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The use of innovative techniques in the classification of dried products for food processing and packaging systems simplifies and brings objectivity to the related processes. In addition, it helps consumers to access quality products. This study aimed to discriminate dried carrot samples subjected to 12 different combinations of drying and pretreatment by using innovative techniques based on chromatic attributes. The drying procedure consists of three different microwave powers (100, 200, 300 W), and pretreatments include 5% gum arabic solution, 5% gum sucrose solution, ultrasound application, and a control (no-pretreatment). The R , G , B , L , a , b , H , S, and V color channels were extracted from dried carrot images, and the exploration of classification models from selected color channels was an innovative aspect of the study. In all color channels, high overall accuracies were obtained with the values of 96.33, 96.50, and 97.00% for the logistic model tree, k-nearest neighbor, and random forest, respectively. The confusion matrix showed the lowest mixing among carrots dried at H-US (300 W + ultrasound) vs. H-C (300 W + control), H-S (300 W + sucrose) vs. H-C (300 W + control), and M-S (200 W + sucrose) vs. H-S (300 W + sucrose). The highest L, a, and b color values for the dried carrot slices were obtained as M-S (75.26), H-GA (30.93), and M-GA (44.75), respectively. In addition, hue values varied between 17.26 and 13.27, whereas saturation values varied between 50.93 and 59.90. The finding revealed that assessment of the effect of pretreatment and drying on carrot slices could be performed by machine learning and image analysis in a practical, rapid, and non-destructive manner. • Eight different machine learning algorithms was applied for classification. • 12 carrot classes subjected to various treatments were distinguished. • Innovative models built based on image textures were developed. • LMT, kNN, and RF was more successful for classification of dried carrot slices. • The developed procedure was non-destructive, objective, and effective. [ABSTRACT FROM AUTHOR]

Establishing quantitative parameters for differentiating between healthy and diseased cartilage tissues by examining collagen fibril degradation patterns facilitates the understanding of tissue characteristics during disease progression. These findings could also complement existing clinical methods used to diagnose cartilage-related diseases. In this study, cartilage samples from normal, osteoarthritis (OA), and rheumatoid arthritis (RA) tissues were prepared and analyzed using polarization-resolved second harmonic generation (P-SHG) imaging and quantitative image texture analysis. The enhanced molecular contrast obtained from this approach is expected to aid in distinguishing between healthy and diseased cartilage tissues. P-SHG image analysis revealed distinct parameters in the cartilage samples, reflecting variations in collagen fibril arrangement and organization across different pathological states. Normal tissues exhibited distinct χ33/χ31 values compared with those of OA and RA, indicating collagen type transition and cartilage erosion with chondrocyte swelling, respectively. Compared with those of normal tissues, OA samples demonstrated a higher degree of linear polarization, suggesting increased tissue birefringence due to the deposition of type-I collagen in the extracellular matrix. The distribution of the planar orientation of collagen fibrils revealed a more directional orientation in the OA samples, associated with increased type-I collagen, while the RA samples exhibited a heterogeneous molecular orientation. This study revealed that the imaging technique, the quantitative analysis of the images, and the derived parameters presented in this study could be used as a reference for disease diagnostics, providing a clear understanding of collagen fibril degradation in cartilage. [ABSTRACT FROM AUTHOR]

Fresh grapes are characterized by a short shelf life and are often subjected to quality losses during post-harvest storage. The quality assessment of grapes using image analysis may be a useful approach using non-destructive methods. This study aimed to compare the effect of different storage methods on the grape image texture parameters of the fruit outer structure. Grape bunches were stored for 4 weeks using 3 storage methods (– 18 °C, + 4 °C, and room temperature) and then were subjected subsequently to image acquisition using a flatbed scanner and image processing. The models for the classification of fresh and stored grapes were built based on selected image textures using traditional machine learning algorithms. The fresh grapes and stored fruit samples (for 4 weeks) in the freezer, in the refrigerator and in the room were classified with an overall accuracy reaching 96% for a model based on selected texture parameters from images in color channels R, G, B, L, a, and b built using Random Forest algorithm. Among the individual color channels, the carried-out classification for the R color channel produced the highest overall accuracies of up to 92.5% for Random Forest. As a result, this study proposed an innovative approach combining image analysis and traditional machine learning to assess changes in the outer structure of grape berries caused by different storage conditions. [ABSTRACT FROM AUTHOR]

The integration of advanced image analysis using artificial intelligence (AI) is pivotal for the evolution of autonomous vehicles (AVs). This article provides a thorough review of the most significant datasets and latest state-of-the-art AI solutions employed in image analysis for AVs. Datasets such as Cityscapes, NuScenes, CARLA, and Talk2Car form the benchmarks for training and evaluating different AI models, with unique characteristics catering to various aspects of autonomous driving. Key AI methodologies, including Convolutional Neural Networks (CNNs), Transformer models, Generative Adversarial Networks (GANs), and Vision Language Models (VLMs), are discussed. The article also presents a comparative analysis of various AI techniques in real-world scenarios, focusing on semantic image segmentation, 3D object detection, vehicle control in virtual environments, and vehicle interaction using natural language. Simultaneously, the roles of multisensor datasets and simulation platforms like AirSim, TORCS, and SUMMIT in enriching the training data and testing environments for AVs are highlighted. By synthesizing information on datasets, AI solutions, and comparative performance evaluations, this article serves as a crucial resource for researchers, developers, and industry stakeholders, offering a clear view of the current landscape and future directions in autonomous vehicle image analysis technologies. [ABSTRACT FROM AUTHOR]

