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

Currently, a reliable and easy-to-use method to monitor flocculation in the water treatment process is highly demanded, especially for small water purification stations. For this problem, in situ images were used to analyze the flocculation process under different conditions via jar tests. A texture feature of the gray level co-occurrence matrix was found to be helpful for monitoring the floc status, such as growth rate and settling velocity. To further verify this finding, we established the correlation between the texture time sequence curve (TTSC) and its corresponding floc status. The slope of the TTSC during the growth phase and during the settling phase can describe the growth rate and the settling velocity, respectively, i.e., the higher the slope, the higher the growth rate and settling velocity. In addition, significant differences between the TTSCs in various abnormal conditions and the normal condition of coagulation can be identified. By using the TTSC for detecting abnormal conditions, we again verified that the texture feature can reliably reflect the flocculation process. Our study helps to develop a low-cost, stable, and simple method for monitoring flocculation and detecting abnormal conditions, which can effectively be used in the operation and management of water treatment plants. HIGHLIGHTS Compared with the morphological features, the texture features are more stable.; The texture time sequence curve (TTSC) can describe flocculation.; Variation characteristics of TTSC can detect abnormal coagulation conditions.; TTSC-based methods can be readily applied online.;

A novel method of disease diagnosis, based on images that capture every part of a diseased plant, such as the leaf, the fruit, the root, etc., is presented in this paper. As is well known, the plant genotypic and phenotypic characteristics can significantly impact how plants are affected by viruses, bacteria, or fungi that cause disease. Assume that these data are unknown at the outset and that the appropriate precautions are not taken to prevent classifications skewed toward uninteresting traits. An approach to avoid categorization bias brought on by the morphology of leaves is suggested in this study. The basis of this approach is the extraction of textural features. Additionally, Bayesian Optimization is suggested to obtain training hyperparameters that enable the creation of better-trained artificial neural networks. First, we initially pre-processed the images from the PlantVillage dataset to remove background noise. Then, tiles from images were used to reduce any potential bias from leaf form. Finally, several cutting-edge tiny convolutional neural networks (CNNs), created for contexts with little processing power, were trained on a new dataset of 85 × 85 × 3 px images. MobileNet, which had a 96.31% accuracy rate, and SqueezeNet, which had a 95.05% accuracy rate, were the models that predicted the best performance. The results were then examined using Precision and Recall measures, which are important for identifying plant diseases.

Liver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws’ method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k -nearest neighbors ( k -NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k -NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws’ texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons. [ABSTRACT FROM AUTHOR]

Calculation of co-occurrence probabilities is a popular method for determining texture features within remotely sensed digital imagery. Typically, the co-occurrence features are calculated by using a grey level co-occurrence matrix (GLCM) to store the co-occurring probabilities. Statistics are applied to the probabilities in the GLCM to generate the texture features. This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities when applying the statistics. An improvement on the GLCM method is to utilize a grey level co-occurrence linked list (GLCLL) to store only the non-zero co-occurring probabilities. The GLCLL suffers since, to achieve preferred computational speeds, the list should be sorted. An improvement on the GLCLL is to utilize a grey level co-occurrence hybrid structure (GLCHS) based on an integrated hash table and linked list approach. Texture features obtained using this technique are identical to those obtained using the GLCM and GLCLL.The GLCHS method is implemented using the C language in a Unix environment. Based on a Brodatz test image, the GLCHS method is demonstrated to be a superior technique when compared across various window sizes and grey level quantizations. The GLCHS method required, on average, 33.4% (σ=3.08%) of the computational time required by the GLCLL. Significant computational gains are made using the GLCHS method. [Copyright &y& Elsevier]

Quality of machined surfaces and its integrity are strongly dependent on tool wear, which consequently depends on the tool life or machining time. Therefore, prediction of the machining time of the cutting tool during machining processes is very important in order to obtain high precision parts and to reduce manual fit operations and production cost. In this work, the relationship between texture features of the gray-level co-occurrence matrix and the machining time of the cutting tool in turning operations has been investigated, and the results show that four texture features are highly correlated with machining time. These highly correlated texture features were utilized to calculate machining time of the cutting tool from captured images of machined parts. The verification results showed that the maximum percentage of error between the actual and calculated machining time ranged between −4.65% and 7.79%. [ABSTRACT FROM PUBLISHER]

Measuring surface roughness is vital to quality control of the machined workpiece. In recent years, vision systems have made image analysis easier and more flexible for measuring surface roughness by using texture features. In this paper, the texture features of the grey-level co-occurrence matrix (GLCM) have been utilized to estimate surface roughness of specimens machined by turning operations. The relationship between GLCM texture features and surface roughness has been investigated to discover which texture features can be used to estimate surface roughness. The correlation coefficient between each texture feature and the arithmetic average height (Ra) was calculated and discussed. The investigation showed that six texture features are highly correlated with Ra. Therefore, these texture features were used to estimate surface roughness for similar specimens with known values of Ra. The results showed that the maximum percentage of error between the actual Ra and the estimated Ra was about ±7 per cent. [ABSTRACT FROM AUTHOR]

