Grayscale Threshold Research Articles

Myocardial Fibrosis Quantification Methods by Cardiovascular Magnetic Resonance Imaging in Patients with Fabry Disease.

Background/Objectives: The presence of late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) in patients with Fabry disease (FD) is a predictor of adverse cardiac events. The aim of this study was to establish the most reliable and reproducible technique for quantifying LGE in patients with FD. Methods: Twenty FD patients with LGE who underwent CMR on the same scanner and LGE sequence were included. LGE quantifications were done using gray-scale thresholds of 2, 3, 4, 5 and 6 standard deviations (SD) above the mean signal intensity of the remote myocardium, the full width at half maximum method (FWHM), visual assessment with threshold (VAT) and the fully manual method (MM). Results: The mean amount of fibrosis varied between quantification techniques from 36 ± 19 at 2SD to 2 ± 2 g using the FWHM (p < 0.0001). Intraobserver reliability was excellent for most methods, except for the FWHM which was good (ICC 0.84; all p < 0.05). Interobserver reliability was excellent for VAT (ICC 0.94) and good for other techniques (all p < 0.05). Intraobserver reproducibility showed the lowest coefficient of variation (CV, 6%) at 5SD and at 2SD and VAT (35% and 38%) for interobserver reproducibility. The FWHM revealed the highest CV (63% and 94%) for both intra- and interobserver reproducibility. Conclusions: The available methods for LGE quantification demonstrate good to excellent intra- and interobserver reproducibility in patients with FD. The most reliable and reproducible techniques were VAT and 5SD, whereas the FWHM was the least reliable in the setting of our study. The total amount of LGE varies strongly with the quantification technique used.

Advancing primate surveillance with image recognition techniques from unmanned aerial vehicles.

Using unmanned aerial vehicles (UAVs) for surveys on thermostatic animals has gained prominence due to their ability to provide practical and precise dynamic censuses, contributing to developing and refining conservation strategies. However, the practical application of UAVs for animal monitoring necessitates the automation of image interpretation to enhance their effectiveness. Based on our past experiences, we present the Sichuan snub-nosed monkey (Rhinopithecus roxellana) as a case study to illustrate the effective use of thermal cameras mounted on UAVs for monitoring monkey populations in Qinling, a region characterized by magnificent biodiversity. We used the local contrast method for a small infrared target detection algorithm to collect the total population size. Through the experimental group, we determined the average optimal grayscale threshold, while the validation group confirmed that this threshold enables automatic detection and counting of target animals in similar datasets. The precision rate obtained from the experiments ranged from 85.14% to 97.60%. Our findings reveal a negative correlation between the minimum average distance between thermal spots and the count of detected individuals, indicating higher interference in images with closer thermal spots. We propose a formula for adjusting primate population estimates based on detection rates obtained from UAV surveys. Our results demonstrate the practical application of UAV-based thermal imagery and automated detection algorithms for primate monitoring, albeit with consideration of environmental factors and the need for data preprocessing. This study contributes to advancing the application of UAV technology in wildlife monitoring, with implications for conservation management and research.

Study on lung CT image segmentation algorithm based on threshold-gradient combination and improved convex hull method

Lung images often have the characteristics of strong noise, uneven grayscale distribution, and complex pathological structures, which makes lung image segmentation a challenging task. To solve this problems, this paper proposes an initial lung mask extraction algorithm that combines threshold and gradient. The gradient used in the algorithm is obtained by the time series feature extraction method based on differential memory (TFDM), which is obtained by the grayscale threshold and image grayscale features. At the same time, we also proposed a lung contour repair algorithm based on the improved convex hull method to solve the contour loss caused by solid nodules and other lesions. Experimental results show that on the COVID-19 CT segmentation dataset, the advanced lung segmentation algorithm proposed in this article achieves better segmentation results and greatly improves the consistency and accuracy of lung segmentation. Our method can obtain more lung information, resulting in ideal segmentation effects with improved accuracy and robustness.

