Statistical Texture Features Research Articles

BA-GCA Net: Boundary-Aware Grid Contextual Attention Net in Osteosarcoma MRI Image Segmentation.

Osteosarcoma is one of the most common bone tumors that occurs in adolescents. Doctors often use magnetic resonance imaging (MRI) through biosensors to diagnose and predict osteosarcoma. However, a number of osteosarcoma MRI images have the problem of the tumor shape boundary being vague, complex, or irregular, which causes doctors to encounter difficulties in diagnosis and also makes some deep learning methods lose segmentation details as well as fail to locate the region of the osteosarcoma. In this article, we propose a novel boundary-aware grid contextual attention net (BA-GCA Net) to solve the problem of insufficient accuracy in osteosarcoma MRI image segmentation. First, a novel grid contextual attention (GCA) is designed to better capture the texture details of the tumor area. Then the statistical texture learning block (STLB) and the spatial transformer block (STB) are integrated into the network to improve its ability to extract statistical texture features and locate tumor areas. Over 80,000 MRI images of osteosarcoma from the Second Xiangya Hospital are adopted as a dataset for training, testing, and ablation studies. Results show that our proposed method achieves higher segmentation accuracy than existing methods with only a slight increase in the number of parameters and computational complexity.

Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach.

The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the random forest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposed method attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive, 82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can be used for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time required to hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost of care and treatment for hospitalized individuals.

Segmentation of Natural and Anthropogenic Objects by Panchromatic Satellite Images Using Statistical Textural Features

Segmentation of Natural and Anthropogenic Objects by Panchromatic Satellite Images Using Statistical Textural Features

GLCMs Based multi-inputs 1D CNN Deep Learning Neural Network for COVID-19 Texture Feature Extraction and Classification

Coronavirus disease 2019 epidemic (COVID-19) is an infectious disease that appeared because of the newest version of discovered coronavirus. The advent and rapid spread of this disease over the world necessitated a concerted effort to contain and eradicate it. Computer Tomography (CT) imaging and X-Ray images are considered as one of the important medical examinations used for disease diagnosis. To speed up and confirm the correctness of the medical diagnosis, many artificial intelligence techniques and machine learning methods are proposed. In this paper, a new and efficient proposed system is introduced to extract appropriate and meaningful features for CT scans and X-Ray COVID-19 images. The proposed method depends on extracting statistical texture features of the images using the GLCM method. The GLCMs matrices are extracted from different three quantized versions of the original image in different distances and directions. New multi-inputs 1D CNN architecture of the deep neural network is implemented to extract the effective features directly from GLCMs matrices after reducing its dimensions using the PCA technique. Three datasets are used to evaluate our method that includes SARS-CoV-2 CT-scan, COVID-CT, and DLAI3 Hackathon COVID-19 Chest X-Ray datasets. The proposed system achieved a classification improvement in terms of accuracy, F1 score, and AUC metrics compared with other methods and exceeds 98%, 89%, and 93% for three datasets, respectively.

Superpixel-Based Cropland Classification of SAR Image With Statistical Texture and Polarization Features

Cropland classification can be used to monitor cropland distribution and its change over time. In this letter, a new superpixel-based cropland classification method is proposed for synthetic aperture radar (SAR) imagery through the integration of statistical texture, polarization, and spatial information. First, the method combines random forest algorithm and superpixels, which are generated using simple linear iterative clustering algorithm with polarization features of Pauli decomposition and spatial information. Superpixel-based spatial context information is used to reduce the influence of coherent speckle and misclassification in cropland blocks. Second, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G^{0}$ </tex-math></inline-formula> statistical texture feature is used to reduce the interference of background targets such as woodland in cropland classification. Comparison experiments of different methods using C-band airborne SAR (AIRSAR) polarimetric data acquired in early July show that the proposed method has better classification performance, with an overall accuracy of 88.62%. The classification accuracy of corn and soybean is above 95% and 91%, respectively. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G^{0}$ </tex-math></inline-formula> statistical texture feature is helpful to eliminate woodland that may cause crop misclassification using single-date SAR image.