The sea-sky line is an important basis for marine unmanned equipment to perceive the sea environment. However, due to the interference of many external factors, such as sea fog, illumination, and sea target occlusion, sea-sky line detection is challenging. Therefore, we present a texture feature-based local adaptive Otsu segmentation and Hough transform for sea-sky line detection. In this method, image texture features and weighted texture quantization are used to determine the sea-sky area. Based on this sea-sky area, a longitudinal block strategy is introduced, and a new adaptive Otsu segmentation method is applied to obtain the binary image of the sea-sky area. The obtained binary image is then post-processed to enhance the sea-sky line edge information. On this basis, a new adaptive Canny edge detector is applied, and the sea-sky line is extracted by the Hough transform. The experimental results show that the proposed sea-sky line detection method has high accuracy and robustness when handling images of complex marine environments. Compared with other algorithms, the detection error and the error standard deviation of the proposed method are relatively small, indicating that this method is more stable and accurate than other algorithms. [ABSTRACT FROM AUTHOR]

Around one in eight women will be diagnosed with breast cancer at some time. Improved patient outcomes necessitate both early detection and an accurate diagnosis. Histological images are routinely utilized in the process of diagnosing breast cancer. Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels. No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer. A strategy for detecting breast cancer is provided in the context of this investigation. Histopathology image texture data is used with the wavelet transform in this technique. The proposed method comprises converting histopathological images from Red Green Blue (RGB) to Chrominance of Blue and Chrominance of Red (YCBCR), utilizing a wavelet transform to extract texture information, and classifying the images with Extreme Gradient Boosting (XGBOOST). Furthermore, SMOTE has been used for resampling as the dataset has imbalanced samples. The suggested method is evaluated using 10-fold cross-validation and achieves an accuracy of 99.27% on the BreakHis 1.0 40X dataset, 98.95% on the BreakHis 1.0 100X dataset, 98.92% on the BreakHis 1.0 200X dataset, 98.78% on the BreakHis 1.0 400X dataset, and 98.80% on the combined dataset. The findings of this study imply that improved breast cancer detection rates and patient outcomes can be achieved by combining wavelet transformation with textural signals to detect breast cancer in histopathology images. [ABSTRACT FROM AUTHOR]

The identification of sedimentary structures in lithofacies is of great significance to the exploration and development of Paleogene shale in the Boxing Sag. However, due to the scale mismatch between the thickness of laminae and the vertical resolution of conventional wireline logs, the conventional lithofacies division method fails to realize the accurate classification of sedimentary structures and cannot meet the needs of reservoir research. Therefore, it is necessary to establish a lithofacies identification method with higher precision from advanced logs. In this paper, a method integrating the gray level co-occurrence matrix (GLCM) and random forest (RF) algorithms is proposed to classify shale lithofacies with different sedimentary structures based on formation micro-imager (FMI) imaging logging and elemental capture spectroscopy (ECS) logging. According to the characteristics of shale laminae on FMI images, GLCM, an image texture extraction tool, is utilized to obtain texture features reflecting sedimentary structures from FMI images. It is proven that GLCM can depict shale sedimentary structures efficiently and accurately, and four texture features (contrast, entropy, energy, and homogeneity) are sensitive to shale sedimentary structures. To accommodate the correlation between the four texture features, the random forest algorithm, which has been proven not to be affected by correlated input features, is selected for supervised lithofacies classification. To enhance the model's ability to differentiate between argillaceous limestone and calcareous mudstone, the carbonate content and clay content calculated from the ECS logs are involved in the input features. Moreover, grid search cross-validation (CV) is implemented to optimize the hyperparameters of the model. The optimized model achieves favorable performance on training data, validation data, and test data, with average accuracies of 0.84, 0.79, and 0.76, respectively. This study also discusses the application of the classification model in lithofacies and production prediction. [ABSTRACT FROM AUTHOR]

Image forgery is a crucial part of the transmission of misinfor)mation, which may be illegal in some jurisdictions. The powerful image editing software has made it nearly impossible to detect altered images with the naked eye. Images must be protected against attempts to manipulate them. Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications. Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images. Because image tampering detection targets processing techniques such as object removal or addition, identifying altered images remains a major challenge in research. In this study, a novel image texture feature extraction model based on the generalized k-symbol Whittaker function (GKSWF) is proposed for better image forgery detection. The proposed method is divided into two stages. The first stage involves feature extraction using the proposed GKSWF model, followed by classification using the “support vector machine” (SVM) to distinguish between authentic and manipulated images. Each extracted feature from an input image is saved in the features database for use in image splicing detection. The proposed GKSWF as a feature extraction model is intended to extract clues of tam)pering texture details based on the probability of image pixel. When tested on publicly available image dataset “CASIA” v2.0 (Chinese Academy of Sciences, Institute of Automation), the proposed model had a 98.60% accuracy rate on the YCbCr (luminance (Y), chroma blue (Cb) and chroma red (Cr)) color spaces in image block size of 8 × 8 pixels. The proposed image authentication model shows great accuracy with a relatively modest dimension feature size, supporting the benefit of utilizing the k-symbol Whittaker function in image authentication algorithms. [ABSTRACT FROM AUTHOR]