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 25 March and 14 April 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. [ABSTRACT FROM AUTHOR]

The speckle noise inherent in synthetic aperture radar (SAR) imaging has long posed a challenge for SAR data processing, significantly affecting image interpretation and recognition. Recently, deep learning-based SAR speckle removal algorithms have shown promising results. However, most existing algorithms rely on convolutional neural networks (CNN), which may struggle to effectively capture global image information and lead to texture loss. Besides, due to the different characteristics of optical images and synthetic aperture radar (SAR) images, the results of training with simulated SAR data may bring instability to the real-world SAR data denoising. To address these limitations, we propose an innovative approach that integrates swin transformer blocks into the prediction noise network of the denoising diffusion probabilistic model (DDPM). By harnessing DDPM's robust generative capabilities and the Swin Transformer's proficiency in extracting global features, our approach aims to suppress speckle while preserving image details and enhancing authenticity. Additionally, we employ a post-processing strategy known as pixel-shuffle down-sampling (PD) refinement to mitigate the adverse effects of training data and the training process, which rely on spatially uncorrelated noise, thereby improving its adaptability to real-world SAR image despeckling scenarios. We conducted experiments using both simulated SAR image datasets and real SAR image datasets, evaluating our algorithm from subjective and objective perspectives. The visual results demonstrate significant improvements in noise suppression and image detail restoration. The objective results demonstrate that our method obtains state-of-the-art performance, which outperforms the second-best method by an average peak signal-to-noise ratio (PSNR) of 0.93 dB and Structural Similarity Index (SSIM) of 0.03, affirming the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The feature extraction and analysis of gymnastics images is an essential foundation for estimating human posture. The primary step is to obtain various joint points of athletes based on basic information such as human texture and contour in the sports images. The reconstruction and analysis of the human skeleton is completed based on the feature data of the joint points. In this process, traditional algorithm models often have certain shortcomings in the accuracy of feature extraction for motion images. This paper combines multi-scale feature fusion algorithms to construct a gymnastics motion image feature extraction model, which can achieve more accurate and efficient analysis and research for the feature extraction process of motion images, further improving the detection accuracy of joint points in motion images; this lays an essential foundation for feature extraction of gymnastics images. At the same time, it also provides more methods for skeleton reconstruction based on image feature information during the motion process, improving the efficiency and accuracy of reconstruction. [ABSTRACT FROM AUTHOR]

Metallographic image analysis is vital in the field of metal science due to its potential to automate the sensing process for grain and particle size estimation. To ensure the good quality and reliability of metal products, analysis of the integrity of metallic components is required. In contemporary manufacturing processes, microscopic analysis is a crucial step, mainly when complex systems like gearboxes, turbines, or engines are assembled using various components from multiple suppliers. A final product's quality, durability, and lifespan are determined via the quality analysis of properties of a material with proper tolerances. A flaw in a single component can cause the breakdown of the entire finished product. To ensure the good quality of a material, micro-structural analysis is necessary, which includes the routine measurement of inclusions. The particle and grain sizes of particulate samples are the most crucial physical characteristics of metals. Their measurement is routinely conducted across various industries, and they are frequently considered essential parameters in the creation of many products. This paper discusses the role of sensors in enhancing the accuracy and efficiency of metallographic image analysis, as well as the challenges and limitations associated with this technology. The paper also highlights the potential applications of sensor-assisted metallographic image analysis in various industries, such as aerospace, automotive, and construction. The paper concludes by identifying future research directions for this emerging field, including the development of more sophisticated algorithms for grain and particle size estimation, the integration of multiple sensors for more accurate measurements, and the exploration of new sensing modalities for metallographic image analysis. [ABSTRACT FROM AUTHOR]

Computer tomography (CT), combined with advanced image processing techniques, can be used to visualize the complex internal structures of living and non-living media in a non-destructive, intuitive, and precise manner in both two and three-dimensional spaces. Beyond its clinical uses, CT has been extensively employed within the field of geotechnical engineering to provide both qualitative and quantitative analyses of the microstructural properties of loess. This technology has been successfully applied in many fields. However, with the rapid development of CT technology and the expansion of its application scope, a reassessment is necessary. In recent years, only a few documents have attempted to organize and review the application cases of CT in the field of loess microstructure research. Therefore, the objectives of this work are as follows: (1) to briefly introduce the development process of CT equipment and the basic principles of CT and image processing; (2) to determine the current state and hotspots of CT technology research based on a bibliometric analysis of the literature from the past three decades in the Web of Science Core Collection and CNKI databases; and (3) to comprehensively review the application of CT to explore the microstructural characteristics (such as particle size, shape, arrangement, and the connectivity, orientation, and pore throats of pores, etc.) and the evolution of structural damage in loess within geotechnical science. In addition, the progress and deficiencies of CT applications in the field of loess microstructure are summarized, and future prospects are proposed. [ABSTRACT FROM AUTHOR]

