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• The spectral characteristics of different types of deterioration are analyzed. • The normalized spectral index is constructed to preliminarily identify the deterioration. • The relationship between spectral reflectance and Schmidt rebound value is established. • The deterioration identification models are established based on hyperspectral image texture features. Deterioration investigation is an essential foundation for understanding the preservation status of stone cultural heritage, as well as for carrying out emergency and preventive conservation. Traditional photogrammetry method for deterioration investigation in stone cultural heritage heavily relies on personnel experience and has low automation. To accurately evaluate the degree of deterioration and quantify its scale, different algorithms are used to establish the rebound value prediction model and deterioration identification model based on the hyperspectral image. The effects of different wavelength selection methods and different classification models are compared. The results show that the rebound value inversion model constructed by CARS and PLS delivers the most accurate forecasts, with R2 being no less than 0.85. The maximum error of the model when applied in the field does not exceed 20%. Different types of deterioration can be initially identified by the normalized spectral index constructed from the 530 nm and 675 nm wavelengths. In addition, all four classification models based on hyperspectral imaging texture features can identify different types of deterioration. The LGBM model has the highest identification accuracy of 0.98. It also has good performance in field identification. This study provides a new method for deterioration investigation in stone cultural heritage. [ABSTRACT FROM AUTHOR]

The objective was to perform placental ultrasound image texture (UPIA) in first (T1), second(T2) and third(T3) trimesters of pregnancy using machine learning(ML). In this prospective observational study the 2D placental ultrasound (US) images from 11-14 weeks, 20-24 weeks, and 28-32 weeks were taken. The image data was divided into training, validating, and testing subsets in the ratio of 80%, 10%, and 10%. Three different ML techniques, deep learning, transfer learning, and vision transformer were used for UPIA. Out of 1008 cases included in the study, 59.5% (600/1008) had a normal outcome. The image texture classification was compared between T1&T2, T2 &T3 and T1&T3 pairs. Using Inception v3 model, to classify T1& T2 images, gave the accuracy, Cohen Kappa score of 83.3%, 0.662 respectively. The image classification between T1&T3 achieved best results using EfficientNetB0 model, having the accuracy, Cohen Kappa score, sensitivity and specificity of 87.5%, 0.749, 83.4%, and 88.9% respectively. Comparison of placental image texture among cases with materno-fetal adverse outcome and controls was done using Efficient Net B0. The F1 score, was found to be 0.824 , 0.820, and 0.892 in T1, T2 and T3 respectively. The sensitivity and specificity of the model was 77.4% at 80.2% at T1 but increased to 81.0% and 93.9% at T2 &T3 respectively. The study presents a novel technique to classify placental ultrasound image texture using ML models and could differentiate first and third-trimester normal placenta and normal and adverse pregnancy outcome images with good accuracy. • Study presents a novel technique to classify placental ultrasound image texture. • Images of each trimester were classified using artificial intelligence (AI). • Images of 1st and 3rd were best classified using transfer learning AI model. • This technique proved good in differentiating normal outcome and adverse pregnancy outcome. [ABSTRACT FROM AUTHOR]

Global biodiversity loss is most pronounced in the tropics. Monitoring of broad-scale patterns of habitat is essential for biodiversity conservation. Image texture measures derived from satellite data are proxies for habitat heterogeneity, but have not been tested in tropical forests. Our goal was to evaluate image texture to predict tropical forest bird distributions across Thailand for different guilds. We calculated a suite of texture measures from cumulative productivity (1-km fPAR-MODIS data) for Thailand's forests, and assessed how well texture measures predicted distributions of 86 tropical forest bird species in relation to body size, and nesting guild. Finally, we compared the predictive performance of combining (a) satellite image texture measures, (b) habitat composition, and (c) habitat fragmentation. We found that texture measures predicted occurrences of tropical forest birds well (AUC = 0.801 ± 0.063). Second-order homogeneity was the most predictive texture measure. Our models based on texture were significantly better for birds with larger body size (p < 0.05), but did not differ among nesting guilds (p > 0.05). Models that combined texture with habitat composition measures (AUC = 0.928 ± 0.038) outperformed models that combined fragmentation with habitat composition measures (AUC = 0.905 ± 0.047) (p < 0.05). The incorporation of texture, composition, and fragmentation variables significantly improved model accuracy over texture-only models (AUC = 0.801 ± 0.063 to AUC = 0.938 ± 0.034; p < 0.05). We suggest that texture measures are a valuable tool to predict bird distributions at broad scales in tropical forests. • Satellite image texture can be a proxy for habitat heterogeneity. • We found that image texture predicted tropic bird distributions well. • Second-order homogeneity had the highest predictive power. • Texture predicted large birds especially well. • Predicting species distributions with image texture can support conservation. [ABSTRACT FROM AUTHOR]

Timely and accurate detection of crop water stress is vital for precision irrigation. Whether the accuracy of the prevailing diagnosis of crop water stress using vegetation indices (VIs) and spectral reflectance can be improved still remains to be investigated. The crop surface characteristics such as grayscale or color vary under different water stress, so in this study one more variable, image texture, was utilized together to diagnose water stress. For this end, the canopy image of winter wheat in bloom was obtained by unmanned aerial vehicle (UAV) equipped with multispectral sensor, and the effect of soil background was eliminated using vegetation index threshold method. On this basis, Grey level co-occurrence matrix (GLCM) was used to calculate the mean (MEA), variance (VAR), homogeneity (HOM), contrast (CON), dissimilarity (DIS), entropy (ENT), second moment (SEC) and correlation (COR) of the image texture under different spatial resolutions (0.008 m, 0.01 m, 0.02 m, 0.05 m, 0.1 m and 0.2 m). Next, the canopy vegetation indices were obtained by mathematical transformation of canopy reflectance, and then sensitive image texture and vegetation indices by full subset regression method. Finally, Cubist, BPNN (Back Propagation Neural Network) and ELM (Extreme Learning Machine) methods were adopted to build the estimation models of the stomatal conductance (Gs) of winter wheat (between the sensitive image texture and Gs, and between vegetation index and Gs), and the water stress map was plotted based on the optimal Gs estimation model. The result showed: (i) the image texture obtained from the high-resolution multispectral image had a high correlation with Gs, and the image texture (VAR, HOM, CON, DIS, ENT and SEC) at 550 nm had the most significant correlation; (ii) the higher the ground resolution, the higher the correlation between the Gs and the image texture, the vegetation indices, respectively. The image texture with a ground resolution of 0.008 m combined with VIs and Gs had the highest correlation, and combining image texture and vegetation index can significantly improve the estimation accuracy of winter wheat Gs; (iii) Among the three estimation models, the BPNN model constructed by combining the image texture and VIs (MEA, VAR, ENT, DWSI and EXG) had the best estimation performance (Calibration: R c 2 = 0.899, RMSE c = 0.01, MAE c = 0.006; Validation: R c 2 = 0.834, RMSE v =;0.018, MAE v = 0.014), and an accurate estimation could even be achieved at a lower Gs value. Compared with the BPNN model solely based on VIs or image texture, the R c 2 of the BPNN model based on the combined variables increased by 24% and 22.48%, respectively. Therefore, combining UAV multispectral image texture and VIs to estimate Gs provides a feasible and accurate method for water stress diagnosis of winter wheat. • The incorporation of texture improved the accuracy of water stress diagnoses with VIs. • BPNN model with VIs and texture provided the highest accuracy of Gs estimation. • UAV provides a feasible and accurate method for water stress diagnosis of winter wheat. • Removing soil background and screening methods improve the quality of remote sensing data. [ABSTRACT FROM AUTHOR]

• Propose a computer vision method for detecting asphalt pavement segregation. • Employ image texture analysis for characterizing pavement surface condition. • Extreme gradient boosting machine and deep neural network are used for classification. • Attractive repulsive center-symmetric local binary pattern is used for texture computation. • XGBoost has achieved the best detection accuracy with accuracy rate = 0.95. Aggregate segregation is a major form of defect that accelerates the pavement deterioration rate. Therefore, asphalt pavement segregation needs to be detected accurately and early during the quality survey process. This study proposes and verifies a computer vision based method for automatic identification of aggregate segregation. The new method includes Extreme Gradient Boosting Machine integrated with Attractive Repulsive Center-Symmetric Local Binary Pattern (ARCSLBP-XGBoost) and Deep Convolutional Neural Network (DCNN). Experimental results obtained from a repetitive random data sampling process with 20 runs show that the ARCSLBP-XGBoost is a capable approach for detecting asphalt pavement segregation with outstanding performance measurement metrics (classification accuracy rate = 0.95, precision = 0.93, recall = 0.98, and F1 score = 0.95). [ABSTRACT FROM AUTHOR]

