Coal–rock interface detection on the basis of image texture features

Based on the stability and inequality of texture features between coal and rock,this study used the digital image analysis technique to propose a coal–rock interface detection method.By using gray level co-occurrence matrix,twenty-two texture features were extracted from the images of coal and rock.Data dimension of the feature space reduced to four by feature selection,which was according to a separability criterion based on inter-class mean difference and within-class scatter.The experimental results show that the optimized features were effective in improving the separability of the samples and reducing the time complexity of the algorithm.In the optimized low-dimensional feature space,the coal–rock classifer was set up using the fsher discriminant method.Using the 10-fold cross-validation technique,the performance of the classifer was evaluated,and an average recognition rate of 94.12%was obtained.The results of comparative experiments show that the identifcation performance of the proposed method was superior to the texture description method based on gray histogram and gradient histogram.

Texture features analysis on micro-structure of paste backfill based on image analysis technology

The strength of cement-based materials,such as mortar,concrete and cement paste backfill(CPB),depends on its microstructures(e.g.pore structure and arrangement of particles and skeleton).Numerous studies on the relationship between strength and pore structure(e.g.,pore size and its distribution)were performed,but the micro-morphology characteristics have been rarely concerned.Texture describing the surface properties of the sample is a global feature,which is an effective way to quantify the micro-morphological properties.In statistical analysis,GLCM features and Tamura texture are the most representative methods for characterizing the texture features.The mechanical strength and section image of the backfill sample prepared from three different solid concentrations of paste were obtained by uniaxial compressive strength test and scanning electron microscope,respectively.The texture features of different SEM images were calculated based on image analysis technology,and then the correlation between these parameters and the strength was analyzed.It was proved that the method is effective in the quantitative analysis on the micro-morphology characteristics of CPB.There is a significant correlation between the texture features and the unconfined compressive strength,and the prediction of strength is feasible using texture parameters of the CPB microstructure.

Role of the texture features of images in the diagnosis of solitary pulmonary nodules in different sizes

Objective： To explore the role of the texture features of images in the diagnosis of solitary pulmonary nodules （SPNs） in different sizes. Materials and methods： A total of 379 patients with pathologically confirmed SPNs were enrolled in this study. They were divided into three groups based on the SPN sizes： ≤10, 11-20, and 〉20 mm. Their texture features were segmented and extracted. The differences in the image features between benign and malignant SPNs were compared. The SPNs in these three groups were determined and analyzed with the texture features of images. Results： These 379 SPNs were successfully segmented using the 2D Otsu threshold method and the self-adaptive threshold segmentation method. The texture features of these SPNs were obtained using the method of grey level co-occurrence matrix （GLCM）. Of these 379 patients, 120 had benign SPNs and 259 had malignant SPNs. The entropy, contrast, energy, homogeneity, and correlation were 3.5597±0.6470, 0.5384±0.2561, 0.1921±0.1256, 0.8281±0.0604, and 0.8748±0.0740 in the benign SPNs and 3.8007±0.6235, 0.6088±0.2961, 0.1673±0.1070, 0.7980±0.0555, and 0.8550±0.0869 in the malignant SPNs （all P〈0.05）. The sensitivity, specificity, and accuracy of the texture features of images were 83.3%, 90.0%, and 86.8%, respectively, for SPNs sized 〈10 mm, and were 86.6%, 88.2%, and 87.1%, respectively, for SPNs sized 11-20 mm and 94.7%, 91.8%, and 93.9%, respectively, for SPNs sized 〉20 mm. Conclusions： The entropy and contrast of malignant pulmonary nodules have been demonstrated to be higher in comparison to those of benign pulmonary nodules, while the energy, homogeneity correlation of malignant pulmonary nodules are lower than those of benign pulmonary nodules. The texture features of images can reflect the tissue features and have high sensitivity, specificity, and accuracy in differentiating SPNs. The sensitivity and accuracy increase for larger SPNs.

Mesomechanics coal experiment and an elastic-brittle damage model based on texture features

To accurately describe damage within coal, digital image processing technology was used to determine texture parameters and obtain quantitative information related to coal meso-cracks. The relationship between damage and mesoscopic information for coal under compression was then analysed. The shape and distribution of damage were comprehensively considered in a defined damage variable, which was based on the texture characteristic. An elastic-brittle damage model based on the mesostructure information of coal was established. As a result, the damage model can appropriately and reliably replicate the processes of initiation, expansion, cut-through and eventual destruction of microscopic damage to coal under compression. After comparison, it was proved that the predicted overall stress-strain response of the model was comparable to the experimental result.