Mapping Dongting Lake Wetland Utilizing Time Series Similarity, Statistical Texture, and Superpixels With Sentinel-1 SAR Data

Wetland is of significant ecological value, which is very important and challenging for large-scale mapping. Sentinel-1 can continuously record wetland changes with its all-day, all-weather working capability. How to fully utilize multidimensional information, such as time and statistical texture of Synthetic Aperture Radar (SAR) image, to classify wetlands more accurately has become a research focus. Thus, this paper constructed Time Series Similarity parameters to describe the temporal change information of targets in wetland and introduced G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> statistical texture parameters to describe texture characteristics of SAR images. Combining multi-features and superpixels, we proposed a classification method based on Random Forest (RF) to map Dongting Lake wetland. The overall classification accuracy was 97.57%, and the Kappa coefficient was 0.97. The classification accuracy of reed beach, grass marsh etc. was above 95%. The results showed the proposed SAR Time Series Similarity features could effectively utilize dynamical information among classes and was helpful to identify mudflat, grass marsh and reed beach with high dynamics in wetland. The introduced statistical texture features expressed the heterogeneity of targets, and enhanced the recognition and extraction ability of forest beach, mudflat and reed beach. Compared with Support Vector Machine (SVM), Decision Tree (DT) and RF classification methods, RF with superpixels optimized not only got high precision but also could effectively reduce the pepper-salt error in classification, because of the consideration of superpixels context information.

Automatic classification of severity of COVID-19 patients using texture feature and random forest based on computed tomography images.

Severity assessment of the novel Coronavirus (COVID‐19) using chest computed tomography (CT) scan is crucial for the effective administration of the right therapeutic drugs and also for monitoring the progression of the disease. However, determining the severity of COVID‐19 needs a highly expert radiologist by visual assessment, which is time‐consuming, boring, and subjective. This article introduces an advanced machine learning tool to determine the severity of COVID‐19 to mild, moderate, and severe from the lung CT images. We have used a set of quantitative first‐ and second‐order statistical texture features from each image. The first‐order texture features extracted from the image histogram are variance, skewness, and kurtosis. The second‐order texture features extraction methods are gray‐level co‐occurrence matrix, gray‐level run length matrix, and gray‐level size zone matrix. Finally, using the extracted features, CT images of each person are classified using random forest (RF) as an ensemble method based on majority voting of the decision trees outputs to four classes. We have used a dataset of CT scans labeled as being normal (231), mild (563), moderate (120), and severe (42) determined by expert radiologists. The experimental results indicate the combination of all feature extraction methods, and RF achieves the highest result compared with the other strategies in detecting the four classes of severity of COVID‐19 from CT images with an accuracy of 90.95%. This proposed system can work well and can be used as an assistant diagnostic tool for quantification of lung involvement of COVID‐19 to monitor the progression of the disease.

Machine Learning Classifiers for Surface Crack Detection in Fracture Experiments

Correctly determining the onset of fracture is crucial when performing mechanical experiments. Commonly carried out by visual inspection, here an image-based machine learning approach is proposed to classify cracked and un-cracked specimens. It yields the potential to objectify and automate crack detection, thereby removing sources of uncertainty and error from the post-processing of experiments. More than 30’000 speckle-pattern images obtained from 77 experiments on three specimen geometries are evaluated. They comprise uniaxial tension, notched tension as well as axisymmetric V-bending experiments. Statistical texture features are extracted from all images. They include both first-order (variance, skewness, kurtosis) and higher-order statistical texture features, i.e. Haralick features. The discriminatory power of the texture information is evaluated based on the Fisher's Discriminant Ratio and feature correlations are identified and quantified. Image texture feature subsets of high discriminatory power are used to parse neural network architectures of different complexities from simple perceptron to feed-forward and cascade neural networks. It is found that a small subset of the investigated texture features is highly significant for all experiments. Using this feature subset in conjunction with multi-layer, non-linear and low complexity feed-forward network architectures classification accuracies in the order of 99% are obtained. At the same time, it is shown that linear classifiers are not sufficient to robustly distinguish the state of the specimens, even when high discriminatory power features are used.Graphical Abstract: [Display omitted]

An efficient approach for no-reference image quality assessment based on statistical texture and structural features