This paper presents an image watermarking technique for authentication and self-recovery called AuSR2. The AuSR2 scheme partitions the cover image into 3 × 3 non-overlapping blocks. The watermark data is embedded into two Least Significant Bit (LSB), consisting of two authentication bits and 16 recovery bits for each block. The texture of each block is preserved in the recovery data. Thus, each tampered pixel can be recovered independently instead of using the average block. The recovery process may introduce the tamper coincidence problem, which can be solved using image inpainting. The AuSR2 implements the LSB shifting algorithm to increase the imperceptibility by 2.8%. The experimental results confirm that the AuSR2 can accurately detect the tampering area up to 100%. The AuSR2 can recover the tampered image with a PSNR value of 38.11 dB under a 10% tampering rate. The comparative analysis proves the superiority of the AuSR2 compared to the existing schemes. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis of its severity for machine health monitoring can be stated as a nested classification problem. For a faulty bearing, each fault location whether belonging to inner race, outer race or the ball can be seen as multiclass classification with three classes while the varying degree of severity in each class can be viewed as a sub classification task. The peculiar vibration patterns generated from the flaws in different bearing parts and with varying degree of distortion can be classified into various classes and subclasses for analysis of vibration signatures. This paper proposes a multiclass support vector machines (MSVMs) based fault classification approach for fault diagnosis of ball bearings. The one dimensional vibration signals are converted to two dimensional gray scale images resulting in textural patterns which are then enhanced using the wave atom transform. Features such as semivariance, skewness and entropy are extracted from the texture images and the MSVM is then trained using feature matrices generated from feature vectors. The MSVM is trained in two phases; in the first phase, the classifier categorizes the location of the fault and in the second phase the classifier does the diagnosis regarding the size of the fault at that particular location. Simulation results show that the proposed technique is highly robust in locating the fault and its severity. [ABSTRACT FROM AUTHOR]

• Cities are dominated by private lands which presents a challenge for field study. • LiDAR data characterizes indices of avifaunal habitat on private residential lands. • NDVI, image texture, and land cover data are weaker predictors of urban avifauna. • LiDAR combined with street tree data has the strongest predictive power for urban birds. • High-resolution remote sensing data should be utilized in urban avifaunal studies. Urban ecosystems are dominated by private lands which poses a significant hurdle to performing field-based assessment of wildlife. An alternative approach is to characterize indices of animal habitat in difficult-to-access areas using data from airborne remote sensing platforms. Characterizing indices of wildlife habitat using remotely sensed data is common in natural systems but has received less attention within urban ecosystems. We tested the utility of using remotely sensed data from high-resolution airborne sensors, including LiDAR, a measure of vertical habitat structure, NDVI, a measure of greenness, image texture, a measure of horizontal habitat structure, and parcel level land-cover data, along with field-based street-tree measurements to predict bird abundance and richness across Greater Los Angeles, California, USA. We surveyed birds and gathered street-tree data on public lands of residential neighborhoods and processed the remote sensing data in 50-m and 300-m circular buffers of bird survey locations to capture data primarily on private, residential land across three winter field seasons (2016–18, 2019/20) at 23 locations along a tree-canopy cover gradient. Data from LiDAR processed as an index for the density of trees summarized in the 50-m and 300-m extents were the strongest univariate predictors of avifaunal abundance and richness explaining 75 % and 74 % of the likelihood in fitted models. NDVI, image texture, land cover, and street-tree density measures were weaker univariate predictors than models fitted with LiDAR data. Models including LiDAR and ground-based street-tree measurements accounted for upwards of 80 % of the variability in avifaunal abundance and richness, particularly for bird species associated with trees and shrubs. We recommend the prioritization of high-resolution remote sensing data, particularly LiDAR, in combination with field-based habitat measures e.g., street trees, to characterize indices of avifaunal habitat on public and private lands of cities, which could help to improve our understanding of the distribution of birds across urban areas. [ABSTRACT FROM AUTHOR]

Due to the emphasis on sustainable development and environmental protection, searching for new technologies in the production of fruit and vegetables is essential. Organic farming using natural preparations is aimed to produce healthy food, rich in nutrients that is unmodified and uncontaminated. Pepper fruit is characterized by its valuable chemical composition and dietary and taste values. The available information on the influence of natural preparations on pepper fruit is insufficient. Therefore, this study was aimed at evaluating the influence of the natural preparations and plant extracts, such as organic fertilizer - Bio-algeen S90, growth and development stimulator - Natural Crop® SL, nettle extract (fertilizer with a killing and nutritional effect) on the chemical composition, structure (image texture), and sensory quality of Devito F 1 and Sprinter F 1 pepper fruit in organic greenhouse cultivation under the conditions for organic farming. The combination of measurements of chemical properties, flesh image features, and sensory analysis for assessing the quality of pepper treated with these preparations is a great novelty of this study. The obtained results showed that the use of Bio-algeen S90 and Natural Crop® SL had an impact on the increase in the total yield compared to the control samples. Furthermore, the application of Bio-algeen S90 and Natural Crop® SL increased the content of nutrients and health-promoting compounds, such as total sugars, l -ascorbic acid, total polyphenols, and carotenoids for both pepper cultivars Devito F 1 and Sprinter F 1. The applied natural preparations had an impact on the flesh structure, which was greater for pepper Devito F 1. The selected image textures allowed for distinguishing pepper slices in terms of natural preparations with higher accuracies for Devito F 1 than Sprinter F 1. However, in the case of both cultivars, the samples treated with NaturalCrop® SL were the most different from the control. Natural preparations were not affecting significantly the sensory attributes of pepper fruit. All treated samples were characterized by high sensory quality. • The influence of the Bio-algeen S90, Natural Crop® SL, and nettle extract on pepper was examined. • The chemical properties, image parameters, and sensory attributes were determined. • The highest effect of NaturalCrop® SL on pepper fruit was confirmed. [ABSTRACT FROM AUTHOR]

Pavement management systems play a vital role in maintaining transportation infrastructures by evaluating pavement distress to perform maintenance tasks efficiently. Severity analysis is an important step in this process. With an increasing focus on automating the pavement distress inspection, challenges persist, including limited attention to severity analysis of texture-based distresses and lack of applying fuzzy systems in this analysis despite the linguistic and qualitative description of severity levels. Accordingly, this paper presents a methodology leveraging computational intelligence frameworks such as fuzzy logic and metaheuristic optimization to develop a reliable system for severity analysis, particularly focusing on asphalt pavement bleeding. Employing GLSM and statistical feature extraction in conjunction with a fuzzy classifier, optimized with metaheuristic-based algorithms like GA, HBA, GWA, ARA, and SSA, the proposed boosted fuzzy classifier achieves an impressive accuracy of 93% and notable improvements in performance metrics, underscoring its superiority over classic fuzzy classifiers. • Automatic analysis of asphalt pavement bleeding severity using a 2D image-based system. • Introducing a new CI-based method for assessing pavement distress severity whose levels are defined linguistically. • Evaluating the efficiency of image texture features in assessing bleeding severity using GLCM and image pixel statistics. • Developing a fuzzy classifier boosted by metaheuristic algorithms for severity analysis of asphalt pavement bleeding. • Reducing pavement inspection efforts for texture-based distress severity analysis using the developed method. [ABSTRACT FROM AUTHOR]

Traditional signal processing methods based on acceleration signals can determine whether a fault has occurred in a planetary gearbox. However, acceleration signals are severely affected by interference, causing difficulties in fault identification. This study proposes a gear fault classification method based on root strain and pseudo images. Firstly, fiber optic sensors are employed to directly acquire strain data from the ring gear root. Next, the strain signals are preprocessed using resampling and a time-domain synchronous averaging algorithm. The processed signals are encoded into two-dimensional images using Gramian Angular Fields (GAF). Then, CN-EfficientNet with contrast learning is proposed to analyze and extract deeper fault features from the image texture features. In the classification experiments for different types of faults, the accuracy reached 96.84%. The results indicate that the method can effectively accomplish the task of fault classification in planetary gearboxes. Comparative experiments with other common classification models further indicate the superior performance of the proposed learning model. Visualization based on Grad-CAM provides interpretability for the fault recognition network's results and reveals the underlying mechanism for its excellent classification performance. • A framework for planetary gearbox fault classification is proposed. • A method for measuring the root strain of ring gear tooth is designed. • A pseudo image-based gear strain signal processing algorithm is designed. • CN-EfficientNet network was built to classify pseudo images. [ABSTRACT FROM AUTHOR]

