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Under long-term synergy effect of multi-factors, especially the thermal stress, insulating paper will be degraded and its insulation performance will decline due to carbonization and degradation of cellulose. This paper presents an optical approach for aging feature extraction of the insulating paper, where one of the image processing methods called texture analysis is utilized. By conducting laboratory accelerated thermal aging tests, insulating paper samples with different aging conditions for both Nomex and Kraft, evaluated with the aging time, are prepared. After taking optical microscopic images of insulating paper samples belong to different aging groups, up to 14 texture features are extracted using the gray-level co-occurrence matrix (GLCM). With different feature selection methods applied, several of them are finally selected to represent the aging condition of insulating. Numerical tests with both supervised and unsupervised algorithms, as well as a linear regression method verifies the validity of these features in characterizing the aging condition of the insulating paper. [ABSTRACT FROM PUBLISHER]

Sessile drying droplets manifest distinct morphological patterns, encompassing diverse systems, viz., DNA, proteins, blood, and protein-liquid crystal (LC) complexes. This study employs an integrated methodology that combines drying droplet, image texture analysis (features from First Order Statistics, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, and Gray Level Dependence Matrix), and statistical data analysis (Generalized Additive Modeling and K-means clustering). It provides a comprehensive qualitative and quantitative exploration by examining LC-protein droplets at varying initial phosphate buffered concentrations (0x, 0.25x, 0.5x, 0.75x, and 1x) during the drying process under optical microscopy with crossed polarizing configuration. Notably, it unveils distinct LC-protein textures across three drying stages: initial, middle, and final. The Generalized Additive Modeling (GAM) reveals that all the features significantly contribute to differentiating LC-protein droplets. Integrating the K-means clustering method with GAM analysis elucidates how textures evolve through the three drying stages compared to the entire drying process. Notably, the final drying stage stands out with well-defined, non-overlapping clusters, supporting the visual observations of unique LC textures. Furthermore, this paper contributes valuable insights, showcasing the efficacy of drying droplets as a rapid and straightforward tool for characterizing and classifying dynamic LC textures. [ABSTRACT FROM AUTHOR]

The composition statistics of gynecological microscopic images play an important role in the diagnosis and treatment of gynecology. However, current methods mainly rely on manual analysis of microscopic images, which is time-consuming and subjective. Therefore, the purpose of this paper is to develop a gynecological microscopic image component statistics method based on optical medical equipment and medical intelligent system to improve the accuracy and efficiency of gynecological diagnosis. The method of combining optical medical equipment and medical intelligent system is used to realize the automatic analysis of gynecological microscopic images. A large number of gynecological microscopic image samples were collected, and image processing technology was used to preprocess the images. Then computer vision algorithm and machine learning method are used to extract and classify the image features. Finally, the new gynecological microscopic images were analyzed statistically according to the trained model. The experimental results show that the method presented in this paper has a good effect on the composition statistics of gynecological microscopic images. Compared with the traditional manual analysis method, this method can analyze gynecological microscopic images more quickly and accurately, and provide effective diagnostic results. [ABSTRACT FROM AUTHOR]

Splicing the shredded paper means the technology that, according the paper, which has been shredded to design a particular Algorithm to splice and recover the original paper. This paper introduced the algorithm of splicing the shredded paper based on the matching to texture feature. By means of this algorithm, we modeled for the problem of splicing the shredded paper and solved it. And, we used the algorithm to splice both pieces of English shredded paper and Chinese shredded paper. The recovered paper had proved the accuracy of the algorithm. [ABSTRACT FROM AUTHOR]

Assistive medical image classifiers can greatly reduce the workload of medical personnel. However, traditional machine learning methods require large amounts of well-labeled data and long learning times to solve medical image classification problems, which can lead to high training costs and poor applicability. To address this problem, a novel unsupervised breast cancer image classification model based on multiscale texture analysis and a dynamic learning strategy for mammograms is proposed in this paper. First, a gray-level cooccurrence matrix and Tamura coarseness are used to transfer images to multiscale texture feature vectors. Then, an unsupervised dynamic learning mechanism is used to classify these vectors. In the simulation experiments with a resolution of 40 pixels, the accuracy, precision, F1-score and AUC of the proposed method reach 91.500%, 92.780%, 91.370%, and 91.500%, respectively. The experimental results show that the proposed method can provide an effective reference for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Multi-image steganography, a technique for concealing information within multiple carrier mediums, finds remote sensing images to be particularly apt carriers due to their complex structures and abundant texture data. These characteristics bolster the resilience against steganalysis and enhance steganographic capacity. The efficacy of multi-image steganography hinges on the diplomatic strategy of cover selection and the meticulous allocation of the payload. Nevertheless, the majority of current methods, which are empirically formulated, predominantly focus on the texture complexity of individual images, thereby potentially undermining overall security. This paper introduces a security-oriented approach to steganographic payload allocation for multiple remote sensing images aimed at fortifying the security of multi-image steganography. Our primary contributions include employing a steganalysis pre-trained network to quantify texture complexity in remote sensing cover images, directly correlating it with security. Additionally, we have developed an adaptive payload allocation strategy for multiple images, which embeds a payload proximate to each image's maximal steganographic capacity while concurrently ensuring the security of the embedding process. Experimental results corroborate that our methodology excels in cover selection and payload allocation and achieves better undetectability against modern steganalysis tools. [ABSTRACT FROM AUTHOR]

The binary pattern family has drawn wide attention for texture representation due to its promising performance and simple operation. However, most binary pattern methods focus on local neighborhoods but ignore center pixels. Even if some studies introduce the center based sub-pattern to provide complementary information, existing center based sub-patterns are much weaker than other local neighborhood based subpatterns. This severe unbalance limits the classification performance of fusion features significantly. To alleviate this problem, this paper designs a multiple threshold center pattern (MTCP) to provide a more discriminative and complementary local texture representation with a compact form. First, a multiple threshold encoding strategy is designed to encode the center pixel that generates three 1-bit binary patterns. Second, it adopts a compact multi-pattern encoding strategy to combine them into a 3-bit MTCP. Furthermore, this paper proposes a completed multiple threshold encoding pattern by fusing the MTCP, local sign pattern, and local magnitude pattern. Comprehensive experimental evaluations on three popular texture classification benchmarks confirm that the completed multiple threshold encoding pattern achieves superior texture classification performance. [ABSTRACT FROM AUTHOR]

Correct classification of skin lesions is a key step in skin cancer screening, which requires high accuracy and interpretability. This paper proposes a multimodal method for differentiating various clinical forms of basal cell carcinoma and benign neoplasms that includes machine learning. This study was conducted on 37 neoplasms, including benign neoplasms and five different clinical forms of basal cell carcinoma. The proposed multimodal screening method combines diffuse reflectance spectroscopy, optical coherence tomography and high-frequency ultrasound. Using diffuse reflectance spectroscopy, the coefficients of melanin pigmentation, erythema, hemoglobin content, and the slope coefficient of diffuse reflectance spectroscopy in the wavelength range 650–800 nm were determined. Statistical texture analysis of optical coherence tomography images was used to calculate first- and second-order statistical parameters. The analysis of ultrasound images assessed the shape of the tumor according to parameters such as area, perimeter, roundness and other characteristics. Based on the calculated parameters, a machine learning algorithm was developed to differentiate the various clinical forms of basal cell carcinoma. The proposed algorithm for classifying various forms of basal cell carcinoma and benign neoplasms provided a sensitivity of 70.6 ± 17.3%, specificity of 95.9 ± 2.5%, precision of 72.6 ± 14.2%, F1 score of 71.5 ± 15.6% and mean intersection over union of 57.6 ± 20.1%. Moreover, for differentiating basal cell carcinoma and benign neoplasms without taking into account the clinical form, the method achieved a sensitivity of 89.1 ± 8.0%, specificity of 95.1 ± 0.7%, F1 score of 89.3 ± 3.4% and mean intersection over union of 82.6 ± 10.8%. [ABSTRACT FROM AUTHOR]