This paper presents a rotation-invariant and computationally efficient no-reference image quality assessment (NR-IQA) model. It estimates the image quality based on texture and structural information associated with the images. The human visual system (HVS) uses perceptual features such as texture and structure as primary information to understand the visual scene and the image content. Moreover, the texture and structural information capture the loss of naturalness due to distortions in the image. Therefore, in this work, the important texture features are extracted using the local binary patterns (LBP).The modified LBP, also known as the hyper-smoothing LBP (H-LBP) and Laplacian of H-LBP (LH-LBP), represents the image structure. Further, the image quality prediction model computes the quality of the image based on the statistical feature measures of the texture and structural information. In the proposed approach, the image quality prediction model uses support vector regression (SVR) to measure image quality. Various experimentations are carried out on the LIVE and TID2013 database to test the effectiveness of the proposed NR-IQA model. The performance metrics such as Spearman rank-ordered correlation coefficient, Pearson linear correlation coefficient, and root mean square error is computed to show the efficiency of the presented approach. The experimental results illustrate a high correlation between the predicted quality score and the human visual perceptions. It is also found to be competitive with the best-performing full-reference and no-reference IQA models.

Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study.

Applying deep learning to the field of preclinical in vivo studies is a new and exciting prospect with the potential to unlock decades worth of underutilized data. As a proof of concept, we performed a feasibility study on a colitis model treated with Sulfasalazine, a drug used in therapeutic care of inflammatory bowel disease. We aimed to evaluate the colonic mucosa improvement associated with the recovery response of the crypts, a complex histologic structure reflecting tissue homeostasis and repair in response to inflammation. Our approach requires robust image segmentation of objects of interest from whole slide images, a composite low dimensional representation of the typical or novel morphological variants of the segmented objects, and exploration of image features of significance towards biology and treatment efficacy. Both interpretable features (eg. counts, area, distance and angle) as well as statistical texture features calculated using Gray Level Co-Occurance Matrices (GLCMs), are shown to have significance in analysis. Ultimately, this analytic framework of supervised image segmentation, unsupervised learning, and feature analysis can be generally applied to preclinical data. We hope our report will inspire more efforts to utilize deep learning in preclinical in vivo studies and ultimately make the field more innovative and efficient.

Medical, Aromatic, and Narcotic Plants Classification using an Artificial Neural Network

Medical, Aromatic, and Narcotic plants are a natural treasure that grows in the desert without human being interference. They can be used in pharmaceutical industries (medicines), medical usage (medical anesthetic), perfumes industries, and cooking. Thus, they are very useful, available, and can be utilized for the sake of human beings. On the other hand, some of these plants are harmful to our bodies and must be strictly prohibited. So, it is necessary to design and implement an image processing system to detect these plants. This system can be applied by the Ministry of Agriculture and Armed Force. After surveying deserts and taking photos of plants by a small camera attached to a drone, they can be inserted into the system to detect the type of captured plant and take action. In this paper, an automatic computer vision system is proposed to identify six types of desert plants. First, a nine-class collected database is prepared. Second, an artificial neural network-based framework, which uses color, DWT, the ratio between the major and the minor axes of the plants, and Tamura statistical texture features, is employed to classify plants. Outcomes and the results of the suggested system have competed with several techniques such as the SVM, the Naive Bayes, the KNN, the decision tree, and discriminant analysis classifiers. Results reveal that the proposed system has the highest overall recognition rate, which is 94.3%, among other techniques.

An Enhanced Version of Second-Order Synchrosqueezing Transform Combined with Time-Frequency Image Texture Features to Detect Faults in Bearings

Signals with multiple components and fast-varying instantaneous frequencies reduce the readability of the time-frequency representations obtained by traditional synchrosqueezing transforms due to time-frequency blurring. We discussed a vertical synchrosqueezing transform, which is a second-order synchrosqueezing transform based on the short-time Fourier transform and compared it to the traditional short-time Fourier transform, synchrosqueezing transform, and another form of the second-order synchrosqueezing transform, the oblique synchrosqueezing transform. The quality of the time-frequency representation and the accuracy of mode reconstruction were compared through simulations and experiments. Results reveal that the second-order frequency estimator of the vertical synchrosqueezing transform could obtain accurate estimates of the instantaneous frequency and achieve highly energy-concentrated time-frequency representations for multicomponent and fast-varying signals. We also explored the application of statistical feature parameters of time-frequency image textures for the early fault diagnosis of roller bearings under fast-varying working conditions, both with and without noise. Experiments showed that there was no direct positive correlation between the resolution of the time-frequency images and the accuracy of fault diagnosis. However, the early fault diagnosis of roller bearings based on statistical texture features of high-resolution images obtained by the vertical synchrosqueezing transform was shown to have high accuracy and strong robustness to noise, thus meeting the demand for intelligent fault diagnosis.