Marine oil spills are one of the major threats to marine ecological safety, and the rapid identification of oil films is of great significance to the emergency response. Marine radar can provide data for marine oil spill detection; however, to date, it has not been commonly reported. Traditional marine radar oil spill research is mostly based on grayscale segmentation, and its accuracy depends entirely on the selection of the threshold. With the development of algorithm technology, marine radar oil spill extraction has gradually come to focus on artificial intelligence, and the study of oil spills based on machine learning has begun to develop. Based on X-band marine radar images collected from the Dalian 716 incident, this study used image texture features, the BP neural network classifier, and threshold segmentation for oil spill extraction. Firstly, the original image was pre-processed, to eliminate co-channel interference noise. Secondly, texture features were extracted and analyzed by the gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA); then, the BP neural work was used to obtain the effective wave region. Finally, threshold segmentation was performed, to extract the marine oil slicks. The constructed BP neural network could achieve 93.75% classification accuracy, with the oil film remaining intact and the segmentation range being small; the extraction results were almost free of false positive targets, and the actual area of the oil film was calculated to be 42,629.12 m2. The method proposed in this paper can provide a reference for real-time monitoring of oil spill incidents. [ABSTRACT FROM AUTHOR]

Striped stem‐borer (SSB) larvae are one of the most serious insect pests that damages rice. In this study, the combined hyperspectral imaging and chemometric method are proposed detect the early SSB larvae infestation in rice stems. Firstly, hyperspectral reflectance data (390–1040 nm) of rice stems were collected for normal, 3 days of infection, 6 days of infection, and 9 days of infection, respectively. On the spectra, the average spectra of the region of interest of the rice stalk samples were extracted, and 19 characteristic wavelengths were screened by the successive projection algorithm (SPA). Morphological processing was performed on the pseudo‐color (generally known as RGB) images of rice stalk samples on the images. Then five image texture features based on the gray‐scale co‐occurrence matrix (GLCM) and nine image color features based on color moments were obtained. The evaluation models of SSB larval infestation based on full spectra, characteristic wavelengths, GLCM, color moments, and data fusion were developed using partial least squares discriminant analysis (PLS‐DA), respectively. The results show that performance of the PLS‐DA model based on the fusion of characteristic wavelengths and GLCM is best. The accuracy of both the comprehensive modeling set and the prediction set is above 97%. The accuracy of the degree of infestation classification is good for all categories, and only the samples on the third day of infection are slightly less accurate. In conclusion, the hyperspectral imaging technology can be used to effectively detect the infection degree situation of early SSB larval infestation. Practical Applications: Chemical control using insecticides to manage SSB infestation on rice is still the farmers' first choice. However, in the middle and late stages of rice growth, SSB larvae may moth into the inside of the rice stalk, and the rice provides a shelter for SSB so that the sprayed pesticides cannot reach SSB larvae, which produce a large number of overwintering SSB. This leads to the misuse of chemical pesticides, which causes serious pollution to the environment, pesticide residues also cause serious harm to the human body, and it also makes the pest resistance to pesticides rapidly increase. Therefore, it is necessary to find a rapid and effective detection method to detect the early infestation of SSB larvae on rice to reduce the use of pesticides and improve rice yield. [ABSTRACT FROM AUTHOR]

Soil is an important environment for crop growth. Quick and accurately access to soil nutrient content information is a prerequisite for scientific fertilization. In this work, hyperspectral imaging (HSI) technology was applied for the classification of soil types and the measurement of soil total nitrogen (TN) content. A total of 183 soil samples collected from Shangyu City (People's Republic of China), were scanned by a near-infrared hyperspectral imaging system with a wavelength range of 874-1734 nm. The soil samples belonged to three major soil types typical of this area, including paddy soil, red soil and seashore saline soil. The successive projections algorithm (SPA) method was utilized to select effective wavelengths from the full spectrum. Pattern texture features (energy, contrast, homogeneity and entropy) were extracted from the gray-scale images at the effective wavelengths. The support vector machines (SVM) and partial least squares regression (PLSR) methods were used to establish classification and prediction models, respectively. The results showed that by using the combined data sets of effective wavelengths and texture features for modelling an optimal correct classification rate of 91.8%. could be achieved. The soil samples were first classified, then the local models were established for soil TN according to soil types, which achieved better prediction results than the general models. The overall results indicated that hyperspectral imaging technology could be used for soil type classification and soil TN determination, and data fusion combining spectral and image texture information showed advantages for the classification of soil types. [ABSTRACT FROM AUTHOR]

