Image Analysis Methodology Research Articles

Doppler Sonography Evaluation of Median Nerve Intraneural Blood Flow: A Systematic Review

Objective: This systematic review aims to determine the extent, scope, and nature of research using the sonographic measurement of intraneural blood flow within the median nerve and to identify, characterize, and compare image acquisition and analysis protocols that have been reported as potential candidate techniques for standardizing research and clinical applications. Materials and Methods: This systematic review summarizes image acquisition protocols and analysis methodologies from 52 current research studies using Doppler ultrasound to examine median nerve intraneural blood flow. Results: Four types of Doppler technologies were identified: power Doppler, color Doppler, spectral Doppler, and Superb Microvascular Imaging, but there were inconsistencies in how images were acquired and analyzed. Intraneural blood flow measurements were categorized into four types based on a combination of measurement level (e.g., binary, ordinal, continuous) and type of flow indicator (e.g., pixel count, intensity, velocity). Conclusion: Standardized imaging protocols and reporting guidelines are needed to improve consistency. Future studies should evaluate the accuracy and reliability of different image acquisition methods and analysis measurements.

Use of confocal microscopy to measure the porosity of cement containing sulfocalcic binder: A comparative study

This work proposes an effective methodology for measuring the porosity of mixed hydraulic binders applying image analysis (IA) with confocal microscopy (CM). CM offers a certain simplification of sample preparation compared to the standard procedure EN 480-11, as the steps of sample staining and pore masking are eliminated. A set of cement and a hydraulic clinker-free sulfocalcic binder pastes was prepared with various aeration. Total air content, micro air content and the air pore spacing factor according to EN 480-11 standard were determined for hardened pastes using measuring chords. Subsequently, a new simple Surface porosity evaluation for IA methodology using CM was proposed and results were compared with the standard evaluation. The results of both IA evaluations achieved high agreement and the new porosity Surface evaluation was found to be promising for the determination of total air content. Porosity values measured by both IA methods were further compared with MIP.

Pest Detection and Classification in Peanut Crops

Recent advancements in image processing have significantly improved pest detection and classification in peanut crops. Our study introduces an innovative approach that optimizes image features for accurate pest identification. Leveraging insights from successful image analysis methodologies, our model employs a tailored architecture for pest detection, segmentation, and classification tasks. By integrating dual branch segment representations and a dual-layer transformer encoder, we aim to enhance image representations and consolidate pest image segments of varying sizes. We evaluate our approach using three distinct pest datasets—Aphids, Wireworm, and Gram Caterpillar—ensuring comprehensive analysis and model validation. Prior to training, we preprocess the datasets extensively, employing feature extraction techniques and addressing image quality issues. We then apply normalization procedures to standardize the data for seamless integration into our model architecture. Our methodology focuses on extracting key features through self-attention mechanisms and standardized scaling processes to enhance predictive capabilities. Comprehensive experimentation demonstrates the superiority of our approach, outperforming established benchmarks in pest detection and classification with high accuracy rates. In summary, our study presents a novel framework that optimizes feature extraction and enhances predictive accuracy in pest detection and classification for peanut crops, addressing the unique challenges of agricultural pest identification.

Sexual Dimorphism of Elastic Fibers in Prenatal Lung Mice

Scientific data has revealed the existence of lung gender differences and therefore sparked a renewed interest in understanding the underlying mechanisms and their effect in the healthy lung development and/or in pathological conditions. Elastic fibers have an important role in lung development during pre and post-natal stages, because a well-developed pulmonary elastic fibers favour pre-natal lung maturation and enhance alveolarization. Sexual differences studies on lung elastic fibers content are focused essentially on the post-natal stage, with scarce data on pre-natal lung development. Using an experimental mice model, we developed this research work to study gender differences in the lungs elastic fibers during gestational days E15-E19, using image analysis and elastin HPLC methodologies. Our results show significant sexual dimorphism in lung elastin and elastic fibers content pre-natal stage, which is more evident in the last two gestational days (E18 and E19). Female’s mice have more elastin and elastic fibers which could mean that the elastogenesis process begins earlier than males. These results are an important contributes to understand the underlying factors involved in physiology and lung development sexual difference.

