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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]

In China, cemented paste backfill (CPB) is a common treatment method after the exploitation of basic energy. The homogeneity of slurry influences the performance of CPB. However, the online monitoring and characterization of homogeneity lack relevant technologies and unified standards. This article discusses an online image analysis technique applied to the online monitoring of cemented paste backfill mixing, which is based on the evolution of the texture of images taken at the surface of the mixing bed. First, the grayscale distribution of the image obtained by the high-speed camera in the CPB preparation process was analyzed by Matlab and its variance (s2) was solved, and the texture features of the image were analyzed by the variance of grayscale distribution. Then, a homogeneity discriminant model ( c s t ) was established. The results show that the variance value of the grayscale distribution of the slurry image increases rapidly at first, then gradually decreases, and becomes stable in the final stage since it turns a constant value. When the s2 value tends to be stable, the slurry gradually reaches homogenization, and the discriminant coefficient of paste homogenization based on the homogenization discriminant model reaches 0.05. The homogenization prediction of CPB proves to be consistent with the backfill performance comparison results. The evolution of the texture allows obtaining important information on the evolution of different formulations during mixing, which can be used for intelligent monitoring of CPB preparation process. [ABSTRACT FROM AUTHOR]

Equine odontoclastic tooth resorption and hypercementosis (EOTRH) is one of the horses' dental diseases, mainly affecting the incisor teeth. An increase in the incidence of aged horses and a painful progressive course of the disease create the need for improved early diagnosis. Besides clinical findings, EOTRH recognition is based on the typical radiographic findings, including levels of dental resorption and hypercementosis. This study aimed to introduce digital processing methods to equine dental radiographic images and identify texture features changing with disease progression. The radiographs of maxillary incisor teeth from 80 horses were obtained. Each incisor was annotated by separate masks and clinically classified as 0, 1, 2, or 3 EOTRH degrees. Images were filtered by Mean, Median, Normalize, Bilateral, Binomial, CurvatureFlow, LaplacianSharpening, DiscreteGaussian, and SmoothingRecursiveGaussian filters independently, and 93 features of image texture were extracted using First Order Statistics (FOS), Gray Level Co-occurrence Matrix (GLCM), Neighbouring Gray Tone Difference Matrix (NGTDM), Gray Level Dependence Matrix (GLDM), Gray Level Run Length Matrix (GLRLM), and Gray Level Size Zone Matrix (GLSZM) approaches. The most informative processing was selected. GLCM and GLRLM return the most favorable features for the quantitative evaluation of radiographic signs of the EOTRH syndrome, which may be supported by filtering by filters improving the edge delimitation. [ABSTRACT FROM AUTHOR]

The use of innovative techniques in the classification of dried products for food processing and packaging systems simplifies and brings objectivity to the related processes. In addition, it helps consumers to access quality products. This study aimed to discriminate dried carrot samples subjected to 12 different combinations of drying and pretreatment by using innovative techniques based on chromatic attributes. The drying procedure consists of three different microwave powers (100, 200, 300 W), and pretreatments include 5% gum arabic solution, 5% gum sucrose solution, ultrasound application, and a control (no-pretreatment). The R , G , B , L , a , b , H , S, and V color channels were extracted from dried carrot images, and the exploration of classification models from selected color channels was an innovative aspect of the study. In all color channels, high overall accuracies were obtained with the values of 96.33, 96.50, and 97.00% for the logistic model tree, k-nearest neighbor, and random forest, respectively. The confusion matrix showed the lowest mixing among carrots dried at H-US (300 W + ultrasound) vs. H-C (300 W + control), H-S (300 W + sucrose) vs. H-C (300 W + control), and M-S (200 W + sucrose) vs. H-S (300 W + sucrose). The highest L, a, and b color values for the dried carrot slices were obtained as M-S (75.26), H-GA (30.93), and M-GA (44.75), respectively. In addition, hue values varied between 17.26 and 13.27, whereas saturation values varied between 50.93 and 59.90. The finding revealed that assessment of the effect of pretreatment and drying on carrot slices could be performed by machine learning and image analysis in a practical, rapid, and non-destructive manner. • Eight different machine learning algorithms was applied for classification. • 12 carrot classes subjected to various treatments were distinguished. • Innovative models built based on image textures were developed. • LMT, kNN, and RF was more successful for classification of dried carrot slices. • The developed procedure was non-destructive, objective, and effective. [ABSTRACT FROM AUTHOR]