The quantitative measure of irregular arrangement, configuration and geometric pattern of microstructural features is often termed as fractal dimension (D f). The measurement of D f of a complex mixture of multiphase microstructure, comprised of crystals consisting of martensite, bainite, Fe 3 C and martensite/austenite (M/A) constituents' chaos networks in a typical dual phase steel (matrix ferrite) is carried out and interpreted with its respective phase fractions and resulting mechanical responses as a function of intercritical annealing schedule. Image–texture characteristics have also been analyzed to comprehend the s t o r e d – e n e r g y and c o n f i g u r a t i o n a l – s t a b i l i t y of such complex phase-mixture with annealing temperature. The invasive-type fractal characteristics, complexities and chaos of such micro layered hierarchical multiphases' networks in the multiphase steel has been convincingly revealed. • Multiphase DP steel is chosen at different intercritical annealed states. • Box-counting method is employed to estimate fractal dimension of second phase mixture. • Image–texture analysis is performed to understand the relative 'energy generation' of multiphases. • Fractality-image texture-microhardness correlated at different microstructural states. [Display omitted] [ABSTRACT FROM AUTHOR]

This study innovatively applies digital imaging techniques to quantify the irregular nanoscale roughness of the surfaces of plasma-etched, hydrophobic polymer films. The main objective is to verify meaningful roughness indicators that predict the surface wettability by quantifying the surface nanostructures in a simple way. Five texture parameters were extracted from SEM images by box-counting and a gray-level co-occurrence matrix (GLCM) algorithm, which together provide 2D spatial information of roughness features. Also, five parameters related to height and aspect ratio profiles of roughness were obtained using AFM analysis. The 2D texture parameters and AFM roughness profiles were statistically correlated, and either set could be used to predict surface wettability with high explanatory power. The prediction was most powerful when variables of fractal dimension , arithmetic mean height Ra of roughness features in the assessed area, and skewness Rsk of height in the assessed area were used in the regression model. This result indicates that both the height profile and 2D spatial distribution of roughness features strongly affect surface wettability. The correlation of gray levels with the neighboring pixels was statistically correlated with Ra and Rsk , and a simple predictive model was developed using fractal dimension and correlation. This research is significant in that it explored a novel analytical approach in assessing the roughness characteristics to predict surface wettability. Ultimately, we anticipate that the developed approach can be applied in designing relevant process parameters to control the wetting properties. • This study explores use of digital imaging techniques to predict surface wettability of plasma-etched, hydrophobic films. • Image texture parameters could be used as alternatives to AFM roughness parameters to quantify irregular nano-roughness. • Image texture parameters could be used to predict surface wettability with high explanatory power. • Both the height profile and 2D spatial distribution of roughness features strongly affect surface wettability. [ABSTRACT FROM AUTHOR]

A few well-annotated datasets for land-cover semantic segmentation have recently been introduced to advance the field of earth observation technologies. However, these datasets overlook the significant diversity among geographic areas with different climates, which can greatly impact and diversify land cover. Consequently, this leads to a domain gap in remote sensing images and severe performance degradation of the segmentation models. To enhance land-cover semantic segmentation with improved generalization ability, we conducted the first investigation into the impact of climate on this task. In this paper, we present a unique large-scale Climate-Aware Satellite Images Dataset (CASID) specifically designed for domain adaptive land-cover semantic segmentation. It consists of 980 satellite images with a size of 5000 × 5000 pixels, collected from 30 different regions around Asia, covering over 24,500 square kilometers. These images are gathered from four distinct climate zones, namely temperate monsoon, subtropical monsoon, tropical monsoon, and tropical rainforest. It includes four sub-datasets/domains, each representing one of the aforementioned climate zones. This characteristic makes CASID the first climate-aware land-cover semantic segmentation dataset with multiple domains. Additionally, we provide a comprehensive analysis of the samples from the four climate zones, emphasizing differences in global image features, image texture, category distribution, spectral value, and object shape. These analyses offer valuable insights for subsequent research in this field. Moreover, we conduct extensive experiments to evaluate the latest semantic segmentation and unsupervised domain adaptation methods on the CASID dataset. These results serve as a robust baseline for future research endeavors. Our dataset will be made publicly available soon at the following link: https://github.com/Linwei-Chen/CASID. [ABSTRACT FROM AUTHOR]

• An almost complete review of current methods for feature extraction from underwater acoustic signals. • Developing a reliable and accurate model for distinguishing Underwater vessel-radiated acoustical noise (UVRAN), which is robustness to noise. • Converting complex marine signals to images using the Gramian angular field (GAF) technique and extracting second-order image statistics by image texture analysis for the first time. • extracting more discriminative information from the UVRAN by signal-to-image transformation. • Designing a parameter called spectral amplitude mean difference function (SAMDF) that is more suitable for encoding to an image than the original signal. Underwater vessel-radiated acoustical noise (UVRAN) is a major factor for classification in the sea by the SONAR. Due to unsteady and complex maritime ambient, analyzing underwater sound signals is a challenging issue that has lately received attention in the marine field. In the conventional feature extraction methods, to reduce the effect of ocean noise, the de-noising procedure is performed before complexity measurement by mode decomposition techniques. Based on this, we propose a novel insight for the first time to distinguish the objects which made the underwater noises as the hydro-acoustic signature, using a signals-to-image conversion without noise removal. After pre-processing, the spectral amplitude mean difference function is encoded into an image using Gramian angular field (GAF) technique. Subsequently, image texture analysis is performed in which GAF images are subjected to the gray-level co-occurrence matrix (GLCM). Finally, the second-order image statistic (i.e., 2-D permutation entropy) is calculated. Compared with other methods, results demonstrate that the proposed method has a high degree of separation and stability between the various kinds of underwater targets, suggesting that the methodology is superior to the existing methods. Moreover, our model is robust to noise. The approach perhaps opens an alternative path for UVRAN discrimination. [ABSTRACT FROM AUTHOR]

Crop lodging detrimentally affects crop yield and mechanical harvest efficiency. Traditional remote sensing-based methods primarily focus on the identification and area extraction of lodging using image texture and spectrum. However, the response of image texture and spectrum to lodging is indirect and varies under diverse conditions. Moreover, other important finer details of lodging phenotyping, such as lodging angle and lodging type, have frequently been neglected. In this study, a robust and accurate method was developed for investigating lodging phenotypes in the field. The method was based on the three-dimensional morphological information of rapeseed (Brassica napus L.) canopy reconstructed from unmanned aerial vehicle (UAV) images. In contrast to traditional remote sensing methods that only identify lodging targets and their respective areas, the novel method in this study calculated the total lodging angle (TLA), root lodging angle (RLA), stem lodging angle (SLA = TLA - RLA), and lodging types according to a morphological method and a lodging classification model. Initially, the method employed a geometric model to characterize the stalk shape of lodged rapeseed. After assessing numerous lodging samples from individual rapeseed plants, the circle function was identified as the optimal geometric model. With this optimal function, the canopy height derived from the UAV images was found effective in calculating TLA, RLA, and SLA across 24 rapeseed cultivars in five climatic zones within the Yangtze River Basin (YRB) in China. Results showed that the average root mean square error (RMSE) was 8.3° for TLA and 7.4° for RLA. Subsequently, based on field measured data of SLA and RLA, a decision tree model was constructed to classify lodging types and an accuracy of 95.4% was achieved. Using the classification model and estimated values of RLA and SLA, the spatial distribution information and specific area estimates for different lodging types were obtained. Based on the analysis of these results, the rapeseed cultivars Zhongshuang 11 and Dadi 199 were determined to be the dominant cultivars with lodging resistance in the YRB, even though they did not achieve the mean high yields in multiple climatic zones. However, the lodging-prone cultivars such as Qinyou7 and Qinyou33 fell under the low-yield level in all climatic zones. The robust and cost-effective method proposed in this study for acquiring detailed crop lodging phenotyping data has the potential to enhance mechanized harvesting, accurately estimate the risk of low yield, and assess the lodging status of various crops. • Circle functions can simulate most stalk shapes of lodged rapeseed. • Two phases of UAV images are found effective to estimate total lodging angle. • Root and stem lodging angle can be estimated using circle functions and UAV images. • Lodging types can be distinguished by root lodging angle and stem lodging angle. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most prevalent cancers in women. Reliable pathology identification can help histopathologists make accurate diagnosis of breast cancer but require specialized histopathological knowledge and a significant amount of manpower and medical resources. In this study, we fuse the coordinated attention mechanism to enhance the image texture analysis capability of the DenseNet, and build the CA-BreastNet model to classify microscopic histopathological images of specific types of breast cancers in the BreakHis dataset. More crucially, convolutional decision trees based on the specialized enhanced classifying strategy(SECS) are built to increase the overall accuracy of the network by reducing the model's accuracy restriction imposed by dataset structures. The related experimental results show that our network has strong performance and the SECS offers researchers reliable and effective performance enhancement guidelines. The accuracy of the convolutional decision trees reaches 99.75% for binary classification and 95.69% for eight-class classification, which means our model and strategy will be useful in the field of automatic diagnosis of breast cancer. [ABSTRACT FROM AUTHOR]