The objective of this study was to validate a novel computerized method of ultrasound image analysis to determine chemical composition of pectoralis major muscles in broiler chickens. Ultrasonograms of pectoral muscles in the longitudinal and transverse planes were obtained from 40 birds just before slaughter. All chemical constituents of muscle samples were determined with the validated laboratory techniques, and the results served as a benchmark for developing the present algorithmic estimates of chicken meat composition. An inhouse developed algorithm (r-Algo) was used to normalize the ultrasonograms and to identify pixel intensity ranges for which linear correlations between mean numerical pixel values and the content of various chemical constituents were the strongest (based on the values of correlation coefficients), using a stepwise sequestration of ultrasound bitmaps. Percentages of chemical constituents were the dependent (accepted) variables and the results of echotextural analyses (luminance or pixel intensity), carried out with a commercially available image analysis software (ImageProPlus®), were the explanatory variables. The predictive regression equations were determined in 30 randomly selected algorithm-training experimental units, and their accuracy was tested in a subset of 10 birds allocated to the algorithm-validation group. Significant determination coefficients were found for all chemical constituents studied, with the accuracy ranging from 62.70% (linoleic acid, transverse plane, pixel range of 141–142) to 96.65% (total hypocholesterolemic acids, longitudinal plane, pixel range of 136–150). The present validation results indicate that accurate prediction of muscle chemical composition using echotextural image analyses is feasible after identifying specific pixel intensity ranges. [ABSTRACT FROM AUTHOR]

Introduction: Although the incidence and mortality rates of colorectal cancer exhibit significant variability, it remains one of the most prevalent cancers worldwide. Endeavors to prevent colorectal cancer development focus on detecting precursor lesions during colonoscopy. The diagnosis of endoscopically resected polyps relies on hematoxylin and eosin staining examination. For challenging cases like adenomatous polyps with epithelial misplacement, additional diagnostic methods could prove beneficial. Methods: This paper aims to underscore stromal changes observed in malignant polyps and polyps with pseudoinvasion, leveraging two-photon excitation microscopy (TPEM), a technique extensively employed in the medical field in recent years. Results and discussions: Both the subjective and quantitative analysis of TPEM images revealed distinct distributions and densities of collagen at the invasion front in malignant polyps compared to areas of pseudoinvasion. TPEM holds potential in discerning true invasion in malignant polyps from pseudoinvasion, offering enhanced visualization of local stromal changes. [ABSTRACT FROM AUTHOR]

Background: The assessment of in vitro wound healing images is critical for determining the efficacy of the therapy-of-interest that may influence the wound healing process. Existing methods suffer significant limitations, such as user dependency, time-consuming nature, and lack of sensitivity, thus paving the way for automated analysis approaches. Methods: Hereby, three structurally different variations of U-net architectures based on convolutional neural networks (CNN) were implemented for the segmentation of in vitro wound healing microscopy images. The developed models were fed using two independent datasets after applying a novel augmentation method aimed at the more sensitive analysis of edges after the preprocessing. Then, predicted masks were utilized for the accurate calculation of wound areas. Eventually, the therapy efficacy-indicator wound areas were thoroughly compared with current well-known tools such as ImageJ and TScratch. Results: The average dice similarity coefficient (DSC) scores were obtained as 0.958 ∼ 0.968 for U-net-based deep learning models. The averaged absolute percentage errors (PE) of predicted wound areas to ground truth were 6.41%, 3.70%, and 3.73%, respectively for U-net, U-net++, and Attention U-net, while ImageJ and TScratch had considerable averaged error rates of 22.59% and 33.88%, respectively. Conclusions: Comparative analyses revealed that the developed models outperformed the conventional approaches in terms of analysis time and segmentation sensitivity. The developed models also hold great promise for the prediction of the in vitro wound area, regardless of the therapy-of-interest, cell line, magnification of the microscope, or other application-dependent parameters. [ABSTRACT FROM AUTHOR]

Retained surgical items (RSIs) pose significant risks to patients and healthcare professionals, prompting extensive efforts to reduce their incidence. RSIs are objects inadvertently left within patients' bodies after surgery, which can lead to severe consequences such as infections and death. The repercussions highlight the critical need to address this issue. Machine learning (ML) and deep learning (DL) have displayed considerable potential for enhancing the prevention of RSIs through heightened precision and decreased reliance on human involvement. ML techniques are finding an expanding number of applications in medicine, ranging from automated imaging analysis to diagnosis. DL has enabled substantial advances in the prediction capabilities of computers by combining the availability of massive volumes of data with extremely effective learning algorithms. This paper reviews and evaluates recently published articles on the application of ML and DL in RSIs prevention and diagnosis, stressing the need for a multi-layered approach that leverages each method's strengths to mitigate RSI risks. It highlights the key findings, advantages, and limitations of the different techniques used. Extensive datasets for training ML and DL models could enhance RSI detection systems. This paper also discusses the various datasets used by researchers for training the models. In addition, future directions for improving these technologies for RSI diagnosis and prevention are considered. By merging ML and DL with current procedures, it is conceivable to substantially minimize RSIs, enhance patient safety, and elevate surgical care standards. [ABSTRACT FROM AUTHOR]