Interactive machine learning for segmenting pores of sandstone in computed tomography images

In the realm of gas field development and CO2/H2 geological storage, the precise characterization of reservoir parameters stands as a critical role. In recent years, digital rock technology has emerged as a cutting-edge tool for examining micro-pore structures, permeability parameter characteristics, and reservoir flow mechanisms. However, there are still certain defects in image segmentation, where low-density pore bodies in low-resolution images make it difficult to clearly distinguish between pores and solid minerals, affecting the accuracy of pore segmentation. This article aims to improve the accuracy of sandstone pore segmentation. We utilized the interactive machine learning segmentation method to segment pore structures and compare the segmented images obtained with the traditional ImageJ grayscale threshold segmentation methods. Furthermore, we evaluated the effectiveness of different segmentation methods quantitatively using the confusion matrix. The research results indicate that the segmentation quality (F1 score) of the machine learning method is higher than the grayscale methods, and the segmentation effectiveness of machine learning method is more stable. To further validate the accuracy of the machine learning method, we simulated the permeability and the results were compared with the experimental results, and the results show that the deviation in permeability segmented through machine learning methods is the smallest. Therefore, our results demonstrate the effective application of the machine learning method in segmenting sandstone pores in the digital rock images.

High-accuracy centroid location of CCD imaging retro-reflective targets in close-range photogrammetry

Depending on the size, shape, and gray intensity distribution of charge-coupled device (CCD) imaging retro-reflective targets (RRTs) in close-range photogrammetry, and based on conventional grayscale centroiding, this paper proposes grayscale threshold variable-index weighted centroiding (GTVIWC). The centroid location accuracy of CCD imaging RRTs was analyzed and compared using simulated and measured target images, respectively. The experimental results demonstrated that the centroid location accuracy of the algorithms used in the experiment was relatively high, reaching the subpixel level. Among them, GTVIWC has the highest location accuracy. The root mean square error (RMSE) of the centroid location for the simulated and measured CCD imaging RRTs reaches 0.0011 pixels and 0.0122 pixels, respectively. The correctness, reliability, and high accuracy of the proposed algorithm are verified.

Comparative analysis of late gadolinium enhancement assessment techniques for monitoring fibrotic changes in myocarditis follow-up

ObjectivesTo compare the repeatability and interrelation of various late gadolinium enhancement (LGE) assessment techniques for monitoring fibrotic changes in myocarditis follow-up.Materials and methodsLGE extent change between baseline and 3-month cardiovascular magnetic resonance (CMR) was compared in patients with acute myocarditis using the full width at half maximum (FWHM), gray-scale thresholds at 5 and 6 standard deviations (SD5 and SD6), visual assessment with threshold (VAT) and full manual (FM) techniques. In addition, visual presence score (VPS), visual transmurality score (VTS), and a simplified visual change score (VCS) were assessed. Intraclass-correlation (ICC) was used to evaluate repeatability, and methods were compared using Spearman’s correlation.ResultsForty-seven patients (38 male, median age: 27 [IQR: 21; 38] years) were included. LGE extent change differed among quantitative techniques (p < 0.01), with variability in the proportion of patients showing LGE change during follow-up (FWHM: 62%, SD5: 74%, SD6: 66%, VAT: 43%, FM: 60%, VPS: 53%, VTS: 77%, VCS: 89%). Repeatability was highest with FWHM (ICC: 0.97) and lowest with SD5 (ICC: 0.89). Semiquantitative scoring had slightly lower values (VPS ICC: 0.81; VTS ICC: 0.71). VCS repeatability was excellent (ICC: 0.93). VPS and VTS correlated with quantitative techniques, while VCS was positively associated with VPS, VTS, VAT, and FM, but not with FWHM, SD5, and SD6.ConclusionFWHM offers the least observer-dependent LGE follow-up after myocarditis. VPS, VTS, and VCS are practical alternatives, showing reliable correlations with quantitative methods. Classification of patients exhibiting either stable or changing LGE relies on the assessment technique.Clinical relevance statementThis study shows that LGE monitoring in myocarditis is technique-dependent; the FWHM method yields the most consistent fibrotic tracking results, with scoring-based techniques as reliable alternatives.Key PointsRecognition of fibrotic changes during myocarditis follow-up is significantly influenced by the choice of the quantification technique employed.The FWHM technique ensures highly repeatable tracking of myocarditis-related LGE changes.Segment-based visual scoring and the simplified visual change score offer practical, reproducible alternatives in resource-limited settings.

