover, RFT can provide relevant information about regional sarcopenia and probably has an accurate correlation with QoL in comparison with the other methods.

Convolutional Neural Networks (CNNs) present drawbacks for modeling geometric transformations, such as scaling and rotation, caused by the convolution operation’s locality. Deformable convolution (DCON), a mechanism that substitutes standard convolution, increasing the receptive field to capture relevant features, is a promising approach to solve this drawback and improve the robustness of CNNs. However, the optimal way to replace the standard convolution with its deformable counterpart in a CNN model is unclear. In this study, we clarify this aseveration by conducting several experiments using deformable convolutions applied in the layers that conform a small four-layer CNN model. We also use deformable convolutions on the four-layers of several ResNet CNNs with depths 18, 34, 50, and 101. The models were tested in binary balanced classes with 2D data for image classification: Cats &amp; Dogs, EyesPACS, Spyders &amp; Chickens, and Shapes. After this testing, we evaluated DCON in 3D data for action recognition: UCF101 and Human2 (a dataset we compiled to control movement, clothing and background). The contribution of this research lies in a guideline to use DCON. It can be summarized as follows: if DCON is used on the first layers of the proposal of model (with simple features), the computational resources expressed as the quantity of Flops will tend to increase and produce bigger misclassification than the standard CNN. However, if the DCON is used at the end layers, the quantity of Flops used in the training and testing will decrease, and the classification accuracy will improve by up to 20% about the base model. Moreover, it gains robustness when using deformable convolutions because it can adapt to the region of interest. Also, the best kernel size of the DCON is three. It showed better results than size five. In the last case, the quantity of Flops increase quadratically, but their performance does not increase significantly. With these results, we propose a guideline to use the DCON and contribute to understanding the impact of DCON on the robustness of CNNs.

Precision agriculture is a farm management technology that involves sensing and then responding to the observed variability in the field. Remote sensing is one of the tools of precision agriculture. The emergence of small unmanned aerial vehicles (sUAV) have paved the way to accessible remote sensing tools for farmers. This paper describes the comparison of two popular off-the-shelf sUAVs: 3DR Iris and DJI Phantom 2. Both units are equipped with a camera gimbal attached with a GoPro camera. The comparison of the two sUAV involves a hovering test and a rectilinear motion test. In the hovering test, the sUAV was allowed to hover over a known object and images were taken every second for two minutes. The position of the object in the images was measured and this was used to assess the stability of the sUAV while hovering. In the rectilinear test, the sUAV was allowed to follow a straight path and images of a lined track were acquired. The lines on the images were then measured on how accurate the sUAV followed the path. Results showed that both sUAV performed well in both the hovering test and the rectilinear motion test. This demonstrates that both sUAVs can be used for agricultural monitoring.

This paper introduces a novel framework for zero-shot learning (ZSL), i.e., to recognize new categories that are unseen during training, by distilling knowledge from foundation models. Specifically, we first employ ChatGPT and DALL-E to synthesize reference images of unseen categories from text prompts. Then, the test image is aligned with text and reference images using CLIP and DINO. Finally, the predicted logits are aggregated according to their confidence to produce the final prediction.Experiments are conducted on multiple datasets, including CIFAR-10, CIFAR-100, and TinyImageNet. The results demonstrate that our model can significantly improve classification accuracy compared to previous approaches, achieving AUROC scores above 96\% across all test datasets. Our code is available at https://github.com/1134112149/MICW-ZIC.

Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography re-view paper provides a systematic review of computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The systematic review is meticulously carried out, adhering closely to the preferred reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are compre-hensively reviewed. The evaluation of a deep learning architecture based on self-extracted features for classification tasks demonstrated outclass performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations' sustainability de-velopment goals.

This paper describes the first results of an Image Recognition Based Location (IRBL) for mobile application focusing on the procedure to generate a Database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surrounding is needed. In order to achieve this objective a complete 3D survey of two different environment (Bangbae metro station of Seoul and E.T.R.I. building in Daejeon – Republic of Korea) was performed using LiDAR (Light Detection And Ranging) instrument and the obtained scans were processed in order to obtain a spatial model of the environments. From this, two databases of reference images were generated using a specific software realized by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allow to generate synthetically different RGB-D images) centered in the each scan position in the environment. Later, the external parameters (X, Y, Z, ω, φ, κ) and the range information extracted from the DB images retrieved, are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper the survey operations, the approach for generating the RGB-D images and the IRB strategy are reported. Finally the analysis of the results and the validation test are described.

Whey protein was pretreated with multi-frequency ultrasound in mono, dual, and tri-frequency modes. The effect of multi-frequency ultrasound pretreatment on the hardness, chemical forces, and microstructure of acid-induced whey protein gel was investigated. Whey protein gels pretreated with dual and tri-frequency ultrasound showed higher hardness (p3.35 mm) in those samples were resisted to gastric digestion. Moreover, the tri-frequency ultrasound pretreatment of whey protein gel released the least free amino group during gastric digestion. In contrast, whey protein gel with the mono-frequency ultrasound pretreatment released the highest amount of free amino acid group during intestinal digestion. Findings from this study suggested that gel hardness and network density could modulate the digestion behaviors of protein gels.

Rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can obtain images with centimeter or even subcentimeter ground resolution, which provides a new efficient and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we propose a real-time edge reconstruction crack detection network (ERNet) which adopts multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and background. To capture global dependencies across spatial and channel levels, we propose an efficient bilateral decomposed convolutional attention module (BDAM) that combines depth separable convolution and dilated convolution to capture global dependencies across spatial and channel levels. To enhance the accuracy of crack detection, we use a coordinate-based fusion module that integrates spatial, semantic, and edge reconstruction information. In addition, we propose an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. Experimental results demonstrate that our network achieves the best balance between accuracy and inference speed.

As the fast growth of information technology (IT), the safety of image transmission and the storing of images are increasingly concerned. Traditional image encryption algorithms have certain limitations in transmission and security, so there is an urgent need for a secure and reliable image encryption algorithm. A universal compression sensing (CS) image encryption (IE) algorithm based on DNA-triploid mutation (DTM) is presented in this paper. Firstly, by using CS algorithm an image is compressed while obtaining a range of chaotic sequences by iteration of chaotic map. Then DNA sequences are generated by encoding the image, and based on the DTM, new mutant DNA sequences are generated according to specific rules. Next, the chaotic sequences are operated at the DNA level to do confusion and diffusion operations on the image to ensure the security of the data. At last, DNA decoding is carried out to obtain the compressed-encrypted image. Both simulation experiments and performance tests fully show that a high level of security and reliability of the proposed algorithm in protecting image privacy is achieved.

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

With the development of deep learning, the Super-Resolution (SR) reconstruction of microscopic images has improved significantly. However, the scarcity of microscopic images for training, the underutilization of hierarchical features in original Low Resolution (LR) images, and the high-frequency noise unrelated with the image structure generated during reconstruction process are still challenges in the Single Image Super-Resolution (SISR) field. Faced with these issues, we first collected sufficient microscopic images through Motic, a company engaged in the design and production of optical and digital microscopes, to establish a dataset. Secondly, we proposed a Residual Dense Attention Generative Adversarial Network (RDAGAN). The network comprises a generator, an image discriminator, and a feature discriminator. The generator includes a Residual Dense Block (RDB) and a Convolutional Block Attention Module (CBAM), focusing on extracting the hierarchical features of the original LR image. Simultaneously, the added feature discriminator enables the network to generate high-frequency features pertinent to the image’s structure. Finally, we conducted experimental analysis and compared our model with six classic models. Compared with the best model, our model improved PSNR and SSIM by about 1.5dB and 0.2, respectively.

Magnetomotive ultrasound (MMUS) is a promising imaging modality for detecting magnetic nanoparticles. In MMUS, an external oscillating magnetic field induces motion of the injected magnetic nanoparticles within tissue, and phase-based tracking algorithms are used to detect the motion. However, the subwavelength scale of theses displacements (often a few micrometers) makes direct visualization on conventional ultrasound B-mode images impossible. In this work, we adapt the Eulerian motion magnification technique to create a novel ultrasound display mode for identifying the nanoparticle locations, eliminating the need for displacement tracking algorithms. Phantom and in-vivo experiments demonstrate that our technique successfully magnifies magnetomotion and the associated shear wave propagation in ultrasound B-mode imaging and pinpoints the nanoparticle vibration source, even in low-concentration scenarios. Additionally, motion magnification magnified the displacement of nanoparticles, having the potential to improve the localization accuracy of displacement tracking algorithms in MMUS.

We are introducing a highly efficient 48-channel ultrasound beamforming system ideal for ultrasound endoscopy applications. The system includes a transmitter and a receiver, which allow for low-area, high-resolution imaging acquisition. The transmitter uses a charge redistribution HV (high voltage) scheme to generate three-level pulses that actuate the transducer, implemented with the standard CMOS process for optimal cost and power savings. Meanwhile, the receiver features a sub-array structure and a delay generator that reduces area usage. To achieve high-resolution ultrasound imaging acquisition with low computational power, we developed the Shift Coherence Factor (SCF) algorithm, which is hardware-friendly. This approach delivers a lateral resolution over 20% better than the conventional delay and sum (DAS) algorithm, with a contrast ratio of over 30 dB. The system was implemented in a 180 nm standard CMOS process with an area of 24.98 mm2, power consumption of 8.23 mW per channel, achieving a delay resolution of 8.33 ns, and a low-area implementation of 0.52 mm2 per channel. The system offers high-quality imaging acquisition with minimal additional area and power consumption, which has great potential for 3D imaging or catheterized ultrasound systems.