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

In recent years, various data-driven algorithms have been applied to the classification and staging of brain glioma MRI detection. However, the restricted availability of brain glioma MRI data in purely data-driven deep learning algorithms has presented challenges in extracting high-quality features and capturing their complex patterns. Moreover, the analysis methods designed for 2D data necessitate the selection of ideal tumor image slices, which does not align with practical clinical scenarios. Our research proposes an novel brain glioma staging model, Medical Cognition Embedded (MCE) model for 3D data. This model embeds knowledge characteristics into data-driven approaches to enhance the quality of feature extraction. Approach includes the following key components: (1) Deep feature extraction, drawing upon the imaging technical characteristics of different MRI sequences, has led to the design of two methods at both the algorithmic and strategic levels to mimic the learning process of real image interpretation by medical professionals during film reading; (2) We conduct an extensive Radiomics feature extraction, capturing relevant features such as texture, morphology, and grayscale distribution; (3) By referencing key points in radiological diagnosis, Radiomics feature experimental results, and the imaging characteristics of various MRI sequences, we manually create diagnostic features (Diag-Features). The efficacy of proposed methodology is rigorously evaluated on the publicly available BraTS2018 and BraTS2020 datasets. Comparing it to most well-known purely data-driven models, our method achieved higher accuracy, recall, and precision, reaching 96.14%, 93.4%, 97.06%, and 97.57%, 92.80%, 95.96%, respectively. [ABSTRACT FROM AUTHOR]

An advantage of masticators is the calibration and possible standardization of intra- and inter-individual mastication variability. However, mastication of soft, sticky and melting products, such as processed cream cheeses, is challenging to reproduce with a masticator. The objectives of this work were, for the cheese studied: (1) to compare child and adult mastication and (2) to find in vitro parameters which best reproduce their in vivo chewing. Five parameters influencing mastication (mouth volume, quantity consumed, saliva volume, mastication time and number of tongue–palate compressions) were measured in 30 children (5–12 years old) and 30 adults (18–65 years old) and compared between the two populations. They were then transposed to a masticator (Oniris device patent). The initial cheese, a homogeneous white paste, was surface-colored to investigate its in-mouth destructuring. In vivo boli were collected at three chewing stages (33, 66 and 99% of mastication time) and in vitro boli were obtained by varying the number of tongue–palate compressions and the rotation speed. In vivo and in vitro boli were compared by both image and texture analysis. Child masticatory parameters were proportionally smaller than those of adults. The in vivo child boli were less homogeneous and harder than adult ones. Comparison of in vivo and in vitro bolus color and texture enabled the successful determination of two in vitro settings that closely represented the mastication of the two populations studied. [ABSTRACT FROM AUTHOR]

Effective features play an important role in synthetic aperture radar (SAR) image interpretation. However, since SAR images contain a variety of terrain types, it is not easy to extract effective features of different terrains from SAR images. Deep learning methods require a large amount of labeled data, but the difficulty of SAR image annotation limits the performance of deep learning models. SAR images have inevitable geometric distortion and coherence speckle noise, which makes it difficult to extract effective features from SAR images. If effective semantic context features cannot be learned for SAR images, the extracted features struggle to distinguish different terrain categories. Some existing terrain classification methods are very limited and can only be applied to some specified SAR images. To solve these problems, a jigsaw puzzle self-supervised learning (JPSSL) framework is proposed. The framework comprises a jigsaw puzzle pretext task and a terrain classification downstream task. In the pretext task, the information in the SAR image is learned by completing the SAR image jigsaw puzzle to extract effective features. The terrain classification downstream task is trained using only a small number of labeled data. Finally, fully connected conditional random field processing is performed to eliminate noise points and obtain a high-quality terrain classification result. Experimental results on three large-scene high-resolution SAR images confirm the effectiveness and generalization of our method. Compared with the supervised methods, the features learned in JPSSL are highly discriminative, and the JPSSL achieves good classification accuracy when using only a small amount of labeled data. [ABSTRACT FROM AUTHOR]

In recent years, deep convolutional neural networks (DCNNs) have shown promising performance in medical image analysis, including breast lesion classification in 2D ultrasound (US) images. Despite the outstanding performance of DCNN solutions, explaining their decisions remains an open investigation. Yet, the explainability of DCNN models has become essential for healthcare systems to accept and trust the models. This paper presents a novel framework for explaining DCNN classification decisions of lesions in ultrasound images using the saliency maps linking the DCNN decisions to known cancer characteristics in the medical domain. The proposed framework consists of three main phases. First, DCNN models for classification in ultrasound images are built. Next, selected methods for visualization are applied to obtain saliency maps on the input images of the DCNN models. In the final phase, the visualization outputs and domain-known cancer characteristics are mapped. The paper then demonstrates the use of the framework for breast lesion classification from ultrasound images. We first follow the transfer learning approach and build two DCNN models. We then analyze the visualization outputs of the trained DCNN models using the EGrad-CAM and Ablation-CAM methods. We map the DCNN model decisions of benign and malignant lesions through the visualization outputs to the characteristics such as echogenicity, calcification, shape, and margin. A retrospective dataset of 1298 US images collected from different hospitals is used to evaluate the effectiveness of the framework. The test results show that these characteristics contribute differently to the benign and malignant lesions' decisions. Our study provides the foundation for other researchers to explain the DCNN classification decisions of other cancer types. [ABSTRACT FROM AUTHOR]