As internet speeds increase and user demands for video quality grow, video coding standards continue to evolve. H.266/Versatile Video Coding (VVC), as the new generation of video coding standards, further improves compression efficiency but also brings higher computational complexity. Despite the significant advancements VVC has made in compression ratio and video quality, the introduction of new coding techniques and complex coding unit (CU) partitioning methods have also led to increased encoding complexity. This complexity not only extends encoding time but also increases hardware resource consumption, limiting the application of VVC in real-time video processing and low-power devices.To alleviate the encoding complexity of VVC, this paper puts forward a Bayesian and texture-feature-based fast splitting algorithm for coding intraframe bloc of VVC, which aims to reduce unnecessary computational steps, enhance encoding efficiency, and maintain video quality as much as possible. In the stage of rapid coding, the video frames are coded by the original VVC test model (VTM), and Joint Rough Mode Decision (JRMD) evaluation cost is used to update the parameter in the Bayesian algorithm to come and set the two thresholds to judge whether the current coding block continues to be split or not. Then, for coding blocks larger than those satisfying the above threshold conditions, the predominant direction of the texture within the coding block is ascertained by calculating the standard deviations along both the horizontal and vertical axes so as to skip some unnecessary splits in the current coding block patterns. The findings from our experiments demonstrate that our proposed approach improves the encoding rate by 1.40% on average, and the execution time of the encoder has been reduced by 49.50%. The overall algorithm has optimized the VVC intraframe coding technology and reduced the coding complexity of VVC. [ABSTRACT FROM AUTHOR]

Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for improving GPP estimation. The key parameter, maximum carboxylation rate at the top of the canopy ( V c m a x , 0 25 ), was quantified using various spatial information representation methods, including mean (μref) and standard deviation (σref) of reflectance, gray-level co-occurrence matrix (GLCM)-based features, local binary pattern histogram (LBPH), and convolutional neural networks (CNNs). Our models were evaluated using a two-year eddy covariance (EC) system and UAV measurements. The result shows that incorporating spatial information can vastly improve the accuracy of V c m a x , 0 25 and GPP estimation. CNN methods achieved the best V c m a x , 0 25 estimation, with an R of 0.94, an RMSE of 19.44 μmol m−2 s−1, and an MdAPE of 11%, and further produced highly accurate GPP estimates, with an R of 0.92, an RMSE of 6.5 μmol m−2 s−1, and an MdAPE of 23%. The μref-GLCM texture features and μref-LBPH joint-driven models also gave promising results. However, σref contributed less to V c m a x , 0 25 estimation. The Shapley value analysis revealed that the contribution of input features varied considerably across different models. The CNN model focused on nir and red-edge bands and paid much attention to the subregion with high spatial heterogeneity. The μref-LBPH joint-driven model mainly prioritized reflectance information. The μref-GLCM-based features joint-driven model emphasized the role of GLCM texture indices. As the first study to leverage the spatial information from high-resolution UAV imagery for GPP estimation, our work underscores the critical role of spatial information and provides new insight into monitoring the carbon cycle. [ABSTRACT FROM AUTHOR]

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

Splicing Image Forgery Identification controls and picture tampering with no proof being left behind has turned out to be exceptionally moderate and exceedingly utilized, because of presence of to a great degree intense altering apparatuses, for example, Adobe Photoshop. Along these lines, there has been a quick expansion of the digitally adjusted pictures on the Internet and prevailing press. The genuineness of a digital image experiences extreme dangers because of the ascent of capable digital image altering devices that effectively adjust the image substance without leaving any obvious hints of such changes. The splicing forgery should be possible by replicated a one/more area from source image and pasted into an objective picture to create a composite image called spliced image. In this manner, this sort of forgery is viewed as challenge issue difficult from tamper identification perspective. To influence the issue most exceedingly awful some to post preparing impacts, for example, blurring, JPEG compression, rotation and scaling perhaps presented in the spliced image. This study aims to perform quantification and data analysis following feature extraction using computational techniques to detect interesting textural and anatomical changes, these extracted features then can be used as a key to distinguish between different classes (Splicing Image and non-Splicing image). This paper presents a new framework to identify the spliced image by exploiting the image texture features, and to automatically identification of spliced images. To accurately identify the spliced image, the proposed solution uses different texture features to capture deferent texture related to the edge of the object and the colour features. We have combine these features to produce a good vector to describe the splice object. Two models have been proposed in this paper first combine the vectors of the three features and feed them to the ANN classifier. Second, use the majority voting of the result of three features to take the decision. This is followed by a ANN classifier; in this model we have trained the system with 30 training 20% validation 50% testing. We evaluated the effectiveness of the classification framework for identifying spliced images by compare the result with the manual label which is done by the people who have created the data sets. In this approach we have combine different features to capture different information and feed them to the neural network to identify the splicing image. The findings outcome from this study have shown an improved approach that automatically splicing image forgery identification. We have evaluated the splicing image forgery identification using the texture features. The identification accuracy in the technique used is about 98.06%., with 99.03% sensitivity and 96.07% specificity. [ABSTRACT FROM AUTHOR]

