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The microtexture of the asphalt pavement, which is critical for the skid resistance, is dictated by the microtexture of the coarse aggregates used in the surface courses. The existing standard test methods can only capture the combined measure of coarse aggregate surface texture which can be influenced by other aggregate characteristics such as shape and angularity. Consequently, the surface texture of aggregates cannot be quantified explicitly using these tests, and therefore image analysis methods that can extract textural features are gaining more significance. In the present study, various image texture analysis methods are evaluated to identify a reliable texture indicator. Methods include traditional statistical approaches such as histogram method, Grey Level Co-occurrence Matrix (GLCM) methods, and transform-based wavelet texture analysis. The image analysis was carried out on the grey-scale scanning electron microscope (SEM) images before and after accelerated polishing. To compare the image texture parameters, the average surface roughness (Sa) measured from aggregate surface profile data obtained from a 3D Optical Surface Profilometer (3D OSP) was considered as a direct measure of surface texture. A detailed correlation analysis with Sa indicates that the surface texture index (STI) from the wavelet analysis quantifies the microtexture more accurately compared to statistical methods. [ABSTRACT FROM AUTHOR]

Recognition of spalling on surface of concrete wall is crucial in building condition survey. Early detection of this form of defect can help to develop cost-effective rehabilitation methods for maintenance agencies. This study develops a method for automatic detection of spalled areas. The proposed approach includes image texture computation for image feature extraction and a piecewise linear stochastic gradient descent logistic regression (PL-SGDLR) used for pattern recognition. Image texture obtained from statistical properties of color channels, gray-level cooccurrence matrix, and gray-level run lengths is used as features to characterize surface condition of concrete wall. Based on these extracted features, PL-SGDLR is employed to categorize image samples into two classes of "nonspall" (negative class) and "spall" (positive class). Notably, PL-SGDLR is an extension of the standard logistic regression within which a linear decision surface is replaced by a piecewise linear one. This improvement can enhance the capability of logistic regression in dealing with spall detection as a complex pattern classification problem. Experiments with 1240 collected image samples show that PL-SGDLR can help to deliver a good detection accuracy (classification accuracy rate = 90.24%). To ease the model implementation, the PL-SGDLR program has been developed and compiled in MATLAB and Visual C# .NET. Thus, the proposed PL-SGDLR can be an effective tool for maintenance agencies during periodic survey of buildings. [ABSTRACT FROM AUTHOR]

Abstract: Motivation: Physical properties of coal such as particle size distribution have a large influence on the stability and operational behavior of fluidized bed reactors and metallurgical furnaces. In particular, the presence of large amounts of “fine” particles invariably has a drastic effect on plant performance as a result of impaired gas permeability characteristics of the coal or ore burden. Therefore, monitoring and control of particle size distribution profiles of such aggregate material on reactor feed streams, such as moving conveyor belts, is critical for predictable operation of these processes. Traditionally, the method of sieve analysis using stock or belt cut samples has been widely used in industry. Unfortunately, the reliability and usefulness of belt cut techniques are constrained by frequency of sampling as well as laboratory analysis turnaround times. For real-time monitoring and control purposes, automated sampling and analysis methods are more desirable. Methods: In this study, the problem of estimating the particle size distribution profile of material on a moving conveyor belt is formulated within a texture classification framework, which has its basis in machine vision and incorporates elements from statistical pattern recognition. Using exemplar images of coal particles taken on a process stream, a set of local features that compactly describes the textural properties of each image are expressed in terms of localized nonlinear features called textons. Representation of image information using textons is primarily motivated by insights from neuroscience research on the optimality of linear oriented basis functions as models of perception in early processing of visual information in the cortex regions of the human brain. Using these representations for different textures, nearest neighbor and support vector machine classification models are subsequently used to classify test images. Results: Using a comprehensive evaluation, it is shown that the use of texton representation obtained from decomposing images with linear oriented basis functions can be sufficiently discriminative compared to the use of the widely used second-order statistical features or features from other baseline models. In particular, model performance obtained with appropriately tuned filters suggest the importance of including shape and spatial structure information in an image representation for texture classification of coal particles. Furthermore, using nonlinear support vector machines rather than nearest neighbor classifiers significantly improved classification performance. A texture classification approach to particle size profile estimation has potential applications in the online monitoring of the proportion of “fines” in coal material on moving conveyor belts. [Copyright &y& Elsevier]