An electronic equipment for marbling meat grade detection based on digital image processing and support vector machine

This work proposes an electronic equipment which determines the marbling grade in beef rib eye according to the American grading scale using digital image processing and machine learning, achieving an 88.89 % coincidence level with grading done by beef specialists. Existing solutions which use image processing usually require calibration methods due to working in non-controlled environments. Furthermore, they only acquire the fat distribution from the longissimus dorsi muscle with an approximate accuracy of 80 %, without referring the distribution to any quality standard. In this work, meat samples are placed in a food grade stainless-steel enclosure with a touch screen and a digital RGB camera. The device acquires an image of the rib eye, which is then analyzed using techniques such as adaptive histogram analysis based on the HSV color model, histogram peaks detection for grayscale thresholding and a linear Support Vector Machine (SVM). The SVM determines the marbling grade based on the American Standard and shows it via a graphical user interface. The classifier was compared with a k-Nearest Neighbors (kNN) and Random Forest (RF) models, to choose the one with the best performance for marbling grade prediction. The SVM and the kNN models obtained a better performance than RF in identifying the marbling level. The estimated American Standard grade was compared to gold standard reference tests performed by specialists from the National Agrarian University in Lima-Peru, where the SVM achieved the aforementioned 88.89 % coincidence level.

Potassium citrate-activated pure BOF slag-based mortars utilizing carbonated and autoclaved BOF slag aggregates

This study proposes novel basic oxygen furnace (BOF) slag mortars formulated with tri-potassium citrate (TPC) activated BOF slag as binder and treated BOF slag (carbonated (CB) and autoclaved (AB)) as aggregates, aiming to maximize the utilization of BOF slag in the construction industry. Untreated BOF slag (UB) and natural sand (NS) were employed as reference aggregates. The effects of carbonation and autoclaving were investigated, focusing on the interfacial transition zone (ITZ) properties through SEM-BSE greyscale thresholding and constituent segmentation analysis. Results indicate that both treatments reduce the free CaO, promote CaCO3 formation, and modify the aggregate surface. The 28 d compressive strength of pure BOF slag-based mortars with UB, CB, and AB aggregates are 25.8, 29.4, and 34.8 MPa (average at varying TPC dosages), significantly higher than that with NS (19.6 MPa). Pure BOF slag-based mortars also show narrower ITZ thicknesses and the absence of bond cracks. Mortars with CB and AB aggregates exhibit fewer unreacted particles and more hydration products within the integral binder region compared to those with UB. Overall, pure BOF slag-based mortars present a more robust aggregate/binder interface that is further enhanced by the aggregate treatments. Leaching values of all mortars are well below the Dutch legal limits. The findings suggest promising application prospects for pure BOF slag-based mortars in substituting cement mortars and valorizing BOF slag.

Self-similarity study based on the particle sizes of coal-series diatomite

Coal-series diatomite (CSD) is widely distributed in China and has poor functional and structural properties and exhibits limited utilization of high value-added materials, resulting in a serious waste of resources and tremendous pressure on the environment. Moreover, due to differences in the mineralogical characteristics of CSD, different particle size scales (PSSs) have different functional structures and exhibit different self-similarities. In this study, we took CSD as the research object and PSS as the entry point and carried out a self-similarity study based on gas adsorption and an image processing method to illustrate the microstructures and self-similarities of different PSSs. The results showed that the pore structure of the CSD was dominated by mesopores and macropores and basically lacked micropores. The fractal dimensions were calculated with the Frenkel-Haisey-Hill (FHH) model and Menger model, and the DF1 values for − 0.025 mm and − 2 mm were 2.51 and 2.48, respectively, and the DM1 values were 3.75 and 3.79, respectively, indicating that the mesopore structure of the fine PSS was complex, whereas macropores were present in the coarse PSS. MATLAB was programmed to obtain grayscale thresholds, binarized images, grayscale histograms, three-dimensional (3D) reconstruction images and box dimensions, which enabled us to observe the microstructures and self-similarities of the CSD. Self-similarity studies based on particle sizes are very important for functional application of CSD.Please note that article title mismatch between MS and JS we have followed MS, kindly check and cofirm.Yes, I have checked and confirmed.Kindly check and confirm corresponding author mail id are correctly identified.Yes, I have checked and confirmed.