Small intestinal stromal tumor (SIST) is a common gastrointestinal tumor. Currently, SIST diagnosis relies on clinical radiologists reviewing CT images from medical imaging sensors. However, this method is inefficient and greatly affected by subjective factors. The automatic detection method for stromal tumors based on computer vision technology can better solve these problems. However, in CT images, SIST have different shapes and sizes, blurred edge texture, and little difference from surrounding normal tissues, which to a large extent challenges the use of computer vision technology for the automatic detection of stromal tumors. Furthermore, there are the following issues in the research on the detection and recognition of SIST. After analyzing mainstream target detection models on SIST data, it was discovered that there is an imbalance in the features at different levels during the feature fusion stage of the network model. Therefore, this paper proposes an algorithm, based on the attention balance feature pyramid (ABFP), for detecting SIST with unbalanced feature fusion in the target detection model. By combining weighted multi-level feature maps from the backbone network, the algorithm creates a balanced semantic feature map. Spatial attention and channel attention modules are then introduced to enhance this map. In the feature fusion stage, the algorithm scales the enhanced balanced semantic feature map to the size of each level feature map and enhances the original feature information with the original feature map, effectively addressing the imbalance between deep and shallow features. Consequently, the SIST detection model's detection performance is significantly improved, and the method is highly versatile. Experimental results show that the ABFP method can enhance traditional target detection methods, and is compatible with various models and feature fusion strategies. [ABSTRACT FROM AUTHOR]

As one of fundamental texture classification methods, LBP-based descriptors have attracted considerable attention due to the efficiency, simplicity, and high performance. However, most of binary pattern methods cannot effectively capture the texture information with scale changes. Inspired by this, this paper proposes a multi-scale threshold integration encoding strategy for texture classification. The essence of this strategy is to introduce the multi-scale local texture information in the view of thresholding. Based on this, we propose the local multi-scale center pattern, local multi-scale sign pattern, and local multi-scale magnitude pattern to extract and describe the multi-scale local texture information. Then, the three sub-patterns are jointly combined to generate the final descriptor for texture classification tasks. The experimental results on three popular texture databases significantly demonstrate that the proposed texture descriptor is very discriminative and powerful for visual texture classification tasks. [ABSTRACT FROM AUTHOR]

Ice aprons are defined as very small ice bodies covering steep rock slopes. They have only been the subject of increased scientific interest for a few years, despite the fact that they are a condition for mountaineering and obvious elements in the high-alpine landscapes. However, very little is known about their distribution, evolution and physical characteristics. In this paper, we review the existing knowledge on ice aprons, which have almost exclusively been investigated in the Mont-Blanc massif, Western Alps. We supplement this review with novel results from recent surveys of ice aprons. We used a wide array of methodologies, from remote sensing (multi-source imagery) to in situ (stakes and thermal monitoring) and laboratory (radiocarbon dating and texture analysis) glaciological investigations. In the Mont-Blanc massif, ice aprons occupy 4.2 km2 within the alpine permafrost zone. Temperature measured at the ice–rock interface is indeed largely negative. Thinness of ice aprons coupled with the cold context implies a quasi-stationary shear regime without basal Sliding. Only ice at the surface can possibly melt in warm periods. After a shrinking period from the end of the Little Ice Age to the mid-to-late-1960s, ice aprons experienced a short period of expansion, followed by an accelerated shrinkage since the beginning of the 21st century. This shrinkage now favours rockfall triggering and poses a serious threat to a glaciological heritage since ice aprons host several-thousand-year-old ice. Finally, we synthesize this information to assess the existing definition of ice aprons, and propose some future research directions. [ABSTRACT FROM AUTHOR]

The objective of this paper is to develop a Non Destructive Testing (NDT) method for the detection and classification of defects in composite materials at a micro level and to devise methodologies to analyse the corrosion resistance behavior using Scanned Electron Microscope (SEM) imagery. The defects on the Stainless Steel – Molybdenum (SS-Mo) Nanocomposite coating is estimated from their Scanning Electron Micrographs by using Image Processing algorithms. For this, the SS-Mo Nano Composite coatings are fabricated using a DC magnetron sputtering process using an indigenously prepared sputtering target. Depositions are carried out on Glass substrate for the evaluation of structural, morphological, chemical composition and corrosion resistance of the coatings prepared under different conditions (deposition of SS at 300°C and RT (Room Temperature); deposition of SS + Mo at 300°C and RT). The structural and compositional analysis performed with X-ray Diffraction (XRD) and Energy-Dispersive X-ray spectroscopy (EDX) has confirmed the formation of Stainless Steel Molybdenum Composite, when the deposition is at 300°C. The SS-Mo composite deposited at 300°C is also observed to yield high corrosion resistance of the order 0.058 mm/year. A novel texture – morphology based image feature descriptor has been proposed for corrosion resistance to evaluate the composite material in a Non-destructive manner. The analysis of SEM image of the developed coatings using the proposed feature along with machine learning algorithm reveals the superior property for SS-Mo coatings deposited at 300°C which is also demonstrated by the laboratory experiments. [ABSTRACT FROM AUTHOR]

In medical research and clinical applications, the utilization of MRI datasets from multiple centers has become increasingly prevalent. However, inherent variability between these centers presents challenges due to domain shift, which can impact the quality and reliability of the analysis. Regrettably, the absence of adequate tools for domain shift analysis hinders the development and validation of domain adaptation and harmonization techniques. To address this issue, this paper presents a novel Domain Shift analyzer for MRI (DSMRI) framework designed explicitly for domain shift analysis in multi-center MRI datasets. The proposed model assesses the degree of domain shift within an MRI dataset by leveraging various MRI-quality-related metrics derived from the spatial domain. DSMRI also incorporates features from the frequency domain to capture low- and high-frequency information about the image. It further includes the wavelet domain features by effectively measuring the sparsity and energy present in the wavelet coefficients. Furthermore, DSMRI introduces several texture features, thereby enhancing the robustness of the domain shift analysis process. The proposed framework includes visualization techniques such as t-SNE and UMAP to demonstrate that similar data are grouped closely while dissimilar data are in separate clusters. Additionally, quantitative analysis is used to measure the domain shift distance, domain classification accuracy, and the ranking of significant features. The effectiveness of the proposed approach is demonstrated using experimental evaluations on seven large-scale multi-site neuroimaging datasets. [ABSTRACT FROM AUTHOR]