In the last few decades, many studies have been performed with the main hope of utilizing imaging methods so as to detect static (bubble size and shape, color, texture) and dynamic (velocity and stability) features of froth that involve crucial information about the process state in order to assess and monitor the performance of flotation process. Although several types of flotation automated control system problems are being successfully solved using the various techniques of features extraction from froth images, there are still a number of unresolved subjects and obstacles. Hence, a suitable review of these methods is needed. After reviewing the technical aspects of froth images structural features in part 1 of this two-part review paper, this work provides an overview of several different approaches for the sake of analyzing and classifying froth image texture and dynamic features. Finally, we conclude our review by linking concentrate grade and recovery with the froth image features described in this two-part paper. In the close future, it is expected with ever-growing application needs and research progress, image analysis systems are becoming even more effective solutions for monitoring and controlling flotation performance. This review provides a platform for future initiatives and potential developments in this area. [ABSTRACT FROM AUTHOR]

In order to realize the evaluation of regional comprehensive disaster reduction capacity in a complex environment, an evaluation model of regional comprehensive disaster reduction capacity in a complex environment based on remote sensing monitoring and data image feature analysis is proposed. According to the geographical location and scale of disaster spots and the parameter analysis of the model of disaster-bearing bodies around the disaster spots, the remote sensing monitoring method is adopted to extract the geographical remote sensing images of regional disaster spots in a complex environment. The collected geographical remote sensing images of regional disaster points under the complex environmental background are filtered and preprocessed, and the texture parameters of the geographical remote sensing images of regional disaster points under the complex environmental background are recognized by combining the method of image texture feature extraction. Based on the method of tone mapping, the rapid filtering and feature analysis of the geographical remote sensing images of regional disaster points under the complex environmental background are carried out, and the time, position, damage, and so on in the geographical remote sensing images of regional disaster points under the complex environmental background are analyzed. By using the method of parameter analysis and gradient operator operation, a comparison model of geographical remote sensing images of regional disaster points under the complex environmental background is established, and the reliability evaluation of regional comprehensive disaster reduction ability under the complex environmental background is realized according to the method of contrast and detail significance enhancement. The test shows that this method has high accuracy in evaluating regional comprehensive disaster reduction capability under a complex environment, high accuracy in marking the geographical location of regional disaster points under a complex environment, and good fusion performance and reliability of regional comprehensive disaster reduction capability evaluation parameters. [ABSTRACT FROM AUTHOR]

Image texture extraction and analysis are fundamental steps in computer vision. In particular, considering the biomedical field, quantitative imaging methods are increasingly gaining importance because they convey scientifically and clinically relevant information for prediction, prognosis, and treatment response assessment. In this context, radiomic approaches are fostering large-scale studies that can have a significant impact in the clinical practice. In this work, we present a novel method, called CHASM (Cuda, HAralick & SoM), which is accelerated on the graphics processing unit (GPU) for quantitative imaging analyses based on Haralick features and on the self-organizing map (SOM). The Haralick features extraction step relies upon the gray-level co-occurrence matrix, which is computationally burdensome on medical images characterized by a high bit depth. The downstream analyses exploit the SOM with the goal of identifying the underlying clusters of pixels in an unsupervised manner. CHASM is conceived to leverage the parallel computation capabilities of modern GPUs. Analyzing ovarian cancer computed tomography images, CHASM achieved up to ∼ 19.5 × and ∼ 37 × speed-up factors for the Haralick feature extraction and for the SOM execution, respectively, compared to the corresponding C++ coded sequential versions. Such computational results point out the potential of GPUs in the clinical research. [ABSTRACT FROM AUTHOR]

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

Texture analysis is an important topic in Ultrasound (US) image analysis for structure segmentation and tissue classification. In this work a novel approach for US image texture feature extraction is presented. It is mainly based on parametrical modelling of a signal version of the US image in order to process it as data resulting from a dynamical process. Because of the predictive characteristics of such a model representation, good estimations of texture features can be obtained with less data than generally used methods require, allowing higher robustness to low Signal-to-Noise ratio and a more localized US image analysis. The usability of the proposed approach was demonstrated by extracting texture features for segmenting the thyroid in US images. The obtained results showed that features corresponding to energy ratios between different modelled texture frequency bands allowed to clearly distinguish between thyroid and non-thyroid texture. A simple k-means clustering algorithm has been used for separating US image patches as belonging to thyroid or not. Segmentation of thyroid was performed in two different datasets obtaining Dice coefficients over 85%. [ABSTRACT FROM AUTHOR]