Background: The aim of this study is to investigate the added value of combining tumour blood flow (BF) and metabolism parameters, including texture features, with clinical parameters to predict, at baseline, the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with newly diagnosed breast cancer (BC). Methods: One hundred and twenty-eight BC patients underwent a 18F-FDG PET/CT before any treatment. Tumour BF and metabolism parameters were extracted from first-pass dynamic and delayed PET images, respectively. Standard and texture features were extracted from BF and metabolic images. Prediction of pCR was performed using logistic regression, random forest and support vector classification algorithms. Models were built using clinical (C), clinical and metabolic (C+M) and clinical, metabolic and tumour BF (C+M+BF) information combined. Algorithms were trained on 80% of the dataset and tested on the remaining 20%. Univariate and multivariate features selections were carried out on the training dataset. A total of 50 shuffle splits were performed. The analysis was carried out on the whole dataset (HER2 and Triple Negative (TN)), and separately in HER2 (N=76) and TN (N=52) tumours. Results: In the whole dataset, the highest classification performances were observed for C+M models, significantly (p-value<0.01) higher than C models and better than C+M+BF models (mean balanced accuracy of 0.66, 0.61, and 0.64 respectively). For HER2 tumours, equal performances were noted for C and C+M models, with performances higher than C+M+BF models (mean balanced accuracy of 0.64, and 0.61 respectively). Regarding TN tumours, the best classification results were reported for C+M models, with better performances than C and C+M+BF models but not significantly (mean balanced accuracy of 0.65, 0.63, and 0.62 respectively). Conclusion: Baseline clinical data combined with global and texture tumour metabolism parameters assessed by 18F-FDG PET/CT provide a better prediction of pCR after NAC in patients with BC compared to clinical parameters alone for TN, and HER2 and TN tumours together. In contrast, adding BF parameters to the models did not improve prediction, regardless of the tumour subgroup analysed. [ABSTRACT FROM AUTHOR]

Hardness is one of the important attributes in determining the quality of dried fruits. Hardness assessment is normally carried out by manual inspection. This method is time consuming, laborious, expensive and subjective. The objective of this study was to develop a computer vision system with a monochrome camera to classify dates based on hardness. Date samples were obtained from three different growing regions in Oman and graded into soft, semi-hard, and hard classes based on hardness. A total of 1800 date samples were imaged individually using a monochrome camera (600 dates / class). Histogram and texture features were extracted from the acquired monochrome images and used in the classification models. The overall classification accuracies in three class model (soft, semi-hard, and hard) were 66% and 71% for linear discriminant analysis (LDA) and artificial neural network (ANN), respectively. It was improved to 84% and 77% in LDA and ANN, respectively while using two class model (soft and hard (semi-hard and hard together)). The histogram features were more contributing in the date classification based on hardness than image texture features. Computer vision technique has great potential to develop online quality monitoring systems for dates and other dried fruits. [ABSTRACT FROM AUTHOR]

Summary: Background: The purpose of this study was to assess the correlation between textural features from 18fluorodeoxyglucose positron emission tomography (18F-FDG-PET) or computed tomography (CT) and EGFR mutation status in patients with stage IV adenocarcinoma lung cancer. Methods: In all, 71 patients who were diagnosed with stage IV adenocarcinoma lung cancer between April 2014 and August 2018 were included in this study. 18F-FDG-PET/CT scanning and EGFR mutation tests were performed before targeted molecular therapy. Textural features were extracted from manually segmented volumes of tumors, and highly dependent features were excluded. Multivariate logistic regression analysis was used to establish predictive models for detection of EGFR mutations. Receiver operating characteristic (ROC) curves were applied to evaluate areas under the curves (AUCs) of each model. Results: Of the 71 patients, 39 (54.9%) were EGFR mutation and 32 (45.1%) showed wild-type. EGFR mutation status was significantly associated with female sex (P = 0.026). In multivariate analysis, three PET (co-occurrence contrast, intensity-size-zone low-intensity large-zone emphasis, and texture spectrum max spectrum) and two CT quantitative features (intensity-size-zone high-intensity zone emphasis and normalized co-occurrence second angular moment) were independent predictors of EGFR mutation status. The predictive model generated from combined clinical and textural features showed a better predictive value than the model from textural features alone (AUC 0.897 vs 0.864). Conclusions: Textural features combined with clinical features could establish a model for improving the predicting power of EGFR mutation status in patients with stage IV adenocarcinoma lung cancer. [ABSTRACT FROM AUTHOR]

In land-use classification of hyperspectral remote sensing (RS) images, traditional classification methods often experience large amount of datasets and low efficiency. To solve these problems, a fast machine-learning method, the extreme learning machine (ELM) algorithm, was introduced. However, basic use of the ELM usually encounters problems of unstable classification results and low classification accuracy. Hence, in this paper, optimization methods for ELM-based RS image classification were mainly discussed and applied to solve the bottleneck problems. From the three perspectives of ensemble learning, making full use of image texture features, and deep learning, three classification optimization methods have been designed and implemented. The results show that: 1) To some extent, all the three methods can achieve a balance between classification accuracy and efficiency, i.e., they can maintain the advantage of ELM algorithm in classification efficiency and speed while have better classification accuracy; 2) The image texture feature optimization method (LBP-KELM) solves the problem of unsatisfactory classification results experienced by the ensemble learning optimization method (Ensemble-ELM) and further improves classification accuracy. However, the classification results are sensitive to the type of dataset; and 3) Fortunately, the optimization method combined with deep learning (CNN-ELM) can meet the application needs of multiple datasets. Furthermore, it can also further improve classification accuracy.