INSURANCE AMOUNT PREDICTION BASED ON ACCIDENTAL CAR DAMAGE LEVEL USING CNN

There is a paradigm shift in the automotive industry with the advancement of technology, especially in the area of ​​insurance claims handling. In this study, we propose a new method for predicting insurance claims based on car damage levels, utilizing advanced artificial intelligence algorithms for image analysis, particularly employing Convolutional Neural Networks (CNN) for image processing and classification. Our strategy is to provide car damage estimates through picture analysis, making insurance claims in the automobile sector simpler. In order to facilitate user interaction with Flask and increase the predictability of our models, we have also implemented enhancements to the user interface. By combining image analysis with a user-friendly interface, our method offers a practical solution for insurance companies to expedite claim processing, reduce fraudulent activities, and ensure fair settlements for policyholders. This research contributes to the ongoing transformation of insurance claim processing within the automotive industry, highlighting the potential of merging image analysis methodologies with user interfaces for enhanced efficiency and usability. Keywords: Insurance claim prediction, Convolutional Neural Networks (CNNs), Image analysis, User interface, Flask, Automotive industry.

The use of scanning electron microscopy and fixation methods to evaluate the interaction of blood with the surfaces of medical devices

Testing the hemocompatibility of medical devices after their interaction with blood entails the need to evaluate the activation of blood elements and the degree of their coagulation and adhesion to the device surface. One possible way to achieve this is to use scanning electron microscopy (SEM). The aim was to develop a novel SEM-based method to assess the thrombogenic potential of medical devices and their adhesiveness to blood cells. As a part of this task, also find a convenient procedure of efficient and non-destructive sample fixation for SEM while reducing the use of highly toxic substances and shortening the fixation time. A polymeric surgical mesh was exposed to blood so that blood elements adhered to its surface. Such prepared samples were then chemically fixed for a subsequent SEM measurement; a number of fixation procedures were tested to find the optimal one. The fixation results were evaluated from SEM images, and the degree of blood elements’ adhesion was determined from the images using ImageJ software. The best fixation was achieved with the May–Grünwald solution, which is less toxic than chemicals traditionally used. Moreover, manipulation with highly toxic osmium tetroxide can be avoided in the proposed procedure. A convenient methodology for SEM image analysis has been developed too, enabling to quantitatively evaluate the interaction of blood with the surfaces of various medical devices. Our method replaces the subjective assessment of surface coverage with a better-defined procedure, thus offering more precise and reliable results.

Image enhancement and microstructure characterization of energy dispersive X-ray spectroscopy images of blended cement pastes

Energy dispersive X-ray spectroscopy (EDS) image analysis has been widely used to study the microstructure of cementitious materials, whereas in some cases the efficiency of the characterization suffered from the extremely long time for image acquisition. In this study, we proposed an EDS image analysis methodology including image enhancement, phase extraction, and characterization of reaction products. The guided filter and Logarithm correction were combined and employed to improve the resolvability of low-quality EDS images. Gaussian Mixture Model (GMM) algorithm was applied to segment the unreacted phases and two different groups of reaction products from the enhanced EDS images. Moreover, the spatial distribution and chemical composition of reaction products were characterized using the lineal-path function and simple lineal iterative clustering (SLIC) algorithm. Results showed that low-quality EDS images after enhancing could achieve the comparable performance with high-quality EDS images in terms of the phase proportions. This study offers a practical and effective methodology for enhancing and analyzing the low-quality EDS images, giving insights into the phase assemblage and microstructure evolution of cementitious materials.