This paper investigates deep learning (DL)-based semantic segmentation of textured mosaics. Existing popular datasets for mosaic texture segmentation, designed prior to the DL era, have several limitations: (1) training images are single-textured and thus differ from the multi-textured test images; (2) training and test textures are typically cut out from the same raw images, which may hinder model generalization; (3) each test image has its own limited set of training images, thus forcing an inefficient training of one model per test image from few data. We propose two texture segmentation datasets, based on the existing Outex and DTD datasets, that are suitable for training semantic segmentation networks and that address the above limitations: SemSegOutex focuses on materials acquired under controlled conditions, and SemSegDTD focuses on visual attributes of textures acquired in the wild. We also generate a synthetic version of SemSegOutex via texture synthesis that can be used in the same way as standard random data augmentation. Finally, we study the performance of the state-of-the-art DeepLabv3+ for textured mosaic segmentation, which is excellent for SemSegOutex and variable for SemSegDTD. Our datasets allow us to analyze results according to the type of material, visual attributes, various image acquisition artifacts, and natural versus synthetic aspects, yielding new insights into the possible usage of recent DL technologies for texture analysis. Article highlights: We propose two texture segmentation datasets that address the limitations of existing texture segmentation datasets. Experiments with materials and attributes shed a new light on recent deep learning technologies for texture analysis. Our results also suggest that synthetic textures can be used for data augmentation to improve segmentation results. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop an analytical model to predict different texture patterns obtained with Radial Ultrasonic Vibration Assisted Machining (RUVAM) and estimate the dimensions of their characteristic features. It is also validated experimentally by performing advanced surface metrology on C45 specimens. The model is based on considering the machining of craters along radial tracks as the main texturing mechanism due to the change in the effective depth of cut provoked by the ultrasonic vibration of the insert. Mathematical formulae composing the model are then developed by considering how the machined craters in those tracks interact radially and axially, describing the final texture as the result of a multidirectional geometrical interaction. To predict how those interactions take place depending on the manufacturing parameters chosen, threshold values for the feed rate and the depth of cut are proposed. They also allow the validation of the formulae that estimate the different geometrical descriptors of craters (radial and axial heights and widths). According to the different combinations of parameters, the model succeeds in proposing diverse potential texturing scenarios. In the second part of this paper, a full factorial design is planned and applied on C45 specimens to validate the results with a RUVAM tool of 40 kHz and a 1 μm-amplitude. The plan includes three factors (insert nose radius, depth of cut and feed rate). Their levels are defined according to the threshold values for feed and depth of cut defined by the model to reproduce different textures. Results prove that the proposed threshold values allow a very good assessment of the resulting microtexturing structures on the surfaces. The measured values of widths and heights are in agreement with the values predicted by the model. With this technique, the heights of the textured surfaces span from 5 to 400 μm and widths from 45 to 720 μm, depending on the selected parameters. In the radial direction, the vibratory motion of the insert is robustly imprinted on the material, with radial heights and widths of around 2 μm and 39.6 μm, agreeing with the model. In overall, errors comparing the experimental measurements and the values predicted by the model rise up to 9%, although this error can reach 15% at some cases on radial descriptors because of their higher sensitivity to the F-filtering. A last texturing mechanism can be attributed to RUVAM related to the shaping of craters, as different oval permeters can be obtained by combining the parameters adequately. All these results lead to potential future studies related with the functional implications of all the detected textures. • An analytical model to predict microtexturing through RUVAM was developed and validated. • RUVAM microtexturing is the result of distributing craters in the radial and axial directions. • The model explains RUVAM microtexturing as a combination of different mechanisms. • The triplet nose radius – depth of cut – feed govern resulting textures given a fixed RUVAM tool. • Different RUVAM texture patterns can be obtained with one type of insert. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to study the influence of dimple textures on the friction and wear properties of tapered roller bearings (TRBs) with many pattern parameters, e.g. diameter, depth and area density under starved lubrication. Design/methodology/approach: The pattern parameters include the dimple diameter (D; 60, 100 and 200 µm), dimple depth (H; 5, 10 and 20 µm) and area density (S; 6%, 12% and 24%). Dimples were fabricated on the outer ring (OR) of TRBs using a laser marking machine. The tribological properties of dimple-textured TRBs under starved lubrication were studied on a vertical universal friction wear tester with special friction pairs. The effect mechanisms of dimple textures on the tribological properties of TRBs are presented and summarized through experiments and discussions. Findings: When dimple-textured TRBs revolve under starved lubrication, the average coefficients of friction (ACOF) and wear losses are markedly lower than those of non-textured bearings. S has the greatest influence on the COF curve. When D is 100 µm and S is 24%, the ACOF and wear losses are both lowest, i.e. 0.00426 and 0.51 mg, respectively. Under the same test conditions, compared with the non-textured group, its COF and wear loss decreased by 35.6% and 62.5%, respectively. Originality/value: This work provides a useful reference for the research on the raceways of textured TRBs. [ABSTRACT FROM AUTHOR]

This paper presents a study on the effectiveness of texture analysis of remote sensing imagery depending on the type and spatial resolution of the source image. The study used the following image types: near-infrared band, red band, first principal component, second principal component and normalized difference vegetation index images of pixel size from 2 m to 30 m, generated from a multispectral WorldView-2 image. The study evaluated the separability of the selected pairs of the following land cover classes: bare soil, low vegetation, coniferous forest, deciduous forest, water reservoirs, built-up areas. The tool used for texture analysis was granulometric analysis based on morphological operations—one of less popular methods which, however, as demonstrated by previous studies, shows high effectiveness in separating classes of different texture. The conducted study enabled researchers to evaluate the significance of image type and resolution for visibility of texture in the image and the possibility of using texture to differentiate between classes. The obtained results showed that there is no single, universal combination of conditions of texture analysis, which would be the best from the point of view of all classes. For most of the analyzed pairs of classes, the best results were obtained for the highest spatial resolution of the image (2–3 m), but the class of built-up areas stands out in this comparison—the best distinction was obtained with the average spatial resolution (10–15 m). Research has also shown that there is no single type of image that is universally the best basis for texture analysis. While for the majority of classes the image of the first principal component was the best, for the class of built-up areas it was the image of the red channel. [ABSTRACT FROM AUTHOR]

Example-based texture synthesis is a fundamental topic of many image analysis and computer vision applications. Consequently, its representation is one of the most critical and challenging topics in computer vision and pattern recognition, attracting much academic interest throughout the years. In this paper, a new statistical method to synthesize textures is proposed. It consists in using two indexed random coefficients autoregressive (2D-RCA) models to deal with this problem. These models have a good ability to well detect neighborhood information. Simulations have demonstrated that the 2D-RCA models are very suitable to represent textures. So, in this work, to generate textures from an example, each original image is splitted into blocks which are modeled by the 2D-RCA. The proposed algorithm produces approximations of the obtained blocks images from the original image using the generalized method of moments (GMM). Different sizes of windows have been used. This study offers some important insights into the newly generated image. Satisfying obtained results have been compared to those given by well-established methods. The proposed algorithm outperforms the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Extensive network receptive field is key for unsupervised affine registration because instead of deformable registration that takes care of local subtleties, the affine registration is global so that the last layers need to see big patches of the organ‐in‐interest. To extend the network's receptive field, we need to go for deeper networks, which causes producing complex models. On the other hand, affine transformation is restricted by its low degree‐of‐freedom (DoF) where larger models increasingly develop the hazard of overfitting. To worsen the situation, the regularizer module cannot be applied to the affine transformation with such a restricted DoF. In this paper, we propose a differentiable computational layer to convert the affine transformation outputted by the network to its corresponding dense displacement field. Such an affine‐to‐field layer enables us to apply different regularization terms on the outputted transformation in order to avoid the overfitting phenomenon while deepening the network. The proposed approach was evaluated on an annotated hard multimodal dataset containing 1109 pairs of CT/MR images of the brain with different heterogeneity for example, variety in scanners, setups and resolutions. Based on the results, the proposed customized layer is fully successful to handle the overfitting for deeper networks that are able to produce richer transformations than the shallower networks from different evaluation metrics for example, in target registration error the proposed network with seven layers has a 13.3% (or 9.1 mm) improvement in performance. The implementation of the proposed customized affine‐to‐field layer in the Python, Keras package with the Tensorflow backend can be publically accessed via https://github.com/boveiri/Deep-coReg. [ABSTRACT FROM AUTHOR]