• Gray zone heterogeneity predicts cardiac events in patients with heart failure. • Heterogeneity can be quantified by the texture analysis technique. • Texture parameters of heterogeneity are prognostic predictors. • The net reclassification is improved by texture parameters. Previous research showed that gray zone detected by late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) imaging could help identify high-risk patients. In this study, we investigated whether LGE-CMR gray zone heterogeneity measured by image texture features could predict cardiovascular events in patients with heart failure (HF). This is a retrospective cohort study. Patients with systolic HF undergoing CMR imaging were enrolled. Cine and LGE images were analyzed to derive left ventricular (LV) function and scar characteristics. Entropy and uniformity of gray zones were derived by texture analysis. A total of 82 systolic HF patients were enrolled. After a median 1021 (25%–75% quartiles, 205–2066) days of follow-up, the entropy (0.60 ± 0.260 vs. 0.87 ± 0.28, p = 0.013) was significantly increased while the uniformity (0.68 ± 0.14 vs. 0.53±0.15, p = 0.016) was significantly decreased in patients with ventricular tachycardia or ventricular fibrillation (VT/VF). The percentage of core scar (21.9 ± 10.6 vs. 30.6 ± 10.4, p = 0.029) was higher in cardiac mortality group than survival group while the uniformity (0.55 ± 0.17 vs. 0.67 ± 0.14, p = 0.018) was lower in cardiac mortality group than survival group. A multivariate Cox regression model showed that higher percentage of gray zone area (HR = 8.805, 1.620−47.84, p = 0.045), higher entropy (>0.85) (HR = 1.391, 1.092−1.772, p = 0.024) and lower uniformity (≦0.54) (HR = 0.535, 0.340−0.842, p = 0.022) were associated with VT/VF attacks. Also, higher percentage of gray zone area (HR = 5.716, 1.379−23.68, p = 0.017), core scar zone (HR = 1.939, 1.056−3.561, p = 0.025), entropy (>0.85) (HR = 1.434, 1.076−1.911, p = 0.008) and lower uniformity (≦0.54) (HR = 0.513, 0.296−0.888, p = 0.009) were associated with cardiac mortality during follow-up. Gray zone heterogeneity by texture analysis method could provide additional prognostic value to traditional LGE-CMR substrate analysis method. [ABSTRACT FROM AUTHOR]

Today's forensic science introduces a new research area for digital image analysis formultimedia security. So, Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered images. Exiting forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing forgeries. However, an image can contain one or more types of forgeries. This study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial domain. Firstly, images are divided into equal blocks to get scale-invariant features.Weber law has been used for getting texture features, and finally, XGBOOST is used to classify both Copy- Move and splicing forgery. The proposed method classified three types of forgeries, i.e., splicing, Copy-Move, and healthy. Benchmarked (CASIA 2.0, MICCF200) and RCMFD datasets are used for training and testing. On average, the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation, which is far better than existing methods. [ABSTRACT FROM AUTHOR]

Context: Due to the change and advancement in technology, day by day the internet service usages are also increasing. Smartphones have become the necessity for every person these days. It is used to perform all basic daily activities such as calling, SMS, banking, gaming, entertainment, education, etc. Therefore, malware authors are developing new variants of malwares or malicious applications especially for monetary benefits. Objective: Objective of this research paper is to develop a technique that can be used to detect malwares or malicious applications on the android devices that will work for all types of packed or encrypted malicious applications, which usually evade decompiling tools. Method: In the proposed approach, visualization method is used for the detection of malware. In the first phase, application files are converted into images and then in second phase, texture feature of images are extracted using Grey Level Co-occurrence Matrix (GLCM). In the last phase, machine learning classification algorithms are used to classify the malicious and benign applications. Results: The proposed approach is run on different datasets collected from various repositories. Different efficiency parameters are calculated and the proposed approach is compared with the existing approaches. Conclusion: We have proposed a static technique for efficient detection of malwares. The proposed technique performs better than the existing technique. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motors (PMSMs) are extensively utilized in production and manufacturing fields due to their wide speed range, high output torque, fast speed response, small size and light weight. PMSMs are susceptible to inter-turn short circuit faults, demagnetization faults, bearing faults, and other faults arising from irregular vibrations and frequent start–brake cycles. While fault diagnosis for PMSMs offers an effective means to enhance operational efficiency, the multi-sensor information fusion is often overlooked. In industrial production processes, the collected data inevitably suffers from noise contamination, which can adversely impact diagnostic outcomes. To enhance the robustness of diagnostic methods in noisy environments and mitigate the risk of overfitting, a PMSM fault diagnosis method based on image features of multi-sensor fusion is proposed. Firstly, the vibration acceleration signals of the PMSM at different positions were acquired. Then, the newly designed multi-signal Gramian Angular Difference Fields (MGADF) method combines sensor signals from three different installation locations into a single image. Next, the multi-texture features are fused to extract the features of the image. Various machine models are compared in the fault feature learning and classification, and the results show that the proposed diagnostic method has good diagnostic accuracy and robustness, with an average diagnostic accuracy of 99.54% and a standard deviation of accuracy of 0.19. It has excellent performance even in noisy environments. The method is non-invasive and can be extended and applied to the condition monitoring and diagnosis of industrial motors. [ABSTRACT FROM AUTHOR]