Exploring CIE Lab Color Characteristics for Skin Lesion Images Detection: A Novel Image Analysis Methodology Incorporating Color-based Segmentation and Luminosity Analysis

Accurate classification of malignant and benign skin lesions is crucial in dermatology. In this novel research, we propose robust image analysis methodology for skin lesion classification that integrates color-based segmentation with luminosity analysis. Our approach is evaluated on a dataset of 400 skin images, with equal representation of malignant and benign samples. By computing mean color values for the Red Channel Color (RCC), Green Channel Color (GCC), and Blue Channel Color (BCC) in groups of 10 samples, we establish a classification range for precise diagnosis, this research introduces a novel dimension by harnessing the potential of the CIE Lab Color characteristics for skin lesion detection as the most reliable scale for distinguishing between benign and malignant samples. The smaller and more thought variety ranges saw in the glow examination improve difference and perceivability, consequently working with prevalent sore separation. By featuring the meaning of mean histograms for each variety channel, this complete exploration adds to propelling the area of dermatology and presents an imaginative methodology that holds guarantee for PC helped conclusion frameworks in skin malignant growth discovery.

Image-Derived Skeletal Muscle Activation Metric Map: a Forearm Study Using Ultra-Fast Ultrasound Imaging and High-Density Electromyography.

Quantification of the skeletal muscle response can help better understand the fundamentals of the musculoskeletal system and can serve as a diagnostic measure or recovery assessment tool during rehabilitation for neurological injuries. Surface electromyography (EMG) is commonly used to measure muscle activity, but it is limited to detecting myoelectric signals without anatomy associated information. In this study, we proposed to use ultra-fast ultrasound imaging and introduced a new image analysis methodology to quantify a muscle's spatialtemporal mechanical response. The methodology is based on analyzing the spatial-temporal change of the impulsive kinetic energy during the period of muscle contraction. The analysis can derive an anatomy-registered muscle activation metric map that localizes regions of muscle activation. To demonstrate this, we intentionally evoked regional muscle responses in five participants without disabilities by electrically stimulating the median nerve and individual forearm muscle groups, respectively. Both ultrasound images and high-density EMG (HD-EMG) data were recorded and processed. We presented the ultrasound image-derived activation localization from five participants and compared the results with HD-EMG measurements. The comparison indicates a good resemblance for describing muscle recruitment pattern. The proposed methodology can potentially become an alternative or complementary approach to surface EMG for the study of skeletal muscle activation and for diagnosis and prognosis in clinical settings.

Evaluation of the effect of nanocellulose edible coating on strawberries inoculated with Aspergillus flavus through image analysis

The impact of a coating solution containing chitosan, lignocellulosic nanofibers (LCNF), and raspberry leaf extract on strawberry quality was assessed. Strawberries were coated, inoculated with Aspergillus flavus (a major aflatoxin producer), and stored at 5 ± 1 °C for 16 days. An automated fruit quality evaluation system was developed, and daily images were taken for analysis. ImageJ software was used to evaluate colour, size loss, and fungal damage. Coating solution significantly reduced fruit size loss, with coated strawberries showing 18.4 ± 12.8% size loss after 16 days, compared to 30.2 ± 8.4% in the control group and 25 ± 10.7% in uncoated strawberries. Regarding colour, lower colour changes were observed in coated fruit compared to the control during storage (p ≤ 0.05). Fungal damage did not significantly differ between treatments during storage (p > 0.05). Furthermore, the study demonstrated that image analysis and visual estimation methods for assessing fruit fungal damage yielded similar results, with a strong correlation (R2 = 0.87). This suggests that the proposed image analysis methodology is effective for the assessment of fungal damage in strawberries compared to visual estimation. Chitosan-LCNF-raspberry extract coating solution shows promise in enhancing strawberry quality by reducing size loss during storage and managing fungal development.