Predicting carbonate rock dissolution using multi-scale residual neural networks with prior knowledge

The factors that affect carbonate dissolution are complex, and it is crucial to acquire efficient and accurate knowledge of carbonate dissolution characteristics for CO2 capture and storage (CCS) projects. In current research, the most precise outcomes can be achieved through experimental or simulation techniques, but they are frequently computationally demanding and time-consuming. Data-driven machine learning methods can efficiently perform regression prediction tasks. In this paper, we propose a multi-scale hierarchical regression model for the dissolution problem based on residual neural networks, incorporating prior knowledge. We have developed new equations for carbonate rock dissolution and have incorporated the factors that influence this process into 3D image data by introducing Dissolution Degree (Dd) parameters. This allows the image data to include both the pore space structure and dissolution information with physical meaning. We utilize the pore phase and matrix phase grayscale thresholds (Pt, Mt) to eliminate voxel noise in the characteristic maps obtained from the model. This ensures that the predicted characteristics of carbonate rock dissolution are consistent with practical physics knowledge. We selected a total of 5 core samples to test the model. Among them, three samples are from the test set, and the additional two core samples selected have strong and weak correlations with the training set samples, respectively. The predictions were evaluated using semantic segmentation evaluation parameters, porosity, geometrical and topological structure parameters, Péclet and Damköhler numbers, porous media flow field simulations, and absolute permeability. The results of both visual comparisons and quantitative analyses demonstrated a high degree of consistency between predicted and experimental results, and the trained multi-scale hierarchical regression residual neural network with prior knowledge (MSR-Net) demonstrates good accuracy and generalization ability. The results of this study demonstrate that model based on MSR-Net can be utilized to predict the dissolution properties of the pore space in the formation where the core was extracted, along with the related formation.

3D visualization of oil-abundant limon flavedo layer under drying

This study investigated the transformations of lemon flavedo tissues during freeze-drying based on high-resolution X-ray micro-computed tomography (μ-CT) and confocal laser scanning microscopy (CLSM). Contrast-enhanced segmentation, traversal statistic algorithmic analysis, and 3D visualization of lemon peel texture are studied. To avoid pseudo-imaging caused by precipitation of contrast enhancer, low concentration cesium iodide was used to improve X-ray contrast and decrease enlarged inter-cellular region. X-ray μ-CT showed the precipitation of contrast enhancers at the intercellular boundary after freeze-drying. The grayscale thresholding method was presented as the most suitable segmentation method for detecting cell walls due to the high noise level caused by low contrast enhancer concentration in the reconstructed images. The faster water loss at the primary drying stage caused the disintegration of oil glands and induced high porosity of 0.7 % in fresh to 0.84 % in 0.5 h freeze-dried. The results suggest that noise filtering based on voxel statistics provides an efficient approach for the inter and intra-cellular microstructural analysis of excessively watery plant tissues with low contrast enhancer concentration.

Accelerated myocardial fibrosis in young to middle-aged patients with hypertrophic cardiomyopathy

The extent of late gadolinium enhancement (LGE) on cardiovascular magnetic resonance (CMR) in patients with hypertrophic cardiomyopathy (HCM) is associated with an increased risk of sudden cardiac death events. However, the clinical significance of age-specific longitudinal changes in LGE is not well characterized in HCM. We sought to assess whether the risk of LGE progression diverges between young to middle-aged (ages 20-59 years) and older (≥ 60) adults with HCM. A total of 102 HCM patients (age <60 years; n=75, age ≥60 years; n=27) undergoing serial CMR studies from two tertiary medical centers were evaluated. The median time interval between initial and follow-up CMR scans was 3.7 years. LGE was semiautomatically quantified by measuring regions with signal intensity >6 SD above the nulled remote myocardium and manually adjusting a grayscale threshold. LGE was identified at baseline in 61 of the 102 HCM patients (60%), occupying 4.8 ± 3.9% of the left ventricular (LV) mass. At the end of the follow-up period, 53 of the 61 patients (87%) demonstrated an increase in the extent of LGE to 7.7 ± 5.4%, and 8 patients had no change. In 5 patients (5%), LGE increased to extensive with >15% of the LV mass. The rate of LGE progression was 0.7 ± 1.0%/year, including 21 patients (21%) with particularly accelerated progression of ≥1%/year. The risk of LGE progression ≥1%/year was significantly higher in patients <60 years than those ≥ 60 years (25% vs. 7%, p=0.03). The odds of LGE progression ≥1%/year was almost 4 times greater for patients <60 years compared with those ≥ 60 years (odds ratio, 4.2; 95%CI, 1.1-27.9). Age<60 years and LGE extent ≥ 10% were significant baseline predictors for future LGE progression ≥1%/year, even after adjustment for other potential risk factors. In HCM, progressive fibrosis occurs more frequently in young to middle-aged patients, underscoring the importance of repeating CMR to re-evaluate for potential LGE progression in this age group.