Land cover mapping is an important part of resource management, planning, and economic predictions. Improvements in remote sensing, machine learning, image processing, and object based image analysis (OBIA) has made the process of identifying land cover types increasingly faster and reliable but these advances have not been able to utilize all of the information encompassed within ultra-high (sub-meter) resolution imagery. There have been few known attempts to try and maximize this detailed information in high resolution imagery using advanced textural components. Hierarchical land classes are also rarely used as an attribute within the machine learning step of object-based image analysis. In this study we try to circumnavigate the inherent problems associated with high resolution imagery by combining well researched data transformations that aid the OBIA process with a seldom used texture transformation in Geographic Object Based Image Analyses (GEOBIA/OBIA) known as the Gabor Transform and the hierarchal organization of landscapes. We will observe the difference made in segmentation and classification accuracy of a random forest classifier when we fuse a Gabor transformed image to a Normalized Difference Vegetation Index (NDVI), high resolution multi-spectral imagery (RGB and NIR) and Light Detection and Ranging (LiDAR) derived canopy height model (CHM) within a riparian area in Southeast Iowa, United States. Additionally, we will observe the effects on classification accuracy when adding multi-scale land cover data to objects. Both, the addition of hierarchical information and Gabor textural information, could aid the GEOBIA process in delineating and classifying the same objects that human experts would delineate within this riparian landscape. [ABSTRACT FROM AUTHOR]

In order to improve the retrieval ability of super-resolution multi-space block images, a texture retrieval method of block images based on depth hash is proposed. A texture feature analysis model of super-resolution multi-dimensional partitioned images is constructed, which combines texture spatial structure mapping method to realize depth information fusion of partitioned images, adopts edge feature detection and texture sparse feature clustering to realize texture hierarchical structure feature decomposition of super-resolution multi-dimensional partitioned images, and adopts deep image parameter analysis method to construct pixel structure recombination model of multi-dimensional partitioned images. Multi-dimensional texture parameter structure analysis and information clustering are realized for the collected partitioned images in multi-dimensional space. According to the information clustering results, the texture retrieval and extraction of partitioned images are realized by using the deep hash fusion algorithm, and the information detection and feature recognition capabilities of partitioned images in multi-dimensional space are improved. Simulation results show that this method has higher precision and better feature resolution in texture retrieval of partitioned images in multidimensional space, which improves the texture retrieval and recognition ability of partitioned images. [ABSTRACT FROM AUTHOR]