Idiopathic acute pancreatitis (IAP) presents a diagnostic challenge and refers to cases where the cause of acute pancreatitis remains uncertain despite a comprehensive diagnostic evalua-tion. Endoscopic ultrasound (EUS) has emerged as a valuable tool in the diagnostic workup of IAP. This review explores the pivotal role of EUS in detecting the actual cause of IAP, as-sessing its accuracy, timing, safety, and future technological improvement. Following PRISMA guidelines, 60 pertinent studies were selected and analysed. EUS emerges as a cru-cial diagnostic tool, particularly when conventional imaging fails. It can offer intricate visu-alization of the pancreas, biliary system, and adjacent structures. High accuracy of EUS is evident in detecting the actual causes of IAP such as microlithiasis, small bile duct stones, pancreas divisum, and small pancreatic tumors. Optimal timing for EUS is post-resolution of the acute phase of the disease. EUS, when conducted by experienced endoscopists, it boasts minimal safety concerns, and is crucial to prevent complications. EUS-guided interventions, including fine-needle aspiration, collection drainage, and biopsies, aid in cytological analy-sis. With high diagnostic accuracy, safety, and therapeutic potential, EUS is able to improve patient outcomes when managing IAP. Further refinement of EUS techniques and cost-effectiveness assessment of EUS-guided approaches need to be explored in multicenter prospective studies. This review underscores EUS as a transformative tool in unraveling IAP's enigma and advancing diagnostic and therapeutic strategies.

A machine learning method for classifying Lung UltraSound is here proposed to pro- vide a point of care tool for supporting a safe, fast and accurate diagnosis, that can also be useful during a pandemic like as SARS-CoV-2. Given the advantages (e.g. safety, rapidity, portability, cost-effectiveness) provided by the ultrasound technology over other methods (e.g. X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest LUS public dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art. The complexity of this solution keeps the number of parameters in the same order as an EfficientNet-b0 by adopting specific design choices that are adaptive ensembling with a combination layer, ensembling performed on the deep features, minimal ensemble only two weak models. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where the focus is on an inaccurate weak model versus an accurate model.

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings to detect COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on ultrasound-based AI studies for COVID-19 detection that use public or private lung ultrasound datasets. We surveyed articles that used publicly available lung ultrasound datasets for COVID-19 and reviewed publicly available datasets and organize ultrasound-based AI studies per dataset. We analyzed and tabulated studies in several dimensions, such as data preprocessing, AI models, cross-validation, and evaluation criteria. In total, we reviewed 42 articles, where 28 articles used public datasets, and the rest used private data. Our findings suggest that ultrasound-based AI studies for the detection of COVID-19 have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.

Conclusions: The quantitative method provides a large-scale overview of the biometry measurements of an entire practice. Image audits can then be focused on sonographers whose measurements are outliers, avoiding the time, complexity, and expense of auditing those whose measurements fall within the expected range. We encourage commercial software developers to include tools in their ultrasound reporting software to facilitate such quantitative review.

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

Herbal Coffee is getting more popular due to its unique flavor and higher concentration of bioactive compounds, mainly polyphenols. Rosemary is a unique herb with potent antioxidant properties. A green technique called ultrasound aids in releasing bioactive substances from the plant cell wall. In this context, ultrasound-assisted herbal coffee prepared and their anti-diabetic, anti-Alzheimer, and antioxidant activities were measured while the LC/MS was used to determine the bioactive components from the herbal coffee infusion. A total of 95 secondary metabolites were putatively reported, including alkaloids, phenolic acids, and flavonoids, which we identified in the herbal coffee infusion. The total phenolic content (61.5 ± 5.1 mg GAE/g), total flavonoid content (19.3 ± 0.9 mg QE/g), and total condensed tannin (7.6 ± 0.7 mg CE/g) of herbal coffee infusion were observed to be highest of the two remaining samples. The herbal coffee extracts were used to determine all samples' antioxidant, anti-diabetic, and anti-Alzheimer activities. Using LC-MS/MS, we semi-quantified 19 phenolic metabolites. The results show numerous bioactive components in herbal coffee infusion with more potent antioxidant, antidiabetic, and anti-Alzheimer potential. Moreover, more studies must be conducted to confirm how the metabolomic profile of herbal coffee affects its flavor profile.

Endobronchial Ultrasound has been widely used in the lymph node staging of lung tumors and the diagnosis of mediastinal diseases. In the last decades this procedure has evolved in several technical aspects, with new tools available to optimize the tissue sampling and to increase the diagnostic yield, like elastography, different types of needles and, most recently, miniforceps and cryobiopsy. Then, also the indications for EBUS have extended beyond the thoracic boundaries to sample lesions of the liver, left adrenal gland and retroperitoneal lymph nodes via the gastroesophageal tract. In this review we will analyze the actual indication of EBUS, even the more unusual, to underline the multiple use of EBUS in clinical practise.

(1) Background: adnexal masses during pregnancy detection are increased due to the use of first trimester screening and to advanced maternal age. Although the low risk of malignancy, other risks are associated: torsion, rupture or labour obstruction. Correct diagnosis and management are fundamental to guarantee both maternal and fetal safety. Adnexal masses may be troublesome to classify during pregnancy due to the increase in the uterus volume and to pregnancy-related hormonal changes. Management should be based on ultrasound examination to provide the best treatment. (2) Methods: the aim was to describe the ultrasound features of ovarian masses detected during pregnancy. We evaluated patient’s management, perinatal outcomes and follow-up. (3) Results: our study demonstrated that the management should be guided by an accurate ultrasound examination performed by an expert operator according to standardized techniques. Secondly, our results confirm the data present in the literature: the masses are most frequently benign, ultrasound follow-up is the best management strategy and obstetric outcome is not considerably affected. (4) Conclusions: the management of patients with ovarian masses detected during pregnancy should be based on ultrasound examination and a centralization in referral centers for ovarian masses should be considered.

Fundus image registration plays a crucial role in the clinical evaluation of ocular diseases, such as diabetic retinopathy and macular degeneration, necessitating meticulous monitoring. The alignment of multiple fundus images enables the longitudinal analysis of patient progression, widening the visual scope, or augmenting resolution for detailed examinations. Currently, prevalent methodologies rely on feature-based approaches for fundus registration. However, certain methods exhibit high feature point density, posing challenges in matching due to point similarity. This study introduces a novel fundus image registration technique integrating U-Net for the extraction of feature points employing FIVES for its training and evaluation, a novel and large dataset for blood vessels segmentation, prioritizing point distribution over abundance. Subsequently, the method employs medial axis transform and pattern detection to obtain feature points characterized by the Fast Retina Keypoint (FREAK) descriptor, facilitating matching for transformation matrix computation. Assessment of the vessel segmentation achieves 0.7559 for Intersection Over Union (IoU), while evaluation on the Fundus Image Registration Dataset (FIRE) demonstrates the method’s comparative performance against existing methods, yielding a registration error of 0.596 for area under the curve, refining similar earlier methods and suggesting promising performance comparable to prior methodologies.

In this paper, a new approach to retrieve semantic images based on shape and geometric features of image in conjunction with multi-class support vector machine is proposed. Zernike moment as shape feature is to verify the invariance of objects for silhouette image. In addition, a set of geometrical features is to explore the objects shape using two features of rectangularity and circularity. Then the extracted features are normalized and employed for multi-class support vector machine either for learning or retrieving processes. The retrieving process relies on three main tasks which namely Query Engine, Matching Module and Ontology Manger, respectively. Query Engine is to build the input text or image query using SPARQL language. The matching module extracts the shape and geometric features of image’s objects and employ them to Ontology Manger which in turn inserts them in ontology knowledge base. Benchmark mammals have been conducted to empirically conclude the outcome of proposed approach. Our experiment on text and image retrieval yields efficient results to problematic phenomena than previously reported.

Background: Bioelectrical impedance (BIA) and ultrasound (US) have become popular for estimating body fat percentage (BF%) due to their low cost and clinical convenience. However, agreement of these devices with gold standard method still require investigation. The aim was to analyze the agreement between a gold standard %BF assessment method with BIA and US devices. Methods: Twenty-three men (age 30.1 ± 7.7 years, 82.5 ± 14.9 kg, 1.77 ± 0.05 m) underwent Dual Energy X-Ray Absorptiometry (DXA), BIA (tetrapolar) and US (three-site method) %BF assessment. Pearson and concordance correlations were analyzed. T-test compared means between methods, and Bland Altman plots analyzed agreement and proportional bias. Alpha was set at &lt; .05. Results: Pearson coefficients between BIA and US with DXA were high (BIA = 0.94; US = 0.89; both p &lt; .001). Concordance coefficient was high for BIA (0.80) and moderate for US (0.49). BF% measured by BIA (24.5 ± 7.5) and US (19.4 ± 7.0) were on average 4.4% and 9.6% lower than DXA (29.0 + 8.5%), respectively (p &lt; .001). Lower and upper agreement limits between DXA and BIA were -1.45 and 10.31, while between DXA and US were 2.01 and 17.14, respectively. There was a tendency of both BIA (p = .09) and US (p = .057) to present proportional bias and underestimate BF%. Conclusions: Despite the correlation, the mean difference between the methods were significant, and the agreement limits are very wide. This indicate that BIA and US, as measured in this study, have limited potential to accurately measure %BF compared to DXA, especially in individuals with higher body fat.

End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational complexity, learned image compression methods inherently align with learning-based data processing and analytic pipelines due to their well-suited internal representations. The concept of Video Coding for Machines has garnered significant attention from both academic researchers and industry practitioners. This concept reflects the growing need to integrate data compression with computer vision applications. In light of these developments, we present a comprehensive survey and review of lossy image compression methods. Additionally, we provide a concise overview of two prominent international standards, MPEG Video Coding for Machines and JPEG AI. These standards are designed to bridge the gap between data compression and computer vision, catering to practical industry use cases.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

Abstract Background: Occipital neuralgia is a debilitating condition and traditional treatments often provide limited or temporary relief. Recently, ultrasound-guided hydrodissection of the greater occipital nerve (GON) has emerged as a promising minimally invasive approach. Objectives: To describe two novel ultrasound-guided hydrodissection with 5% dextrose for GON and discuss their advantages, disadvantages, and considerations. Methods: Two case reports are presented. Case 1 describes a lateral decubitus approach for hydrodissecting the GON between the semispinalis capitis (SSC) and obliquus capitis inferior (OCI) muscles. Case 2 details a cranial-to-caudal approach for hydrodissecting the GON within the SSC and upper trapezius (UT) muscles when the GON pass through these two muscles. Results: Both patients experienced significant and sustained pain relief with improvements in function and quality of life. Conclusions: Ultrasound-guided GON hydrodissection using 5% dextrose is a promising treatment for occipital neuralgia. The lateral decubitus and cranial to caudal approaches provide additional options to address patient-specific anatomical considerations and preferences.