Powerful, workflow-agnostic and interactive visualisation is essential for the ad hoc, human-in-the-loop workflows typical of cryo-electron tomography (cryo-ET). While several tools exist for visualisation and annotation of cryo-ET data, they are often integrated as part of monolithic processing pipelines, or focused on a specific task and offering limited reusability and extensibility. With each software suite presenting its own pros and cons and tools tailored to address specific challenges, seamless integration between available pipelines is often a difficult task. As part of the effort to enable such flexibility and move the software ecosystem towards a more collaborative and modular approach, we developed blik, an open-source napari plugin for visualisation and annotation of cryo-ET data (source code: https://github.com/brisvag/blik). blik offers fast, interactive, and user-friendly 3D visualisation thanks to napari, and is built with extensibility and modularity at the core. Data is handled and exposed through well-established scientific Python libraries such as numpy arrays and pandas dataframes. Reusable components (such as data structures, file read/write, and annotation tools) are developed as independent Python libraries to encourage reuse and community contribution. By easily integrating with established image analysis tools—even outside of the cryo-ET world—blik provides a versatile platform for interacting with cryo-ET data. On top of core visualisation features—interactive and simultaneous visualisation of tomograms, particle picks, and segmentations—blik provides an interface for interactive tools such as manual, surface-based and filament-based particle picking, and image segmentation, as well as simple filtering tools. Additional self-contained napari plugins developed as part of this work also implement interactive plotting and selection based on particle features, and label interpolation for easier segmentation. Finally, we highlight the differences with existing software and showcase blik's applicability in biological research. A common challenge encountered by researchers working on cryo-ET data is the incompatibility between different software tools. This study develops a napari-based 3D visualization and analysis tool, named blik, that is compatible with commonly used cryo-ET processing packages. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is a serious health concern because of its high morbidity and mortality. The prognosis of HCC largely depends on the disease stage at diagnosis. Computed tomography (CT) image textural analysis is an image analysis technique that has emerged in recent years. Objective: To probe the feasibility of a CT radiomic model for predicting early (stages 0, A) and intermediate (stage B) HCC using Barcelona Clinic Liver Cancer (BCLC) staging. Methods: A total of 190 patients with stages 0, A, or B HCC according to CT-enhanced arterial and portal vein phase images were retrospectively assessed. The lesions were delineated manually to construct a region of interest (ROI) consisting of the entire tumor mass. Consequently, the textural profiles of the ROIs were extracted by specific software. Least absolute shrinkage and selection operator dimensionality reduction was used to screen the textural profiles and obtain the area under the receiver operating characteristic curve values. Results: Within the test cohort, the area under the curve (AUC) values associated with arterial-phase images and BCLC stages 0, A, and B disease were 0.99, 0.98, and 0.99, respectively. The overall accuracy rate was 92.7%. The AUC values associated with portal vein phase images and BCLC stages 0, A, and B disease were 0.98, 0.95, and 0.99, respectively, with an overall accuracy of 90.9%. Conclusion: The CT radiomic model can be used to predict the BCLC stage of early-stage and intermediate-stage HCC. [ABSTRACT FROM AUTHOR]

Generating realistic road scenes is crucial for advanced driving systems, particularly for training deep learning methods and validation. Numerous efforts aim to create larger and more realistic synthetic datasets using graphics engines or synthetic-to-real domain adaptation algorithms. In the realm of computer-generated images (CGIs), assessing fidelity is challenging and involves both objective and subjective aspects. Our study adopts a comprehensive conceptual framework to quantify the fidelity of RGB images, unlike existing methods that are predominantly application-specific. This is probably due to the data complexity and huge range of possible situations and conditions encountered. In this paper, a set of distinct metrics assessing the level of fidelity of virtual RGB images is proposed. For quantifying image fidelity, we analyze both local and global perspectives of texture and the high-frequency information in images. Our focus is on the statistical characteristics of realistic and synthetic road datasets, using over 28,000 images from at least 10 datasets. Through a thorough examination, we aim to reveal insights into texture patterns and high-frequency components contributing to the objective perception of data realism in road scenes. This study, exploring image fidelity in both virtual and real conditions, takes the perspective of an embedded camera rather than the human eye. The results of this work, including a pioneering set of objective scores applied to real, virtual, and improved virtual data, offer crucial insights and are an asset for the scientific community in quantifying fidelity levels. [ABSTRACT FROM AUTHOR]