Simple Summary: Detecting horse state after exercise is critical for maximizing athletic performance. The horse's response to fatigue includes exercise termination or exercise continuation at a lower intensity, which significantly limit the results achieved in races and equestrian competition. As conventional methods of detecting and quantifying exercise effort have shown some limitations, infrared thermography was proposed as a method of contactless detection of exercise effect. The promising correlation between body surface temperature and exercise-dependent blood biomarkers has been demonstrated. As the application of conventional thermography is limited by low specificity, advanced thermal image analysis was proposed here to visualize the link between blood biomarkers and texture of thermal images. Twelve horses underwent standardized exercise tests for six consecutive days, and both thermal images and blood samples were collected before and after each test. The images were analyzed using four color models (RGB, red-green-blue; YUV, brightness-UV-components; YIQ, brightness-IQ-components; HSB, hue-saturation-brightness) and eight texture-features approaches, including 88 features in total. In contrast to conventional temperature measures, as many as twelve texture features in two color models (RGB, YIQ) were linked with blood biomarker levels as part of the horse's response to exercise. As the detection of horse state after exercise is constantly developing, a link between blood biomarkers and infrared thermography (IRT) was investigated using advanced image texture analysis. The aim of the study was to determine which combinations of RGB (red-green-blue), YUI (brightness-UV-components), YIQ (brightness-IQ-components), and HSB (hue-saturation-brightness) color models, components, and texture features are related to the blood biomarkers of exercise effect. Twelve Polish warmblood horses underwent standardized exercise tests for six consecutive days. Both thermal images and blood samples were collected before and after each test. All 144 obtained IRT images were analyzed independently for 12 color components in four color models using eight texture-feature approaches, including 88 features. The similarity between blood biomarker levels and texture features was determined using linear regression models. In the horses' thoracolumbar region, 12 texture features (nine in RGB, one in YIQ, and two in HSB) were related to blood biomarkers. Variance, sum of squares, and sum of variance in the RGB were highly repeatable between image processing protocols. The combination of two approaches of image texture (histogram statistics and gray-level co-occurrence matrix) and two color models (RGB, YIQ), should be considered in the application of digital image processing of equine IRT. [ABSTRACT FROM AUTHOR]

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

Recent asteroid missions have revealed that many sub-kilometer asteroids are rubble piles. Large parts of their surfaces are covered with boulders larger than tens of centimeters. An evaluation of the abundance and size distribution of boulders provides clues to understand surface processes on boulder-covered asteroids. Here we report a new method that automatically measures the abundance of small boulders (sub-pixel to a few pixels), whose boundaries cannot be recognized with visual inspection, by quantifying the surface radiance variation that occurs during the spinning of the asteroid. After validating our approach with previous boulder counting data, we apply this method to images of the asteroids Ryugu and Itokawa, which were visited by JAXA's Hayabusa and Hayabusa2, and obtain a global distribution of the boulders larger than 0.75–3 ​m, which corresponds to 1.5–6 pixels. We find that the boulder number density of this size range is smaller (1) on the western bulge than on the eastern hemisphere and (2) on the equatorial ridge than on the higher latitudes, both of which exceed the number density of boulders ​> ​5 ​m by an order of magnitude. The boulder size distribution at 1.25–20 ​m shows that the boulders smaller than 1 ​m are more abundant at the equator than at mid-latitudes, while those larger than 1 ​m in diameter are more abundant at mid-latitudes than at the equator. This contrast suggests size-dependent migration of boulders in the latitudinal direction. We also find that the typical boulder size (the size reaching the cumulative areal coverage of 50%) is 1.9 ​m at the equatorial region (10°S-10°N) while it is 2.6 ​m at mid-latitudes (40°S-50°S, 40°N-50°N). The typical boulder size is also smaller in the western bulge (2.0–2.2 ​m). We construct global maps of the power-law index of the size frequency distribution of boulders and find minor variations over the entire surface of Ryugu (−2.53 ​± ​0.03) for boulders larger than 1.25 ​m. This small variation suggests homogeneous size sorting processes on Ryugu. Surface roughness does not show a significant correlation with the v-band albedo but shows a high anti-correlation (R ​= ​−0.73) with the current geological slope on the eastern hemisphere. Our method is useful enhancement of smooth area detection and boulder distribution characterization that will be applicable to other planetary explorations in the future, including those of Phobos and other asteroids. • Automatic analysis of pixel-scale boulders based on pixel radiance variation as asteroid spins. • Distribution of sub-meter boulders on Ryugu indistinguishable from visual counting. • Method detects the east-west dichotomy in the distribution of small boulders (several meters in diameter). • Uniform power-law index of boulder size frequency distribution on Ryugu suggests homogeneous size sorting. • Boulder distribution is correlated with current geologic slope. [ABSTRACT FROM AUTHOR]