Feature extraction is an important part for coal and gangue recognition, which has direct impact on accuracy of recognition. However, the existing feature extraction methods for coal and gangue are not ideal, and so a feature extraction method with multi-fractal is proposed based on energy field normalization for target recognition of coal and gangue in this paper. In the method, the concept of target energy field is established based on 3D grey surface, and the normalized target energy is calculated by using pixels. Then, after analysis of the feature extraction process, a feature extraction algorithm with multi-fractal is proposed based on energy field, in which 3D grey surface is divided by different grid forms, and the pixels in grid are counted to obtain the probability density distribution matrix of pixels. The results of multiple feature extraction is observed visually from probability density distribution, spatial feature distribution, and multi-fractal spectrum to illustrate the measurability of the method for target textures, which is the quantitative attribute of feature extraction. In the experiment, this method is used to quantitatively measure grey texture, and the effectiveness of measured features in coal and gangue recognition is compared with other methods. The experiment results show that the method can achieve effective quantitative measurement for coal and gangue texture, and the recognition accuracy is higher than other methods. [ABSTRACT FROM AUTHOR]

Strawberry anthracnose, caused by Colletotrichum spp., is a major disease that causes tremendous damage to cultivated strawberry plants (Fragaria × ananassa Duch.). Examining and distinguishing plants potentially carrying the pathogen is one of the most effective ways to prevent and control strawberry anthracnose disease. Herein, we used this method on Colletotrichum gloeosporioides at the crown site on indoor strawberry plants and established a classification and distinguishing model based on measurement of the spectral and textural characteristics of the disease-free zone near the disease center. The results, based on the successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), and interval random frog (IRF), extracted 5, 14, and 11 characteristic wavelengths, respectively. The SPA extracted fewer effective characteristic wavelengths, while IRF covered more information. A total of 12 dimensional texture features (TFs) were extracted from the first three minimum noise fraction (MNF) images using a grayscale co-occurrence matrix (GLCM). The combined dataset modeling of spectral and TFs performed better than single-feature modeling. The accuracy rates of the IRF + TF + BP model test set for healthy, asymptomatic, and symptomatic samples were 99.1%, 93.5%, and 94.5%, the recall rates were 100%, 94%, and 93%, and the F1 scores were 0.9955, 0.9375, and 0.9374, respectively. The total modeling time was 10.9 s, meaning that this model demonstrated the best comprehensive performance of all the constructed models. The model lays a technical foundation for the early, non-destructive detection of strawberry anthracnose. [ABSTRACT FROM AUTHOR]

In order to improve the recognition effect of laser images, this study designed an intelligent recognition method of laser images based on big data analysis technology. On the basis of setting up the laser holographic scanning device and parameters, the laser image is obtained by using the calibration method of vision system. In order to avoid the limitation of coordinate system in the process of laser image recognition, a rational function model with general attributes is constructed. Then, convolutional neural network is used to output the feature data of laser images, and Spark parallel support vector machine algorithm is used to complete the classification of laser images. Finally, the SVM classification model based on the big data analysis technology is constructed. The texture feature data can be input to quickly output the classification results of laser images, and then the intelligent classification and recognition of laser images can be realized according to the probability distribution. Experimental results show that this method can accurately identify the tiny features in laser images, and the recognition results have high peak signal-to-noise ratio and high recognition accuracy. [ABSTRACT FROM AUTHOR]

Retinal diseases is one of the major cause of vision impairments worldwide and the variations in fundus images help to identify these retinal diseases. Manual identification and segmentation of these fundus images is a tedious task and is highly prone to errors. Thus, a Dagum probability distribution function (PDF) based on matched filter (MF) for retinal blood vessel segmentation is proposed in this work. Initially, different features are extracted from the fundus images and these features are provided to the ensemble classifier to perform the task of disease classification. The overall performance of the proposed work is evaluated using different popular datasets such as DRIVE, STARE, HRF, IRDiD, Kaggle retinal, ORIGA and RFI. It is found that the specificity of 0.9802 and 0.9838 is achieved on DRIVE and STARE datasets respectively. In addition, the input image is predicted with HRF-glaucoma image '10_g.jpg', having a higher prediction accuracy of 95.45%. [ABSTRACT FROM AUTHOR]