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

Mealiness is a phenomenon in which intercellular adhesions in apples loosen during storage, causing a soft and floury texture at the time of eating, and leading to lower consumer preference. Although apples can be stored and commercially sold throughout the year, the occurrence of mealiness is not monitored during storage. Therefore, the objective of this research was to non-destructively estimate the mealiness of apple fruit by means of laser scattering measurement. This method is based on laser light backscattering imaging but can quantify a wider range of backscattered light than the conventional method by utilizing high dynamic range (HDR) rendering techniques. Lasers with wavelengths of 633 nm and 850 nm were used as a light source, and after acquiring backscattered images, profiles, and images were obtained. Profile features such as curve fitting coefficients and profile slopes and image features such as statistical image features and texture features were extracted from the profiles and images, respectively. PLS, SVM, and ANN models were used for the estimation of mealiness. The results of the estimation based on these features showed that the ANN model combining both wavelengths had a higher performance (R = 0.634, RMSE = 7.621) than the models constructed from features of single wavelength measurements. In order to further improve the performance of the model, we applied various ensemble learning methods to combine different estimation models. As a result, the ensemble model showed the highest performance (R = 0.682, RMSE = 7.281). These results suggest that laser scattering measurement is a promising method for estimating apple fruit mealiness. [ABSTRACT FROM AUTHOR]

Image segmentation is one of the most important aspects of image processing that has many applications in image analysis, computer vision, and machine vision. So far, researchers have proposed many image segmentation algorithms that provide a relatively appropriate response for a particular application and over a specific type of input images. At the same time, they have not yet suggested a simple, fast, and efficient segmentation method that can apply to a wide range of natural and texture images. In this study, the authors present a new adaptive method for selecting Gabor filters using the image frequency content. Adaptive spectral features of the image are then extracted using only these selected filters from a Gabor filter bank. A pre‐setting parameter Gabor filter is used to extract the gradient of each feature in x and y directions. Combining these features gradient, the texture gradient image is calculated, and then by selected markers for different texture regions and applying the marker‐based watershed transform on texture gradient image, the authors present a new image segmentation method that has a less computational cost and yields better results compared to using all other filter banks. [ABSTRACT FROM AUTHOR]

Purpose: Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region. Methods: Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong's test was used to compare the different ROCs in terms of AUC performance. Results: The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05). Conclusions: The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors. [ABSTRACT FROM AUTHOR]

There are mainly two types of wafer map failure pattern recognition, i.e., traditional classification based and deep learning based approaches. Traditional classification usually needs feature engineering, and deep learning requires lower human intervention and feature engineering. The joint requirement is noise filtering and identical input dimension size. Feature engineering and dimension resizing are artificial work and vary from study to study. In our study, we proposed an image hashing based wafer map resizing and pattern information retrieval framework. Traditional classification methods and CNN are employed for the wafer map failure pattern recognition. The experiments show that the image hashing based framework is the more appropriate method when compared to feature extraction and image resizing methods. We also found that proper input data manipulation can result in acceptable performance whether the used method is traditional classification algorithms or CNN. [ABSTRACT FROM AUTHOR]

Highlights • Propose texture features based on a modified version of the local binary pattern (LBP) descriptor. • To improve the texture discrimination ability, the local spatial information of the image is taken into account in the LBP code computation. • For each pixel, the scale parameter and the threshold value of the LBP code are determined using bilateral filter-based multi-scale image analysis. • The effectiveness of the proposed strategy is supported by the analysis of different texture patterns. Graphical abstract Abstract Texture characterization aims at describing the spatial arrangement of local structures within an image. However, mixed pixels that are generally located near boundaries of the regions represent challenge to perform accurate image texture discrimination. To address this problem, this paper proposes a robust discriminating texture features relying on an efficient Local Binary Pattern (LBP) descriptor, where the spatial information within image is taken into account. To determine for each pixel both a proper scale parameter and a threshold value to compute the LBP code, an efficient way relying on bilateral filter-based multi-scale image analysis is used. First, the difference of Gaussian operator is used to determine the corresponding scale. Second, key points based-approach is used to identify the threshold value of each pixel. This provides the ability to deal with mixed pixels. Then, LBP code is computed to characterize the texture information for each pixel. Experimental results, using both synthetic and real images, show that the proposed appropriate-scale-threshold selection strategy demonstrates a significant improvement in texture discrimination ability. [ABSTRACT FROM AUTHOR]

In unstructured agricultural environments, accurate fruit detection and picking point localization are important for automatic robotic harvesting. In order to solve the machine vision problem of selective mechanized picking of clustered fruit, this study proposed a novel vision algorithm for target recognition and picking point localization based on the shape and growth characteristics of clustered tomatoes. The picking point has to be determined according to its position and posture relative to the fruit or fruit cluster. The shape, texture and color features of tomatoes were extracted and combined to realize accurate tomato recognition. The precision, recall and accuracy of the recognition model were as high as 100%, and the recognition time was less than 1 s. After the recognition of tomato fruit area, this study further developed a two-step picking point positioning algorithm. Firstly, the co-ordinates and radius of the fruit center of mass was acquired, and then the contour line of the whole bunch of fruits were fitted based on Hough circle detection. Secondly, spatially symmetrical spline interpolation method and geometric analysis were applied for peduncle estimation, contour fitting and picking point location. Experimental results showed that located picking points were distributed on the branches, and the position deviation was small, which met the requirements within a certain positioning accuracy range. This indicated that the proposed method can achieve satisfactory picking-points location effect of clustered fruit in complex environments. [ABSTRACT FROM AUTHOR]