Fresh grapes are characterized by a short shelf life and are often subjected to quality losses during post-harvest storage. The quality assessment of grapes using image analysis may be a useful approach using non-destructive methods. This study aimed to compare the effect of different storage methods on the grape image texture parameters of the fruit outer structure. Grape bunches were stored for 4 weeks using 3 storage methods (– 18 °C, + 4 °C, and room temperature) and then were subjected subsequently to image acquisition using a flatbed scanner and image processing. The models for the classification of fresh and stored grapes were built based on selected image textures using traditional machine learning algorithms. The fresh grapes and stored fruit samples (for 4 weeks) in the freezer, in the refrigerator and in the room were classified with an overall accuracy reaching 96% for a model based on selected texture parameters from images in color channels R, G, B, L, a, and b built using Random Forest algorithm. Among the individual color channels, the carried-out classification for the R color channel produced the highest overall accuracies of up to 92.5% for Random Forest. As a result, this study proposed an innovative approach combining image analysis and traditional machine learning to assess changes in the outer structure of grape berries caused by different storage conditions. [ABSTRACT FROM AUTHOR]

Goblet cells (GCs) in the conjunctiva are specialized epithelial cells secreting mucins for the mucus layer of protective tear film and playing immune tolerance functions for ocular surface health. Because GC loss is observed in various ocular surface diseases, GC examination is important for precision diagnosis. Moxifloxacin-based fluorescence microscopy (MBFM) was recently developed for non-invasive high-contrast GC visualization. MBFM showed promise for GC examination by high-speed large-area imaging and a robust analysis method is needed to provide GC information. In this study, we developed a deep learning framework for GC image analysis, named dual-channel attention U-Net (DCAU-Net). Dual-channel convolution was used both to extract the overall image texture and to acquire the GC morphological characteristics. A global channel attention module was adopted by combining attention algorithms and channel-wise pooling. DCAU-Net showed 93.1% GC segmentation accuracy and 94.3% GC density estimation accuracy. Further application to both normal and ocular surface damage rabbit models revealed the spatial variations of both GC density and size in normal rabbits and the decreases of both GC density and size in damage rabbit models during recovery after acute damage. The GC analysis results were consistent with histology. Together with the non-invasive high-contrast imaging method, DCAU-Net would provide GC information for the diagnosis of ocular surface diseases. [ABSTRACT FROM AUTHOR]

Superficial fibromatosis exhibits variable MR signal intensity due to collagenous and fibroproliferative components. Quantifying this signal heterogeneity using image texture analysis and T2-mapping could have prognostic and therapeutic implications. This IRB-approved retrospective study included 13 patients with superficial fibromatosis, managed by observation, electron beam radiotherapy (EBT), or pentoxifylline/vitamin E. Two-dimensional regions of interest (ROIs) were drawn on proton-density or T2-weighted MRI for radiomics feature analysis, and corresponding T2-maps. Comparisons were made between baseline and follow-up T2 relaxation times and radiomics features: Shannon's entropy, kurtosis, skewness, mean of positive pixels (MPP), and uniformity of distribution of positive gray-level pixel values (UPP). There were 19 nodules in 13 subjects. Mean patient age was 60 years; 62% (8/13) were female; mean follow-up was 9.7 months. Nodule diameter at baseline averaged 18.2 mm (std dev 16.2 mm) and decreased almost 10% to 16.6 mm (p = 0.1, paired t -test). Normalized T2 signal intensity decreased 23% from 0.71 to 0.55 (p = 0.03, paired t -test). T2 relaxation time decreased 16% from 46.5 to 39.1 ms (p < 0.001, paired t -test). Among radiomics features, skewness increased to 0.71 from 0.41 (p = 0.03, paired t -test), and entropy decreased from 8.37 to 8.03 (p = 0.05, paired t-test); differences in other radiomics features were not significant. Radiomics analysis and T2-mapping of superficial fibromatosis is feasible; robust decreases in absolute T2 relaxation time, and changes in image textural features (increased skewness and decreased entropy) offer novel imaging biomarkers of nodule collagenization and maturation. • T2 mapping, image texture analysis show longitudinal changes in superficial fibromatoses. • Treated nodules show decreased T2 relaxation times, reflecting increased collagenization. • Increased skewness and decreased entropy indicate homogeneity accompanies response. [ABSTRACT FROM AUTHOR]