Bridges enable communications and transportation of goods nationally and internationally, underpinning economic and social activities, thus they are pylons of our prosperity and mobility. Bridges worldwide are constantly subjected to physical wear, ageing, deterioration, hazards, environmental influences, and increased loading. Loss of performance and functionality of bridge structures would have a crucial impact on overall infrastructural resilience and would cause significant negative economic and social consequences. Monitoring their behaviour for different loading conditions relies on accurate estimations of the stress-strain state of various critical components and remaining capacities. These activities are of high importance for better planning lifespan prolongation, that is the extension of their service life and prevention of unforeseen collapses, in line with sustainability principles of well-informed maintenance. In many cases, access to the structure is limited or even impossible, which causes the need for the deployment of remote and contactless methods. One such innovative technique, which has recently attracted attention in scientific and practical applications is the digital image correlation (DIC). DIC is a contactless approach applicable for long-term measurements, which enables obtaining the full field of strains and deformations. Although the DIC approach has been widely used in world engineering practice for monitoring bridges and has proved to be a reliable and accurate method, there is a lack of systematic integral review on previous practical applications, revealing limitations and perspectives. This is the main motivation and novelty of this study, which will describe selected case studies, in which DIC was used on real full-scale bridge structures and propose improvements for the method.

Multispectral image (MSI) and hyperspectral image (HSI) fusion (MHIF) aims to address the challenge of acquiring high-resolution (HR) HSI images. This field combines a low-resolution (LR) HSI with an HR-MSI to reconstruct HR-HSI. Existing methods directly utilize transformers to perform feature extraction and fusion. Despite the demonstrated success, there exist two limitations: 1) Employing the entire transformer model for feature extraction and fusion fails to fully harness the transformer’s potential in integrating the spectral of the HSI and spatial information of the MSI. 2) HSI has a strong spectral correlation and exhibits sparsity in the spatial domain. Existing transformer-based models do not optimize this physical property, which makes their methods prone to spectral distortion. To accomplish these issues, this paper introduces a novel framework for MHIF called Sparse Mix-Attention Transformer (SAMformer). Specifically, to fully harness the advantages of the Transformer architecture, we propose a Spectral Mix Attention Block (SMAB), which concatenates the keys and values extracted from LR-HSI and HR-MSI to create a new multi-head attention module. This design facilitates the extraction of detailed long-range information across spatial and spectral dimensions. Besides, to address the spatial sparsity inherent in HSI, we incorporated a sparse mechanism within the core of SMAB called Sparse Spectral Mix Attention Block (SSMAB). In the SSMAB, we compute attention maps from queries and keys and select the K highly correlated values as the sparse attention map. This approach enables us to achieve a sparse representation of spatial information while eliminating spatially disruptive noise. Extensive experiments conducted on three benchmark datasets, namely Cave, Harvard, and Pavia Center, demonstrate the SMAformer method outperforms state-of-the-art methods.

In response to the burgeoning global demand for premium agricultural products, particularly within the competitive nut market, this paper introduces an innovative methodology aimed at enhancing the grading process for almonds and their shells. Leveraging state-of-the-art Deep Convolutional Neural Networks (CNNs), specifically the AlmondNet-20 architecture, our study achieves exceptional accuracy exceeding 99%, facilitated by the utilization of a 20-layer CNN model. To bolster robustness in differentiating between almonds and shells, data augmentation techniques are employed, ensuring the reliability and accuracy of our classification system. Our model, meticulously trained over 1000 epochs, demonstrates remarkable performance, boasting an accuracy rate of 99% alongside a minimal loss function of 0.0567. Rigorous evaluation through test datasets further validates the efficacy of our approach, revealing impeccable precision, recall, and F1-score metrics for almond detection. Beyond its technical prowess, this advanced classification system offers tangible benefits to both industry experts and non-specialists alike, ensuring globally reliable almond classification. The application of deep learning algorithms, as showcased in our study, not only enhances grading accuracy but also presents opportunities for product patents, thereby contributing to the economic value of our nation. Through the adoption of cutting-edge technologies such as the AlmondNet-20 model, we pave the way for future advancements in agricultural product classification, ultimately enriching global trade and economic prosperity.

The essence of cross-modal generation and retrieval modeling for image-text interactions lies in semantic consistency. Among these, cross-modal representation alignment serves as the foundational requirement for achieving cross-modal semantic consistency. The crux of our proposed method entails the collaborative training of an image captioning model(referred to as the ’captioner’) and a pair of contrastive encoders for image-text matching(referred to as the ’concoder’). This synergistic approach yields concurrent enhancements in concoder’s performance, captioner’s proficiency, and cross-modal semantic coherence. We coin the proposed method as ‘ReCap’. To begin, we initialize the concoder through knowledge distillation from a pre-trained model. During the training process, we use the output of the initialized concoder as input to a residual attention network, jointly training it with the captioner to achieve semantic consistency. Subsequently, we iteratively update the concoder during the image-text momentum encoding phase using the residual attention network, creating a closed-loop for semantic consistency resolution. Upon completion of training, the concoder and captioner modules can be used independently to perform tasks related to cross-modal retrieval and cross-modal generation of text and images. In order to substantiate the efficacy of our proposed method, we initially conducted empirical experiments encompassing image-text retrieval and image captioning tasks, employing a widely recognized benchmark dataset, MSCOCO. Subsequently, we assessed the fine-grained cross-modal alignment performance of the concoder through an image classification task on the iNaturalist 2018 dataset. The achieved performance metrics notably surpassed those achieved by several incumbent state-of-the-art models, thus validating the proficiency of our method. Finally, motivated by the practical requirements of handling the nature conservation image data, we performed caption annotation for the iNaturalist 2018 dataset. Subsequently, we trained the ReCap on this annotated data. Experimental results demonstrate that our proposed method can maintain semantic consistency between cross-modal retrieval and image caption generation for species with similar visual features but distinct semantics. This achievement signifies a meaningful exploration in the field of cross-modal semantic consistency representation, holding significant practical value in the domain of biological research.

Background: DICER1, cancer predisposition syndrome (CPS), seems to escape timely diagnosis in pediatric patients. Case report 1: A 16-year-old female patient was referred to the endocrinology ward due to a large goiter. Her medical history indicated normal sexual maturation, with menarche occurring at 13.5 years. Over the past 2.5 years, she developed pronounced androgenic symptoms, including a deepened male voice, facial, back and neckline acne, hirsutism, and menstrual irregularities leading to secondary amenorrhoea. A thyroid ultrasound identified a multinodular goiter with cystic-solid lesions containing calcifi-cations. An abdominal ultrasound identified a 5.7x6.9 cm solid mass in the right adnexal region, displacing the uterus to the left. Histopathological examination confirmed a Sertoli-Leydig cell tumor. The patient was subjected to total thyroidectomy. Histopathology revealed benign follicular cell-derived neoplasms. Thyroid follicular nodular disease was diagnosed bilaterally. DNA analysis using NGS, confirmed by the Sanger method, revealed a pathogenic heterozygotic variant c.2953>T [p.Gln985*] in exon 18 of DICER1 gene. Case report 2: A 12-year-old male patient was admitted to the pediatric surgery unit due to a 33 ml goiter. A month prior to admission, the patient discovered a palpable nodule in his neck, accompanied by hoarseness. An ultrasound revealed a multinodular goiter. Molecular analysis revealed a pathogenic heterozygotic variant c.2782C>T [p.Gln928Ter] in exon 17 of DICER1 gene. Subsequently, a total thyroidectomy was performed, and histopathological examination revealed thyroid follicular nodu-lar disease bilaterally. Conclusion: Multinodular goiter, although rare in pediatric population, when accompanied by characteristic ultrasound and histopathological features, and by additional features such as androgen excess, warrants exclusion of DICER1 syndrome.

Embryological development of pancreas is a complex phenomenon and therefore, it can have multiple developmental aberrations. Fortunately, majority of these pancreatic ductal anomalies are asymptomatic with no clinical relevance and are incidentally detected during diagnostic cross sectional imaging or endoscopic retrograde cholangiopancreatography (ERCP) or autopsy. Occasionally, pancreatic duct anomalies can result in symptoms like abdominal pain or recurrent pancreatitis. Also, accurate pre-operative diagnosis of ductal anomalies can prevent inadvertent duct injury during surgery. Conventionally, ERCP had been used for accurate diagnosis of pancreatic duct anomalies. However, because of being invasive and associated with risk of pancreatitis, it has been replaced with magnetic resonance cholangiopancreatography (MRCP). MRCP has demonstrated high sensitivity and specificity for diagnosis of ductal anomalies which can be further improved with use of secretin enhanced MRCP. Endoscopic ultrasound (EUS) is a new diagnostic and interventional tool in the armamentarium of endoscopists and has demonstrated promising results in detection of pancreatic duct variations and anomalies. In this article, we will focus on the role of EUS in diagnosis of various pancreatic duct anomalies.

Understanding the distribution of rock glaciers provides key information for investigating and recognizing the status and changes of the periglacial environment. Deep learning algorithms and red-green-blue (RGB) bands from high-resolution satellite images have been extensively employed to map rock glaciers. However, the near-infrared (NIR) band offers rich spectral information and sharp edge features that could significantly contribute to semantic segmentation tasks, but it is rarely utilized in constructing rock glacier identification models due to the limitation of three input bands for classical semantic segmentation networks, like DeeplabV3+. In this study, a dual-encoder DeeplabV3+ network (DEDNet) was designed to overcome the flaws of classical DeeplabV3+ network (CDNet) when identifying rock glaciers using multispectral remote sensing images by extracting spatial and spectral features from RGB and NIR bands, respectively. This network, trained with manually labeled rock glacier samples from QiLian Mountains, established a model with accuracy, precision, recall, specificity, and mIoU (Mean Intersection over Union) of 0.9131, 0.9130, 0.9270, 0.9195, and 0.8601, respectively. The well-trained model was applied to identify new rock glaciers in a test region, achieving a producer's accuracy of 93.68% and a user's accuracy of 94.18%. Furthermore, the model was employed to two study areas in northern Tien Shan (Kazakhstan) and Daxue Shan (Hengduan Shan, China) with high accuracy, which proved that the DEDNet offers an innovative solution to more accurately map rock glaciers on a larger scale due to its robustness across diverse geographic regions.