Precise information on tree species composition is critical for forest management and conservation, but mapping tree species with satellite data over large areas is still a challenge. Since 2017, Sentinel-2A/B provide multi-spectral time series with global coverage at an unprecedented spatial and temporal resolution. This is a new opportunity for mapping tree species over large areas that has not yet been fully explored. Because of the high spatial and temporal resolution, Sentinel-2 time series improve the characterization of vegetation phenology and canopy structure, parameters that are intrinsically linked to tree species. The objective of this study was to test the utility of a Sentinel-2 time-series based approach for mapping tree species in a temperate forest region in Central Europe. Using stand-wise forest inventory data for single species stands we assess how well main and minor tree species can be mapped, and if the addition of environmental variables and spatial texture metrics improves the classification accuracy. Our time series approach utilizes all available Sentinel-2 observations and an ensemble of radial basis convolution filters to build cloud-free 5-day time series for each spectral band. The time series are then used as input features to classify seventeen tree species. Our results show the potential of Sentinel-2 time-series based classification, but they also show the challenges associated with mapping a diverse portfolio of tree species. Accuracy of the nine main species, with an area proportion greater than 0.5%, ranged between 98.9% and 66.8%, which is promising for a large area. Adding detailed environmental data and texture metrics to the spectral model only marginally increased the accuracy of a few minor tree species. Overall, the eight minor tree species with area proportions less than 0.5% were most strongly affected by classification errors. Although the absolute mapped area of minor species correlated well with the estimated reference area, the small class areas of minor species lead to high classification errors in relative terms. Mapping minor tree species is challenging for statistical reasons (i.e., class imbalance, small sample size and class variance). Using all available Sentinel-2 data allows building dense time series at high spatial resolution that are mandatory for improved tree species mapping. We were able to show that the spectral time series is the prime explanatory information, even when complementing our analyses with texture information and various environmental data. The results suggest that with the applied data harmonization approach precise regional tree species mapping is feasible. • Sentinel-2A/B time series used for mapping of 17 tree species on regional scale. • Adaptive ensemble filter produced cloud-free 5-day time series over varying data density. • Major species with area shares >0.5% were mapped with medium to high accuracy. • Maps reflect area proportions of minor tree species but with low spatial accuracy. • Environmental variables and texture metrics did not substantially improve accuracy. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have performed well in image denoising. Although some CNNs enlarge convolutional kernels and increase stacked convolutional layers to overcome the locality defect of convolutional operations, they may increase computational costs. In this paper, we propose a parallel and serial denoising network (PSDNet) for image denoising to preserve image texture. Specifically, the proposed PSDNet contains a parallel block (PB), a serial block (SB), and a reconstruction block (RB). A PB uses two heterogeneous sub-networks with a deformable convolution in a parallel way to extract comparative information for better-recovering image texture. A SB utilizes an enhanced residual dense architecture via combinations of a batch normalization, ReLU, and convolutional layer in a serial way to refine obtained features for obtaining more accurate noise information. A RB is responsible for reconstructing images. Experimental results reveal that our PSDNet is very effective in image denoising, according to quantitative analysis and visual analysis. Codes can be obtained at https://github.com/hellloxiaotian/PSDNet. • Heterogeneous architecture with deformable convolution can better filter noise. • An enhanced residual architecture is used to remove redundant features. • Combining a parallel and serial way can improve effects of images denoising. • Proposed network is effective for synthesized and real noisy image denoising. [ABSTRACT FROM AUTHOR]

Dehazing of nighttime road images holds significant practical relevance for autonomous driving and intelligent transportation systems operating in nocturnal conditions. To address the limitations of traditional atmospheric scattering models in accurately describing the imaging process in low illumination environments such as nighttime, this study constructs a low illumination environment atmospheric scattering model. Based on this newly constructed model, we propose an algorithm specifically designed for dehazing road images at nighttime. The proposed model incorporates an incident light attenuation factor, an additive noise factor, and a global compensation factor related to the scene's depth. The proposed nighttime road image dehazing algorithm firstly suppresses the interference of additive noise in the original image using wavelet decomposition and soft thresholding operation. Then, a transmittance-solving method containing the primary structural information of the image is designed based on the color attenuation linear model, and the solved transmittance is refined based on the interval gradient image texture structure method. Consequently, the global light map estimation process is formulated as an optimization problem to ensure the accuracy of the atmospheric light value calculation. In the final stages, a novel RGB color equalization method is introduced to address the issue of non-uniform incident light color bias in the dehazed images. Additionally, to eliminate the halo problem in the dehazed images, the adaptive histogram equalization algorithm is modified by incorporating a normalized gamma correction function. The experimental results show that the proposed nighttime road image dehazing algorithm exhibits robust performance and generality across various nighttime scenes. Compared with the representative algorithms for nighttime image dehazing, it can get more excellent dehazing results and has more outstanding performance in all numerical evaluation indexes. • We develop an atmospheric scattering model that accurately describes the imaging process in low-light environments. • We propose a method for calculating atmospheric light values for optimal global light map estimation. • We propose a method for color bias and halo suppression of non-uniform incident light in images after dehazing. [ABSTRACT FROM AUTHOR]

To ensure the timely detection of safety hazards in transmission lines and to enhance accurately the detection of insulator defects in complicated environments, this study proposes a 3D attention-focused pure convolutional target detection algorithm (CMYOLOv7) based on you only look once v7 (YOLOv7) for defect detection on insulators in complicated environments. Firstly, to address the incapacity of focusing on the target in the backbone feature extraction networks, this study proposes a focused pure convolutional feature extraction module (ConvSimCB) to enhance the extraction and focus capability on insulator defect features. Secondly, to solve the problem of loss of key detail feature information caused by maximum pooling in spatial pyramid pooling module (SPPCSPC), this study proposes a mixed spatial pyramid pooling module (MIXPCSPC) to retain abundant image texture detail information and increase accuracy in detection of tiny insulator defects. Finally, a lightweight generic upsampling operator (CARAFE) is introduced to enhance the feature map resolution to address image distortion caused by the Nearest Neighbor Method of upsampling. This study proposed CMYOLOv7 achieves 98.37% precision, 90.59% recall, 95.68% mean average precision (MAP), and 94% F1 score, higher than YOLOv7 by 0.61%, 8.64%, 5.41%, and 5%. [ABSTRACT FROM AUTHOR]

This study was aimed at assessing the effect of microwave drying at 100, 200, or 300 W on the quality of Cavendish banana slices without pretreatment and with pretreatment using 5% ascorbic acid solution, 5% citric acid solution, 5% gum arabic solution, and ultrasound. Banana slices were imaged using a digital single-lens reflex (SLR) camera. The acquired images were processed to extract texture parameters. The classification models were developed based on image texture parameters selected from a big dataset of 2172 textures of images in different color channels using artificial neural networks. Wide Neural Network, Bilayered Neural Network, Medium Neural Network and three classifiers from the group of function, such as RBF (Radial basis function) Network, Multilayer Perceptron, and WiSARD were applied. Banana slices belonging to 15 classes with different combinations of pretreatment and microwave drying were distinguished with an average accuracy of up to 97.2% for a model built using Multilayer Perceptron. For most models, banana samples microwave-dried at 200 W without pretreatment were classified with the highest correctness. The performed study revealed that the objective, non-destructive, correct, and robust quality assessment of pretreated and microwave-dried banana slices may be performed using image processing and artificial intelligence. • Banana slices were subjected to different combinations of pretreatment and microwave drying. • Samples were classified using image processing and artificial neural networks. • The obtained classification accuracy reached 97.2%. • The most successful model was developed using Multilayer Perceptron. [ABSTRACT FROM AUTHOR]