Since two years ago, the COVID-19 virus has spread strongly in the world and has killed more than 6 million people directly and has affected the lives of more than 500 million people. Early diagnosis of the virus can help to break the chain of transmission and reduce the death rate. In most cases, the virus spreads in the infected person's chest. Therefore, the analysis of a chest CT scan is one of the most efficient methods for diagnosing a patient. Until now, various methods have been presented to diagnose COVID-19 disease in chest CT-scan images. Most recent studies have proposed deep learning-based methods. But handcrafted features provide acceptable results in some studies too. In this paper, an innovative approach is proposed based on the combination of low-level and deep features. First of all, local neighborhood difference patterns are performed to extract handcrafted texture features. Next, deep features are extracted using MobileNetV2. Finally, a two-level decision-making algorithm is performed to improve the detection rate especially when the proposed decisions based on the two different feature set are not the same. The proposed approach is evaluated on a collected dataset of chest CT scan images from June 1, 2021, to December 20, 2021, of 238 cases in two groups of patient and healthy in different COVID-19 variants. The results show that the combination of texture and deep features can provide better performance than using each feature set separately. Results demonstrate that the proposed approach provides higher accuracy in comparison with some state-of-the-art methods in this scope. [ABSTRACT FROM AUTHOR]

Medical image segmentation is fundamental and essential for the analysis of medical images. Although prevalent success has been achieved by convolutional neural networks (CNN), challenges are encountered in the domain of medical image analysis by two aspects: 1) lack of discriminative features to handle similar textures of distinct structures and 2) lack of selective features for potential blurred boundaries in medical images. In this paper, we extend the concept of contrastive learning (CL) to the segmentation task to learn more discriminative representation. Specifically, we propose a novel patch-dragsaw contrastive regularization (PDCR) to perform patch-level tugging and repulsing. In addition, a new structure, namely uncertainty-aware feature re- weighting block (UAFR), is designed to address the potential high uncertainty regions in the feature maps and serves as a better feature re- weighting. Our proposed method achieves state-of-the-art results across 8 public datasets from 6 domains. Besides, the method also demonstrates robustness in the limited-data scenario. The code is publicly available at https://github.com/lzh19961031/PDCR_UAFR-MIShttps://github.com/lzh19961031/PDCR_UAFR-MIS. [ABSTRACT FROM AUTHOR]

The aim of this research was to develop an appropriate algorithm that can automatically detect and classify liver cancers. Digital image processing (DIP) provides different techniques for the segmentation and classification purposes. The sample was taken from 36 abdominal CT scans from Tikur Anbessa Specialized Hospital in DICOM stored in Addis Ababa. Semi-automatic techniques were used to segment liver images from the abdominal CT images then artificial neural network (ANN) was applied for classification of liver out of the given sample through nine texture features into 12 normal and 24 abnormal images. Then, liver tumors were segmented from the 24 abnormal liver images using the histogram-based Otsu method. The classification of liver tumors was performed by using ANN through six morphological features into 12 benign and 12 malignant. The classification results are presented in this paper using the confusion matrix, which shows 95.8 % maximum accuracy rate of tumor classification. However, the performance of the classifier could be improved by more sample images. The outcome of this work may help radiologists to identify tumors at early stages and to classify them as benign or malignant. The methodology proposed here might reduce the fatality rate of liver cancers in Ethiopia. [ABSTRACT FROM AUTHOR]

Pneumonia is one of the diseases that seriously endangers human health, and it is also the leading cause of death of children under the age of five in China. The most commonly used imaging examination method for radiologists is mainly based on chest X-ray images. Still, imaging errors often result during imaging examinations due to objective factors such as visual fatigue and lack of experience. Therefore, this paper proposes a feature fusion model, FC-VGG, based on the fusion of texture features (local binary pattern LBP and directional gradient histogram HOG) and depth features. The model improves model performance by adding detailed information in texture features to the convolutional neural network while making the model more suitable for clinical use. We input the X-ray image with texture features into the modified VGG16 model, C-VGG, and then add the Add fusion method to C-VGG for feature fusion so that FC-VGG is obtained, so FC-VGG has texture features detailed information and abstract information of deep features. Through experiments, our model has achieved 92.19% accuracy in recognizing children's pneumonia images, 93.44% average precision, 92.19% average recall, and 92.81% average F1 coefficient, and the model performance exceeds existing deep learning models and traditional feature recognition algorithms. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to study a new liquid-phase assisted texture treatment method to improve the tribological properties of 304 stainless steel. Design/methodology/approach: Three groups of textured type (KY, KJ and YJ) were prepared on 304 stainless steel surface using laser circular and cross scanning method in air and liquid assisted condition. The surface morphology and element content of test samples were measured with scanning electron microscope, energy spectrum. Then, the tribological test was carried out using MWF-500 reciprocating friction and wear testing machine under dry and oil lubrication condition. Findings: The experimental results showed that the textured surface of laser processing in air was obviously blackened, and the oxygen content was increased from 16.9% to 24%. These cases did not occur on liquid-assisted laser textured surface, which induced a better wettability and surface texture processing quality. For friction test, the friction coefficient of cross-scanning textured surface prepared in assisted liquid (YJ) was the smallest. It is reduced by 55% in oil lubrication case compared to the original surface (YS). The cross-scanning textured surface prepared in air (KJ) was a little worse in friction coefficient and a little better in wear quantity than the cross-scanning textured surface prepared in assisted liquid (YJ). It is indicated that the laser processing surface with assisted liquid has obvious advantages in surface texture quality and interfacial tribological property. The main reason is that the assisted liquid plays a role in cooling and protecting action of the machined surface. The bubbles, generated at the solid–liquid interface because of the laser heat effect, scatter the laser beam and carry out the processed melt meanwhile. The lubricating medium is easier to penetrate and store in the contact interfaces because of the higher surface textured performance and wettability. Originality/value: The main contribution of this work is in providing a new surface texture processing method that has a better surface micropits quality and interfacial tribology regulation ability. [ABSTRACT FROM AUTHOR]

To solve the long-tail problem and improve the testing efficiency for autonomous navigation systems of unmanned surface vehicles (USVs), a visual image-based navigation scene complexity perception method is proposed. In this paper, we intend to accurately construct a mathematical model between navigation scene complexity and visual features from the analysis and processing of image textures. First, the typical complex elements are summarized, and the navigation scenes are divided into four levels according to whether they contain these typical elements. Second, the textural features are extracted using the gray level cogeneration matrix (GLCM) and Tamura coarseness, which are applied to construct the feature vectors of the navigation scenes. Furthermore, a novel paired bare bone particle swarm clustering (PBBPSC) method is proposed to classify the levels of complexity, and the exact value of the navigation scene complexity is calculated using the clustering result and an interval mapping method. By comparing different methods on the classical and self-collected datasets, the experimental results show that our proposed complexity perception method can not only better describe the level of complexity of navigation scenes but also obtain more accurate complexity values. [ABSTRACT FROM AUTHOR]