Background: The transversus abdominis plane (TAP) block has gained increasing importance as a very effective pain relief in for caesarean section. This study aimed to compare bupivacaine (Group B), bupivacaine with dexmedetomidine (Group BD) and bupivacaine with dexamethasone (Group BDx) applied in ultrasound-guided TAP block in the treatment of CS postoperative pain. Material and Method: American Society of Anesthesiologists I and II pregnant women who underwent cesarean section were included in the study. Forty patients were admitted in each group. All patients were applied TAP block with ultrasound guidance by administering the medications of the group in which they were included. Results: Body mass index (BMI) and operation duration were higher in Group B (p*=0.01 and 0.004, respectively). Instant tramadol requirement and amount were higher in Group B at some of follow up times. Patient satisfaction scale were better in Group BD and Group BDx. Conclusion: TAP block significantly reduces pain after cesarean section, prolongs the time to first analgesic request, and reduces the need for additional opioid analgesics. The addition of adjuvant to the TAP block usually improves the postoperative clinical condition. However, some special conditions such as body mass index (BMI) may negatively affect the clinical situation. Comprehensive, standardized, randomized controlled studies are needed to compare results under different conditions.

Imaging of the ocular vasculature can provide new insights into the pathophysiology of ocular diseases. This study proposes a novel high-frequency super-resolution ultrasound localization microscopy (SRULM) technique and evaluates its ability to measure in vivo perfusion changes in the rat eye at elevated intraocular pressure (IOP). A 38.4 MHz center frequency linear array transducer on a VisualSonics Vevo F2 imaging platform was used to collect high frame rate (1 kHz) radiofrequency data of the posterior rat eye following systemic microbubble contrast injection. Following clutter and spatiotemporal non-local means filtering, individual microbubbles were localized and tracked. The microbubble tracks were accumulated over 10,000 frames to generate vascular images quantifying perfusion velocity and direction. Experiments were performed using physiologic relevant controlled flow states for algorithm validation and subsequently performed in vivo on the rat eye at 10 mm Hg IOP increments from 10 to 60 mm Hg. The posterior vasculature of the rat eye, including the ophthalmic artery, long posterior ciliary arteries and their branches, central retinal artery and retinal arterioles and venules were successfully visualized, and velocities quantified at each IOP level. Significant reductions in arterial flow were measured as IOP was elevated. High-frequency SRULM can be used to visualize and quantify perfusion velocity of the rat eye in both the retrobulbar and intraocular vasculature simultaneously. The ability to detect ocular perfusion changes throughout the depth of the eye may help elucidate the role ischemia has in the pathophysiology of ocular diseases such as glaucoma.

This study aims to test a model linking destination image, perceived value, tourist satisfaction, and tourist loyalty. Based on a sample of 300 tourists travelling by car from the World Natural Heritage Site of Tianchi, China, a new model of destination image was explored and data were analysed using partial least squares structural equation modelling (PLS-SEM). The results show that perceived value and satisfaction are direct antecedents of destination loyalty. Above all, perceived value and tourist satisfaction mediate the relationship between destination image and loyalty. Finally, this study discusses the theoretical and management implications of the findings in order to boost the tourism industry in the context of car trips.

Underwater image often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network's capability to extract key features, improvements were made based on the symmetrically structured Blind Super-Resolution Generative Adversarial Network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrate that the proposed method achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Underwater Image Quality Measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications.

Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

With the progression of AI, embedding advanced AI technologies into small robotics and mobile devices has become essential, driving research towards lightweighting AI models. Our study enhances the EPINET depth estimation model for light field images, aiming for compactness and faster inference while preserving accuracy. We conducted two-step experiments aimed at enhancing inference efficiency: Initially, by adjusting input streams and convolution layers, we simplified the CNN model, achieving faster inference times at the cost of reduced accuracy. To address this reduction in accuracy, we then applied knowledge distillation, allowing the simplified model to learn from the original model’s more complex patterns. In our quantitative experiments using two error metrics, MSE (Mean Squared Error) and BadPix, we identified optimal knowledge positions and evaluated the required complexity for the student model. As a result, our method improved MSE by 21% and BadPix by 14% compared to training without it. Furthermore, the student model achieved an inference speed 13% faster than the teacher model and surpassed its accuracy by 10% in MSE. Additionally, we demonstrated that repeatedly applying our approach could further enhance both model compactness and accuracy.

Accurate and prompt determination of fire types is essential for effective firefighting and reducing damages. However, traditional methods such as smoke detection, visual analysis, and wireless signals pose unavailability in identifying fire types. This paper introduces FireSonic, an acoustic sensing system that leverages commercial speakers and microphones to actively probe the fire using acoustic signals, effectively identifying indoor fire types. By incorporating beamforming technology, FireSonic first enhances signal clarity and reliability, thus mitigating signal attenuation and distortion. To establish a reliable correlation between fire types and sound propagation, FireSonic quantifies the Heat Release Rate (HRR) of flames by analyzing the relationship between fire heated areas and sound wave propagation delays. Furthermore, the system extracts spatiotemporal features related to fire from channel measurements. Experimental results demonstrate that FireSonic attains an average fire type classification accuracy of 95.5% and a detection latency of less than 400 ms, satisfying the requirements for real-time monitoring. This system significantly enhances the formulation of targeted firefighting strategies, boosting fire response effectiveness and public safety.

Visible light photography diagnostic images are coloured ex vivo medical images popularly used in Dermatology and Endoscopy for diagnosis and monitoring. The need to protect the integrity of these images as well as associated patient data calls for techniques such as image steganography and watermarking. This research explores and compares the effect of watermarking on the YIQ and YCbCr colour transforms used in processing digital coloured images and video in recent times. Using a new spread spectrum watermarking algorithm, it was found that YIQ has better distortion performance than YCbCr in the order of 3dB while YCbCr had lower BER for accurate watermark retrieval and tamper detection in the order of 1.3 x 10-3.

Endoscopic ultrasound-guided gallbladder drainage (EUS-GBD) is an alternative to surgery for acute cholecystitis (AC) in poor operative candidates. However, the role of EUS-GBD in non-cholecystitis (NC) indications has not been well studied. We compared clinical outcomes of EUS-GBD for AC and NC indications. Consecutive patients undergoing EUS-GBD for all indications at a single center were retrospectively analyzed. Fifty-one patients underwent EUS-GBD during the study period. Thirty-nine (76%) patients had AC while 12 (24%) had NC indications. NC indications included malignant biliary obstruction (n = 8), symptomatic cholelithiasis (n = 1), gallstone pancreatitis (n = 1), choledocholithiasis (n = 1), and Mirizzi’s syndrome (n = 1). Technical success was noted in 92% (36/39) for AC and 92% (11/12) for NC (p &gt; 0.99). Clinical success rate was 94% and 100%, respectively (p &gt; 0.99). There were 4 adverse events in the AC group and 3 in the NC group (p = 0.62). Procedure duration (median 43 vs 45 minutes, p = 0.37), post-procedure length of stay (median 3 vs 3 days, p = 0.97), and total gallbladder related procedures (median 2 vs 2, p = 0.59) were similar. EUS-GBD for NC indications is similarly safe and effective as EUS-GBD in AC.

Abstract: Background. The newer wireless portable scanners seem to permit a quicker bedside ultrasound (US) assessment, shortening the transfer times of the portable US unit and without the hindering of connection cables. However, no substantial evidence is currently available regarding of the clinical effectiveness of such new generation devices. Objective. The aim of the current study was to evaluate the efficacy of a wireless US system with respect to a high-end US unit in a cohort of consecutive respiratory patients. Materials and Methods. This prospective observational study included a total of 72 respiratory patients admitted in the department of Internal Medicine. The US examination were independently performed by two experienced blinded examiners with a high-end Esaote MyLab-Twice scanner and a wireless double-head linear and convex probe OTE-L/C501CD. The findings resulting from the high-end US assessment and the wireless US examination were then compared each other. All the patients had a confirmation chest CT scan. Results. The high-end US machine identified 64 positive cases for pleuro-pulmonary alterations, in comparison to which the wireless US unit resulted in 13 false negative results. The respective overall sensitivity and specificity for the portable wireless US unit were of 79.69% and 100.00%, respectively. The resolution of the portable wireless US device was judged as good in 22 (31%) cases, affected by minor diagnostic limitations in 26 (36%) cases and affected by major diagnostic limitations in 24 (33%). The main diagnostic strength of the wireless probe was in the detection of pleural effusion (sensitivity 100%). The diagnostic sensitivity of the wireless probe in assessing abnormalities of the pleural line (67%), pulmonary subpleural consolidations (65%), pleural lesions (33%) and pericardial effusion (0%) was reduced. Conclusions. The newer wireless US probes may have clinical roles for the detection and management of pleural effusion when used by experienced examiners. However, sensitivity in detecting other pleuro-pulmonary pathologies is not comparable to high-end ultrasound machines.

EUS–TA in unresectable pancreatic cancer requires not only a tissue diagnosis but also tissue collection in anticipation of comprehensive genomic profiling. However, the optimal puncture target remains controversial. Therefore, the Primary and Metastatic Lesions in Pancreatic Cancer (PRIMATE) study was designed to clarify the optimal target by comparing the success rates for meeting OncoGuide NCC Oncopanel (NOP) analysis criteria on pre-check primary and metastatic lesion specimens obtained during the same EUS–TA session in patients with invasive pancreatic ductal adenocarcinoma. In this ongoing prospective study, two specimens, each from primary and metastatic lesions, are obtained by EUS–TA (typically using a 19G fine-needle biopsy needle) in patients with invasive pancreatic ductal adenocarcinoma. The primary endpoint is the proportion of EUS–TA specimens that meet NOP analysis criteria during pre-check (i.e., tumor cellularity ≥ 20% and tissue area ≥ 4 mm2), which are then compared between primary and metastatic lesions. This study has been approved by the National Cancer Center Institutional Review Board (Research No. 2022-168). The results of this study will be reported at an international conference and published in an international peer-reviewed journal. The trial registration number is UMIN 000048966.