Introduction: Neonatal cranial ultrasound plays an important role in the evaluation of neonatal brain development and related diseases. Methods: This paper preliminarily explored the analysis and interpretation methods of neonatal brain ultrasound images, and applied the relevantmedical image analysis methods to analyze the relevant neonatal brain ultrasound images in more detail. Results: Compared with other types of imaging methods, ultrasound has its unique advantages and characteristics in such applications as neonatal head imaging. Discussion: The analysis steps and schemes adopted in this paper have certain reference significance for the analysis of other neonatal brain pictures. [ABSTRACT FROM AUTHOR]

Digital pathology technologies, including whole slide imaging (WSI), have significantly improved modern clinical practices by facilitating storing, viewing, processing, and sharing digital scans of tissue glass slides. Researchers have proposed various artificial intelligence (AI) solutions for digital pathology applications, such as automated image analysis, to extract diagnostic information from WSI for improving pathology productivity, accuracy, and reproducibility. Feature extraction methods play a crucial role in transforming raw image data into meaningful representations for analysis, facilitating the characterization of tissue structures, cellular properties, and pathological patterns. These features have diverse applications in several digital pathology applications, such as cancer prognosis and diagnosis. Deep learning-based feature extraction methods have emerged as a promising approach to accurately represent WSI contents and have demonstrated superior performance in histology-related tasks. In this survey, we provide a comprehensive overview of feature extraction methods, including both manual and deep learning-based techniques, for the analysis of WSIs. We review relevant literature, analyze the discriminative and geometric features of WSIs (i.e., features suited to support the diagnostic process and extracted by “engineered” methods as opposed to AI), and explore predictive modeling techniques using AI and deep learning. This survey examines the advances, challenges, and opportunities in this rapidly evolving field, emphasizing the potential for accurate diagnosis, prognosis, and decision-making in digital pathology. [ABSTRACT FROM AUTHOR]

Featured Application: The present work contributes to the investigation on the robustness and reproducibility of radiomic studies, a fundamental step for bringing radiomics towards clinical implementation. Radiomic analysis of 18F[FDG] PET/CT images might identify predictive imaging biomarkers, however, the reproducibility of this quantitative approach might depend on the methodology adopted for image analysis. This retrospective study investigates the impact of PET segmentation method and the selection of different target lesions on the radiomic analysis of baseline 18F[FDG] PET/CT images in a population of newly diagnosed diffuse large B-cell lymphoma (DLBCL) patients. The whole tumor burden was segmented on PET images applying six methods: (1) 2.5 standardized uptake value (SUV) threshold; (2) 25% maximum SUV (SUVmax) threshold; (3) 42% SUVmax threshold; (4) 1.3∙liver uptake threshold; (5) intersection among 1, 2, 4; and (6) intersection among 1, 3, 4. For each method, total metabolic tumor volume (TMTV) and whole-body total lesion glycolysis (WTLG) were assessed, and their association with survival outcomes (progression-free survival PFS and overall survival OS) was investigated. Methods 1 and 2 provided stronger associations and were selected for the next steps. Radiomic analysis was then performed on two target lesions for each patient: the one with the highest SUV and the largest one. Fifty-three radiomic features were extracted, and radiomic scores to predict PFS and OS were obtained. Two proportional-hazard regression Cox models for PFS and OS were developed: (1) univariate radiomic models based on radiomic score; and (2) multivariable clinical–radiomic model including radiomic score and clinical/diagnostic parameters (IPI score, SUVmax, TMTV, WTLG, lesion volume). The models were created in the four scenarios obtained by varying the segmentation method and/or the target lesion; the models' performances were compared (C-index). In all scenarios, the radiomic score was significantly associated with PFS and OS both at univariate and multivariable analysis (p < 0.001), in the latter case in association with the IPI score. When comparing the models' performances in the four scenarios, the C-indexes agreed within the confidence interval. C-index ranges were 0.79–0.81 and 0.80–0.83 for PFS radiomic and clinical–radiomic models; 0.82–0.87 and 0.83–0.90 for OS radiomic and clinical–radiomic models. In conclusion, the selection of either between two PET segmentation methods and two target lesions for radiomic analysis did not significantly affect the performance of the prognostic models built on radiomic and clinical data of DLBCL patients. These results prompt further investigation of the proposed methodology on a validation dataset. [ABSTRACT FROM AUTHOR]

With the development of Computer-aided Diagnosis (CAD) and image scanning techniques, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital histopathology. Since 2004, WSI has been used widely in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computer algorithms, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists to obtain more stable and quantitative results with minimum labor costs and improved diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning techniques in WSI segmentation, classification, and detection are reviewed. Finally, the existing methods are studied, and the application prospects of the methods in this field are forecasted. [ABSTRACT FROM AUTHOR]