Efficient and Lightweight Automatic Wheat Counting Method with Observation-Centric SORT for Real-Time Unmanned Aerial Vehicle Surveillance

The number of wheat ears per unit area is crucial for assessing wheat yield, but automated wheat ear counting still faces significant challenges due to factors like lighting, orientation, and density variations. Departing from most static image analysis methodologies, this study introduces Wheat-FasterYOLO, an efficient real-time model designed to detect, track, and count wheat ears in video sequences. This model uses FasterNet as its foundational feature extraction network, significantly reducing the model’s parameter count and improving the model’s inference speed. We also incorporate deformable convolutions and dynamic sparse attention into the feature extraction network to enhance its ability to capture wheat ear features while reducing the effects of intricate environmental conditions. To address information loss during up-sampling and strengthen the model’s capacity to extract wheat ear features across varying feature map scales, we integrate a path aggregation network (PAN) with the content-aware reassembly of features (CARAFE) up-sampling operator. Furthermore, the incorporation of the Kalman filter-based target-tracking algorithm, Observation-centric SORT (OC-SORT), enables real-time tracking and counting of wheat ears within expansive field settings. Experimental results demonstrate that Wheat-FasterYOLO achieves a mean average precision (mAP) score of 94.01% with a small memory usage of 2.87MB, surpassing popular detectors such as YOLOX and YOLOv7-Tiny. With the integration of OC-SORT, the composite higher order tracking accuracy (HOTA) and counting accuracy reached 60.52% and 91.88%, respectively, while maintaining a frame rate of 92 frames per second (FPS). This technology has promising applications in wheat ear counting tasks.

A comprehensive intelligent framework for interpreting and quantifying the microstructure of waste-derived alkali-activated materials: A case study of fly ash/slag pastes

The alkali-activated fly ash/slag (AAFS) pastes exhibit complex compositions and heterogeneous spatial distributions of phases, necessitating a comprehensive tool capable of quantifying phase assemblage, characterizing ion diffusion, and unveiling microstructure evolution. However, existing techniques have limitations in providing interpretability and visual analyses. This study introduces a versatile analysis methodology for energy dispersive X-ray spectroscopy (EDS) images, encompassing data preprocessing, data interpretation, and four downstream applications: phase inference, phase quantification, ion diffusion characterization, and quantification of phase spatial distributions. Through the analysis of five representative elements, this study successfully identifies and quantifies three components in precursors (slag, quartz, mullite) and four reaction products (slag-products, quartz-products, mullite-products, mixture products) in the AAFS pastes. Moreover, by characterizing ion diffusion and quantifying spatial distributions, insights into the interactions among various precursors and reaction products are elucidated. The proposed EDS image analysis methodology is expected to yield valuable understandings of the reaction mechanisms and phase evolution involved in utilizing solid waste as cementitious materials.

Integument colour change: Tracking delayed growth of Oppia nitens as a sub-lethal indicator of soil toxicity

Growth is an important toxicity end-point in ecotoxicology but is rarely used in soil ecotoxicological studies. Here, we assessed the growth change of Oppia nitens when exposed to reference and heavy metal toxicants. To assess mite growth, we developed an image analysis methodology to measure colour spectrum changes of the mite integument at the final developmental stage, as a proxy for growth change. We linked the values of red, green, blue, key-black, and light colour of mites to different growth stages. Based on this concept, we assessed the growth change of mites exposed to cadmium, copper, zinc, lead, boric acid, or phenanthrene at sublethal concentrations in LUFA 2.2 soil for 14 days. Sublethal effects were detected after 7 days of exposure. The growth of O. nitens was more sensitive than survival and reproduction when exposed to copper (EC50growth = 1360 mg/kg compared to EC50reproduction = 2896 mg/kg). Mite growth sensitivity was within the same order of magnitude to mite reproduction when exposed to zinc (EC50growth = 1785; EC50reproduction = 1562 mg/kg). At least 25% of sublethal effects of boric acid and phenanthrene were detected in the mites but growth was not impacted when O. nitens were exposed to lead. Consistent with previous studies, cadmium was the most toxic metal to O. nitens. The mite growth pattern was comparable to mite survival and reproduction from previous studies. Mite growth is a sensitive toxicity endpoint, ecologically relevant, fast, easy to detect, and can be assessed in a non-invasive fashion, thereby complimenting existing O. nitens testing protocols.