Despite the inherent stability of dried and cured products, such as pastourma, appropriate refrigeration remains essential for preserving their optimal characteristics. This study explored quality and safety characteristics in lamb, beef, and buffalo pastourma during 16-day refrigeration storage after package opening. The comprehensive approach employed Attenuated Total Reflection–Fourier-Transform Infrared (ATR-FTIR) spectroscopy, colorimetry, and image analysis, alongside physicochemical and microbiological analyses, to shed light on these alterations. The findings reveal a reduction in textural uniformity and color vibrancy (fading reds and yellows) across all samples during storage, with lamb pastourma exhibiting the most pronounced effects. Notably, image analysis emerged as a powerful tool, enabling the accurate classification of samples based on storage duration. Additionally, significant variations were observed in moisture content, hue angle, firmness, and TBARS levels, highlighting their influence on pastourma quality. The study documented a gradual decrease in lactic acid bacteria and aerobic plate count populations over time. ATR-FTIR spectra's interpretation revealed the presence of lipids, proteins, carbohydrates, and water. Protein secondary structures, demonstrably influenced by the meat type used, exhibited significant changes during storage, potentially impacting the functional and textural properties of pastourma. Overall, the findings contribute to a deeper understanding of pastourma spoilage during storage, paving the way for the development of improved preservation and storage strategies. [ABSTRACT FROM AUTHOR]

As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the most fatal skin lesions, capable of leading to fatality if not detected in its early stages. The characteristics of skin lesions are similar in many of the early stages of skin lesions. The AI in categorizing diverse types of skin lesions significantly contributes to and helps dermatologists to preserve patients' lives. This study introduces a novel approach that capitalizes on the strengths of hybrid systems of Convolutional Neural Network (CNN) models to extract intricate features from dermoscopy images with Random Forest (Rf) and Feed Forward Neural Networks (FFNN) networks, leading to the development of hybrid systems that have superior capabilities early detection of all types of skin lesions. By integrating multiple CNN features, the proposed methods aim to improve the robustness and discriminatory capabilities of the AI system. The dermoscopy images were optimized for the ISIC2019 dataset. Then, the area of the lesions was segmented and isolated from the rest of the image by a Gradient Vector Flow (GVF) algorithm. The first strategy for dermoscopy image analysis for early diagnosis of skin lesions is by the CNN-RF and CNN-FFNN hybrid models. CNN models (DenseNet121, MobileNet, and VGG19) receive a region of interest (skin lesions) and produce highly representative feature maps for each lesion. The second strategy to analyze the area of skin lesions and diagnose their type by means of CNN-RF and CNN-FFNN hybrid models based on the features of the combined CNN models. Hybrid models based on combined CNN features have achieved promising results for diagnosing dermoscopy images of the ISIC 2019 dataset and distinguishing skin cancers from other skin lesions. The Dense-Net121-MobileNet-RF hybrid model achieved an AUC of 95.7%, an accuracy of 97.7%, a precision of 93.65%, a sensitivity of 91.93%, and a specificity of 99.49%. [ABSTRACT FROM AUTHOR]

Due to the nature of Selective Laser Melting process, the built parts suffer from high chances of defects formation. Powders quality have a significant impact on the final attributes of SLM-manufactured items. From a processing standpoint, it is critical to ensure proper powder distribution and compaction in each layer of the powder bed, which is impacted by particle size distribution, packing density, flowability, and sphericity of the powder particles. Layer-by-layer study of the process can provide better understanding of the effect of powder bed on the final part quality. Image-based processing technique could be used to examine the quality of parts fabricated by Selective Laser Melting through layerwise monitoring and to evaluate the results achieved by other techniques. In this paper, a not supervised methodology based on Digital Image Processing through the build-in machine camera is proposed. Since the limitation of the optical system in terms of resolution, positioning, lighting, field-of-view, many efforts were paid to the calibration and to the data processing. Its capability to individuate possible defects on SLM parts was evaluated by a Computer Tomography results verification. [ABSTRACT FROM AUTHOR]

The geotechnical characterization of the subsurface is a key requirement for most soil investigations, incl. those for reclaiming landfills and waste dumps associated with mining operations. New sensor technology, combined with intelligent analysis algorithms, allow for a faster and less expensive acquisition of the necessary information without loss of data quality. The use of advanced technologies to support and back up common site investigation techniques, such as cone penetration testing (CPT), can enhance the underground characterization process. This study aims to investigate the possibilities of image analysis for material recognition to advance the geotechnical characterization process. The grey level co-occurrence matrix (GLCM) image processing technique is used in a wide range of study fields to estimate textures, patterns and structure anomalies. This method was adjusted and applied to process the video recorded during a CPT sounding, in order to distinguish soil types by its changing surface characteristics. From the results of the video processing, it is evident that the GLCM technique can identify transitions in soil types that were captured in the video recording. This enables the prospect of image analysis not just for soil investigations, but also for monitoring of the conveyor belt in the mining field, to allow for efficient preliminary decision making, material documentation and quality control by providing information in a cost effective and efficient manner. [ABSTRACT FROM AUTHOR]