Identification of oral squamous cell carcinoma in optical coherence tomography images based on texture features

Surgical excision is an effective treatment for oral squamous cell carcinoma(OSCC),but exact intraoperative differentiation OSCC from the normal tissue is the first premise.As a noninvasive imaging technique,optical coherence tomography(OCT)has the nearly same resolution as the histopathological examination,whose images contain rich information to assist surgeons to make clinical decisions.We extracted kinds of texture features from OCT images obtained by a home-made swept-source OCT system in this paper,and studied the identification of OSCC based on different combinations of texture features and machine learning classifiers.It was demonstrated that different combinations had different accuracies,among which the combination of texture features,gray level co-occurrence matrix(GLCM),Laws'texture measnres(LM),and center symmetric auto-correlation(CSAC),and SVM as the classifier,had the optimal comprehensive identification effect,whose accuracy was 94.1%.It was proven that it is feasible to distinguish OSCC based on texture features in OCT images,and it has great potential in helping surgeons make rapid and accurate decisions in oral clinical practice.

Effect of MR Field Strength on the Texture Features of Cerebral T2-FLAIR Images: A Pilot Study

Objective To investigate effect of MR field strength on texture features of cerebral T2 fluid attenuated inversion recovery(T2-FLAIR)images.Methods We acquired cerebral 3 D T2-FLAIR images of thirty patients who were diagnosed with ischemic white matter lesion(WML)with MR-1.5 T and MR-3.0 T scanners.Histogram texture features which included mean signal intensity(Mean),Skewness and Kurtosis,and gray level co-occurrence matrix(GLCM)texture features which included angular second moment(ASM),Contrast,Correlation,Inverse difference moment(IDM)and Entropy,of regions of interest located in the area of WML and normal white matter(NWM)were measured by ImageJ software.The texture parameters acquired with MR-1.5 T scanning were compared with MR-3.0 T scanning.Results The Mean of both WML and NWM obtained with MR-1.5 T scanning was significantly lower than that acquired with MR-3.0 T(P<0.001),while Skewness and Kurtosis between MR-1.5 T and MR-3.0 T scanning showed no significant difference(P>0.05).ASM,Correlation and IDM of both WML and NWM acquired with MR-1.5 T revealed significantly lower values than those with MR-3.0 T(P<0.001),while Contrast and Entropy acquired with MR-1.5 T showed significantly higher values than those with MR-3.0 T(P<0.001).Conclusion MR field strength showed no significant effect on histogram textures,while had significant effect on GLCM texture features of cerebral T2-FLAIR images,which indicated that it should be cautious to explain the texture results acquired based on the different MR field strength.

A Method of Soil Salinization Information Extraction with SVM Classification Based on ICA and Texture Features

Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.

Retrieval of High Resolution Satellite Images Using Texture Features

In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.

Improvement of Liver Segmentation by Combining High Order Statistical Texture Features with Anatomical Structural Features

Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robustness. A crucial stage of the liver segmentation is the selection of the image features for the segmentation. This paper presents an accurate liver segmentation algorithm. The approach starts with a texture analysis which results in an optimal set of texture features including high order statistical texture features and anatomical structural features. Then, it creates liver distribution image by classifying the original image pixelwisely using support vector machines. Lastly, it uses a group of morphological operations to locate the liver organ accurately in the image. The novelty of the approach is resided in the fact that the features are so selected that both local and global texture distributions are considered, which is important in liver organ segmentation where neighbouring tissues and organs have similar greyscale distributions. Experiment results of liver segmentation on CT images using the proposed method are presented with performance validation and discussion.

Image Splicing Detection Based on Texture Features with Fractal Entropy

Over the past years,image manipulation tools have become widely accessible and easier to use,which made the issue of image tampering far more severe.As a direct result to the development of sophisticated image-editing applications,it has become near impossible to recognize tampered images with naked eyes.Thus,to overcome this issue,computer techniques and algorithms have been developed to help with the identification of tampered images.Research on detection of tampered images still carries great challenges.In the present study,we particularly focus on image splicing forgery,a type of manipulation where a region of an image is transposed onto another image.The proposed study consists of four features extraction stages used to extract the important features from suspicious images,namely,Fractal Entropy(FrEp),local binary patterns(LBP),Skewness,and Kurtosis.The main advantage of FrEp is the ability to extract the texture information contained in the input image.Finally,the“support vector machine”(SVM)classification is used to classify images into either spliced or authentic.Comparative analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection methods.Overall,the proposed algorithm achieves an ideal balance between performance,accuracy,and efficacy,which makes it suitable for real-world applications.