Tridimensional characterization of open cells and hollow strut cavities from SiC and ZrO2 foams: A study accomplished with open-source software tools

The characterization of ceramic foams via X-ray microtomography imaging method is often restricted to a general overview of samples, either to perform qualitative or quantitative analyses. To assess the foam with a focus on some specific components of its structure, such as hollow struts’ cavities and open cells, the generalized characterizations must be overcome. This work presents image analysis methodologies, based on open-source software, tools, and plugins, to achieve the aforementioned characterizations. SiC and ZrO2 foams samples were analyzed and some of the results were compared with those accomplished with pieces of commercial software. The results show good agreement between open-source and commercial software applications, indicating that the presented methodologies can be freely applied by any researcher to analyze their foams.

Prediction of lymphoma response to CAR T cells by deep learning-based image analysis.

Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lymphomas. We therefore tested the feasibility of a deep-learning (DL)-based image analysis methodology on pre-treatment diagnostic computed tomography (dCT), low-dose CT (lCT), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) images and rule-based reasoning to predict treatment response to chimeric antigen receptor (CAR) T-cell therapy in B-cell lymphomas. Pre-treatment images of 770 lymph node lesions from 39 adult patients with B-cell lymphomas treated with CD19-directed CAR T-cells were analyzed. Transfer learning using a pre-trained neural network model, then retrained for a specific task, was used to predict lesion-level treatment responses from separate dCT, lCT, and FDG-PET images. Patient-level response analysis was performed by applying rule-based reasoning to lesion-level prediction results. Patient-level response prediction was also compared to prediction based on the international prognostic index (IPI) for diffuse large B-cell lymphoma. The average accuracy of lesion-level response prediction based on single whole dCT slice-based input was 0.82+0.05 with sensitivity 0.87+0.07, specificity 0.77+0.12, and AUC 0.91+0.03. Patient-level response prediction from dCT, using the "Majority 60%" rule, had accuracy 0.81, sensitivity 0.75, and specificity 0.88 using 12-month post-treatment patient response as the reference standard and outperformed response prediction based on IPI risk factors (accuracy 0.54, sensitivity 0.38, and specificity 0.61 (p = 0.046)). Prediction of treatment outcome in B-cell lymphomas from pre-treatment medical images using DL-based image analysis and rule-based reasoning is feasible. This approach can potentially provide clinically useful prognostic information for decision-making in advance of initiating CAR T-cell therapy.

Quantifying the microstructure evolution and phase assemblage of solid waste as construction materials by EDS image analysis: A case study of carbonated basic oxygen furnace slag aggregates

The diversities of raw materials and manufacturing techniques lead to large fluctuations in the upcycling potential of solid waste as construction materials. A quantitative energy dispersive X-ray spectroscopy (EDS) image analysis methodology was developed to measure the microstructure evolution and phase assemblage of solid waste. Based on the proposed EDS image enhancement algorithm, the component complexity and structural heterogeneity of basic oxygen furnace slag (BOFS) can be fully revealed by the EDS images. Thereafter, the proportions of major phases (i.e., pores, reaction products, and unreacted components) and the specific composition information of involved phases can be determined. The spatial distributions of reaction products and unreacted components could be studied by the lineal-path distribution function and the phase assemblage of reaction products could be studied by the elemental ratios. The results showed that the effects of subsequent hydration on the microstructural and mineralogical alterations of carbonation products were more prominent than the consumption of unreacted components. The continuous dissolution of the Ca and Si ions upon the hydration significantly promoted the transformation of carbonation products and microstructure densification of BOFS aggregates, which was the main contributor to the development of mechanical performance.