In Indonesia, types of meat are still identified manually due to increasing prices for beef and buf-falo ahead of Idul Fitri. Traders mix beef with pork to thwart the beef scam. Haar wavelet and GLCM, GLCM with angles of 0°, 45°, 90°, and 135°, as well as matrices using contrast, correlation, energy, homogeneity, and entropy, are used in feature extraction. The following are the results of meat image classification testing using k-NN, Haar wavelet, and GLCM: The k-NN algorithm shows superiority in identifying fresh (99%), frozen (99%), and rotten (96%) meat texture images with the most fantastic accuracy results in every situation. Meanwhile, GLCM routinely provides good results, especially regarding the texture image of fresh meat (183.21) and rotten meat (115.79). However, despite delivering less throughput than k-NN and GLCM, Haar wavelets are still helpful, especially when dealing with fresh meat texture images (89,96).

Background: This study assesses the effects of the ‘Radiant’ image enhancement technique on fetal nuchal translucency (NT) measurements during first trimester sonographic ex-ams. Methods: A retrospective analysis of 263 ultrasound images of first trimester midsagittal sections was conducted. NT measurements were obtained using a semi-automatic tool. Statistical methods were applied to compare NT measurements with and without ‘Radi-ant’ enhancement. An in-vitro setup with predefined line distances provided additional data. Results: Incremental increases in NT measurements were observed with varying levels of ‘Radiant’ application: An average increase of 0.19 mm with ‘Radiant min’, 0.24 mm with ‘Radiant mid’ and 0.30 mm with ‘Radiant max.’ The in-vitro results supported these findings, showing consistent effects on line thickness and measurement accuracy, with the smallest mean deviation occurring at the ‘Radiant mid’ setting. Conclusions: ‘Radiant’ image enhancement leads to significant increases in NT measure-ments. To avoid systematic biases in clinical assessments, it is advisable to disable ‘Radi-ant’ during NT measurement procedures. Further studies are necessary to corroborate these findings and to consider updates to the NT reference tables based on this technology.

Background: Hashimoto’s thyroiditis (HT) is an autoimmune disease that progressively leads to thyroid gland destruction and hypothyroidism. Patients produce antibodies against two thyroid-specific antigens: thyroglobulin and thyroid peroxidase. As a result of the abnormal amount of thyroid hormone produced and its autoimmune etiology, patients with HT display a higher frequency of either autoimmune or non-autoimmune dermatological disorders. On ultrasound examination, HT can be detected by observing an enlarged thyroid gland with hypoechoic parenchymal regions or by observing a small gland indicating atrophy and diffusely heterogeneous parenchyma. Changes in the parenchymal vascularity can be observed as decreased or increased levels of vascularization. Objective: This study aimed to analyze the role of clinical data and ultrasound evaluation of patients with HT in suggesting an association with dermatological disorders. Patients and methods: Complete dermatological and endocrinological assessments as well as ultrasonography of the thyroid and lymphnodes were performed in HT patients. Results: The data obtained showed that a family history of thyroid disease, increased diameter of the inferior thyroid artery, and the presence of cervical lymphadenopathy were more frequent in the group of patients with dermatological diseases. Taken together, our results suggest the usefulness of ultrasound in determining a possible association between HT and dermatological disorders.

This paper describes an image processing method that makes use of image parts instead of neural parts. Neural networks excel at image or pattern recognition and they do this by constructing complex networks of weighted values that can cover the complexity of the pattern data. These features however are integrated holistically into the network, which means that they can be difficult to use in an individual sense. A different method might scan individual images and use a more local method to try to recognise the features in it. This paper suggests such a method, where a trick during the scan process can not only recognise separate image parts, as features, but it can also produce an overlap between the parts. It is therefore able to produce image parts with real meaning and also place them into a positional context. Tests show that it can be quite accurate, on some handwritten digit datasets, but not as accurate as a neural network, for example. The fact that it offers an explainable interface could make it interesting however. It also fits well with an earlier cognitive model, and an ensemble-hierarchy structure in particular.

Saliency detection in synthetic aperture radar (SAR) image is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR image. Firstly, we extract four features of SAR image as the input of the MSD model, which include the intensity, orientation, uniqueness and global contrast. Then, the saliency map is generated by the multitask sparsity pursuit (MTSP) which integrates the multiple features collaboratively. Subjective and objective evaluation of the MSD model verifies its effectiveness. Based on the saliency maps of the source images, an image fusion method is proposed for the SAR and color optical image fusion. The experimental results of real data show the proposed image fusion method is superior to the presenting methods in terms of several universal quality evaluation indexes, as well as in the visual quality. The salient areas in the SAR image can be highlighted and the spatial and spectral details of color optical image can also be preserved in the fusion result.

The primary objective of this study was to demonstrate the potential of digital image analysis as a tool to identify microplastic (MP) particles in surface waters and to facilitate their characterisation in terms of 2D and 3D morphology. Digital image analysis preceded by microscopic analysis was used for an exhaustive quantitative and qualitative evaluation of MPs isolated from the Vistula river. Using image processing procedures, 2D and 3D shape descriptors were determined. The Principal Components Analysis (PCA) was used to interpret the relationships between the parameters studied, characterising MP particle geometry, type and colour. This multivariate analysis of the data allowed 3 or 4 main factors to be extracted, explaining approximately 90% of the variation in the data characterising MP morphology. It was found that the first principal component for granules, flakes and films was largely represented by strongly correlated with 2D shape descriptors (area, perimeter, equivalent area diameter) and 3D shape descriptors (CSF, Compactness, Dimensionality). Considering the scraps, PC1 was represented by only five of the above descriptors, and the Compactness variable had the largest contribution to PC2. In addition, for granules, flakes and films, a relationship between 2D shape and the colour of their particles could be observed. For the most numerous MP group identified of multicoloured scraps, no such association was found. The results of our study can be used for further multivariate analysis regarding the presence of microplastic floating on the river surface, with a particular focus on particles of secondary origin. This is of key importance for optimising future efforts in conducting small-scale and multidimensional monitoring of and reducing plastics in the aquatic environment.

Bisphenol-A (BPA) represents an important co-monomer for obtaining polycarbonate, epoxy resins, flame retardants, in paint, and other chemicals products. Products containing BPA are widely used, and thus, we can find it in the environment: in the air, in the soil, in natural surface water and in sediments, underground water in landfills, and in wastewater. BPA disturbs human and animals’ health. This influences the enzymatic, androgenic, neurological, liver and reproductive systems at different stages of human life: like in fetal stage, children and adults’ stages. Taking these inconveniences into account, it is very important to remove BPA from water. Ultrasonic technology can be considered a verry sustainable and efficient method to remove BPA from water. The advantages of this method is easy to implement on existing water treatment and purification facilities, it does not produce residual compounds that produce sludge, the time required for degradation is of the order of 1-2 hours and the level of degradation is very high. In this work, we presented the studies on the efficiency of ultrasonics under air atmosphere on the degradation of BPA. The influence of the frequency and of some additional compounds such as carbon tetrachloride, FeSO4 7H2O (FS), and ethyl anthraquinone (EAC) was studied. Three different frequencies were used: 1146 KHz, 864 KHz, 580 KHz, at a power of 50 W. It was identified the efficiency of BPA degradation over a period of 60 minutes, with sampling every 15 minutes. Using the LC-MS/MS technique, the degradation compounds were identified that result from the application of ultrasound processes. Pathways of BPA degradation were also proposed.

Background: Intracavitary contrast-enhanced ultrasound is widely accepted as a highly informative, safe and easily reproducible technique for diagnosis, treatment and follow- up of different pathologies of the biliary tree. Methods: This article reports the combined conclusions of personal experience with pictorial examples and literature review of applications for CEUS in intracavitary biliary scenarios. Different methodologies, choices of equipment, indications for the procedure and expected complications were identified and described from the authors’ own experience as well as from a review of published studies with total of 208 patients for the period between 2009 and 2015. Results: Indications were biliary obstruction by various etiologies, including postoperative strictures, evaluation of the biliary tree of liver donors (n=12) and evaluation of localization of a drainage catheter. One of the studies found a statistically significant difference in the diagnostic accuracy of PTC and PUSC. Conclusion: Intracavitary CEUS brings all the positive features of US together with the virtues of contrast- enhanced imaging, proving comparable accuracy to the listed techniques for diagnostics of biliary tree diseases.

Infrared and visible light image fusion technology integrates image feature information from two different modalities to generate an image that integrates complementary information from the source images. However, in low-light scenarios, the degradation of lighting in visible light images leads to existing fusion methods being unable to effectively extract texture detail information from the scene. At this time, the target prominence information provided by infrared images alone is far from sufficient. To address this challenge, this paper proposes a lightweight infrared and visible light image fusion method based on low-light enhancement, named LLE-Fuse. Firstly, the method improves the MobileOne Block by embedding the Sobel operator in the Edge-MobileOne Block to perform feature extraction and downsampling on the source images. Then, intermediate features of different scales are obtained and fused through a cross-modal attention fusion module, followed by image reconstruction using a decoder with upsampling. Next, the CLAHE algorithm is used for image enhancement of infrared and visible light images, and the enhanced loss guides the network model to learn the capability of low-light enhancement. Finally, upon completion of the network training, the method optimizes the Edge-MobileOne Block into the direct connection structure of MobileNetV1 through structural re-parameterization, effectively reducing the computational resource consumption of the model network. Through extensive experimental comparisons, this paper's method demonstrates excellent performance in both visual appeal and evaluation metrics, providing satisfactory fusion results even in low-light scenarios.