To solve the long-tail problem and improve the testing efficiency for autonomous navigation systems of unmanned surface vehicles (USVs), a visual image-based navigation scene complexity perception method is proposed. In this paper, we intend to accurately construct a mathematical model between navigation scene complexity and visual features from the analysis and processing of image textures. First, the typical complex elements are summarized, and the navigation scenes are divided into four levels according to whether they contain these typical elements. Second, the textural features are extracted using the gray level cogeneration matrix (GLCM) and Tamura coarseness, which are applied to construct the feature vectors of the navigation scenes. Furthermore, a novel paired bare bone particle swarm clustering (PBBPSC) method is proposed to classify the levels of complexity, and the exact value of the navigation scene complexity is calculated using the clustering result and an interval mapping method. By comparing different methods on the classical and self-collected datasets, the experimental results show that our proposed complexity perception method can not only better describe the level of complexity of navigation scenes but also obtain more accurate complexity values. [ABSTRACT FROM AUTHOR]

Photon-counting spectral computed tomography (CT) is capable of material characterization and can improve diagnostic performance over traditional clinical CT. However, it suffers from photon count starving for each individual energy channel which may cause severe artifacts in the reconstructed images. Furthermore, since the images in different energy channels describe the same object, there are high correlations among different channels. To make full use of the inter-channel correlations and minimize the count starving effect while maintaining clinically meaningful texture information, this paper combines a region-specific texture model with a low-rank correlation descriptor as an a priori regularization to explore a superior texture preserving Bayesian reconstruction of spectral CT. Specifically, the inter-channel correlations are characterized by the low-rank representation, and the inner-channel regional textures are modeled by a texture preserving Markov random field. In other words, this paper integrates the spectral and spatial information into a unified Bayesian reconstruction framework. The widely-used Split-Bregman algorithm is employed to minimize the objective function because of the non-differentiable property of the low-rank representation. To evaluate the tissue texture preserving performance of the proposed method for each channel, three references are built for comparison: one is the traditional CT image from energy integration detection. The second one is spectral images from dual-energy CT. The third one is individual channels images from custom-made photon-counting spectral CT. As expected, the proposed method produced promising results in terms of not only preserving texture features but also suppressing image noise in each channel, comparing to existing methods of total variation (TV), low-rank TV and tensor dictionary learning, by both visual inspection and quantitative indexes of root mean square error, peak signal to noise ratio, structural similarity and feature similarity. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present an analysis of several 3 D texture parameters for the entire wear scars obtained in severe regime, on a four-ball tester. The aim of this analysis is to correlate the tribological parameter as wear scar diameter to texture parameters. Design/methodology/approach: Tested lubricants were rapeseed oil, rapeseed oil additivated with 1% Wt nano TiO2 and rapeseed oil additivated with 1%Wt nano ZnO. The severe regime was applied for 1400 rpm and for loads increasing in steps of 50 N, from 500 to 900 N. Several analyzed roughness parameters (height parameters and functional ones) could be related to the evolution of a wear parameter, the wear scar diameter. Comparing the values for neat rapeseed oil and additivated variants, the texture parameters allow for evaluating if the additives protect or not the worn surfaces. Findings: Measurements pointed out two groups of roughness parameters: one that has an evolution depending on wear scar diameter (WSD) and load (Sa, St, functional parameters) and one including Ssk that has shown no dependence on load and WSD. Also, the functional parameters Spk and Svk follow in a similar manner the wear parameter, WSD, but Sk is the least dependent on load. For the highest load, amplitude parameters such as Sa and St are following the tendency of WSD. Each lubricant has its particular correlation between wear parameters and texture quality, expressed by the help of a set of roughness parameters. Research limitations/implications: Such studies help tribologists to rank lubricants based on a combined analysis with wear parameters and texture parameters. Practical implications: The results allow for evaluating new formulated lubricants. Originality/value: The study on the quality on worn surfaces introduces the original idea of analyzing the entire wear scar surface (approximated by an ellipse with the axes as those experimentally measured) by the help of a set of 3 D roughness parameters. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the influence of sinusoidal texture (ST) with different morphology parameters on the corresponding tribological effects. Design/methodology/approach: The STs with different amplitudes, ranging from 0.05 to 0.3 mm, and frequencies, ranging from 5 to 17.5, are fabricated using nanosecond pulsed laser equipment. The friction experiments and the finite element analysis method are combined to investigate the tribological properties, under dry friction conditions. Findings: Test results show that when the amplitude is 0.15 mm and frequency is 10, ST surface has the lowest friction coefficient of 0.373, and exhibits great anti-friction effect. It also possesses a complete texture edge after friction. The friction reduction effect of ST with larger or smaller amplitude and frequency is worse. Originality/value: The results of this study can provide a guidance for the design optimization of ST of reciprocating sliding contact surfaces, under dry friction conditions. [ABSTRACT FROM AUTHOR]

Blind noise-level estimation (NLE) is a fundamental issue in digital image processing. This paper provides a noise-level estimator for additive white Gaussian noise and multiplicative Gaussian noise using principal texture patches (PTPs). The proposed algorithm first identifies the principal texture of the noisy image by using the principal component analysis, and then, it chooses PTPs to calculate the noise level. The major contributions of this paper toward addressing the challenges in the NLE literature include: 1) analyzing the nonlinear relationship between the smallest eigenvalue, true noise level, number of chosen patches, and block size; 2) extracting PTPs; and 3) computing noise level using PTPs. The abundant experimental results show that the proposed method works well for various natural images over a large range of noise levels and it performs well for multiplicative noise. Compared with some state-of-the-art approaches, our method has the best performance and a faster running speed. [ABSTRACT FROM AUTHOR]

Methodology for analysing textural properties of sugar beet roots in the laboratory has previously been established. It has been shown to be reliable and of value in exploring relationships between textural properties, damage rates, and storability of varieties. In this paper, a methodology for the assessment of textural properties in-field, prior to harvest, using an inexpensive handheld penetrometer is examined. Three sugar beet varieties were grown in Belgium, the Netherlands, and Sweden during 2019. Textural properties were assessed in-field with the handheld penetrometer 2, 1 and 0 months prior to harvest, and with the laboratory penetrometer directly after harvest. Comparison of the results showed generally strong correlations. A power analysis suggests a difference in mean Handheld Pressure of 0.10 MPa could be found significant within a large trial with a block design. The reliability of the handheld penetrometer was further assessed in the Swedish national variety trials over three years (2019-2021). Correlation coefficients of 0.86 and 0.94 were found between mean Handheld Pressure for 2019 and 2020, and 2020 and 2021 respectively. The handheld penetrometer can be applied as an economic means of quantifying differences in textural properties of sugar beet varieties. Clear operating procedure and training must exist. [ABSTRACT FROM AUTHOR]

Urban trees have the potential to mitigate some of the harm brought about by rapid urbanization and population growth, as well as serious environmental degradation (e.g. soil erosion, carbon pollution and species extirpation), in cities. This paper presents a novel urban tree extraction modelling approach that uses discrete laser scanning point clouds and object-based textural analysis to (1) develop a model characterised by four sub-models, including (a) height-based split segmentation, (b) feature extraction, (c) texture analysis and (d) classification, and (2) apply this model to classify urban trees. The canopy height model is integrated with the object-level local binary pattern algorithm (LBP) to achieve high classification accuracy. The results of each sub-model reveal that the classification of urban trees based on the height at 47.14 (high) and 2.12 m (low), respectively, while based on crown widths were highest and lowest at 22.5 and 2.55 m, respectively. Results also indicate that the proposed algorithm of urban tree modelling is effective for practical use. [ABSTRACT FROM AUTHOR]