DeepFake Videos Detection Based on Texture Features

In recent years,with the rapid development of deep learning technologies,some neural network models have been applied to generate fake media.DeepFakes,a deep learning based forgery technology,can tamper with the face easily and generate fake videos that are difficult to be distinguished by human eyes.The spread of face manipulation videos is very easy to bring fake information.Therefore,it is important to develop effective detection methods to verify the authenticity of the videos.Due to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in the forgery process,the texture details of the fake face are insufficient.Therefore,in this paper,a new method is proposed to detect DeepFake videos.Firstly,the texture features are constructed,which are based on the gradient domain,standard deviation,gray level co-occurrence matrix and wavelet transform of the face region.Then,the features are processed by the feature selection method to form a discriminant feature vector,which is finally employed to SVM for classification at the frame level.The experimental results on the mainstream DeepFake datasets demonstrate that the proposed method can achieve ideal performance,proving the effectiveness of the proposed method for DeepFake videos detection.

Recognizing Breast Cancer Using Edge-Weighted Texture Features of Histopathology Images

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 1.040X dataset,98.95%on the BreakHis 1.0100X dataset,98.92%on the BreakHis 1.0200X dataset,98.78%on the BreakHis 1.0400X 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.

Morphologic and texture features in classifying the malignant and benign breast nodules in ultrasonography

Objective To develop a computer-aided diagnosis(CAD)system with automatic contouring and morphologic and textural analysis to aid on the classification of breast nodules on ultrasound images.Methods A modified Level Set method was proposed to automatically segment the breast nodules(46malignant and 60benign nodules).Following,16morphologic features and 17texture features from the extracted contour were calculated and principal component analysis(PCA)was applied to find the optimal feature vector dimensions.Fuzzy C-means classifier was utilized to identify the breast nodule as benign or malignant with selected principal vectors.Results The performance of morphologic features was 78.30%for accuracy,67.39%for sensitivity and 86.67%for specificity,while the latter was 72.64%,58.70%and 83.33%,respectively.After the combination of the two features,the result was exactly the same with the morphologic performance.Conclusion This system performs well in classifying the malignant breast nodule from the benign breast nodule.

Psychometric Properties of Geometrical Texture Features for Tactile Roughness Sensation of Fabric Surfaces

The lack of understanding of the psychometric properties on the basic texture features forming tactile texture sense hinders the development of haptic rendering technology of textiles. The differential threshold and Weber fraction were investigated for a deep understanding of how surface texture features of fabrics affect the perceived roughness sensation by the constant stimulus method and the paired comparison method. The results showed that the differential threshold for the mean deviation of surface profile was0. 86 μm,and that the differential threshold of texture spatial period(TSP) was 2. 48 mm. And also,the difference thresholds and Weber fraction were affected by the reference stimulus intensity. As there is a significant interaction between four extracted texture feature indexes,any of the indexes alone cannot represent roughness sensation of fabrics.

Combining Spectral with Texture Features into Objectoriented Classification in Mountainous Terrain Using Advanced Land Observing Satellite Image

Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in mountainous terrain. Mountainous terrain mapping using ALOS image faces numerous challenges. These include spectral confusion with other land cover features, topographic effects on spectral signatures （such as shadow）. At first, topographic radiometric correction was carried out to remove the illumination effects of topography. In addition to spectral features, texture features were used to assist classification in this paper. And texture features extracted based on GLCM （Gray Level Co- occurrence Matrix） were not only used for segmentation, but also used for building rules. The performance of the method was evaluated and compared with Maximum Likelihood Classification （MLC）. Results showed that the object-oriented method integrating spectral and texture features has achieved overall accuracy of 85.73% with a kappa coefficient of 0.824, which is 13.48% and o.145 respectively higher than that got by MLC method. It indicated that texture features can significantly improve overall accuracy, kappa coefficient, and the classification precision of existing spectrum confusion features. Object-oriented method Integrating spectral and texture features is suitable for land use extraction of ALOS image in mountainous terrain.

STUDY ON THE TECHNIQUE TO DETECT TEXTURE FEATURES IN SAR IMAGES

This letter studies on the detection of texture features in Synthetic Aperture Radar (SAR) images. Through analyzing the feature detection method proposed by Lopes, an improved texture detection method is proposed, which can not only detect the edge and lines but also avoid stretching edge and suppressing lines of the former algorithm. Experimental results with both simulated and real SAR images verify the advantage and practicability of the improved method.