• Cotton water status was estimated using vegetation indices and textural information. • Combining vegetation indices with textural information enhanced estimation accuracy. • The XGBoost_VIs + TFs + TIs model performed the best in estimating CWSI. • The estimated CWSI predictive map could diagnose cotton canopy water stress. Precise and timely estimation of crop canopy water status is of great importance for precision irrigation. The unmanned aerial vehicle (UAV)-based remote sensing technology has become increasingly popular for crop canopy water status estimation. Nevertheless, the capability of vegetation indices (VIs) along with textural information from high-resolution imagery for estimating cotton canopy water status has been rarely explored. The VIs, texture features (TFs), and texture indices (TIs) were obtained from UAV multispectral images of cotton with different irrigation levels and nitrogen rates in the southern Xinjiang of China. The performances of three machine learning models, i.e. support vector machine (SVM), back-propagation neural network (BPNN) and extreme gradient boosting (XGBoost) were evaluated for estimating canopy equivalent water thickness (CEWT) and crop water stress index (CWSI) throughout the cotton growing season. The results showed that combining vegetation indices and textural information significantly improved the estimation accuracy of models compared to vegetation indices or textural information alone. The XGBoost_VIs + TFs model exhibited the best accuracy in estimating CEWT (R2 = 0.75, RMSE = 0.01 cm, RE = 19.46 % at upper half-canopy level, and R2 = 0.65, RMSE = 0.02 cm, RE = 24.59 % at all-canopy level), while the XGBoost_VIs + TFs + TIs model performed best in predicting CWSI among the models (R2 = 0.90, RMSE = 0.05, RE = 5.84 %). Although CEWT estimation was fair to some extent, CWSI estimation was more applicable for diagnosing cotton water stress. The CWSI maps created from the optimal XGBoost_VIs + TFs + TIs model intuitively reflected the cotton canopy water status under various irrigation levels and nitrogen rates, which could help farmers implement timely and precision irrigation in cotton production. [ABSTRACT FROM AUTHOR]

The fusion of infrared and visible images aims to extract and fuse thermal target information and texture details to the fullest extent possible, enhancing the visual understanding capabilities of images for both humans and computers in complex scenes. However, existing methods have difficulties in preserving the comprehensiveness of source image feature information and enhancing the saliency of image texture information. Therefore, we put forward a novel infrared and visible image fusion algorithm based on global information-enhanced attention network (GIEA). Specifically, we develop an attention-guided Transformer module (AGTM) to make sure the fused images have enough global information. This module combines the convolutional neural network and Transformer to perform adequate feature extraction from shallow to deep layers, and utilize the attention network for multi-level feature-guided learning. Then, we build the contrast enhancement module (CENM), which enhances the feature representation and contrast of the image so that the fused image contains significant texture information. Furthermore, our network is driven to fully preserve the texture and structure details of the source images with a loss function that consists of content loss and total variance loss. Numerous experiments demonstrate that our fusion approach outperforms other fusion approaches in both subjective and objective assessments. • Combining CNN and Transformer to fully extract complementary features. • Designed an attention network for multi-level feature learning. • Designed a contrast enhancement module to enhance feature saliency. [ABSTRACT FROM AUTHOR]

• A flexible orthoimage mosaicking method is developed considering the cloud coverage. • Dynamic variable patch is defined as the processing unit for orthoimage mosaicking. • This method can remove clouds and also relieve the impact of misalignments on the mosaic. • An area-weighted image blending method is presented to achieve smooth transition. Orthoimage mosaicking plays an important role in remote sensing applications, such as environment monitoring and protection. However, the cloud coverage in remote sensing images often results in substantial loss of information and degradation of data quality, bringing lots of challenges to orthoimage mosaicking. Therefore, this paper proposes a flexible multi-temporal orthoimage mosaicking method based on dynamic variable patches. Different from most existing pixel-based mosaicking methods, the proposed method defines patch as the minimal processing unit for orthoimage mosaicking, and the size and position of each patch are dynamic variable. Patches from different images are rearranged under the image texture consistency constraint to generate the mosaic image. In the proposed method, the initial positions of available patches of all images are determined first, then the initial size of each patch is determined by the cloud coverage and the effective area of image. Subsequently, considering the inevitable misalignments between different orthoimages, the texture similarity between patches is utilized to select the most suitable patch, and the size of the patch is also dynamically adjusted to make the misalignments within each patch controllable. Finally, considering the color inconsistency in the overlapping areas of neighboring images, an area-weighted image blending algorithm is also presented to achieve invisible seams and smooth transitions in the final mosaic. Not only does the proposed method produce mosaic images with minimal cloud coverage areas by excluding cloud pixels as much as possible, but it is also flexible for the mosaicking of images with varying degrees of misalignments, which is achieved by dynamically adjusting the size and position of each patch while maintaining texture consistency constraint. Both simulated experiments and real data experiments are carried out to verify the performance of the proposed method. The experimental results demonstrate that the proposed method can generate the mosaic image with higher spatial continuity and radiometric consistency. [ABSTRACT FROM AUTHOR]

This article focuses on the characterization and analysis of dimple geometry on the published tensile fracture surfaces of a HfNbTaTiZr refractory high entropy alloy of different matrix microstructures, generated through cold deformation and systematic annealing treatments. Changes in different microstructural states which led to different tessellated fracture complexions have been quantified, compared and correlated with the corresponding tensile responses of the alloy. This interpretation allows the use of fracture surface morphology and image texture in a quantitative way. • A HfNbTaTiZr high entropy alloy is chosen for fracture feature analysis at six different initial microstructural states. • Two-dimensional dimple geometry is measured on tensile fractographs. • Quantitative fractography is employed to measure dimple geometry. • Image texture analysis on fractographs is performed to understand relative 'energy generation' during ductile fracture. • Dimple geometry-image texture-mechanical property correlated with microstructural states. [ABSTRACT FROM AUTHOR]

Fractal dimension of a microstructural feature is a measure of its geometric complexity, morphological characteristics and irregularities in configurations. The experimental measurements of fractal dimensions of a novel-flaky microstructure, comprising the crystals with hierarchical 3D nanolayered α/β-Zr networks in Zr-2.5Nb alloy have been performed and correlated with its corresponding mechanical properties as a function of annealing temperature. Image texture analysis has also been performed to understand the stored-energy and thermal stability of these α/β-Zr networks. The invasive fractal-character and chaos of these networks have been convincingly revealed. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Cardiovascular magnetic resonance (CMR) is emerging as an important tool for cardiac allograft assessment. Native T1 mapping may add value in identifying rejection and in assessing graft dysfunction and myocardial fibrosis burden. We hypothesized that CMR native T1 values and features of textural analysis of T1 maps would identify acute rejection, and in a secondary analysis, correlate with markers of graft dysfunction, and with fibrosis percentage from endomyocardial biopsy (EMB). Methods: Fifty cases with simultaneous EMB, right heart catheterization, and 1.5 T CMR with breath-held T1 mapping via modified Look-Locker inversion recovery (MOLLI) in 8 short-axis slices and subsequent quantification of mean and peak native T1 values, were performed on 24 pediatric subjects. A single mid-ventricular slice was used for image texture analysis using nine gray-level co-occurrence matrix features. Digital quantification of Masson trichrome stained EMB samples established degree of fibrosis. Markers of graft dysfunction, including serum brain natriuretic peptide levels and hemodynamic measurements from echocardiography, catheterization, and CMR were collated. Subjects were divided into three groups based on degree of rejection: acute rejection requiring new therapy, mild rejection requiring increased ongoing therapy, and no rejection with no change in treatment. Statistical analysis included student's t-test and linear regression. Results: Peak and mean T1 values were significantly associated with acute rejection, with a monotonic trend observed with increased grade of rejection. Texture analysis demonstrated greater spatial heterogeneity in T1 values, as demonstrated by energy, entropy, and variance, in cases requiring treatment. Interestingly, 2 subjects who required increased therapy despite low grade EMB results had abnormal peak T1 values. Peak T1 values also correlated with increased BNP, right-sided filling pressures, and capillary wedge pressures. There was no difference in histopathological fibrosis percentage among the 3 groups; histopathological fibrosis did not correlate with T1 values or markers of graft dysfunction. Conclusion: In pediatric heart transplant patients, native T1 values identify acute rejection requiring treatment and may identify graft dysfunction. CMR shows promise as an important tool for evaluation of cardiac grafts in children, with T1 imaging outperforming biopsy findings in the assessment of rejection. [ABSTRACT FROM AUTHOR]