In this manuscript, a method that utilizes classical image techniques to assess particle aggregation and segregation, with the primary goal of validating particle size distribution determined by conventional methods, is presented. This approach can represent a supplementary tool in quality control systems for powder production processes in industries such as manufacturing and pharmaceuticals. The methodology involves the acquisition of high-resolution images, followed by their fractal and textural analysis. Fractal analysis plays a crucial role by quantitatively measuring the complexity and self-similarity of particle structures. This approach allows for the numerical evaluation of aggregation and segregation phenomena, providing valuable insights into the underlying mechanisms at play. Textural analysis contributes to the characterization of patterns and spatial correlations observed in particle images. The examination of textural features offers an additional understanding of particle arrangement and organization. Consequently, it aids in validating the accuracy of particle size distribution measurements. To this end, by incorporating fractal and structural analysis, a methodology that enhances the reliability and accuracy of particle size distribution validation is obtained. It enables the identification of irregularities, anomalies, and subtle variations in particle arrangements that might not be detected by traditional measurement techniques alone. [ABSTRACT FROM AUTHOR]

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

In order to improve the data quality of land use remote sensing monitoring images, this article introduces the process of generating satellite image data, elaborates on the content of satellite image data verification in land use remote sensing monitoring, and proposes quality issues and improvement measures for satellite image data. Taking the discovered satellite image data quality issues as an example, compared with quality inspection standards, it was found that the main problems in the results were projection parameter errors, image color distortion, image blurring, and position accuracy exceeding limits. It is recommended to check the above issues during the image production stage, analyze the reasons for exceeding the position accuracy limit, image distortion, and embossing, and provide relevant suggestions. Provided strong technical support for land use surveys. [ABSTRACT FROM AUTHOR]

To develop a fast and effective image deblurring method, the blind recovery of motion‐blurred images based on DeblurGAN(GAN, Generative Adversarial Networks) is researched.Firstly, the number of residual modules in the DeblurGAN network is changed, and an attempt is made to optimize the network structure to achieve better results in the blind recovery of motion‐blurred images. Secondly, an image deblurring method based on the improved DeblurGAN network is proposed.This paper makes corresponding adjustments to the generator and discriminator networks to change their input and output size to 512 × 512 while keeping the overall structure of the network unchanged, and the experimental results show that the quality of the restored images has been greatly improved. In addition, the images were divided into four classes of images,to achieve improved restoration of blurred images with specific content. Experimental results on the benchmark GoPro dataset validate that the improved DeblurGAN achieves more than 1.5 dB improvement than DeblurGAN in terms of peak signal‐to‐noise ratio (PSNR) as trained utilizing the same amount of data, and structural similarity assessment (SSIM) evaluation means and maximum values increased between 0.2 and 0.3. The improved DeblurGAN is more effective in terms of both blur removal and detail recovery. [ABSTRACT FROM AUTHOR]

Acute Respiratory Distress Syndrome (ARDS) is a life-threatening lung injury for which early diagnosis and evidence-based treatment can improve patient outcomes. Chest X-rays (CXRs) play a crucial role in the identification of ARDS; however, their interpretation can be difficult due to non-specific radiological features, uncertainty in disease staging, and inter-rater variability among clinical experts, thus leading to prominent label noise issues. To address these challenges, this study proposes a novel approach that leverages label uncertainty from multiple annotators to enhance ARDS detection in CXR images. Label uncertainty information is encoded and supplied to the model as privileged information, a form of information exclusively available during the training stage and not during inference. By incorporating the Transfer and Marginalized (TRAM) network and effective knowledge transfer mechanisms, the detection model achieved a mean testing AUROC of 0.850, an AUPRC of 0.868, and an F1 score of 0.797. After removing equivocal testing cases, the model attained an AUROC of 0.973, an AUPRC of 0.971, and an F1 score of 0.921. As a new approach to addressing label noise in medical image analysis, the proposed model has shown superiority compared to the original TRAM, Confusion Estimation, and mean-aggregated label training. The overall findings highlight the effectiveness of the proposed methods in addressing label noise in CXRs for ARDS detection, with potential for use in other medical imaging domains that encounter similar challenges. [ABSTRACT FROM AUTHOR]

Background: Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30–40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60–70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response. Methods: H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction. Results: The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD. Conclusion: Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment. [ABSTRACT FROM AUTHOR]

The present research study aimed to examine three different herb extract's effects on the discoloration rate of fresh-cut pear slices using an image analysis technique. Pear slices were sprayed and dip-coated with Ocimum basilicum, Origanum vulgare, and Camellia sinensis (0.1 g/ml) extract solution. During 15 days storage period with three days intervals, all sprayed/dip-coated pear slices were analyzed for the quality attribute (TA) and color parameters notably a*, b*, hue angle (H*), lightness (L*), and total color change (ΔE). Further, order kinetic models were used to observe the color changes and to predict the shelf-life. The results obtained showed that the applicability of image analysis helped to predict the discoloration rate, and it was better fitted to the first-order (FO) kinetic model (R2 ranging from 0.87 to 0.99). Based on the kinetic model, color features ΔE and L* was used to predict the shelf-life as they had high regression coefficient values. Thus, the findings obtained from the kinetic study demonstrated Camellia sinensis (assamica) extract spray-coated pear slices reported approximately 28.63- and 27.95-days shelf-stability without much discoloration compared with all other types of surface coating. [ABSTRACT FROM AUTHOR]