Left ventricular fibrosis in arrhythmic mitral valve prolapse: quantification and comparison of semi-automated techniques assessed by cardiac magnetic resonance

PurposeLeft ventricular (LV) fibrosis has a key role in arrhythmogenesis in patients with mitral valve prolapse (MVP). Cardiac magnetic resonance identifies LV fibrosis by using late gadolinium enhancement (LGE) technique. LGE assessment and quantification in patients with MVP lacks of standardization protocols.Methods66 MVP patients with normal systolic function and without significant regurgitation were enrolled. Semi-automated gray-scale thresholding techniques using full width at half maximum (FWHM) and 2, 3 and 5 standard deviation (SD) above the remote myocardium were used and compared with the visual assessment, considered as the gold standard.ResultsLGE was identified in 41 MVP patients (62%) and quantified. The mean quantity of LGE visually assessed was 2.40 ± 1.07% or 1.40 ± 0.82 g. With FWHM, LGE resulted 3.56 ± 1.23% or 1.99 ± 1.13 g. Using thresholding, the mean LGE quantity was 9.2 ± 3.1% or 4.82 ± 2.28 g for 2-SD, 5.72 ± 1.75% or 3.06 ± 1.47 g for 3-SD and 2.36 ± 0.99% or 1.29 ± 0.79 g for 5-SD. The 5-SD measurement in percentage demonstrated a good correlation with LGE quantification visually assessed (2.40 ± 1.07 vs. 2.363 ± 0.9909, p = 0.543). When compared with the gold standard, the 5-SD threshold quantification, both in percentage and in grams, revealed the least intra-observer (respectively, ICC: 0.976 and 0.966) and inter-observer variability (respectively ICC: 0.948 and 0.935).ConclusionThe 5-SD gray-scale threshold technique in percentage revealed the best correlation with the visual assessment and an optimal reproducibility in MVP patient.

Multiscale method for identifying and marking the multiform fractures from visible-light rock-mass images

Multiform fractures have a direct impact on the mechanical performance of rock masses. To accurately identify multiform fractures, the distribution patterns of grayscale and the differential features of fractures in their neighborhoods are summarized. Based on this, a multiscale processing algorithm is proposed. The multiscale process is as follows. On the neighborhood of pixels, a grayscale continuous function is constructed using bilinear interpolation, the smoothing of the grayscale function is realized by Gaussian local filtering, and the grayscale gradient and Hessian matrix are calculated with high accuracy. On small-scale blocks, the pixels are classified by adaptively setting the grayscale threshold to identify potential line segments and mini-fillings. On the global image, potential line segments and mini-fillings are spliced together by progressing the block frontier layer-by-layer to identify and mark multiform fractures. The accuracy of identifying multiform fractures is improved by constructing a grayscale continuous function and adaptively setting the grayscale thresholds on small-scale blocks. And the layer-by-layer splicing algorithm is performed only on the domain of the 2-layer small-scale blocks, reducing the complexity. By using rock mass images with different fracture types as examples, the identification results show that the proposed algorithm can accurately identify the multiform fractures, which lays the foundation for calculating the mechanical parameters of rock masses.