• Comparison of PE2D with the novel AAPE2D entropy algorithm. • Texture analysis of synthetic and biomedical images. • Both methods statistically differentiate healthy and pneumonia subjects. • AAPE2D features achieve 75.5% accuracy, which is slightly better than PE2D. Entropy algorithms have been applied extensively for time series analysis. The entropy value given by the algorithm quantifies the irregularity of the data structure. For higher irregular data structures, the entropy is higher. Both permutation entropy (PE) and amplitude-aware permutation entropy (AAPE) have been previously used to analyze time series. These two metrics have the advantage, over others, of being computationally fast and simple. However, fewer entropy measures have been proposed to process images. Two-dimensional entropy algorithms can be used to study texture and analyze the irregular structure of images. Herein, we propose the extension of AAPE for two-dimensional analysis (AAPE 2 D). To the best of our knowledge, AAPE 2 D has never been proposed to analyze texture of images. For comparison purposes, we also study the two-dimensional permutation entropy (PE 2 D) to analyze the effect of the amplitude consideration in texture analysis. In this study, we compare AAPE 2 D method with PE 2 D in terms of irregularity discrimination, parameters sensitivity, and artificial texture differentiation. Both AAPE 2 D and PE 2 D appear to be interesting entropy-based approaches for image texture analysis. When applied to a biomedical dataset of chest X-rays with healthy subjects and pneumonia patients, both methods showed to statistically differentiate both groups for P < 0.01. Finally, using a SVM model and multiscale entropy values as features, AAPE 2 D achieves an average of 75.7% accuracy which is slightly better than the results of PE 2 D. Overall, both entropy algorithms are promising and achieve similar conclusions. This work is a new step towards the development of other entropy-based texture measures. [ABSTRACT FROM AUTHOR]

• Ultrahigh-resolution PCD-CT produces images with inherently higher noise level. • QIR reduces noise without affecting image texture. • QIR does not compromise sharpness or stenosis measurements on coronary CTA series. • The most benefit is observed at the highest QIR strength level. To assess the impact of different quantum iterative reconstruction (QIR) levels on objective and subjective image quality of ultra-high resolution (UHR) coronary CT angiography (CCTA) images and to determine the effect of strength levels on stenosis quantification using photon-counting detector (PCD)-CT. A dynamic vessel phantom containing two calcified lesions (25 % and 50 % stenosis) was scanned at heart rates of 60, 80 and 100 beats per minute with a PCD-CT system. In vivo CCTA examinations were performed in 102 patients. All scans were acquired in UHR mode (slice thickness0.2 mm) and reconstructed with four different QIR levels (1–4) using a sharp vascular kernel (Bv64). Image noise, signal-to-noise ratio (SNR), sharpness, and percent diameter stenosis (PDS) were quantified in the phantom, while noise, SNR, contrast-to-noise ratio (CNR), sharpness, and subjective quality metrics (noise, sharpness, overall image quality) were assessed in patient scans. Increasing QIR levels resulted in significantly lower objective image noise (in vitro and in vivo: both p < 0.001), higher SNR (both p < 0.001) and CNR (both p < 0.001). Sharpness and PDS values did not differ significantly among QIRs (all pairwise p > 0.008). Subjective noise of in vivo images significantly decreased with increasing QIR levels, resulting in significantly higher image quality scores at increasing QIR levels (all pairwise p < 0.001). Qualitative sharpness, on the other hand, did not differ across different levels of QIR (p = 0.15). The QIR algorithm may enhance the image quality of CCTA datasets without compromising image sharpness or accurate stenosis measurements, with the most prominent benefits at the highest strength level. [ABSTRACT FROM AUTHOR]

Scattering and attenuation of light in no-homogeneous imaging media or inconsistent light intensity will cause insufficient contrast and color distortion in the collected images, which limits the developments such as vision-driven smart urban, autonomous vehicles, and intelligent robots. In this paper, we propose an all-in-one scene recovery network via multi-knowledge integration (termed AoSRNet) to improve the visibility of imaging devices in typical low-visibility imaging scenes (e.g., haze, sand dust, underwater, and low light). It combines gamma correction (GC) and optimized linear stretching (OLS) to create the detail enhancement module (DEM) and color restoration module (CRM). Additionally, we suggest a multi-receptive field extraction module (MEM) to attenuate the loss of image texture details caused by GC nonlinear and OLS linear transformations. Finally, we refine the coarse features generated by DEM, CRM, and MEM through Encoder-Decoder to generate the final restored image. Comprehensive experimental results demonstrate the effectiveness and stability of AoSRNet compared to other state-of-the-art methods. The source code is available at https://github.com/LouisYuxuLu/AoSRNet. • AoSRNet can enhance imaging quality in hazy, sandy, underwater, and low-light scenes. • Multi-knowledge integration can robustly restores image in unpredictable scenes. • Optimized linear stretching and gamma correction improve AoSRNet's generalization. • We constructed a set of atmospheric light values for haze and sand image synthesis. • Extensive experiments verify AoSRNet's effectiveness with start-of-the-art methods. [ABSTRACT FROM AUTHOR]

• The Contrast Texture Feature Values (CTFV) is proposed to extract subtle changes in the thermal image caused by crack evolution based on the Gray Level Co-occurrence Matrix (GLCM). • The cumulative Crack Texture Thermal Image (CTTI) is reconstructed using CTFV, which can reflect the spatial evolution process of loading coal and rock cracks. • The CTFV can provide readily identifiable precursor information for the coal and rock failure, and the precursor can be divided into high risk, medium risk, and low risk levels based on different gray levels. • An anomaly detection method for CTFV was proposed based on sliding window probability density estimation to achieve real-time and adaptive recognition of rock failure precursors. Early warning of the catastrophic failure of rocks is a challenging rock mechanics problem. This article proposed a failure precursor recognition method based on infrared thermal image texture features for coal and rock. Based on spatiotemporal background noise correction for infrared thermal images, a new thermal image parameter of loading coal and rock, Contrast Texture Feature Values (CTFV), is proposed to extract subtle changes in the thermal image caused by crack evolution based on the Gray Level Co-occurrence Matrix (GLCM). The cumulative Crack Texture Thermal Image (CTTI) is reconstructed using CTFV, which can accurately reflect the spatial evolution process of loading coal and rock cracks. The CTFV remains at 0 in the early stage of loading and gradually increases with stress increase at the unstable crack propagation stage, which can serve as a reference for precursor warning of coal and rock failure. For shale, sandstone, and limestone, the precursor of CTFV at grayscale levels of 7, 8, and 9, can be classified into high failure risk, medium failure risk, and low failure risk, respectively. For coal samples, the CTFV is only applicable for the critical warning of high failure risk when the grayscale level is 7. Then, an adaptive identification method for failure precursors based on the sliding window probability density estimation method is proposed. The research results can enhance the reliability of IR monitoring technology for rock failure and instability early warning and can provide support for the prevention and control of rock engineering and geological disasters. [ABSTRACT FROM AUTHOR]

• ImmunoPET is a potential biomarker for patient selection to immunotherapy. • Patient response classification differs with tumor measurement strategy. • MPM tumor volumetry predicts patient survival and outperforms CT volumetry. • CT image texture has potential to differentiate among MPM tumor histologies. • Skeletal muscle loss in chemotherapy patients demonstrates prognostic potential. Imaging of mesothelioma plays a role in all aspects of patient management, including disease detection, staging, evaluation of treatment options, response assessment, pre-surgical evaluation, and surveillance. Imaging in this disease impacts a wide range of disciplines throughout the healthcare enterprise. Researchers and clinician-scientists are developing state-of-the-art techniques to extract more of the information contained within these medical images and to utilize it for more sophisticated tasks; moreover, image-acquisition technology is advancing the inherent capabilities of these images. This paper summarizes the imaging-based topics presented orally at the 2021 International Conference of the International Mesothelioma Interest Group (iMig), which was held virtually from May 7–9, 2021. These topics include an update on the mesothelioma staging system, novel molecular targets to guide therapy in mesothelioma, special considerations and potential pitfalls in imaging mesothelioma in the immunotherapy setting, tumor measurement strategies and their correlation with patient survival, tumor volume measurement in MRI and CT, CT-based texture analysis for differentiation of histologic subtype, diffusion-weighted MRI for the assessment of biphasic mesothelioma, and the prognostic significance of skeletal muscle loss with chemotherapy. [ABSTRACT FROM AUTHOR]