The aim of the present study was to evaluate Marisol strawberries' (Fragaria × ananassa) physicochemical quality and shelf-life during storage, using an integrated analytical approach. More specifically, the research aimed to assess the strawberries' color, texture, and nutritional quality, over an 11-day storage period, employing physicochemical analyses, spectrophotometric assays, Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy, image analysis, and statistical tools. The results revealed significant changes in the outer surface texture and color characteristics, indicating spoilage progression. Physicochemical parameters such as water activity, moisture content, soluble solids, titratable acidity, and ascorbic acid content exhibited significant alterations, influencing the taste profile and freshness perception. Antioxidant and antiradical activities showed fluctuations, suggesting a potential decrease in phenolic content during storage. Moreover, the ATR-FTIR spectra findings confirmed the results regarding the moisture content, soluble solids, and total phenolic content. The integration of physicochemical and image analysis-derived features through a principal component analysis (PCA) enabled the accurate classification of samples based on storage days. Regression analysis, using these features, successfully predicted the storage day with high accuracy. Overall, this integrated analytical approach provided valuable information on the estimation of Marisol strawberries' shelf-life and the prediction of their quality deterioration, contributing to better fruit management and the minimization of discards. [ABSTRACT FROM AUTHOR]

Sour cherries are a perishable raw material, and their preservation is needed to extend their availability to consumers. Improving drying techniques is desirable to ensure the highest quality of products. This study aimed to determine image textures from color channels R, G, B, L, a, b, X, Y, and Z; color parameters L*, a*, and b*; the color difference (ΔE) of raw materials and dried fruit; and the sensory attributes of dried sour cherry products prepared using an innovative approach. Three sour cherry cultivars, 'Nefris', 'Debreceni Botermo', and 'Łutówka', were used in the experiment. Sour cherries were subjected to freezing and pit removal before drying. The simultaneous osmotic–microwave–vacuum drying was carried out in one process lasting an hour and combining osmotic dehydration using a 40 °Bx sucrose solution and microwave–vacuum drying at microwave powers of 100 W for 900 s, 300 W for 900 s, 250 W for 900 s, and 0 W for stabilization for 900 s and a pressure of 30 ± 2 hPa. After drying, the quality assessment of products was performed using non-destructive image analysis and color measurements, as well as sensory analysis, including non-destructively and destructively assessed attributes. The highest changes in textures occurred for the GHMean (histogram's mean for color channel G) (from 30.69 to 22.64) and LHMean (histogram's mean for color channel L) (from 66.93 to 59.07) of images of the cultivar 'Łutówka', and the smallest changes were found for the cultivar 'Nefris'. Drying had a statistically significant effect on the color parameters of the 'Debreceni Botermo' and 'Łutówka' sour cherries. The value of ΔE was the highest (10.44) for 'Debreceni Botermo' and the smallest (1.98) for 'Nefris'. All cultivars of dried sour cherries had very high values of overall quality, reaching 8.9 for 'Nefris' and 'Debreceni Botermo' and 8.8 for 'Łutówka'. The 'Nefris' sour cherry was characterized by the highest value of flavor of 9.0. All dried samples were attractive in terms of their external appearance. The sensory parameters related to taste, texture, and crunchiness were also satisfactory. Innovative simultaneous osmotic–microwave–vacuum drying allowed for the obtainment of dried sour cherries with a high quality, including acceptable sensory attributes. [ABSTRACT FROM AUTHOR]

The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic. One of the most popular methods for manipulating digital images is image splicing, which involves copying a specific area from one image and pasting it into another. Attempts were made to mitigate the effects of image splicing, which continues to be a significant research challenge. This study proposes a new splicing detectionmodel, combining Sonine functions-derived convex-based features and deep features. Two stages make up the proposed method. The first step entails feature extraction, then classification using the "support vector machine" (SVM) to differentiate authentic and spliced images. The proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image pixels. The proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset "Chinese Academy of Sciences, Institute of Automation" which is a publicly available dataset for forgery classification. The experimental results show that, for image splicing forgery detection, the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy, precision, and recall. Overall, the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature representations. Finding the regions or locations where image tampering has taken place is limited by the study. Future research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions. [ABSTRACT FROM AUTHOR]

Today's forensic science introduces a new research area for digital image analysis formultimedia security. So, Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered images. Exiting forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing forgeries. However, an image can contain one or more types of forgeries. This study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial domain. Firstly, images are divided into equal blocks to get scale-invariant features.Weber law has been used for getting texture features, and finally, XGBOOST is used to classify both Copy- Move and splicing forgery. The proposed method classified three types of forgeries, i.e., splicing, Copy-Move, and healthy. Benchmarked (CASIA 2.0, MICCF200) and RCMFD datasets are used for training and testing. On average, the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation, which is far better than existing methods. [ABSTRACT FROM AUTHOR]