Real-Time Image Processing Review Paper: Methods, Techniques, and Applications

Abstract—In modern days, Image processing is now of utmost importance, especially in real-time scenarios where errors in contemporaneous image processing could have disastrous results; the research and analysis methods of real- time image processing istherefore of extraordinary importance. Real-time image processing research and analysis methodologies are consequently of the utmost significance. This article's main aim is to provide an overview of the currentstate of real-time image processing research (Applications), useful methods, and practice. Keywords— Processing images in real time, Applications of image processing, image processing techniques, and image enhancement methods.

Brightfield vs Fluorescent Staining Dataset–A Test Bed Image Set for Machine Learning based Virtual Staining

Differential fluorescent staining is an effective tool widely adopted for the visualization, segmentation and quantification of cells and cellular substructures as a part of standard microscopic imaging protocols. Incompatibility of staining agents with viable cells represents major and often inevitable limitations to its applicability in live experiments, requiring extraction of samples at different stages of experiment increasing laboratory costs. Accordingly, development of computerized image analysis methodology capable of segmentation and quantification of cells and cellular substructures from plain monochromatic images obtained by light microscopy without help of any physical markup techniques is of considerable interest. The enclosed set contains human colon adenocarcinoma Caco-2 cells microscopic images obtained under various imaging conditions with different viable vs non-viable cells fractions. Each field of view is provided in a three-fold representation, including phase-contrast microscopy and two differential fluorescent microscopy images with specific markup of viable and non-viable cells, respectively, produced using two different staining schemes, representing a prominent test bed for the validation of image analysis methods.

Cross-sectional reliability and concurrent validity of a quantitative 2-dimensional ultrasound image analysis of effusion and synovial hypertrophy in knee osteoarthritis

ObjectiveEffusion-synovitis is related to pain and progression in knee osteoarthritis (OA), but current gold standard ultrasound (US) measures are limited to semi-quantitative grading of joint distension or 1-dimensional thickness measures. A novel quantitative 2-dimensional image analysis methodology is applied to US images of effusion-synovitis; reliability and concurrent validity was assessed in patients with knee OA. MethodsCross sectional analysis of US images collected from 51 patients with symptomatic knee OA were processed in ImageJ and segmented in 3DSlicer to produce a binary mask of the supra-patellar synovitis region of interest (ROI). Area measures (mm2) of total synovitis, effusion and hypertrophy components were exported. Intra-rater reliability and test-retest reliability (1–14 days washout) were estimated with intra-class correlation coefficients (ICCs). Concurrent validity was measured by Spearman correlations between quantitative measures and gold standard OMERACT and caliper measurements of synovitis. ResultsIntra-rater reliability for hypertrophy area was estimated at 0.98, 0.99 for effusion area, and 0.99 for total synovitis area. The test-retest reliability for total synovitis area was 0.63 (SEM 87.8 ​mm2), 0.59 for hypertrophy area (SEM 21.0 ​mm2), and 0.64 for effusion area (SEM 73.8 ​mm2). Correlation between total synovitis area and OMERACT grade was 0.84, 0.81 between total synovitis area and effusion-synovitis calipers, and 0.81 between total effusion area and effusion calipers. ConclusionThis new research tool for image analysis demonstrated excellent intra-rater reliability, good concurrent validity, and moderate test-retest reliability. Quantitative 2D US measures of effusion-synovitis and its individual components may enhance the study and management of knee OA.

Anomaly detection in railway bridges using imaging techniques

The monitoring of the structural health of infrastructures is a very important topic in structural engineering, but unfortunately, there are few established techniques that are applicable in a wide range of situations. In this paper, we present a new method that adapts image analysis tools and methodologies, taken from the field of computer vision, and applies them to the monitoring signals of a railway bridge. We show that our method correctly identifies changes in the structural health of the bridge with very high precision, thus providing a better, simpler, and more general alternative to current methodologies used in the field.