Machine learning, and especially deep learning, is rapidly gaining acceptance and clinical usage in a wide range of image analysis applications and is regarded as providing high performance in detecting anatomical structures and identification and classification of patterns of disease in medical images. However, there are many roadblocks to the widespread implementation of machine learning in clinical image analysis, including differences in data capture leading to different measurements, high dimensionality of imaging and other medical data, and the black-box nature of machine learning, with a lack of insight into relevant features. Techniques such as radiomics have been used in traditional machine learning approaches to model the mathematical relationships between adjacent pixels in an image and provide an explainable framework for clinicians and researchers. Newer paradigms, such as topological data analysis (TDA), have recently been adopted to design and develop innovative image analysis schemes that go beyond the abilities of pixel-to-pixel comparisons. TDA can automatically construct filtrations of topological shapes of image texture through a technique known as persistent homology (PH); these features can then be fed into machine learning models that provide explainable outputs and can distinguish different image classes in a computationally more efficient way, when compared to other currently used methods. The aim of this review is to introduce PH and its variants and to review TDA's recent successes in medical imaging studies. Key points: Topological data analysis (TDA) provides information on the shape of data. In radiology, the shape of 2D and 3D images contains additional information. TDA can be combined with other applications, such as textural analysis. Persistent homology can provide a visual representation of extracted TDA data. [ABSTRACT FROM AUTHOR]

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]

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

Infrared thermography (IRT) was applied as a potentially useful tool in the detection of pregnancy in equids, especially native or wildlife. IRT measures heat emission from the body surface, which increases with the progression of pregnancy as blood flow and metabolic activity in the uterine and fetal tissues increase. Conventional IRT imaging is promising; however, with specific limitations considered, this study aimed to develop novel digital processing methods for thermal images of pregnant mares to detect pregnancy earlier with higher accuracy. In the current study, 40 mares were divided into non-pregnant and pregnant groups and imaged using IRT. Thermal images were transformed into four color models (RGB, YUV, YIQ, HSB) and 10 color components were separated. From each color component, features of image texture were obtained using Histogram Statistics and Grey-Level Run-Length Matrix algorithms. The most informative color/feature combinations were selected for further investigation, and the accuracy of pregnancy detection was calculated. The image texture features in the RGB and YIQ color models reflecting increased heterogeneity of image texture seem to be applicable as potential indicators of pregnancy. Their application in IRT-based pregnancy detection in mares allows for earlier recognition of pregnant mares with higher accuracy than the conventional IRT imaging technique. [ABSTRACT FROM AUTHOR]

Purpose: Computer-assisted tissue image analysis (CATIA) enables an optical biopsy of human tissue during minimally invasive surgery and endoscopy. Thus far, it has been implemented in gastrointestinal, endometrial, and dermatologic examinations that use computational analysis and image texture feature systems. We review and evaluate the impact of in vivo optical biopsies performed by tissue image analysis on the surgeon's diagnostic ability and sampling precision and investigate how operation complications could be minimized. Methods: We performed a literature search in PubMed, IEEE, Xplore, Elsevier, and Google Scholar, which yielded 28 relevant articles. Our literature review summarizes the available data on CATIA of human tissues and explores the possibilities of computer-assisted early disease diagnoses, including cancer. Results: Hysteroscopic image texture analysis of the endometrium successfully distinguished benign from malignant conditions up to 91% of the time. In dermatologic studies, the accuracy of distinguishing nevi melanoma from benign disease fluctuated from 73% to 81%. Skin biopsies of basal cell carcinoma and melanoma exhibited an accuracy of 92.4%, sensitivity of 99.1%, and specificity of 93.3% and distinguished nonmelanoma and normal lesions from benign precancerous lesions with 91.9% and 82.8% accuracy, respectively. Gastrointestinal and endometrial examinations are still at the experimental phase. Conclusions: CATIA is a promising application for distinguishing normal from abnormal tissues during endoscopic procedures and minimally invasive surgeries. However, the efficacy of computer-assisted diagnostics in distinguishing benign from malignant states is still not well documented. Prospective and randomized studies are needed before CATIA is implemented in clinical practice. [ABSTRACT FROM AUTHOR]