• A robustness analysis on quantitative ultrasound texture features was done varying the dynamic range and beamforming method. • Ultrasound image texture parameters are robust to changes in the dynamic range values considered in our study. • Texture parameters are altered when employing different beamforming methods. • Different beamformers could potentially discriminate better between healthy/pathological tissues. Texture analysis of medical images gives quantitative information about the tissue characterization for possible pathology discrimination. Ultrasound B-mode images are generated through a process called beamforming. Then, to obtain the final 8-bit image, the dynamic range value must be set. It is currently unknown how different beamforming techniques or dynamic range values may alter the final image texture. We provide here a robustness analysis of first and higher order texture features using six beamforming methods and seven dynamic range values, on experimental phantom and in vivo musculoskeletal images acquired using two different ultrasound research scanners. To investigate the repeatability of the texture parameters, we applied the multivariate analysis of variance (MANOVA) and estimated the intraclass correlation coefficient (ICC) on the texture features calculated on the B-mode images created with different beamforming methods and dynamic range values. We demonstrated the high repeatability of texture features when varying the dynamic range and showed texture features can differentiate between beamforming methods through a MANOVA analysis, hinting at the potential future clinical application of specific beamformers. [ABSTRACT FROM AUTHOR]

• A novel PDE-constrained optimization problem is proposed for MRI denoising task. • The proposed optimization procedure computes the variable exponent and the clean image for MRI images. • The comparative results demonstrates the performance on two MRI datasets This paper investigates a novel PDE-constrained optimization model with discontinuous variable exponent p (x) identification. Since the parameter p is always related to a better approximation of the image gradient, its computation plays a critical role in preserving the image texture. Analytically, we include results on the approximation of this parameter as well as the resolution of the encountered PDE in a well posed framework. In addition, to resolve the PDE-constrained minimization problem, we proposed a modified primal-dual algorithm. Finally, numerical results are provided to compute the parameter p and also to remove high intensity of noise. The proposed algorithm simultaneously keep safe fine details and important features in medical image applications (Magnetic Resonance Images (MRI)) with numerous comparisons to show the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

[Display omitted] • Gray-level Co-occurrence Matrix was applied to SEM image texture analysisPearson analysis of DEPs with sensory attributes and chemical composition. • 55 differentially expressed proteins (DEPs) significant correlated with sensory properties. • 69 DEPs associated with mutable collagenous tissues (MCTs) structures. The microstructure of the body wall, body wall composition, and collagen fibers of sea cucumber (Stichopus japonicus) under different heating times (1 h, 4 h, 12 h, and 24 h) was investigated based on heat treatment at 80 °C. A Label-Free proteomics technique was applied to study the proteomic changes in the body wall of sea cucumbers under 4 and 12 h of heat treatment. Compared with the fresh group, 981 proteins were found to be differentially expressed proteins (DEPs) after heat treatment at 80 °C (4 h), and 1110 DEPs were observed after heat treatment at the same temperature for 12 h. There were 69 DEPs associated with mutable collagenous tissues (MCTs) structures. The results of correlation analysis showed that 55 DEPs were correlated with sensory properties, among which A0A2G8KRV2 was significantly correlated with hardness and SEM image texture features (SEM_Energy, SEM_Correlation, SEM_Homogeneity, and SEM_Contrast). These findings could be conducive to further comprehension of the structural changes and mechanisms of quality loss in the body wall of sea cucumbers at different heat treatment times. [ABSTRACT FROM AUTHOR]

In-line phase contrast synchrotron tomography combined with in situ mechanical loading enables the characterisation of soft tissue micromechanics via digital volume correlation (DVC) within whole organs. Optimising scan time is important for reducing radiation dose from multiple scans and to limit sample movement during acquisition. Also, although contrasted edges provided by in-line phase contrast tomography of soft tissues are useful for DVC, the effect of phase contrast imaging on its accuracy has yet to be investigated. Due to limited time at synchrotron facilities, scan parameters are often decided during imaging and their effect on DVC accuracy is not fully understood. Here, we used previously published data of intervertebral disc phase contrast tomography to evaluate the influence of i) fibrous image texture, ii) number of projections, iii) tomographic reconstruction method, and iv) phase contrast propagation distance on DVC results. A greater understanding of how image texture influences optimal DVC tracking was obtained by visualising objective function mapping, enabling tracking inaccuracies to be identified. When reducing the number of projections, DVC was minimally affected by image high frequency noise but with a compromise in accuracy. Iterative reconstruction methods improved image signal-to-noise and consequently significantly lowered DVC displacement uncertainty. Propagation distance was shown to affect DVC accuracy. Consistent DVC results were achieved within a propagation distance range which provided contrast to the smallest scale features, where; too short a distance provided insufficient features to track, whereas too long led to edge effect inconsistencies, particularly at greater deformations. Although limited to a single sample type and image setup, this study provides general guidelines for future investigations when optimising image quality and scan times for in situ phase contrast x-ray tomography of fibrous connective tissues. [ABSTRACT FROM AUTHOR]

• Entropy of image texture reveals densification, clarification, and sorting of sludge. • Dynamic entropy effectively differentiates various types of granular sludge. • Prompt sorting during the settling process indicates stable granulation. • Image-based monitoring correlate with respirogram indexes. Aerobic granular sludge is one of the most promising biological wastewater treatment technologies, yet maintaining its stability is still a challenge for its application, and predicting the state of the granules is essential in addressing this issue. This study explored the potential of dynamic texture entropy, derived from settling images, as a predictive tool for the state of granular sludge. Three processes, traditional thickening, often overlooked clarification, and innovative particle sorting, were used to capture the complexity and diversity of granules. It was found that rapid sorting during settling indicates stable granules, which helps to identify the state of granules. Furthermore, a relationship between sorting time and granule heterogeneity was identified, helping to adjust selection pressure. Features of the dynamic texture entropy well correlated with the respirogram, i.e., R2 were 0.86 and 0.91 for the specific endogenous respiration rate (SOUR e) and the specific quasi-endogenous respiration rate (SOUR q), respectively, providing a biologically based approach for monitoring the state of granules. The classification accuracy of models using features of dynamic texture entropy as an input was greater than 0.90, significantly higher than the input of conventional features, demonstrating the significant advantage of this approach. These findings contributed to developing robust monitoring tools that facilitate the maintenance of stable granular sludge operations. [Display omitted] [ABSTRACT FROM AUTHOR]

Uranium ore concentrates (UOCs) are produced in the early stages of the nuclear fuel cycle, prior to conversion to uranium hexafluoride. Because of their high uranium content and the large-scale production, UOCs diversion from civilian use and proliferation are potential risks. This implies the necessity to develop methods able to recognise characteristic parameters correlating each UOC powder to its history and origin. Here, a novel methodology is proposed: first the reflectance spectra of 79 commercial UOCs are acquired and clustered by means of Ward's clustering analysis, then classified by Support Vector Machine (SVM). Second, SVM classification is applied to the image textural features extracted with the Grey Level Co-occurrence Matrix (GLCM) and the Angle Measure Technique (AMT) algorithms for powders in two different colour groups. The developed SVM models present good classification quality: a Matthews correlation coefficient (MCC) of 0.95 is obtained for the classification based on colours while macro-F1 is generally greater than 0.81 (MCC larger than 0.75) for the texture-based classification. These results reveal the potentiality of the present automated classification for the scopes of nuclear forensics in the identification of an unknown uranium ore concentrate sample. [Display omitted] • Illicit trafficking of uranium ore concentrates is a realistic risk. • Nuclear Forensics requires reliable methods to determine uranium ore concentrates origin. • Spectrophotometry and image texture analysis are powerful techniques for powders characterisation. • Support Vector Machine enables the identification of an unknown uranium ore concentrate sample. [ABSTRACT FROM AUTHOR]

The goal of infrared and visible image fusion is to generate an informative image that preserves the complementary information of the two types of source images, such as texture details and infrared targets. Existing methods have designed a variety of means to achieve the fusion of image texture and target complementary information. But they ignore the fact that the two types of source images have the complementarity of illumination, especially the infrared image can mainly supplement the information of the low-light visible image. An image fusion method using infrared and visible light to supplement light interactively is proposed. It performs intrinsic image decomposition on the input infrared and visible light images respectively and then fuses the illumination components and material components of the two types of images respectively. Further, to better meet the needs of image fusion, an improved variational model for the decomposition of image intrinsics is proposed. Experimental results demonstrate the effectiveness of the proposed model with its algorithm. Compared with the state-of-the-art methods, the significant advantage of the proposed method is that it has better fusion accuracy and visual perception at the same time, and the comprehensive metric evaluation is the best (9.2% performance improvement). The code is available at https://github.com/skyworkds/IFICI. • An improved intrinsic image decomposition suitable for image fusion is proposed. • A image fusion method based on interactive compensation illumination is proposed. • Experiments show that our method has obvious advantages. [ABSTRACT FROM AUTHOR]