Deep-learning versus greyscale segmentation of voids in X-ray computed tomography images of filament-wound composites

Filament-wound composites (FWC) are prone to high void contents, with large and complex-shape voids. It is critical to characterise these voids accurately to understand their effect on part strength. The characterization depends on the accuracy of the analysis technique, for example X-ray computed tomography and the subsequent void segmentation. This paper compares conventional greyscale thresholding to deep-learning (DL) based segmentation. The processing steps for both techniques are discussed. The greyscale thresholding contains segmentation errors due to the simple one-parameter algorithm and the pre-processing operations required for segmentation. This reduces the accuracy of void characterisation. The DL-based segmentation is found to be more accurate for characterisation of void size, shape, and location. The processing-time and system requirements are discussed, helping to determine the suitable segmentation technique based on desired results.

Abstract 16938: Epicardial Adipose Tissue on the Risk of Lethal Ventricular Arrhythmias in Patients With Nonischemic Cardiomyopathy Receiving Implantable Cardioverter-Defibrillator

Background: The arrhythmic risk stratification in patients with nonischemic cardiomyopathy (NICM) remains challenging. The implication of epicardial adipose tissue (EAT) on incident sudden cardiac death (SCD) is unclear. Objectives: To assess the relationship between EAT and SCD in NICM patients receiving implantable cardioverter-defibrillators (ICD) and compare this risk to patients with ischemic cardiomyopathy (ICM). Methods: A total of 421 patients scheduled for primary or secondary prevention ICD implantation underwent CMR at 1.5 or 3T, including cine and late gadolinium enhancement (LGE) imaging. EAT volume surrounding both ventricles was quantified with manual delineation on cine short-axis images and calculated by summation of EAT volume of each slice using the modified Simpson rule. LGE was semiautomatically quantified by adjusting a grayscale threshold. The primary endpoint was a composite of ICD therapy, including anti-tachycardia pacing and SCD. Results: We identified 265 NICM patients (mean age 54 years, 73% men), with LGE present in 152 patients (57%), including 85 patients (32%) with secondary prevention ICD. During the median follow-up of 2.9 years, 42 patients (16%) experienced the endpoint. Low EAT was associated with an increased risk of SCD in NICM patients (HR ad per 10ml decrease, 1.54; 95%CI, 1.19-1.96, p&lt;0.001), even after adjustment for other relevant disease variables. A continuous relationship was evident between EAT and subsequent ICD therapies (p=0.001). Even when considering 85 patients with secondary prevention ICD as theoretically having an SCD event, the risk of EAT in predicting SCD remained essentially unchanged. In contrast, EAT was not associated with increased SCD risk in ICM patients. Conclusions: Low EAT was associated with increased SCD risk in NICM patients receiving ICD implantation, especially primary prevention ICD. These data suggest a potential role of EAT in identifying NICM patients at risk of SCD.

Abstract 16793: The Impact of Late Gadolinium Enhancement on Sudden Death Risk Stratification in Older Patients With Hypertrophic Cardiomyopathy

Background: Extensive late gadolinium enhancement (LGE) is now recognized as a risk factor for sudden cardiac death (SCD) in patients with hypertrophic cardiomyopathy (HCM). However, risk for SCD is very low in older (≥60 years) HCM patients, and therefore utility of assessing LGE for risk stratification in this subgroup of patients is uncertain. Objectives: We assessed the relationship between LGE and SCD in older HCM patients and compared this risk to younger patients. Methods: A total of 1735 HCM patients undergoing cardiovascular magnetic resonance (CMR) for initial evaluation from 4 medical centers were followed up for a median of 3.7 years. LGE was semiautomatically quantified by manually adjusting a grayscale threshold. Results: We identified 638 HCM patients ≥60 years of age (up to 90 years), with LGE present in 337 patients (53%), including 28 patients (4%) with extensive LGE occupying ≥15% of left ventricular mass. Neither the presence nor extent of LGE was associated with increased risk for SCD events (HR ad , 1.13; 95%CI, 0.20-6.44, p=0.89 and HR ad per 10% increase, 1.49; 95%CI, 0.46-3.28, p=0.38, respectively), even after adjustment for other relevant disease variables. The risk for SCD at five years was 4% in older HCM patients with extensive LGE ≥15%, and not different compared to older patients with less LGE (1.0%, p=0.25). The performance of the AHA/ACC SCD risk model was not enhanced in older HCM patients by the addition of LGE (continuous net reclassification index, 0.25; 95%CI, -0.35-0.85, p=0.42). In contrast, the extent of LGE was associated with increased SCD risk in patients &lt;60 years, with extensive LGE associated with a 3-fold greater risk. Conclusions: The extent of LGE was not associated with increased SCD risk in HCM patients ≥60 years, even in the presence of other risk markers. These data suggest a limited role for CMR with LGE in older HCM patients to inform SCD risk stratification.