Radiomics aims to support clinical decisions through its workflow, which is divided into: (i) target identification and segmentation, (ii) feature extraction, (iii) feature selection, and (iv) model fitting. Many radiomics tools were developed to fulfill the steps mentioned above. However, to date, users must switch different software to complete the radiomics workflow. To address this issue, we developed a new free and user-friendly radiomics framework, namely matRadiomics, which allows the user: (i) to import and inspect biomedical images, (ii) to identify and segment the target, (iii) to extract the features, (iv) to reduce and select them, and (v) to build a predictive model using machine learning algorithms. As a result, biomedical images can be visualized and segmented and, through the integration of Pyradiomics into matRadiomics, radiomic features can be extracted. These features can be selected using a hybrid descriptive–inferential method, and, consequently, used to train three different classifiers: linear discriminant analysis, k-nearest neighbors, and support vector machines. Model validation is performed using k-fold cross-Validation and k-fold stratified cross-validation. Finally, the performance metrics of each model are shown in the graphical interface of matRadiomics. In this study, we discuss the workflow, architecture, application, future development of matRadiomics, and demonstrate its working principles in a real case study with the aim of establishing a reference standard for the whole radiomics analysis, starting from the image visualization up to the predictive model implementation. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles have large prospects for organizing territory monitoring. To integrate them into this sphere, it is necessary to improve their high functionality and safety. Computer vision is one of the vital monitoring aspects. In this paper, we developed and validated a methodology for terrain classification. The overall classification procedure consists of the following steps: (1) pre-processing, (2) feature extraction, and (3) classification. For the pre-processing stage, a clustering method based on particle swarm optimization was elaborated, which helps to extract object patterns from the image. Feature extraction is conducted via Gray-Level Co-Occurrence Matrix calculation, and the output of the matrix is turned into the input for a feed-forward neural network classification stage. The developed computer vision system showed 88.7% accuracy on the selected test set. These results can provide high quality territory monitoring; prospectively, we plan to establish a self-positioning system based on computer vision. [ABSTRACT FROM AUTHOR]

Objective. 4D-CBCT provides phase-resolved images valuable for radiomics analysis for outcome prediction throughout treatment courses. However, 4D-CBCT suffers from streak artifacts caused by under-sampling, which severely degrades the accuracy of radiomic features. Previously we developed group-patient-trained deep learning methods to enhance the 4D-CBCT quality for radiomics analysis, which was not optimized for individual patients. In this study, a patient-specific model was developed to further improve the accuracy of 4D-CBCT based radiomics analysis for individual patients. Approach. This patient-specific model was trained with intra-patient data. Specifically, patient planning 4D-CT was augmented through image translation, rotation, and deformation to generate 305 CT volumes from 10 volumes to simulate possible patient positions during the onboard image acquisition. 72 projections were simulated from 4D-CT for each phase and were used to reconstruct 4D-CBCT using FDK back-projection algorithm. The patient-specific model was trained using these 305 paired sets of patient-specific 4D-CT and 4D-CBCT data to enhance the 4D-CBCT image to match with 4D-CT images as ground truth. For model testing, 4D-CBCT were simulated from a separate set of 4D-CT scan images acquired from the same patient and were then enhanced by this patient-specific model. Radiomics features were then extracted from the testing 4D-CT, 4D-CBCT, and enhanced 4D-CBCT image sets for comparison. The patient-specific model was tested using 4 lung-SBRT patients’ data and compared with the performance of the group-based model. The impact of model dimensionality, region of interest (ROI) selection, and loss function on the model accuracy was also investigated. Main results. Compared with a group-based model, the patient-specific training model further improved the accuracy of radiomic features, especially for features with large errors in the group-based model. For example, the 3D whole-body and ROI loss-based patient-specific model reduces the errors of the first-order median feature by 83.67%, the wavelet LLL feature maximum by 91.98%, and the wavelet HLL skewness feature by 15.0% on average for the four patients tested. In addition, the patient-specific models with different dimensionality (2D versus 3D) or loss functions (L1 versus L1 + VGG + GAN) achieved comparable results for improving the radiomics accuracy. Using whole-body or whole-body+ROI L1 loss for the model achieved better results than using the ROI L1 loss alone as the loss function. Significance. This study demonstrated that the patient-specific model is more effective than the group-based model on improving the accuracy of the 4D-CBCT radiomic features analysis, which could potentially improve the precision for outcome prediction in radiotherapy. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) has all-weather and all-day observation capabilities and a certain ability to penetrate the surface; therefore, it has unique advantages in many aspects, which other remote sensing methods cannot achieve. However, owing to imaging principles, the interpretation of SAR images is complicated. Therefore, converting SAR images into optical remote sensing images is one method of SAR image interpretation. Therefore, this paper proposes an improved conditional generative adversarial network (cGAN) that includes a generator based on the encoder-decoder structure of the swin transformer feature extraction module. The generator uses a spatial pyramid structure and multi-scale depth feature extraction structure to extract the SAR image features at different scales. A multi-scale discriminator was used to discriminate images on different scales. The introduction of feature matching loss makes the discriminator's feedback to the generator richer and uses perceptual loss based on VGGNet to evaluate the image quality in depth. A series of experiments verified the effectiveness of the method in this study and proved the potential of the transformer structure in the field of SAR image translation. The method presented in this paper has obvious advantages and can translate SAR images into optical remote sensing images. [ABSTRACT FROM AUTHOR]