Statistical Texture Features Based Automatic Detection and Classification of Diabetic Retinopathy

Statistical Texture Features Based Automatic Detection and Classification of Diabetic Retinopathy

Face Recognition Based on Statistical Texture Features

Facial recognition has attracted the attention of researchers and has been one of the most prominent topics in the fields of image processing and pattern recognition since 1990. This resulted in a very large number of recognition methods and techniques with the aim of increasing the accuracy and robustness of existing systems. Many techniques have been developed to address the challenges and reliable recognition systems have been reached but require considerable processing time, suffer from high memory consumption and are relatively complex. The focus of this paper is on extracting subset of descriptors (less correlated and less calculations) from the co-occurrence matrix with the goal of enhancing the performance of Haralick’s descriptors. Improvements are achieved by adding the image pre-processing and selecting the proper method according to the database problem and by extracting features from image local regions.

DAMAD: Database, Attack, and Model Agnostic Adversarial Perturbation Detector.

Adversarial perturbations have demonstrated the vulnerabilities of deep learning algorithms to adversarial attacks. Existing adversary detection algorithms attempt to detect the singularities; however, they are in general, loss-function, database, or model dependent. To mitigate this limitation, we propose DAMAD-a generalized perturbation detection algorithm which is agnostic to model architecture, training data set, and loss function used during training. The proposed adversarial perturbation detection algorithm is based on the fusion of autoencoder embedding and statistical texture features extracted from convolutional neural networks. The performance of DAMAD is evaluated on the challenging scenarios of cross-database, cross-attack, and cross-architecture training and testing along with traditional evaluation of testing on the same database with known attack and model. Comparison with state-of-the-art perturbation detection algorithms showcase the effectiveness of the proposed algorithm on six databases: ImageNet, CIFAR-10, Multi-PIE, MEDS, point and shoot challenge (PaSC), and MNIST. Performance evaluation with nearly a quarter of a million adversarial and original images and comparison with recent algorithms show the effectiveness of the proposed algorithm.

VECTOR-DIFFERENCE TEXTURE SEGMENTATION METHOD IN TECHNICAL AND MEDICAL EXPRESS DIAGNOSTIC SYSTEMS

The study shows the need for express systems, in which it is necessary to perform the analysis of texture images in various areas of diagnosis, for example, in medical express diagnostics of dermatologic disorders. Since the reliability of decision-making in such systems depends on the quality of image segmentation, which, as a rule, have the nature of spectral-statistical textures, it is advisable to develop methods for segmentation of such images and models for their presentation. A model of spectral-statistical texture is proposed, which takes into account the random nature of changes in the field variations and quasi-harmonics. On its basis, a vector-difference method of texture segmentation has been developed, which is based on the vector transformation of images of spectral and statistical textures based on vector algebra. The stages of the vector-difference method are the following: an evaluation of the spectral texture feature; an evaluation of the statistical texture feature; vector-difference transformation of texture images; aboundary detection of the homogeneous regions. For each pixel of the image in the processing aperture, the features of the spectral and statistical texture are evaluated. Statistical texture evaluation was performed by the quadratic-amplitude transformation. At the stage of vector-difference transformation of texture images, a vector of features of each pixel of an image is constructed, the elements of which are estimates of features of a spectral and statistical texture, and the modulus of the difference of two vectors is calculated. At the stage of boundary detection of homogeneous regions, Canny method was applied. The developed vector-difference texture segmentation method was applied both to model images of spectral-statistical texture and to texture images obtained in technical and medical diagnostics systems, namely, for images of psoriasis disease and wear zones of cutting tools. To compare the segmentation results, frequency-detector and amplitude-detector methods of texture segmentation were applied to these images. The quality of segmentation of homogeneous textured regions was evaluated by the Pratt's criterion and by constructing a confusion matrix. The research results showed that the developed vector-difference texture segmentation method has increased noise tolerance at a sufficient processing speed.

Classification of canola seed varieties based on multi-feature analysis using computer vision approach

ABSTRACT This study aims to analyze the potential of the computer vision (CV) approach to classify eight canola varieties. The input images of eight canola varieties were CON-I, CON-II, CON-III, Pakola, Canola Raya, Rainbow, PARC Canola Hybrid, and Tarnab-III. A digital camera acquired these images on an open sunny day without any complex laboratory setup. First-order histogram features, second-order statistical texture features, binary features, spectral features of three bands were, blue (B), green (G), and red (R), were employed in the artificial neural network (ANN). A 10-fold stratified cross-validation method was used for classification. The best results with accuracy ranging from 95% to 98% observed when the data of regions of interest (512 × 512) deployed to the classifier.