The category identification and grade classification of tea are important to food safety and life safety. The category and grade of the whole tea batch are typically determined by random sampling. However, full distribution graphs of tea categories are hardly available. This study analyzed relevant features and model construction to improve model precision for the category identification of tea. Moreover, spectral and texture features of hyperspectral images of tea were discussed through principal component analysis and gray level concurrence matrix. The category identification models of tea based on spectral features, texture features, and the integration of the two were constructed on the basis of the support vector machine model. The optimal model was applied to hyperspectral images to produce the full category distribution graph of tea. Then, its accuracy was verified through an assessment. Results demonstrate that the model based on hyperspectral images integrating spectral and texture features achieves the highest accuracyon the prediction set, and the total accuracy reaches 94.3%. This outcome is higher than the accuracy of the model based on spectral features (88.9%) and the model based on texture features (85%). On the full-image scale, the overall classification accuracy of the model based on hyperspectral images of four kinds of tea reaches 90.3% when the training sample is only 1%. In addition, the corresponding Kappa coefficient is 0.87, indicating that the established model is feasible. The integration of spectral and texture features can overcome the influences of having different spectra for the same object to some extent. The proposed algorithm provides scientific guidance for authenticity verification, grade classification, and other practical production of tea. [ABSTRACT FROM AUTHOR]

Mapping informal settlements' diverse morphological patterns remains intricate due to the unavailability and huge costs of high-resolution data, as well as the spatial heterogeneity of urban environments. The accessibility to high-spatial-resolution PlanetScope imagery, coupled with the convenience of simple non-iterative clustering (SNIC) algorithm within the Google Earth Engine (GEE), presents the potential for Geographic Object-Based Image Analysis (GEOBIA) to map the spatial morphology of deprivation pockets in a complex built-up environment of Durban. Such advances in multi-sensor satellite image inventories on GEE also afford the possibility to integrate data from sensors with different spectral characteristics and spatial resolutions for effective abstraction of informal settlement diversity. The main objective is to exploit Sentinel-1 radar data, Sentinel-2 and PlanetScope optical data fusion for more accurate and precise localization of informal settlements using GEOBIA, within GEE. The findings reveal that the Random Forests classification model achieved informal settlement identification accuracy of 87% (F-score) and overall accuracy of 96%. An assessment of agreement between observed informal settlement extents and ground truth dimensions was conducted through regression analysis, yielding root mean square log error (RMSLE) = 0.69 and mean absolute percent error (MAPE) = 0.28. The results demonstrate reliability of the classification model in capturing variability of spatial characteristics of informal settlements. The research findings confirm efficacy of combined advantages of GEOBIA within GEE, and integrated datasets for more precise capturing of characteristic morphologic informal settlement features. The outcomes suggest a shift from standard static conventional approaches towards more dynamic, on-demand informal settlement mapping through cloud computing, a powerful analysis platform that simplifies access to and the processing of voluminous data. The study has important implications for identifying the most effective ways to map informal settlements in a complex urban landscape, thus providing a benchmark for other regions with significant landscape heterogeneity. [ABSTRACT FROM AUTHOR]

Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy task since it requires highly skilled professionals, making this procedure both costly and time-consuming. The diagnosis process relying on MR images gets even harder in the presence of similar objects in terms of their density, size, and shape. No matter how skilled professionals are, their task is still prone to human error. The main aim of this work is to propose a system that can automatically classify and diagnose glioma brain tumors into one of the four tumor types: (1) necrosis, (2) edema, (3) enhancing, and (4) non-enhancing. In this paper, we propose a combined texture discrete wavelet transform (DWT) and statistical features based on the first- and second-order features for the accurate classification and diagnosis of multiclass glioma tumors. Four well-known classifiers, namely, support vector machines (SVM), random forest (RF), multilayer perceptron (MLP), and naïve Bayes (NB), are used for classification. The BraTS 2018 dataset is used for the experiments, and with the combined DWT and statistical features, the RF classifier achieved the highest average accuracy whether for separated modalities or combined modalities. The highest average accuracy of 89.59% and 90.28% for HGG and LGG, respectively, was reported in this paper. It has also been observed that the proposed method outperforms similar existing methods reported in the extant literature. [ABSTRACT FROM AUTHOR]

This paper aims to develop an automatic feature extraction system for detecting icebergs in Antarctica. Extracting suitable features to discriminate an iceberg from sea ice and land melting based on its content is tedious. Especially in Synthetic Aperture Radar data, high image content is highly affected by speckle noise. Establishing the appropriate spatial relationship between pixels is not producing much accuracy with the standard low-level features. The proposed method introduces the two-level iceberg detection and tracking algorithm. The available samples were used to train the first-level convolution neural network-based features. False-positive predictions have been removed using the multiscale contourlet-based Haralick texture features in the second level. The final detected iceberg movement has been tracked using the temporal image data. The distance moved in both temporal images is computed with the help of latitude and longitude information. The proposed methodology exhibited the best performance over state-of-the-art methods and acquired 79.1% precision and 83.8 F1 score. [ABSTRACT FROM AUTHOR]