High-quality magnetic resonance (MR) images afford more detailed information for reliable diagnoses and quantitative image analyses. Given low-resolution (LR) images, the deep convolutional neural network (CNN) has shown its promising ability for image super-resolution (SR). The LR MR images usually share some visual characteristics: structural textures of different sizes, edges with high correlation, and less informative background. However, multi-scale structural features are informative for image reconstruction, while the background is more smooth. Most previous CNN-based SR methods use a single receptive field and equally treat the spatial pixels (including the background). It neglects to sense the entire space and get diversified features from the input, which is critical for high-quality MR image SR. We propose a wide weighted attention multi-scale network ($\text{W}^{2}$ AMSN) for accurate MR image SR to address these problems. On the one hand, the features of varying sizes can be extracted by the wide multi-scale branches. On the other hand, we design a non-reduction attention mechanism to recalibrate feature responses adaptively. Such attention preserves continuous cross-channel interaction and focuses on more informative regions. Meanwhile, the learnable weighted factors fuse extracted features selectively. The encapsulated wide weighted attention multi-scale block ($\text{W}^{2}$ AMSB) is integrated through a recurrent framework and global attention mechanism. Extensive experiments and diversified ablation studies show the effectiveness of our proposed $\text{W}^{2}$ AMSN, which surpasses state-of-the-art methods on most popular MR image SR benchmarks quantitatively and qualitatively. And our method still offers superior accuracy and adaptability on real MR images. [ABSTRACT FROM AUTHOR]

In medical imaging, quantitative measurements have shown promise in identifying diseases by classifying normal versus pathological parameters from tissues. The support vector machine (SVM) has shown promise as a supervised classification algorithm and has been widely used. However, the classification results typically identify a category of abnormal tissues but do not necessarily differentiate progressive stages of a disease. Moreover, the classification result is typically provided independently as a supplement to medical images, which contributes to an overload of information sources in the clinic. Hence, we propose a new imaging method utilizing the SVM to integrate classification results into medical images. This framework is called disease-specific imaging (DSI) that produces a color overlaid highlight on B-mode ultrasound images indicating the type, location, and severity of pathology from different conditions. In this article, the SVM training was performed to construct hyperplanes that can differentiate normal, fibrosis, steatosis, and pancreatic ductal adenocarcinoma (PDAC) metastases in livers based on ultrasound echoes. Also, cluster centroids for specific diseases define unique disease axes, and the inner product between measured features and any disease axis selected by the SVM quantifies the disease progression. The features were measured from 2794 ultrasound frames using the H-scan analysis, attenuation estimation, and B-mode image analysis. The performance of our proposed DSI method was evaluated for a preclinical model of steatosis (${n} =400$ frames). The contribution of each feature was assessed, and the results were compared with ground truth from histology. Moreover, the images generated by our DSI were compared with earlier imaging methods of B-mode, H-scan, and histology. The comparisons demonstrate that DSI images yield higher sensitivity to monitor progressive steatosis than B-mode and H-scan and provide a comparable performance with the histology. For the parameter comparison, DSI and H-scan resulted in similar correlation with histology (${r}_{s} =0.83$) but higher than attenuation (${r}_{s} =0.73$) and B-mode (${r}_{s} =0.47$). Therefore, we conclude that DSI utilizing the SVM applied to steatosis can visually represent the classification results with color highlighting, which can simplify the interpretation of classification compared to the traditional SVM result. We expect that the proposed DSI can be used for any medical imaging modality that can estimate multiple quantitative parameters at high resolution. [ABSTRACT FROM AUTHOR]