An Implementation Of Statistical Feature Algorithms For The Detection Of Brain Tumor

A member of a population who is at risk of becoming infected by disease is a susceptible individual. Finding disease susceptibility and generating an alert in advance, is valuable for an individual. The aim of the work presented a feature vector using different statistical texture analyses of brain tumors from an MRI image. The statistical feature texture is computed using GLCM (Gray Level Co-occurrence Matrices) of brain tumor cell structure. For this paper, the brain tumor cell segmented using the strip method to implement hybrid Assured Convergence Particle Swarm Optimization (ACPSO) - Fuzzy C-means clustering (FCM). Furthermore, the four angles 0o, 45o, 90o, and 135o have calculated the segmented brain image in GLCM. The four angular directions are calculated using texture features are correlation, energy, contrast and homogeneity. The texture analysis is performed on different types of images using past years. So, the algorithm proposed statistical texture features are calculated for iterative image segmentation. The algorithm FETC (Feature Extraction Tumor Cell) extracts statistical features of GLCM. These results show that MRI images can be implemented in a system of brain cancer detection.

Amalgamation of Machine Learning and Slice-by-Slice Registration of MRI for Early Prognosis of Cognitive Decline

Brain atrophy is the degradation of brain cells and tissues to the extent that it is clearly indicative during Mini-Mental State Exam test and other psychological analysis. It is an alarming state of the human brain that progressively results in Alzheimer disease which is not curable. But timely detection of brain atrophy can help millions of people before they reach the state of Alzheimer. In this study we analyzed the longitudinal structural MRI of older adults in the age group of 42 to 96 of OASIS 3 Open Access Database. The nth slice of one subject does not match with the nth slice of another subject because the head position under the magnetic field is not synchronized. As a radiologist analyzes the MRI image data slice wise so our system also compares the MRI images slice wise, we deduced a method of slice by slice registration by driving mid slice location in each MRI image so that slices from different MRI images can be compared with least error. Machine learning is the technique which helps to exploit the information available in abundance of data and it can detect patterns in data which can give indication and detection of particular events and states. Each slice of MRI analyzed using simple statistical determinants and Gray level Co-Occurrence Matrix based statistical texture features from whole brain MRI images. The study explored varied classifiers Support Vector Machine, Random Forest, K-nearest neighbor, Naive Bayes, AdaBoost and Bagging Classifier methods to predict how normal brain atrophy differs from brain atrophy causing cognitive impairment. Different hyper parameters of classifiers tuned to get the best results. The study indicates Support Vector Machine and AdaBoost the most promising classifier to be used for automatic medical image analysis and early detection of brain diseases. The AdaBoost gives accuracy of 96.76% with specificity 95.87% and sensitivity 87.37% and receiving operating curve accuracy 96.3%. The SVM gives accuracy of 96% with 92% specificity and 87% sensitivity and receiving operating curve accuracy 95.05%.

Research on Anisotropic Fracture Mechanical Behavior of Cortical Bone with Combined Method of Fractal and Gray Level Co-Occurrence Matrix

The irregular fracture surface of cortical bone, which is caused by complex multilevel micro-nanostructure, reflects the mechanical properties and fracture mechanisms. It is of great significance to characterize some characteristic parameters from the fracture surfaces of bone. In this research, anisotropic fracture mechanical properties of bovine femoral cortical bone along transverse, longitudinal and radial direction are firstly obtained by three-point bend experiment. Then the fracture routes and fracture surfaces are observed by scanning electron microscope. The observation shows that the formed fracture surfaces, which are caused by different crack routes, are extremely rough and have complex textures. Lastly, the combined method of fractal and gray level co-occurrence matrix are adopted to describe the morphology of fracture surface of cortical bone objectively and quantitatively. It is shown that the fracture surface of cortical bone has obvious fractal characteristics and four statistical texture feature parameters (contrast,angular second moment, correlation and entropy) of GLCM of fracture surfaces can describe a certain fracture texture character. The relationship between the characteristic parameters and macroscopic mechanical properties are established. The quantitative analysis and automatic class identification for the fracture surfaces of cortical bone can be achieved.