Deep learning image reconstruction (DLIR) algorithms employs convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at low radiation dose. The aim of this study was to assess whether the DLIR algorithm reduces the CT effective dose (ED) and im-proves CT image quality in comparison with filtered back projection (FBP) and iterative reconstruction (IR) algorithms in intensive care unit (ICU) patients. We identified all consecutive patients referred to the ICU of a single hospital who underwent at least two consecutive chest and/or abdominal contrast-enhanced CT scans within a time period of 30 days using DLIR and subsequently FBP or IR algorithm (Advanced Mod-eled Iterative Reconstruction model-based algorithm [ADMIRE], or Adaptive Iterative Dose Reduction 3D [AIDR 3D]) for CT image reconstruction. The radiation ED, noise level, and signal-to-noise ratio (SNR) were compared between the different CT scanners. The non-parametric Wilcoxon test was used for statistical comparison. Statistical significance was set at p < 0.05. A total of 83 patients (mean age, 59 ± 15 years [standard deviation]; 56 men) were included. DLIR vs FBP reduced ED (18.45 ± 13.16 mSv vs 22.06 ± 9.55 mSv, P < 0.05), while DLIR vs FBP and vs ADMIRE and AIDR 3D IR algorithms reduced image noise (8.45 ± 3.24 vs 14.85 ± 2.73 vs 14.77 ± 32.77 and 11.17 ± 32.77\, P < 0.05) and increased SNR (11.53 ± 9.28 vs 3.99 ± 1.23 vs 5.84 ± 2.74 and 3.58 ± 2.74, P < 0.05). CT scanners employing DLIR reduced radiation ED and im-proved SNR compared to CT scanners using FBP, whereas CT scanners using DLIR improved SNR com-pared to CT scanners using FBP or IR algorithms in ICU patients.

Timely and accurate acquisition of information on distribution of the crop planting structure in Loess Plateau of eastern Gansu Province, one of the most important agricultural areas in western China, is crucial for promoting fine management of agriculture and ensuring food security. In this study, the Object-Based Image Classification (OBIC), the Random Forest (RF) and Convolutional Neural Network (CNN) models were employed to classify the crop planting structure of four representative test areas in Qingyang City in a precise manner. Firstly, different optimal segmentation scales for various crops were selected using the Estimation of Scale Parameter 2 (ESP2) tool and the Ratio of Mean Difference to Neighbors(ABS) to Standard Deviation (RMAS)model. The images were then segmented through multiresolution segmentation combined with the Canny Edge Detection algorithm. Secondly, the L1 regularized logistic regression model was utilized to select and optimize 39 spatial feature factors including spectral, textural, geometric, and index features, in conjunction with phenological factors. Lastly, under the multi-level classification framework, the Random Forest (RF) classifier and Convolutional Neural Network (CNN) model, combined with object-based multiresolution segmentation, was used to classify the crop planting structure. The findings show that: Thanks to the Canny Edge Detection algorithm, we can obtain a more complete boundary of the segmented objects and improve the separability. The optimal segmentation scales for corn, vegetables, and buckwheat were found to be 55, 70, and 35, respectively, while wheat and apple had optimal segmentation scales of 65. In addition to phenological characteristics, the number of selected spatial features for corn, vegetables, buckwheat, wheat, and apple were 9, 7, 16, 12, and 10, respectively. The CNN model demonstrated high consistency with the RF model in the classification results, but the accuracy of RF model is higher than that of CNN model on the whole. The overall accuracy of classification using RF model in four test areas registered 91.93%, 94.92%, 89.37% and 90.68%, respectively. This paper introduced crop phenological factors, effectively improving the extraction precision of shattered agricultural planting structure in the Loess Plateau of eastern Gansu Province. Its findings have important application value in crop monitoring, management, food security and other related fields.

Background: clubfoot is a common congenital deformity. The Ponseti technique, involving early corrective manipulations followed by the application of long leg casts and Achilles tenotomy, is widely accepted as the preferred treatment. Rapid healing of the tendon after surgery has been documented, but the aspect regarding tendon structure and properties is not known. Three cases of Achilles tendon rupture in adolescent previously treated for clubfoot have been described in the literature. Since rupture is a rare event in this age group a possible correlation with previous surgery has been hypothesized. The primary aim of the study was to compare the ultrasound findings of the Achilles tendon, in patients treated for clubfoot, between patients treated with casting alone and the ones who underwent surgery (percutaneous tenotomy or Z-plasty lengthening). Methods: 22 asymptomatic patients, (34 feet) median age 12 years, previously treated for clubfoot, were recruited for this study; the patients underwent an Achilles tendon ultrasound examination during a follow-up outpatient visit. Results: a greater thickness and structural alterations with presence of hypoechoic areas of the operated tendons compared to those treated with plaster alone were observed (p value: 0.0498 and

Background and Objectives: Ultrasound (US) is a non-invasive tool for the in-vivo detection of peripheral nerve alterations. Materials and Methods: In this study we applied nerve US to assist the discrimination between the spectrum of Amyotrophic Lateral Sclerosis (ALS, n=11), chronic inflammatory demyelinating polyradiculoneuropathy (CIDP, n=5) and genetically confirmed Charcot-Marie-Tooth disease (CMT, n=5). All participants and n=15 controls without neurological diseases underwent high-resolution US of the bilateral tibial nerve. Nerve cross-sectional area (CSA) and nerve microvascular blood flow were compared between the groups and related to cerebrospinal fluid (CSF) measures, clinical symptoms and nerve conduction studies. Analyses are part of a larger multimodal study on the comparison between US and 7 Tesla (T) magnetic resonance neurography (MRN). Results: Patients and controls were matched with respect to their demographical data. CMT had the longest disease duration, followed by CIDP and ALS. CSA was related to age, weight and disease duration. CSA was larger in CMT and CIDP compared to ALS and controls. Blood flow was greatest in CIDP, and higher than in CMT, ALS and controls. In ALS, greater CSA was correlated with greater CSF total protein and higher albumin quotient. US measures did not correlate with clinical scores or nerve conduction studies in any of the subgroups. Conclusion: Our results point towards the feasibility of CSA and blood flow to discriminate between ALS, CIDP and CMT, even in small sample sized groups. In ALS, larger CSA could indicate an inflammatory disease subtype characterized by reduced blood-nerve barrier integrity. Our upcoming analysis will focus on the additive value of 7T MRN in combination with US to disentangle the spectrum between more inflammatory or more degenerative disease variants along the disease groups.

The analysis of data produced by the MV20/20 sensor, tagged with quality outcomes, is presented with the aim of developing a predictor model for real-time anomaly detection and classification. Three types of inclusions, undesired particles that deteriorate the quality of production, are used to tag the quality data using results from the lab. We explore both unsupervised and supervised learning, which both offer advantages in monitoring and controlling the quality of production. It is discovered that the dataset can be clustered using techniques like K-Means and DBSCAN. Bounding the data within a 95% confidence interval ellipse ensures we can detect anomalous events in real time. For supervised learning, a two-stage classifier is explored, which classifies the outcome of a cast and secondly the inclusion responsible for the negative outcome. We explore models from logistic regression and support vector machines, to two neural networks, namely the multi-layer perceptron and the radial basis function network. While the cast outcome is adequately predicted by all the models, the multi-layer perceptron provides a boundary performance for the inclusion type. A more advanced technique for model optimisation, namely grid search, is applied in order to improve on the results. The outcome for the grid search is not much better, which indicates a global maximum in the learning capacity of the model. Recommendations include the addition of sensor systems and an audit of data collection variation.

The current Covid-19 pandemic has hugely disrupted the delivery of routine and established medical care. Patients can develop a wide range of clinical signs and symptoms from a cough and fever to severe respiratory failure. There is an ongoing argument on a concise investigative pathway to ensure the safety of all healthcare workers. The stethoscope can help with any clinical respiratory assessment but the risk of cross infection is high. Computer tomography should not be routinely performed. There is a potential place for lung ultrasound but outcomes are not yet determined.

This paper presents an improved indoor localization system based on RF and ultrasonic signal which we named SNSH system. This system composes of a transmitter mounted in a mobile target, and a series of receiver nodes which are managed by a coordinator. By measuring Time Delay of Arrival (TDoA) of RF and ultrasonic signal from the transmitter, distance from target to receiver node is calculated and sent to the coordinator through CAN network, and all the information are gathered in PC to estimate 3D position of the target. Sine wave detector and dynamic threshold filter are applied to provide excellent accuracy of range measurement from TDoA result, and multilateration algorithms is realized to optimize coordinate determination accuracy. Specifically, Linear Least Square and Non-linear Least Square techniques are implemented to contrast their performances in target coordinate estimation. RF signal encoding/decoding time, time delay in CAN network and math calculating are carefully considered to ensure optimal system performance and get ready for field application. Experiments show that sine wave detector algorithm has greatly improved range measurement accuracy, with mean error at 2.2mm and maximum error at 6.7 mm, for distance below 5m. In addition, 3D position accuracy is greatly enhanced by multilateration methods, with mean error in position stay under 15mm, and 90% confident error values at 23mm for LLS, 20mm for NLS. Overall system update period has been verified in real system operation, with maximum rate at 25ms, and compared with other existing researches.

Analytical instruments used for scientific research and education are often expensive. This limitation poses a challenge for schools and educators in low-income countries to provide practical science training to students. To address this problem, the improvisation of analytical instruments and instructional materials has emerged as a viable solution. By incorporating practical experiments into science education, students have the opportunity to engage directly with scientific instruments, conduct investigations, and collect data. This hands-on approach fosters a deeper understanding of scientific principles, enhances critical thinking skills, and cultivates scientific mindsets among students. Herein, we review and discuss, step-by-step, the approach of using a smartphone for quantitative analysis, referred to as smartphone digital image colorimetry (SDIC). SDIC, with its practicality and accessibility, utilizes smartphone digital cameras for image acquisition and the freely available image processing programs, such as ImageJ and smartphone Apps, for image quantification. Using SDIC can enable students in low-income countries to develop essential scientific skills, including critical thinking, data interpretation, and an evidence-based approach to scientific research. By leveraging the ubiquity and affordability of smartphone technology, SDIC offers an accessible and cost-effective approach to practical science education, bridging the gap between theory and practice in resource-constrained settings.