Abstract: Extraction of the flooded areas from synthetic aperture radar (SAR) and especially TerraSAR-X data is one of the most challenging tasks in the flood management and planning. SAR data due to its high spatial resolution and its capability of all weather conditions makes a proper choice for tropical countries. Texture is considered as an effective factor in distinguishing the classes especially in SAR imagery which records the backscatters that carry information of kind, direction, heterogeneity and relationship of the features. This paper put forward a computer program for texture analysis for high resolution radar data. Texture analysis program is introduced and discussed using the gray-level co-occurrence matrix (GLCM). To demonstrate the ability and correctness of this program, a test subset of TerraSAR-X imagery from Terengganu area, Malaysia was analyzed and pixel-based and object-based classification were attempted. The thematic maps derived by pixel-based method could not achieve acceptable visual interpretation and for that reason no accuracy assessment was performed on them. The overall accuracy achieved by object-based method was 83.63% with kappa coefficient of 0.8. Results on image texture classification showed that the proposed program is capable for texture analysis in TerraSAR-X image and the obtained textural analysis resulted in high classification accuracy. The proposed texture analysis program can be used in many applications such as land use/cover (LULC) mapping, hazard studies and many other applications. [Copyright &y& Elsevier]

This paper established aircraft models by using 3D-MAX and designed a three-dimensional the aircraft target image library. In this image library, divided different aircraft models into three categories, each category were divided into 24 groups according to flight poses. It discussed the three types of aircraft image texture feature extraction methods. Then it calculated contrast fusion value, entropy of fusion, inverse gap value feature fusion of each type of aircraft image by multi-sensor consistency fusion method based on closeness, and pre-judge the test object classification by the minimum distance method. It also introduced K-L transform, feature subspace and projection coefficient vector, calculated the coefficient vector of each image's projection on gallery feature subspace, and tested image's projection on feature subspace. Then it introduced fisher optimal discriminate vector and discriminate rules. It considered coefficient vector of image projection on feature subspace as an object; derived optimal discriminate vectors through training each set of image's positive samples and negative samples. It judged test target's category and flight attitude through fisher discriminate rules. It proved a high recognition rate of the recognition system by experiments, as well as the judgment of the aircraft complex flight attitude. [ABSTRACT FROM AUTHOR]

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis. Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised. [ABSTRACT FROM AUTHOR]

Accurate assessment of radiological images can help in early diagnosis and therapy of suppurative osteomyelitis (OM). The purpose of this study was to apply texture analysis to MRI as a means of quantitatively evaluating acute OM of the mandible. We analyzed the data from 38 patients who complained of pain and underwent MRI between April 2017 and March 2019. From the MRIs of these patients, with (n = 19) and without OM (n = 19), 279 radiomics features were extracted using short tau inversion recovery, data of the regions of interest and analyzed with MaZda v. 3.3. 10 features, including one histogram feature (90th percentile), eight gray-level co-occurrence matrix features (Sum Averg), and one gray-level run-length matrix feature (Horzl_RLNonUni), were selected using Fisher coefficient and compared between the acute OM and non-OM groups. The two groups were compared using Mann–Whitney U test with p value set at 0.05. All 10 radiomics features showed significant differences between the acute OM and non-OM groups (p < 0.05). MRI texture analysis has potential application in radiomics diagnosis of acute OM of the mandible. [ABSTRACT FROM AUTHOR]

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Abstract: One of the principal causes for image quality degradation is blur. This frequent phenomenon is usually a result of misfocused optics or camera motion, and it is very difficult to undo. Beyond the impaired visual quality, blurring causes problems to computer vision algorithms. In this paper, we present a simple yet powerful image descriptor, which is robust against the most common image blurs. The proposed method is based on quantizing the phase information of the local Fourier transform and it can be used to characterize the underlying image texture. We show how to construct several variants of our descriptor by varying the technique for local phase estimation and utilizing the proposed data decorrelation scheme. The descriptors are assessed in texture and face recognition experiments, and the results are compared with several state-of-the-art methods. The difference to the baseline is considerable in the case of blurred images, but also with sharp images our method gives a highly competitive performance. [Copyright &y& Elsevier]

In diverse computer applications, the analysis of image content plays a key role. This image content might be either textual (like text appearing in the images) or visual (like shape, color, texture). These two image contents consist of image's basic features and therefore turn out to be as the major advantage for any of the implementation. Many of the art models are based on the visual search or annotated text for Content-Based Image Retrieval (CBIR) models. There is more demand toward multitasking, a new method needs to be introduced with the combination of both textual and visual features. This paper plans to develop the intelligent CBIR system for the collection of different benchmark texture datasets. Here, a new descriptor named Information Oriented Angle-based Local Tri-directional Weber Patterns (IOA-LTriWPs) is adopted. The pattern is operated not only based on tri-direction and eight neighborhood pixels but also based on four angles 0 ∘ , 4 5 ∘ , 9 0 ∘ , and 1 3 5 ∘ . Once the patterns concerning tri-direction, eight neighborhood pixels, and four angles are taken, the best patterns are selected based on maximum mutual information. Moreover, the histogram computation of the patterns provides the final feature vector, from which the new weighted feature extraction is performed. As a new contribution, the novel weight function is optimized by the Improved MVO on random basis (IMVO-RB), in such a way that the precision and recall of the retrieved image is high. Further, the proposed model has used the logarithmic similarity called Mean Square Logarithmic Error (MSLE) between the features of the query image and trained images for retrieving the concerned images. The analyses on diverse texture image datasets have validated the accuracy and efficiency of the developed pattern over existing. [ABSTRACT FROM AUTHOR]