• We first noticed the importance of similar and heterogeneous pixel gradients in feature extraction. • Introducing clustering algorithms into the field of image fusion to improve feature extraction capabilities. • Using source image gradients instead of the clustering base layer of traditional algorithms. Image fusion is performed to merge the information of different source images into one image. In this study, a fusion algorithm based on the pixel mean shift and source image gradient was proposed to make the background of the fused image clear and the target prominent. First, a pixel spatial average kernel function was designed to separate the image texture and the significant target during pixel density migration. In this study, a novel approach is presented that distinguishes itself from other methods in two distinct ways. Firstly, it segregates the texture details of dissimilar pixel regions and captures the texture characteristics of analogous pixel regions. Secondly, by utilizing the discrepancy between the minimum and maximum values of infrared and visible images, the proposed technique incorporates the source graph into the fusion process, thereby improving the precision of the information. Additionally, the feature reconstruction function was designed based on the layer features, which could adaptively concentrate the source image information on one image and further improve the clarity and information retention of the fused image. Finally, the results of the qualitative and quantitative comparison of our proposed method with other state-of-the-art methods available, obtained by applying them to public datasets, demonstrated the advantages of our algorithm. Our results retained more appearance details and significantly more target information. [ABSTRACT FROM AUTHOR]

Blind image denoising and edge-preserving are two primary challenges to recover an image from low-level vision to high-level vision. Blind denoising requires a single denoiser can denoise images with any intensity of noise, and it has practical utility since accurate noise levels cannot be acquired from realistic images. On the other hand, edge preservation can provide more image features for subsequent processing which is also important for the denoising. In this paper, we propose a novel blind universal image denoiser to remove synthesis and realistic noise while preserving the image texture. The denoiser consists of noise network and prior network parallelly, and then a fusion block is used to give the weight between these two networks to balance computation cost and denoising performance. We also use the Non-subsampled Shearlet Transform (NSST) to enlarge the size of receptive field to obtain more detailed information. Extensive denoising experiments on synthetic images and realistic images show the effectiveness of our denoiser. [ABSTRACT FROM AUTHOR]

• Multi-block data analysis was used for beef quality assessment. • Different parts of beef were evaluated by HSI, 1H NMR and quality parameters datasets. • ComDim shows Clustering results, contribution of data blocks and their relationships. Several factors affect the quality of beef. In the field of chemometrics, multi-block data analysis methods are useful for examining multiple sources of information from a sample. This study focuses on the application of ComDim, a multi-block data analysis method, to evaluate beef from different parts of hyperspectral spectrum and image texture information, 1H NMR fingerprints, quality parameters and electronic nose. Compared to principal component analysis (PCA) methods based on low-level data fusion, ComDim is more efficient and powerful, because it reveals the relationships between the methods and techniques studied, as well as the variability of beef quality across multiple metrics. The quality and metabolite composition of beef tenderloin and hindquarters were differentiated, with low L* value and high shear tenderloin distinguished from hindquarters with opposite characteristics. The proposed strategy demonstrates that ComDim approach can be used to characterize samples when different techniques describe the same set of samples. [ABSTRACT FROM AUTHOR]

• New texture indices were constructed by texture metrics and effectively used to estimate LAI of maize. • The potential of UAV-based thermal infrared sensor for estimating LAI was proved. • The combination of UAV-based multispectral and thermal infrared data improved the estimation accuracy of LAI. • The RFR model had the strong robustness and achieved the best performance on LAI estimation. Crop leaf area index (LAI) is one of the important indicators to evaluate crop growth and guide field management, and can be used to predict crop yield. Spectral and thermal information extracted by multispectral (MS) and thermal infrared (TIR) sensors mounted on an unmanned aerial vehicle (UAV) can be used for LAI estimation. Image texture is sensitive to the changes in crop surface grayscale or color characteristics, and can be combined with spectral and thermal information to estimate the LAI. But single texture metric has limitations in LAI estimation. Therefore, the purpose of this study is to construct new texture indices based on texture metrics extracted from MS and TIR images, and combined spectral and thermal information to enhance the estimation accuracy of maize LAI. Three replicates of maize experiments under different irrigation treatments were conducted in 2020. The MS and TIR sensors were mounted on a UAV to acquire maize canopy images during critical growth stages and acquire field LAI value of samples synchronously. The LAI estimation models were established using MS data, TIR data, as well as their combination. These models were constructed by Back Propagation Neural Network (BPNN), Partial least squares regression (PLSR), and Random Forest Regression (RFR). Finally, the performance of LAI estimation models was evaluated by the coefficient of determination (R2), root mean square error (RMSE) and relative root mean square error (rRMSE). Results shown that: (i) Among the eight kinds of texture metrics extracted from MS and TIR images, the texture metric mean (MEA) has the best performance. Compared with single texture metrics, texture indices constructed by different metrics has stronger correlation with LAI. (ii) Adding texture indices to estimation models significantly improved model accuracy, especially multispectral three-texture index(MS-TTI) has higher LAI estimation potential than thermal infrared three-texture index(TIR-TTI). (iii) Compared with the use of MS or TIR data alone, the estimation model constructed by combining MS data and TIR data have better performance. The best estimation model obtained by the RFR method (R2 = 0.862, RMSE = 0.246 and rRMSE = 10.20 %) further improved the LAI estimation of maize, with R2 increasing by 6.55 % and 14.48 %, respectively. In conclusion, the combination of MS and TIR data can effectively improve the estimation accuracy of maize LAI, and also provide a feasible method for monitoring crop growth. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT) system for oil field production, oil-well pump leakage is one of the most challenging faults to detect. Once the pump leakage fault occurs, it will severely impact crude oil production. Therefore, accurate diagnosis of oil-well pump leakage faults is highly necessary. However, achieving accurate diagnosis through existing artificial intelligence methods remains a challenge due to the prolonged and progressive nature of oil-well pump leakage faults. This paper proposes a novel methodology for diagnosing oil-well pump leakage. Specifically, we propose a time-series data transformation method that ingeniously transforms difficult-to-identify pump leakage long time-series data variation features into image pixel-level texture features, and based on the characteristics of differences in image texture features under different states, we propose the structure of convolutional residual blocks with attributes and spatial attention for oil-well pump leakage fault diagnosis, which enables more accurate fault diagnosis by assigning more attention weights to extract valuable fault information. Through extensive experimentation, we found that the diagnosis process proposed in this paper outperforms sequence models such as LSTM and GRU, achieving a diagnostic accuracy of 98.36% for oil-well pump leakage faults. Moreover, it leads to a significant improvement of 19.40% and 10.36% in accuracy compared to conventional CNN and ResNet models, respectively. Therefore, in the actual production of oilfields, this method can effectively meet the detection requirements for oil-well pump leakage. • A multivariate time series transformation method for oil well sensor time series data is proposed. • Transformation of time series data into texture features of the image allows for better differentiation of oil well pumps operating states. • An oil-well pump leakage fault diagnosis model with convolutional residual blocks with Attribute and Spatial attention is proposed. [ABSTRACT FROM AUTHOR]

• Pavement raveling detection based on image processing is proposed. • Texture analysis techniques are employed for feature extraction. • Two scenarios of GLCM and LBP-GLCM are utilized in the feature extraction stage. • XGBoost classifier is employed for the classification of raveling and no-raveling images. • The results of LBP-GLCM feature extraction achieved better performance for raveling detection. The raveling of asphalt pavement is the primary cause of decreasing road safety, comfort, and service life. Because of the asphalt's complex texture, automatic raveling detection from image samples is a challenging operation. In this study, a computer vision technique, based on image texture features, for automatic detection of asphalt pavement raveling is proposed and verified. Two scenarios are taken into account for feature extraction. First, texture features from images are extracted using the traditional GLCM (Gray-Level Co-occurrence Matrix) algorithm. Second, the images are subjected to LBP (Local Binary Pattern) and then GLCM is employed to extract texture features. Utilizing the eXtreme Gradient Boost (XGBoost) technique, two models are built using the mentioned feature extraction scenarios and then compared. The results indicate that compared to the first scenarios prediction performance (Accuracy, Precision, Recall, and F1-Score are all approximately equal to %81), the second feature extraction scenario can offer higher prediction performance (Accuracy, Precision, Recall, and F1-Score are all approximately equal to %97). In order to demonstrate the model's generalizability, a separate dataset is tested. Due to the acceptable performance values for this dataset (with more than %97 in terms of Accuracy, Precision, Recall, and F1-Score), the suggested model can be beneficial for transportation agencies to enhance the efficiency of road inspection activities. [ABSTRACT FROM AUTHOR]