Abstract Purpose: This study aims to evaluate the potential of multimodal ultrasound techniques in improving the visibility of liver lesions that are indistinct under conventional ultrasound. Specifically, we will explore the application of fusion imaging, contrast-enhanced ultrasound (CEUS), and high-frequency ultrasound in enhancing the visibility of liver masses. These techniques will be utilized to assist in visualizing liver lesions and to accurately localize them for the purpose of performing percutaneous tissue biopsy. Lastly, we will compare the biopsy results with the final diagnosis to assess whether multimodal techniques can enhance the success rate of percutaneous biopsy for liver masses that are not clearly visible under conventional ultrasound. Furthermore, the potential impact of these techniques on the development of personalized treatment strategies for patients was also discussed. Materials and Results: A retrospective analysis was conducted on 144 patients with liver masses who underwent conventional ultrasound examinations at our hospital from October 2018 to January 2024. These patients had poor visibility on conventional ultrasound, but the tumor location was visible on abdominal computerized tomography (CT) or magnetic resonance imaging (MRI) scans. We excluded 71 patients who did not undergo percutaneous liver tumor biopsy and 8 patients who remained non-visualized under multimodal ultrasound. Finally, 95 patients met the inclusion criteria and their clinical and radiological data were selected for analysis. Computerized tomography or magnetic resonance imaging-ultrasound (CT/MR-US) fusion imaging was performed in 55 patients, CEUS was conducted in 95 patients, and high-frequency ultrasound examination was performed in 21 patients. The visibility of the lesions using these three techniques was evaluated, and the consistency between the biopsy pathology and the final diagnosis was analyzed. Results: In the study, a total of 39 cases were identified as solitary liver lesions, 34 cases as multiple lesions, and an additional 23 cases as diffuse hepatocellular carcinoma. Among them, there were 13 cases (13.7%) of lesions located in the left lobe, 73 cases (76.8%) in the right lobe, and 9 cases (9.4%) at the junction of the left and right lobes. The average maximum diameter of the lesions was 2.7 cm (ranging from 0.7 cm to 10.0 cm). The detection rates of lesions using CT/MR-US fusion imaging, contrast-enhanced ultrasound (CEUS), and high-frequency ultrasound examination were 49.1%, 96.8%, and 76.2%, respectively. All patients in the entire cohort underwent percutaneous ultrasound-guided biopsy after confirming the target location of the lesions, including 9 patients underwent percutaneous microwave ablation. No severe complications were observed. The accuracy rate of the biopsies was 91.6%, and the positive concordance rate was 91.1%. The biopsy pathology included 39 cases of hepatocellular carcinoma (HCC), 16 cases of intrahepatic cholangiocarcinoma (ICC), 22 cases of metastatic adenocarcinoma, 3 cases of hepatic adenoma (HA), 1 case of vascular sarcoma, and 1 case of mixed HCC-ICC. Among the 13 patients with negative pathology findings after biopsy, 8 cases were considered false negatives (based on follow-up laboratory tests and imaging results consistent with malignant tumor characteristics), resulting in a false negative rate of 8.9%. Conclusions: The application of multimodal ultrasound technology can significantly improve the success rate of liver lesion biopsies that are difficult to visualize using conventional ultrasound. Furthermore, it will facilitate the guidance of microwave ablation therapy for lesions using multimodal ultrasound imaging, enabling collaborative precision treatment planning for patients with clinical physicians.

Thermal infrared imagery provides temperature information on target objects, and has been widely applied in non-destructive testing. However, thermal infrared imagery is not always able to display detailed textures of inspected objects, which hampers the understanding of geometric entities consisting of temperature information. Although some commercial software has been developed for 3D thermal model displays, the software requires the use of expensive specific thermal infrared sensors. This study proposes a cost-effective method for 3D thermal model reconstruction based on image-based modeling. Two smart phones and a low-cost thermal infrared camera are employed to acquire visible images and thermal images, respectively, that are fused for 3D thermal model reconstruction. The experiment results demonstrate that the proposed method is able to effectively reconstruct a 3D thermal model which extremely approximates its corresponding entity. The total computation time for the 3D thermal model reconstruction is intensive while generating dense points required for the creation of a geometric entity. Future work will improve the efficiency of the proposed method in order to expand its potential applications to in-time monitoring.

Facial recognition systems are widely used systems around the globe. Their applications in real world scenarios like law enforcement, surveillance, ID card identification, information security, access control and many more has made them very important in today’s world. These systems have shown promising results and excellent performance; however, these systems need tremendous number of facial images for training before they are implemented in the real-world scenarios. The problem arises when we face lack of training images due to which the performance of such systems decreases exponentially. The problem becomes even worse when there is only one image per subject for training our facial recognition system, the probe image might contain variation like illumination, expression and disguise which are hard to be accurately recognized. Generative models have achieved great success for handling the lack of training data by generating synthetic data that resembles the original data. We have created a new method by fine tuning the basic Conditional Generative Adversarial Network (CGAN) to generate synthetic facial images provided only single image per subject. These synthetic facial images contain six variations that are smile, anger, stare, illumination and disguise. We increased the size of the dataset and then applied a convolutional neural network for facial recognition, which improved the accuracy of our system from 0.76 to 0.99.

Abstract: Rationale. Object tracking has significance in many applications ranging from control of unmanned vehicles to autonomous monitoring of specific situations and events, especially when providing safety for patients with certain adverse conditions such as epileptic seizures. Conventional tracking methods face many challenges, such as the need for dedicated attached devices or tags, influence by high image noise, complex object movements, and intensive com-putational requirements. We have developed earlier computationally efficient algorithms for global optical flow reconstruction of group velocities that provide means for convulsive seizure detection and have potential applications in fall and apnea detection. Here, we address the chal-lenge of using the same calculated group velocities for object tracking in parallel. Methods. We propose a novel optical flow-based method for object tracking. It utilizes real-time image se-quences from the camera and directly reconstructs global motion-group parameters of the con-tent. These parameters can steer a rectangular region of interest surrounding the moving object to follow the target. The method successfully applies to multi-spectral data, further improving its effectiveness. Results. Experimental results on simulated tests and complex real-world data demonstrate the method's capabilities. The proposed optical flow reconstruction can provide accurate, robust, and faster results compared to current state-of-the-art approaches.

This study addresses the challenge of treating skin cancer effectively, especially in visible areas like the face, where traditional methods may be undesirable. Current options, including surgery and radiation therapy, have limitations such as complexity, expense, and imprecise dose delivery. The proposed solution involves additively manufacturing personalized patches using diagnostic imaging data, offering a targeted and controlled dose of beta-emitting isotopes like Y-90. The patches, tailored to the tumor's shape and depth, aim to minimize damage to healthy tissues and structures. Through 3D printing technology we demonstrated the feasibility of creating individualized applicators filled with radioisotopes, that have the potential to deliver therapeutic doses while ensuring patient comfort and affordability. This innovative approach, based on radiation delivery simulations, offers a promising future alternative to conventional radiation therapy methods. Clinical trials are required to validate its efficacy and safety, but the potential for reduced radiation exposure and enhanced treatment precision positions this personalized and targeted therapy as a valuable advancement in skin cancer treatment.

Background: Endoscopic Ultrasound (EUS) is widely used for the diagnosis of bilio-pancreatic and gastrointestinal (GI) tract diseases, for the evaluation of subepithelial lesions, and for sampling of lymph nodes and solid masses lo-cated next to the GI tract. The role of Artificial Intelligence in healthcare in growing. This review aimed to provide an overview of the current state of AI in EUS from imaging to pathological diagnosis and training. Methods: AI algorithms can assist in lesion detection and characterization in EUS by analysing EUS images and identifying suspicious areas that may require further clinical evaluation or biopsy sampling. Deep learning tech-niques, such as convolutional neural networks (CNNs), have shown great potential for tumour identification and subepithelial lesion (SEL) evaluation by extracting important features from EUS images and using them to classify or segment the images. Results: AI models with new features can increase the accuracy of diagnoses, provide faster diagnoses, identify subtle differences in disease presentation that may be missed by human eyes, and provide more information and in-sights into disease pathology. Conclusions: The integration of AI in EUS images and biopsies has the potential to improve the diagnostic accura-cy, leading to better patient outcomes and to a reduction of repeated procedures in case of non-diagnostic biopsies.

Ultrasound is a well-known tool to produce thermal and non-thermal effects on cells and tissues. These effects require a correct application of ultrasound in terms of localization and acoustic energy delivered. This article describes a new device that combines ultrasound and Dry Needling treatments. The ultrasound transducer can rotate in 3D space mechanically to align itself in the direction of the needle. The transducer electronically focuses the acoustic pressure automatically on the needle tip and its surroundings. A computer, using graphical interface software, controls the angulation of the array and the focus position.

When the image quality is evaluated, the human visual system (HVS) infers the details in the image through its internal generative mechanism. In this process, the HVS integrates both local and global information of the image, utilizes contextual information to restore the original image information, and compares it with the distorted image information for image quality evaluation. Inspired by this mechanism, a no-reference image quality assessment method is proposed based on a multitask image restoration network. The multitask image restoration network generates a pseudo-reference image as the main task and produces structural similarity index measure map as an auxiliary task. By mutually promoting the two tasks, a higher quality pseudo-reference image is generated. In addition, when predicting the image quality score, both the quality restoration features and the difference features between the distorted and reference images are used, thereby fully utilizing the information from the pseudo-reference image. To enable the model to focus on both global and local features, a multi-scale feature fusion module is proposed. Experimental results demonstrate that the proposed method achieves excellent performance on both synthetically and authentically distorted databases.