Highlights • The paper presents a novel central pixel selection (CPS) strategy for LBP framework. • The proposed strategy can be applied to LBP and its variants. • The results of proposed CPS strategy are significant. Abstract Local binary pattern (LBP) has been successfully used in computer vision and pattern recognition applications, such as biomedical image analysis, remote sensing and image retrieval. However, the current LBP-based features, which assign a fixed sampling radius for all pixels in a single scale, completely ignore the fact that different central pixels actually have different local gray-value distributions and the proper sampling radius should be different for pixels. In this paper, we propose a novel and effective central pixel selection (CPS) strategy by using gradient information to classify central pixels of a texture image into different classes based on their local gray-value distributions. Then, we introduce this CPS strategy into the LBP framework and assign an adaptive sampling radius for each central pixel according to the class it belongs to. As a preprocessing step of LBP framework, this CPS strategy can also be integrated into any other LBP variants so as to extract more effective local texture features. Extensive experiments on five representative texture databases of Outex, UIUC, CUReT, UMD and ALOT validate the efficiency of the proposed central pixel selection (CPS) strategy, which can achieve almost 16% improvement over the original LBP and 1%–10% improvement compared with the best classification accuracy among other benchmarked state-of-the-art LBP variants. [ABSTRACT FROM AUTHOR]

Abstract With the remarkable progress in access to remote sensing imagery data, nowadays research very often utilizes more than one image. We are often able to use multitemporal, hyperspectral, and/or full polarization of microwave radar images. In addition, it has become the general consensus that texture analysis plays an important role in remote sensing. It has been found in several publications that texture analysis was applied to each layer separately; however, this procedure requires a significant amount of computation and produces a massive volume of data. One alternative, and perhaps a better procedure, is to arrange the images into a multi-layered structure and perform texture analysis within some sort of three-dimensional domain. This manuscript extends the concepts of the gray level of co-occurrence matrix (GLCM) texture analysis applied for a single image to a multi-layered set of images, referred to in this paper as 3DGLCM. We then presented an interpretation of the 3DGLCM within the context of building damage identification. A set of 3DGLCM-based features were computed and evaluated as well. As a result, it was observed that some texture features have certain similarities with other methods proposed in previous studies, whereas other features have not been used before. Furthermore, this paper evaluates the performance of the Support Vector Machine (SVM) classifier in learning and detecting collapsed buildings using 3DGLCM-based features. Thus, the empirical evaluation focuses on the identification of collapsed buildings caused by the 2011 Tohoku earthquake and tsunami, where individual polarized TerraSAR-X intensity images are used to compute the texture features, and the collapsed buildings caused by the 2016 Kumamoto earthquake, where LIDAR-based digital surface models are used to compute the texture features. Extensive datasets consisting of building damage states that have been visually inspected by local authorities and research teams are used to set up the training and testing subsets. Furthermore, the proposed texture features are compared with features commonly used to identify collapsed buildings. The study concludes that an SVM trained with 3DGLCM-based features identifies collapsed buildings with high accuracy and outperforms an SVM trained with common features used in previous studies. [ABSTRACT FROM AUTHOR]

In the foam nickel process, texture is the indicator of foam nickel performance. In order to recognize foam nickel surface defects accurately and provide guidance for production operations, this paper proposes a method for extracting foam nickel image textures based on multi-scale texture analysis. First, nonsubsampled contourlet (NSCT) is used to carry out foam nickel image multi-scale decomposition, and the lowfrequency and high-frequency components following decomposition are used to characterize different defect details. Then, the Haralick eigenvalue, which measures the foam nickel image texture information at each sub-band, is calculated. The KPCA and optimal DAG-SVM are adopted in order to reduce the parameter dimension and clarify defects. Tests are carried out on the foam nickel surface image samples, including crack, scratch, pollution, leakage, and indentation tests. The results indicate that the method proposed in this paper can extract different pieces of detailed texture information and can achieve a defect-identifying accuracy of up to 88.9%. [ABSTRACT FROM AUTHOR]

Highlights • First system for Simultaneous Localization and Mapping in large environments from Squared Planar Markers. • Print markers with a regular printer and place them in environment. Use a camera an our system finds the camera position while estimating the pose of the markers at the same time. • Real time operation on CPUs (150 Hz) with a single execution thread. Abstract SLAM is generally addressed using natural landmarks such as keypoints or texture, but it poses some limitations, such as the need for enough textured environments and high computational demands. In some cases, it is preferable sacrificing the flexibility of such methods for an increase in speed and robustness by using artificial landmarks. The recent work [1] proposes an off-line method to obtain a map of squared planar markers in large indoor environments. By freely distributing a set of markers printed on a piece of paper, the method estimates the marker poses from a set of images, given that at least two markers are visible in each image. Afterwards, camera localization can be done, in the correct scale. However, an off-line process has several limitations. First, errors can not be detected until the whole process is finished, e.g., an insufficient number of markers in the scene or markers not properly spotted in the capture stage. Second, the method is not incremental, so, in case of requiring the expansion of the map, it is necessary to repeat the whole process from start. Finally, the method can not be employed in real-time systems with limited computational resources such as mobile robots or UAVs. To solve these limitations, this work proposes a real-time solution to the problems of simultaneously localizing the camera and building a map of planar markers. This paper contributes with a number of solutions to the problems arising when solving SLAM from squared planar markers, coining the term SPM-SLAM. The experiments carried out show that our method can be more robust, precise and fast, than visual SLAM methods based on keypoints or texture. [ABSTRACT FROM AUTHOR]

This paper proposes a local texture feature descriptor which fuses the center pixel information into the Center-Symmetric Local Binary Pattern (CS-LBP) for the purpose of face recognition. Because of its tolerance to illumination changes, and computational efficiency, the CS-LBP is widely used in face recognition. But this operator completely ignores the center pixel information which may affect the discriminative result in some applications. In order to take advantage of more useful information, this paper fuses the center pixel information into CS-LBP descriptor, namely CS-LBP/Center. In face recognition, the face image is first divided into small blocks from which CS-LBP/Center histograms are extracted and then weighted by image entropy. Finally, all the weighted histograms are connected serially to create a final texture descriptor for face recognition. The experimental results on some face datasets show that a higher recognition accuracy can be obtained by employing the proposed method with nearest neighbor classification. [ABSTRACT FROM AUTHOR]

In this paper, we propose a context-driven injection scheme for pansharpening, in which the injection coefficients are computed over superpixel segments obtained by means of a modified Simple Linear Iterative Clustering (t-SLIC) technique applied on the texture descriptors of the PAN image. By using the t-SLIC algorithm, various homogeneous-connected components can be generated according to their spectral properties. The proposed pansharpening method relies on a multiresolution framework by employing the Generalized Laplacian Pyramid (GLP) tailored to the Modulation Transfer Function (MTF) of the MS sensors for extracting the high frequency details. First, the injection gains are locally computed as regression coefficients between the upsampled MS and low-resolution PAN regions at a reduced scale. Then, they are multiplied by a global weighting factor computed per spectral band and defined as the ratio of variance between expanded MS bands and PAN image. Finally, the spatial details are modulated by means of the estimated global-local injection coefficients at superpixel level to produce the high-resolution MS image. The validation is assessed with two datasets acquired by IKONOS and WorldView-3 satellites. The experimental results show that the proposed method achieves a favourable performance both visually and quantitatively compared to the state of-the-art pansharpening algorithms. [ABSTRACT FROM AUTHOR]