Fabric defect detection using ACS-based thresholding and GA-based optimal Gabor filter

Fabric defect detection is closely related to quality control. Therefore, it is an important topic in the modern textile field. In this article, a new fabric defect detection method using ACS-based thresholding and GA-based optimal Gabor filter is proposed. The multi-level gray-scale image thresholding is improved by using Adaptive cuckoo search (ACS) to calculate the best threshold. In order to suppress texture interference, the optimal Gabor filter calculated by genetic algorithm (GA) is used to achieve the optimal feature extraction of fabric defects. A specific image acquisition system is set up to obtain clear defective fabric images and corresponding defect-free images which are used to build the database. The performance of the method is evaluated by extensive experiments on various types of fabric defects. Experimental results show that the proposed method, effective and robust, is superior than the other six methods.

Multi-scale analysis of the mechanism of microbially induced calcium carbonate precipitation consolidation loess.

Microbial-induced calcium carbonate precipitation (MICP) treatment of consolidated loess has the advantages of high efficiency and environmental protection. In this study, changes in the microscopic pore structure of loess before and after MICP treatment were compared and quantified, combined with test results at different scales, to better understand the mechanisms of MICP-consolidated loess. The unconfined compressive strength (UCS) of MICP-consolidated loess is significantly increased, and the stress-strain curve indicates improved strength and stability of the loess. X-ray diffraction (XRD) test results show that the signal strength of calcium carbonate crystals is significantly enhanced after loess consolidation. The microstructure of the loess was determined by scanning electron microscopy (SEM). The loess SEM microstructure images are quantitatively analyzed using comprehensive image processing methods (including gamma adjustment, grayscale threshold selection, median processing). The changes in microscopic pore area and average pore sizes (Feret diameter) of the loess before and after consolidation are described. More than 95% of the pores consist of pores with a pore area of less than 100 μm2 and an average pore size of less than 20μm. The total percentage of pore numbers with pore areas of 100-200 and 200-1000 μm2 decreased by 1.15% after MICP consolidation, while those with 0-1 and 1-100 μm2 increased. The percentage of pore numbers with an average pore size greater than 20μm decreased by 0.93%, while the 0-1, 1-10, and 10-20μm increased. Particle size distributions revealed a significant increase in particle size after MICP consolidation, with an increase of 89μm in D50.

A comprehensive assessment of image processing variability in pore structural investigations: Conventional thresholding vs. machine learning approaches

Broad ion beam - scanning electron microscopy (BIB-SEM) is an important technology for quantitative description of pore space and detailed investigation of pore structure in mudstones and shales. To obtain quantitative data from BIB-SEM images, image processing is required. However, the effect of different image processing approaches on the segmentation results has not been well documented yet. Particularly the interpretation of pore boundaries approaching the maximum image resolution can represent a major challenge. This study addresses the variability in image processing by comparing conventional thresholding with advanced machine learning methods and analysing the sensitivity of results to different segmentation. The test dataset consists of large area BIB-SEM maps of 12 Middle Miocene mudstone samples from the Vienna Basin, Austria. Segmentation of pore space and organic matter was performed using the thresholding method in ImageJ and the machine learning-based pixel classification in ilastik. The results show that the selected greyscale thresholding algorithm in ImageJ significantly affected the resolved porosity (1.7% to 2.8% variation) relative to the average total porosity of 4.5%. Compared to ImageJ, ilastik provides comparable results with better efficiency in separating pores from organic matter. However, the obtained pore information is inevitably influenced by variations in boundary delineation and feature identification due to inherent differences in the image processing methods and varying representativeness of image maps themselves. Overall, the study findings highlight the importance of carefully selecting image processing methods and considering their potential impact on the accuracy and reliability of pore characterization in mudstones and shales.