基于机器视觉的海鲜花螺分类研究

针对目前人工分选海鲜花螺劳动强度大、人工成本高的问题,研究提出一种DPO-SVM海鲜花螺公母分类模型。通过灰度共生矩阵分析提取海鲜花螺外壳间隔纹理特征量,采用SVM作为公母分类模型基体,对不同纹理特征量组合进行分类效果对比,得出使用能量、熵、对比度3种特征量分类效果最好的结论。针对SVM优化问题,以PSO和WOA算法为基础提出DPO算法对SVM的重要参数c、g进行优化;对DPO-SVM性能进行测试,将测试结果与SVM、PSO-SVM、WOA-SVM测试结果对比。相比于其他3种SVM模型,DPOSVM分类准确率大幅度提升,相比于SVM,分类总准确率由85%上升至100%,上升了15%;DPO算法提高了单种群优化算法的寻优性能,相比于PSO算法,DPO算法将最佳适应度从95.26提升至98.68,提升幅度为3.47%。此外,达到最佳适应度的迭代次数由14次减少至6次,下降57.14%,显著优化了收敛速度。研究结果可为自动分拣装置中海鲜花螺公母分类提供技术参考。

基于不同地面分辨率的无人机图像监测水稻白叶枯

【目的】快速无损地监测水稻白叶枯并量化感染程度,从而指导田间作业。【方法】利用无人机获取受白叶枯病菌Xanthomonas oryzae pv. oryzae感染的水稻冠层高分辨率图像,提取颜色和纹理特征,分别构建基于颜色、纹理特征以及两者融合的多元回归模型,对白叶枯感染等级进行预测。探究不同地面分辨率(Ground sampling distances,GSD)对不同模型精度的影响。【结果】基于颜色特征的监测模型的决定系数(Coefficient of determination,R2)为85.9%,均方根误差(Root mean square error,RMSE)为1.43,相对均方根误差(Relative RMSE,RRMSE)为19.1%,相比基于纹理特征的模型,R2上升了2.4个百分点,RRMSE增加了4.6个百分点;与单一种类特征相比,基于颜色和纹理特征融合的预测模型(R2=89.6%,RMSE=1.06,RRMSE=15.1%)精度有较大的提升;通过构建不同GSD模型发现,当GSD为0.2、0.5或0.8 cm时,模型精度较高,R2均在80%以上。【结论】从无人机捕获的低空遥感图像中提取的颜色和纹理特征可用于监测水稻白叶枯病,结果可为无人机遥感监测水稻白叶枯提供有效的技术支持。

基于自适应反馈机制的小差异化图像纹理特征信息数据检索

针对小差异化图像纹理相似度和噪声等因素导致纹理特征挖掘效果较差的问题,设计一种自适应反馈结合局部二值机制的小差异化图像纹理特征挖掘方法.使用规范割策略将图像数据各点拟作节点,使用节点间的连接线权重计算2点的相似度,采用支持向量机训练图像属性参数分类图像属性,进一步归纳图像类别.运用跳跃连接方法传输图像数据,将数据引入卷积神经网络剔除图像噪声.将中心点像素值当作反馈因子,创建自适应反馈判定条件,利用局部二值模式实现小差异化图像纹理特征挖掘.在MATLAB平台进行试验,从卷积神经网络收敛性、图像频谱纹理单元数、平均准确率、图像数据匹配度等方面进行了分析,分析结果表明:随着迭代次数不断增加,精度损失逐渐降低,基本收敛到稳定值,达到了预期训练效果;所提出方法挖掘的图像频谱纹理单元数3800个以上,更贴合人眼视觉信息;平均准确率为0.87,准确率@1、准确率@5和准确率@10的平均值分别为0.90、0.84和0.85;挖掘耗时低于5 s,图像数据匹配度高于90.3%,验证了所提出方法可在图像纹理特征识别操作中发挥应有作用.

腕管综合征患者高频超声纹理特征及其诊断价值分析

目的 采用高频超声分析腕管综合征患者的纹理特征,并分析高频超声纹理特征对腕管综合征的诊断价值,以期为该病的诊断提供更好的方法。方法 以2021年1月—2023年12月湖州市中心医院收治的148例腕管综合征患者为病例组,纳入同期因其他因素就诊的100例非腕管综合征患者为对照组。对比2组患者高频超声纹理特征,绘制ROC曲线分析高频超声纹理特征对腕管综合征的诊断价值。结果 病例组均值、标准差、变异系数以及直方图熵均低于对照组,偏度、峰值、亮度熵以及中值均高于对照组(P<0.05);病例组像素均值比、同深度均值比、中值比、同深度中值比、Q0.5、Q0.6、Q0.7、Q0.8、Q0.9、Q1均低于对照组,Q0、Q0.05、Q0.1以及Q0.15均高于对照组(P<0.05);病例组厚度最大值、厚度中值、厚度均值、长度最大值、长度中值、长度均值、病灶面积、长轴、短轴、离心率以及病灶周长均小于对照组(P<0.05);灰阶共生矩阵、形态学特征以及一阶统计量联合诊断腕管综合征患者的AUC(0.934)显著高于单一灰阶共生矩阵AUC(0.762)、形态学特征AUC(0.884)以及一阶统计量AUC(0.753)。结论 高频超声纹理特征分析中灰阶共生矩阵、形态学特征以及一阶统计量对腕管综合征患者具有较高的诊断价值,且3种联合的诊断价值更高。