For condition monitoring and predictive maintenance of high-end manufacturing equipment, surface roughness is a critical metric to evaluate machining quality. Designing a method that can meet the requirements of on-machine monitoring tasks to confirm the cutters' machinability is of great practical significance. Specifically, data-driven on-machine roughness measurement is a recently emerged non-contact method, which has the characteristics of non-invasive and high efficiency. However, recent studies indicate that, for the manufacturing sectors, the on-machine measurement processes are required to respect the predominant impact from the interference of ambient noise or shocks. Besides, the acquisition of microscopic morphology images such as white light interferometers (WLI) is still a relatively expensive and offline acquisition process. And the scale of available WLI data is limited for most industrial application cases. Considering the above-mentioned analysis, this paper proposes a new hybrid machine condition monitoring method based on interpretable dual tree methods, where the adopted interpretable decision tree models obtain knowledge from both topography analysis and acquired texture images of the white light interferometer. It couples with the Shapley additive explanation (SHAP) theory to analyze the main contribution features. The proposed method provides a promising solution to the poor interpretability of feature contribution analysis in general machine condition monitoring methods, which can accurately identify the machinery's physical states under various operating conditions. [ABSTRACT FROM AUTHOR]

The exploration of new hydrocarbon resources requires a detailed image of the subsurface geological structures. Interpreting seismic sections is one of the most common ways of accurately imaging the Earth's subsurface. Automated seismic section interpretation requires accurate delineation of the target geobody through seismic section segmentation. Texture analysis of images is one of the common tools for seismic section segmentation for target geobody identification. Exploration of geological phenomena e.g. the salt dome, buried channels, etc., is very important in the field of hydrocarbon exploration and production due to the possibility of creating stratigraphic and structural hydrocarbon traps, creating potential for subsurface energy storage and drilling hazards. They have textural differences with their surrounding environment, and therefore the analysis of seismic sections using textural attributes to determine the geometry of these events is one of the challenges facing interpreters. Gray level co-occurrence matrix (GLCM) is the commonly used tool for textural analysis of seismic images, based on which several attributes have been introduced. Optimal adjustment of numerous input parameters in GLCM attributes and their strong dependence on the dip of events are the drawbacks of this method. In this paper, we proposed new seismic attributes based on the newly introduced gray level matrices (GLM) consisting of gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), gray level difference matrix (GLDM), and normalized gray level dependence matrix (NGLDM). The new proposed seismic attributes depend on fewer input parameters for adjustment than conventional attributes while increasing accuracy in event detection, and even GLSZM-based attributes are independent of the phenomena dip. The efficiency of the proposed attributes was evaluated on the real field and synthetic seismic data containing a salt dome and its results were compared with conventional GLCM-based attributes. The qualitative and quantitative results showed that in addition to the methodological superiority of the newly introduced gray level matrices compared to the GLCM, the accuracy of the proposed attributes was also increased in the salt dome detection. Moreover, it seems that the linear transform to grayscale performed better than the non-linear one in distinguishing the salt dome from the surrounding sediments. But the main challenge is distinguishing the salt dome texture from the weak layering which the nonlinear transform has done better than the linear one. • Introduction of new seismic texture attributes based on various GLM e.g., GLRLM, GLDM, GLSZM, and NGLDM. • Reducing the number of input parameters in proposed attributes especially dip in the GLSZM-based attributes. • Comparison of the performance of newly introduced seismic attributes with conventional attributes. • Improved determination of salt dome geobody compared to conventional seismic attributes. • Using a non-linear transform instead of a linear one to convert a seismic image to grayscale in attributes calculations. [ABSTRACT FROM AUTHOR]

Texture classification is widely used in image analysis and some other related fields. In this paper, we designed a texture classification algorithm, named by TCIVG (Texture Classification based on Image Visibility Graph), based on a newly proposed image visibility graph network constructing method by Lacasa et al. By using TCIVG on a Brodatz texture image database, the whole procedure is illustrated. First, each texture image in the image database was transformed to an associated image natural visibility graph network and an image horizontal visibility graph network. Then, the degree distribution measure [ P (k) ] was extracted as a key characteristic parameter to different classifiers. Numerical experiments show that for artificial texture images, a 100% classification accuracy can be obtained by means of a quadratic discriminant based on natural TCIVG. For natural texture images, 94.80% classification accuracy can be obtained by a linear SVM (Support Vector Machine) based on horizontal TCIVG. Our results are better than that reported in some existing literature studies based on the same image database. [ABSTRACT FROM AUTHOR]

Background: A various number of imaging modalities are available (e.g., magnetic resonance, x-ray, ultrasound, and biopsy) where each modality can reveal different structural aspects of tissues. However, the analysis of histological slide images that are captured using a biopsy is considered the gold standard to determine whether cancer exists. Furthermore, it can reveal the stage of cancer. Therefore, supervised machine learning can be used to classify histopathological tissues. Several computational techniques have been proposed to study histopathological images with varying levels of success. Often handcrafted techniques based on texture analysis are proposed to classify histopathological tissues which can be used with supervised machine learning.Methods: In this paper, we construct a novel feature space to automate the classification of tissues in histology images. Our feature representation is to integrate various features sets into a new texture feature representation. All of our descriptors are computed in the complex Shearlet domain. With complex coefficients, we investigate not only the use of magnitude coefficients, but also study the effectiveness of incorporating the relative phase (RP) coefficients to create the input feature vector. In our study, four texture-based descriptors are extracted from the Shearlet coefficients: co-occurrence texture features, Local Binary Patterns, Local Oriented Statistic Information Booster, and segmentation-based Fractal Texture Analysis. Each set of these attributes captures significant local and global statistics. Therefore, we study them individually, but additionally integrate them to boost the accuracy of classifying the histopathology tissues while being fed to classical classifiers. To tackle the problem of high-dimensionality, our proposed feature space is reduced using principal component analysis. In our study, we use two classifiers to indicate the success of our proposed feature representation: Support Vector Machine (SVM) and Decision Tree Bagger (DTB).Results: Our feature representation delivered high performance when used on four public datasets. As such, the best achieved accuracy: multi-class Kather (i.e., 92.56%), BreakHis (i.e., 91.73%), Epistroma (i.e., 98.04%), Warwick-QU (i.e., 96.29%).Conclusions: Our proposed method in the Shearlet domain for the classification of histopathological images proved to be effective when it was investigated on four different datasets that exhibit different levels of complexity. [ABSTRACT FROM AUTHOR]

Multiparametric MRI represents the primary imaging modality to assess diffuse liver disease, both in a qualitative and in a quantitative manner. Diffusion-weighted imaging (DWI) is among the imaging techniques that can be used to assess fibrosis due to its unique capability to assess microstructural changes at the tissue level. DWI is based on water mobility patterns and has the potential to become a non-invasive and non-destructive virtual biopsy to assess diffuse liver disease, overcoming sampling bias errors due to its three-dimensional imaging capabilities. Parallel to DWI, another quantitative method called texture analysis may be used to assess early and advanced diffused liver disease through quantifying spatial relationships in a global and local level, applying to any type of digital imaging technique like MRI or CT. Initial results using texture analysis hold great promise. In the current paper, we will review the role of DWI and texture analysis using MR images in assessing diffuse liver disease. [ABSTRACT FROM AUTHOR]