Thresholding is considered as a statistical-decision making theory which can lessen the average error incurred in allocating pixels to two or more groups. The traditional Bayes decision rule can be applied with the prior knowledge of the Probability Density Function (PDF) of each class. It is surmised that a threshold resulting in the best class separation is the optimal one. In this paper, Otsu’s thresholding for image segmentation has been implemented. The well-known Otsu’s method is to learn a threshold that can maximize the between-class variance or equivalently make light of the within-class variance of the entire image. At first, a color image of a tree is taken. After that, the image is transformed into a grayscale image. Then in the first part, two-level thresholding is conducted, and later on, three-level thresholding is also applied. Again, two-level thresholding, as well as three level thresholding, are also applied to some other images. Finally, the comparison is made between two level thresholding and three level thresholding

Metabolic-associated fatty liver disease (MASLD) is not only associated with traditional cardiovascular risk factors such as T2DM and obesity, but it is also an independent risk factor for the development of cardiovascular disease. MASLD has been shown to be independently related to endothelial dysfunction and atherosclerosis. MASLD is characterized by a chronic proinflammatory response that, in turn, may induce a prothrombotic state. Several mechanisms such as endothelial and platelet dysfunction, changes in the coagulation cascade, decreased fibrinolytic activity can contribute to induce the prothrombotic state. Platelets are players and addresses of metabolic dysregulation; obesity and insulin resistance are related to platelet hyperactivation. Furthermore, platelets can exert a direct effect on liver cells, particularly through the release of mediators from granules. Growing data in literature support the use of antiplatelet agent as a treatment for MASLD. The use of antiplatelets drugs seems to have potential clinical implication for the prevention of HCC patients with MASLD, since platelets contribute to fibrosis progression and cancer development. This review aims to summarize the main data on the role of platelets in the pathogenesis of MAFLD and its main complications such as cardiovascular events and the development of liver fibrosis. Furthermore, we will examine the role of antiplatelet therapy not only in the prevention and treatment of cardiovascular events but also as a possible anti-fibrotic and anti-tumor agent.

Abstract Background: Analysis of different type of consolidation in dogs and cats relay on parenchymal and vascular criteria. Hypothesis: the vascular criteria of the consolidation it’s possible to perform in dogs and cats and vascularisation is different in shred, tissue, nodule, wedge sign and mass. Animals: 347 cats and 634 dogs with different types of consolidations. Methods: Retrospective study between 2018-2023. Lung ultrasound performed in dogs and cats with different types of consolidations. Characteristic of bronchogram: air dynamic and static bronchogram, fluid bronchogram or absence of bronchogram. Types of vascularisations in consolidation: tree like type, residual, chaotic vascularisation and penetrating from the chest wall to consolidation and “vascular sign”. Conclusion and Clinical Importance: Analysis of bronchogram and type of vascularisation in consolidation is possible to perform in dogs and cat. The analysis of mentioned criteria had an impact on subsequent diagnostic and therapeutic decisions

Model-based image reconstruction has brought improvements in terms of contrast and spatial resolution to imaging applications such as magnetic resonance imaging and emission computed tomography. However, their use for pulse-echo techniques like ultrasound imaging is limited by the fact that model-based algorithms assume a finite grid of possible locations of scatterers in a medium -- which does not reflect the continuous nature of real world objects and creates a problem known as off-grid deviation. To cope with this problem, we present a method of dictionary expansion and constrained reconstruction that approximates the continuous manifold of all possible scatterer locations within a region of interest. The expanded dictionary is created using a highly coherent sampling of the region of interest, followed by a rank reduction procedure based on a truncated singular value decomposition. We develop a greedy algorithm, based on the Orthogonal Matching Pursuit (OMP), that uses a correlation-based non-convex constraint set that allows for the division of the region of interest into cells of any size. To evaluate the performance of the method, we present results of 2-dimensional ultrasound image reconstructions with simulated data in a nondestructive testing application. Our method succeeds in the reconstructions of sparse images from noisy measurements, providing higher accuracy than previous approaches based on regular discrete models.

As technology and multimedia production have advanced, there has been a significant rise in attacks on digital media, resulting in duplicated, fraudulent, and altered data and the infringement of copyright laws. This paper presents a robust and secure digital image watermarking technique that has been implemented in the spatial domain and exploits the erratic and chaotic behaviour of the powerful elementary cellular automata rule-30. The crucial characteristics of the watermarking system, i.e., imperceptibility, capacity, and robustness, have been perfectly balanced by the suggested blind watermarking technique. In this approach, prior to embedding, the grayscale watermark image is downsized to its two Most Significant Bits (MSBs). Then, the 2-MSBs watermark is encrypted using an ECA rule-30 so as to level up the security attribute of the system. Then, the host image is scrambled using ECA rule-30 to distribute the watermark pixels throughout the host image and thus achieve the highest robustness against geometrical attacks. Finally, the encrypted watermark data is embedded into the scrambled host image using the ECA rule-30-based embedding key. The proposed method performs better in terms of imperceptibility, capacity, and robustness when compared to several systems with similar competencies. The simulation's findings demonstrate strong imperceptibility as evaluated by the Peak Signal-to-Noise Ratio (PSNR), which has an average value of 58.3735 dB and a high payload. The experimental outcomes, observed across a diverse range of standardized attack scenarios, unequivocally establish the ascendancy of the proposed algorithm over competing methodologies in the realm of image watermarking.

Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, we present a comprehensive approach utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets are based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH Dataset, we deployed an ensemble strategy incorporating VGG16 and ResNet50 architec-tures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique, to preprocess a high-resolution image, which facilitates focused analysis of localized regions of interest. The annotated BACH dataset encompasses 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, we employed the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify mi-croscopic images into eight distinct categories (four benign and four malignant). For both models we leveraged a five-fold cross-validation approach for rigorous training and testing. Preliminary ex-perimental results indicate a Patch classification accuracy of 95.31% (on BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to on-going endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens.

Ultrasound based haptic feedback is a potential technology for human-computer interaction (HCI) with the advantages of low cost, low power consumption and controlled force. In this paper, the phase optimization for multipoint haptic feedback based on ultrasound array is investigated and the corresponding experimental verification is provided. A mathematical model of acoustic pressure is established for the ultrasound array and then a phase optimization model for an ultrasound transducer is constructed. We propose a pseudo-inverse (PINV) algorithm to accurately determine the phase contribution of each transducer in the ultrasound array. By controlling the phase difference of the ultrasound array, the multipoint focusing forces are formed leading to various shapes such as geometries and letters that can be visualized. Because the unconstrained PINV solution results in unequal amplitudes for each transducer, a weighted amplitude iterative optimization is deployed to further optimize the phase solution, by which the uniform amplitude distributions of each transducer are obtained. For the purpose of experimental verifications, a platform of ultrasound haptic feedback consisting of a Field Programmable Gate Array (FPGA), an electrical circuit and an ultrasound transducer array is prototyped. The haptic performances of single point, multiple points and dynamic trajectory were verified by controlling the ultrasound force exerted on the liquid surface. The experimental results demonstrate that the proposed phase optimization model and theoretical results are effective and feasible, and the acoustic pressure distribution is consistent with the simulation results.

The black hole (BH) phenomenon is an intraluminal restenotic lesion. It was identified by intravascular ultrasound (IVUS) and optical coherence tomography (OCT) after intracoronary brachytherapy and drug-eluting stent implantation. Despite the similarity in the mode of action of brachytherapy and drug-eluting stent implantation, the BH phenomenon appears to be uncommon after drug-eluting stent implantation. Specifically, the BH phenomenon is better identified by OCT than by IVUS. Herein, we present a case of in-stent restenosis with suspected BH phenomenon on IVUS confirmed by OCT.

(1) Background: Patella and achilles tendon injuries have become increasingly prevalent due to an increasingly active population. These injuries can cause significant dysfunction which is especially true for elite athletes. Standard B-Mode ultrasound has been useful in the diagnosis of these injuries, however, only to a limited extent. Shear wave elastography (SWE) can aid in evaluation by objectively measuring tissue stiffness. Prior studies have proven shear wave values can be used to evaluate tendon tissue noting differences in healthy versus pathologic tendons. One main limitation remains a lack of normative data for specific tendon tissues which we aim to address in this study; (2) Methods: A cross-sectional study of healthy volunteers aged 18-65 without lower extremity injury was evaluated using A Samsung RS85 Prestige ultrasound machine with a 14L-2 MHz transducer. Shear wave values were collected in a standardized manner for the patellar tendon at one location, as well as the achilles tendon at the midportion and insertional areas; (3) Results: Data for 54 healthy adult patellar and achilles tendons was collected bilaterally. The mean SWE average of the patellar tendon (P1-P3) for all subjects was 96.3 +/- 10.9 kPA. A higher BMI was correlated with a decrease in patella tendon kPA and the mean PT1 was higher for males compared to females (adjusted mean 96.4 kPA for males and 88.5 kPA for females, mean difference = 7.9 kPA; P = 0.009). The mean SWE average of the insertional Achilles (AT1-AT3) for all subjects was 101.7 +/-16.2 kPA and for the midportion achilles (MTP1-MTP5) was 145.6 +/- 18.8 kPA. There was a decrease in kPA for insertional Achilles measurements in those with a higher BMI, but this did not reach statistical significance (p=.12); (4) Conclusion: This study provides normative SWE values for the patella and achilles tendon using the Samsung RS85. These values can be used for comparison to pathological tissue by means of the same examination protocol to better understand tendon degeneration. Further studies are warranted as to how SWE can be applied to the diagnosis of tendinopathy, partial and full-thickness tears, and response to treatment.

Ultrasonography is a relevant diagnostic tool extensively used andrology of human and do-mestic animals, including dogs. The present review aimed to describe all the technologies based on ultrasound, starting with the basic B-Mode ultrasonography to the recent contrast-enhanced ultrasonography (CEUS) and the ultrasound elastography, available to assess the testicle of the dog. The principles of the different technologies and the relevant findings in normal and ab-normal testicular conditions were described and discussed. B-mode ultrasonography aims at the examination of the testis detecting focal lesions but lacks objectivization. Other technologies, such as Doppler ultrasonography, B-Flow, and CEUS allow the definition of vascular patterns, that could be measured with specific applications, such as spectral Doppler or quantitative CEUS. Finally, ultrasound elastography allows the estimation of parenchyma stiffness, in both qualitatively and quantitatively manner. Ultrasound-based technologies assist the andrology in the evaluation of the testicular function and integrity, offering valuable information to define pathological conditions that could have an impact on the health and life quality of the male dog.