The effect of fish diet and cooking modality was described for Gilthead sea bream juveniles (Sparus aurata). Three feeds were tested: a control diet containing fish meal (FM>26%) and fish oil (FO>6%) and two diets where FM and FO were replaced with more sustainable alternatives (FM<14%; FO<5%). Fillets were analyzed raw and after cooking at increasing intensities: sous-vide , boiling, roasting and microwave oven baking. The overall quality of fish fillets was assessed by measuring their appearance by Image Analysis, by characterizing their proximate composition, fatty acid profiles, quality indices, and their microstructural features by Magnetic Resonance Imaging and relaxometry. The analysis of experimental data suggested that fish diet specifically characterized the nutritional quality of cooked fillets by affecting their fatty acid profiles, irrespective of the cooking process. Appearance (color, image texture) and muscle microstructure were only influenced by the intensity of cooking, regardless of diet. As cooking intensity increased, water and oil distribution in myofibrils was progressively reorganized and muscle fibers shrunk, causing relevant microstructural changes, especially in roasted and microwave oven baked fillets. The paper provides a useful guide for the production of ready-to-eat meals derived from sustainable aquaculture processes and characterized by high sensory and nutritional quality. [Display omitted] • Magnetic Resonance relaxometry and Image Analysis can guide fish fillet processing. • Fatty acids, color and muscle microstructure of fillets were differentially affected. • Diet affected nutritional quality while cooking mostly color and microstructure. • Combined effect of fish diet and processing on quality is studied for the first time. [ABSTRACT FROM AUTHOR]

Multifractal analysis was applied to evidence spatial properties, in particular scaling behaviour, of Sentinel-2 surface reflectances and optical indexes acquired over the Southwest region of France. Considering the significant impact of clouds on the spatial properties of optical products, no study to our knowledge has focused on the multifractal analysis of a series of images. Images with minimal cloud cover were identified for the whole year 2016 based on the MACCS-ATCOR Joint Algorithm (MAJA) cloud and shadow detection process. This allowed us to access the dynamics of the scaling properties of surface reflectances, contributing to demonstrate the potential of multifractals for spatial characterization. Different scaling regimes, separated by significant scale breaks, were evident in these data. Through simulations from the Universal Multifractal model, we could explain the scaling properties and especially the position of scale breaks observed in the images studied by two types of phenomena. First, a transition scale located between hundreds of meters and one kilometre was detected on power spectra, structure functions and statistical moments. This specific scale was related to the average size of the agricultural plots of the studied region, revealing the direct link between the scaling behaviour observed and the physical properties of the surface. Second, an instrumental phenomenon was detected on the power spectra, resulting in a systematic transition scale observed near the resolution of the sensor and revealing the impact of the acquisition system on the high frequencies of optical images. In this research work, theoretical and/or empirical validation arguments were provided as to the origin of the scales detected, contributing to demonstrate the value of multifractals to extract image texture from surface reflectance products. • Particular scale breaks were detected on Sentinel-2/MAJA products. • Time variations in scaling regimes were noticed in series of surface reflectances. • The large-scale break was related to the landscape spatial structure. • The fine-scale break was explained by the effect of the optical acquisition system. [ABSTRACT FROM AUTHOR]