Multiresolution analysis (MRA) methods have been successfully used in texture analysis. Texture analysis is widely discussed in literature, but most of the methods which do not employ multiresolution strategy cannot exploit the fact that texture occurs at various spatial scales. This paper proposes a methodology to identify different classes in satellite images using texture features from newly developed multiresolution methods. The proposed method is tested on remotely sensed optical images and a Pauli RGB decomposed version of synthetic aperture radar image. The textural information is extracted at various scales and in different directions from curvelet and contourlet transforms. The results are compared with wavelet-based features. Accuracy assessment is performed and comparative analysis is carried out using minimum distance to mean, support vector machine and random forest classifiers. It is found that the proposed method shows better class discriminating power and classification capability as compared to existing wavelet-based method. [ABSTRACT FROM AUTHOR]

Oil-impregnated Kraft paper samples normally used for power transformer electrical insulation have been artificially aged thermally to produce a sample set with varying levels of insulation deterioration. The samples were aged in an oven at a temperature above the normal operating temperature of a power transformer. Digital images obtained from optical microscopy measurements on the paper samples have been analyzed using a texture analysis method which converts each image into a spatial gray level dependence matrix (SGLDM). The SGLDM contains information about the statistical variation of pixels gray-level intensities in an image and thereby information about the sample texture. Mathematical operators applied to the SGLDM have been used to extract 22 different statistical texture features for each sample image. Optical microscopy images of the thermally aged samples show that thermal deterioration of the insulation paper produces changes in morphology and physical structure. These changes are detectable by the statistical texture features extracted from the SGLDM texture analysis. Statistical classification is performed on the feature set to demonstrate that differentiation between oil-impregnated paper samples with different levels of thermal degradation is reliable with low error rates. Therefore, development of a practical method to assess condition of oil-impregnated paper insulation using optical microscopy and texture analysis is promising. [ABSTRACT FROM AUTHOR]

Image inpainting is an effective method to repair damaged image. In this paper, we mainly studied some typical image inpainting models and analyzed these corresponding repair qualities. Firstly, we studied three typical algorithms based on PDE (Partial Differential Equation) model to repair images whose damaged areas are relative small. And then we studied the image inpainting algorithm based on texture synthesis. This kind of inpainting algorithm is suitable for image with large damaged area. Finally, we studied Criminisi inpainting algorithm based on image segmentation in detail, on which we proposed an improved priority formula image repair algorithm, the simulation results indicate that the improved algorithm of this paper showed better performance in repair quality from both subjective and objective aspects. [ABSTRACT FROM AUTHOR]

Series machines, i.e., machines (which are usually unreliable) arranged in series with no buffering, are pervasive in production systems. In the analysis, design, and optimization of the series-machine system, the efficiency analysis is one of the most fundamental issues. There are not a lot of researches analyzing the efficiency of the series-machine system, and almost all of them assume that the system operates under type-I failure mechanisms (i.e., the breakdown of a machine could make all other series machines forced down) rather than under type-II mechanisms (i.e., the breakdown of a machine does not make any other series machines forced down). The reason that the type-I failure mechanisms are usually assumed in the literature is that the analysis of the series-machine system under type-II mechanisms is much more complex than under type-I mechanisms, although type-II mechanisms are more common in practice. To thoroughly and systematically estimate the efficiency of the series-machine system, in this paper, we propose a unified analytical approach to investigate the efficiency under both type-I and type-II failure mechanisms. Both cases of deterministic and random cycle times are considered. Different from under type-I failure mechanisms, analytical expressions of the efficiency of series-machine systems under type-II failure mechanisms are extremely hard to obtain, and thus, limit bounds of the efficiency are derived and algorithms are developed to calculate its exact value. Results show that the series-machine system under type-II failure mechanisms is more efficient than under type-I mechanisms, which, intuitively making sense, is the reason that type-II mechanisms are more common in the industry. Note to Practitioners—The series-machine system is pervasive in industry and it usually operates under type-II failure mechanisms (i.e., the breakdown of a machine does not make any other series machines forced down). Nevertheless, the existing research on type-II failure mechanisms is very limited. Thus, the efficiency and other performance measures of such systems are, in general, approximately evaluated based on type-I mechanisms (i.e., the breakdown of a machine could make all other series machines forced down). However, the error of this approximation may be large and unacceptable. In this paper, a unified approach is proposed to analyze the efficiency of the series-machine system. Due to the difficulties in deriving closed-form expressions under type-II failure mechanisms, algorithms are developed to calculate the efficiency of the series-machine system. Theoretical results ensure the effectiveness of the algorithms. [ABSTRACT FROM AUTHOR]

The cellular cytoskeleton is a dynamic subcellular structure that can be a marker of key biological phenomena including cell division, organelle movement, shape changes and locomotion during the avascular tumor phase. Little attention is paid to quantify changes in the cytoskeleton while nuclei and cytoplasmic both are present in subcellular microscopic images. In this paper, we proposed a quantitative image analysis method to analyze subcellular cytoskeletal changes using a texture analysis method preceded by segmentation of nuclei, cytoplasm and ruffling regions (area except nuclei and cytoplasm). To test and validate this model we hypothesized that M ammary S erine P rotease I nhibitor (maspin) acts as cytoskeleton regulator that mediates cell-extracellular matrix (ECM) adhesion in tumor. Maspin-a tumor suppressor gene shows multiple tumor suppressive properties such as increasing tumor cell apoptosis and reducing migration, proliferation, invasion, and overall tumor metastasis. The proposed method obtained separated ruffling regions from segmentation steps and then adopted gray–level histograms (GLH) and grey-level co-occurrence matrix (GLCM) texture analysis techniques. In order to verify the reliability, the proposed texture analysis method was used to compare the control and maspin expressing cells grown on different ECM components: plastic, collagen I, fibronectin and laminin. The results show that the texture parameters extracted reflect the different cytoskeletal changes. These changes indicate that maspin acts as a regulator of the cell-ECM enhancement process, while it reduces the cell migration. Overall, this paper not only presents a quantitative image analysis approach to analyze subcellular cytoskeletal architectures but also provides a comprehensive tool for the biologist, pathologist, cancer specialist, and computer scientist to understand cellular and subcellular organization of cells. In long term, this method can be extended to be used in live cell tracking in vivo , image informatics based point-of-care expert system and quantification of various complex architectures in organisms. [ABSTRACT FROM AUTHOR]

Wood recognition is a crucial task for wood sciences and industries, since it leads to the identification of the anatomical features and physical properties of wood. Traditionally, the recognition process relies almost exclusively on human experts, who are based on various characteristics of wood, such as color, structure and texture. However, there are numerous types of wood species in the nature that are difficult to be identified even by experienced scientists. Towards this end, in this paper we propose a novel approach for automated wood species recognition through multidimensional texture analysis. By taking advantage of the fact that static wood images contain periodic spatially-evolving characteristics, we introduce a new spatial descriptor considering each wood image as a collection of multidimensional signals. More specifically, the proposed methodology enables the representation of wood images as concatenated histograms of higher order linear dynamical systems produced by vertical and horizontal image patches. The final classification of images, i.e., histogram representations, into wood species, is performed using a Support Vector Machines (SVM) classifier. For the evaluation of the proposed method, a dataset, namely “WOOD-AUTH”, consisting of more than 4200 wood images (from cross, radial and tangential sections of normal wood structure) of twelve common wood species existing in Greek territory, was created. Experimental results presented in this paper show the great potential of the proposed methodology, which, despite a small number of misclassification cases with regards to both anatomically similar and different species, outperforms a number of state of the art approaches, yielding a classification rate of 91.47% in wood cross sections. [ABSTRACT FROM AUTHOR]