Background: Colon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD. Methods: We downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF). Results: There were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group. Conclusions: These results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future. [ABSTRACT FROM AUTHOR]

Brain tumor is not most common, but truculent type of cancer. Therefore, correct prediction of its aggressiveness nature at an early stage would influence the treatment strategy. Although several diagnostic methods based on different modalities exist, a pre-operative method for determining tumor malignancy state still remains as an active research area. In this regard, the paper presents a new method for the assessment of tumor grades using conventional MR sequences namely, T1, T1 with contrast enhancement, T2 and FLAIR. The proposed method for tumor gradation is mainly based on feature extraction using multiresolution image analysis and classification using support vector machine. Since the wavelet features of different tumor subregions, obtained from single MR sequence, do not carry equally important information, a wavelet fusion technique is proposed based on the texture information content of each voxel. The concept of texture gradient, used in the proposed algorithm, fuses the wavelet coefficients of the given MR sequences. The feature vector is then derived from the co-occurrence of fused wavelet coefficients. As each wavelet subband contains distinct detail information, a novel concept of multispectral co-occurrence of wavelet coefficients is introduced to capture the spatial correlation among different subbands. It enables to convey more informative features to characterize the tumor type. The effectiveness of the proposed method is analyzed, with respect to six classification performance indices, on BRATS 2012 and BRATS 2014 data sets. The classification accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under curve assessed by the ten-fold cross-validation are 91.3%, 96.8%, 66.7%, 92.4%, 88.4%, and 92.0%, respectively, on real brain MR data. [ABSTRACT FROM AUTHOR]

Fractal analysis was used in the study to determine a set of feature descriptors which could be applied in the process of diagnosing bone damage caused by osteoporosis. The subject of the research was CT images of vertebrae on the thoraco-lumbar region. The dataset contained images of healthy patients and patients diagnosed with osteoporosis. On the basis of fractal analysis and feature selection by linear stepwise regression, three descriptors were obtained. These were two fractal dimensions calculated by the variation method and fractal lacunarity calculated by the box counting method. The first two descriptors were obtained as a result of the analysis of gray images, and the third was the result of analysis of binary images. The effectiveness of the descriptors was verified using six popular supervised classification methods: linear and quadratic discriminant analyses, naive Bayes classifier, decision tree, K -nearest neighbors (K -NN) and random forests. The best results were obtained using the K -NN classifier; they were as follows: overall classification accuracy: 81%, classification sensitivity: 78%, classification specificity: 90%, positive predictive value: 90% and negative predictive value: 77%. The results of the research have shown that fractal analysis can be a useful tool to extract features of spinal CT images in the diagnosis of osteoporotic bone defects. [ABSTRACT FROM AUTHOR]

When analyzing financial markets, it needs to mine effective information from massive data. However, it is difficult to obtain information from image information. In order to improve the efficiency of financial market analysis, this paper applies the iris recognition algorithm to financial image data analysis and proposes a feature extraction and recognition algorithm based on morphological skeleton and Gabor filter. The algorithm uses a multi-frequency, multi-directional 2D Gabor filter to extract local features and combines the extracted feature codes with the iris recognition method to complete the identification of intra-class irises and inter-class irises. In addition, in order to verify the effect of the algorithm, this study uses MatalabGUI as a platform to build an experimental model. In summary, in this study, financial images are used as research images for identification and analysis. The research results show that the algorithm proposed in this paper has a certain effect. [ABSTRACT FROM AUTHOR]

Traditional art design models are inefficient and difficult to control the modal characteristics of images. Based on the actual needs of artistic design, this paper builds an intelligent model of artistic design image modal analysis based on machine learning technology and image recognition technology, and proposes a feature extraction method based on wavelet transform and fuzzy logic and an art image classification method based on rotating quaternion wavelet transform. Moreover, this paper calculates the activation intensity value corresponding to each fuzzy area in the fuzzy feature space and normalizes these activation intensity values to form the artistic image feature vector. In addition, this paper uses rotating quaternary wavelet transform to decompose the art image, and then calculate the energy and standard deviation of each decomposed sub-band coefficient. Finally, this paper uses support vector machines to recognize artistic images. The experimental research shows that the model constructed in this paper has certain effects. [ABSTRACT FROM AUTHOR]