Simple Summary: Overloading of the horse's thoracolumbar region is a serious problem mainly affecting sport and school horses during their daily under-saddle work. As the human population becomes heavier, the effect of rider bodyweight on equine welfare and performance requires further investigation. This study used infrared thermography to assess the effect of rider:horse bodyweight ratio on the horse's thoracolumbar region by introducing advanced digital image processing. Twelve horses during regular work were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images of the back region were taken before and after standard exercise and underwent conventional analysis and texture analysis where the thermal images were separated into red, green, and blue components. Four areas of the horse's thoracolumbar region were annotated to represent the withers area, the thoracic spine area, and the left and right areas of back muscles. Among 372 returned features, 75 texture features differed between bodyweight ratio groups, whereas the conventional thermal features did not. Contrary to conventional thermal features, the consistent measurable differences in texture features were evidenced predominantly in the red component of thermal images when the texture heterogeneity measures, such as InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, were considered. Appropriate matching of rider–horse sizes is becoming an increasingly important issue of riding horses' care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body's surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10–12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) images are inherently degraded by speckle noise caused by coherent imaging, which may affect the performance of the subsequent image analysis task. To resolve this problem, this article proposes an integrated SAR image despeckling model based on dictionary learning and multi-weighted sparse coding. First, the dictionary is trained by groups composed of similar image patches, which have the same structural features. An effective orthogonal dictionary with high sparse representation ability is realized by introducing a properly tight frame. Furthermore, the data-fidelity term and regularization terms are constrained by weighting factors. The weighted sparse representation model not only fully utilizes the interblock relevance but also reflects the importance of various structural groups in despeckling processing. The proposed model is implemented with fast and effective solving steps that simultaneously perform orthogonal dictionary learning, weight parameter updating, sparse coding, and image reconstruction. The solving steps are designed using the alternative minimization method. Finally, the speckles are further suppressed by iterative regularization methods. In a comparison study with existing methods, our method demonstrated state-of-the-art performance in suppressing speckle noise and protecting the image texture details. [ABSTRACT FROM AUTHOR]

• Surface roughness is a complex multiscale characteristic and multiple roughness indexes can be defined. • The simplified geostatistical-based algorithm presented describes key aspects of short-range roughness. • Roughness anisotropy is a key morphological characteristic. Surface roughness, i.e. local variations in elevation which indicate less and less local smoothness, is an important concept for the analysis of solid earth/planetary surfaces and of the related surface processes. Surface roughness is a general concept covering multiple aspects of the surface spatial variability structure, that can be characterized at different scales and with different metrics. Accordingly, it is not surprising the wide range of approaches and algorithms adopted for the analysis of surface roughness and the lack of an accepted standard for its evaluation. In this context, geostatistical-based roughness indexes are a valid solution, providing a good balance between flexibility of algorithms and interpretability of the results. However, despite the long record of applications and the well-known and robust theoretical framework, geostatistical-based surface roughness tools have still not gained momentum in the context of geomorphometric analysis. Many geomorphometric studies concerning with the evaluation of roughness indexes are based on popular approaches, such as the ones based on measures of dispersion of normal vectors to surface. Unfortunately, these indexes present some drawbacks in the capability to represent specific aspects of surface roughness and from the perspective of their interpretability. Accordingly, in this paper some of the key aspects and advantages of geostatistical-based approaches for roughness analysis are highlighted, including the relevance of roughness anisotropy. Then, a new approach tailored to the analysis of short-range roughness, requiring a minimum intervention by the user, is introduced discussing the advantages compared to the vector dispersion-based roughness. The new approach is implemented as open-source code both in R language using the functions of the "Terra" package as well as in Python for Esri ArcMap GIS. The methodological considerations and algorithms presented can be applied also in the broader context of image analysis. [ABSTRACT FROM AUTHOR]