3D Gray Level Co-Occurrence Matrix Based Classification of Favor Benign and Borderline Types in Follicular Neoplasm Images

Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation of experienced pathologists. However, it is difficult to separate the favor benign from borderline types. Thus, this paper presents a classification approach based on 3D nuclei model to classify favor benign and borderline types of follicular thyroid adenoma (FTA) in cytological specimens. The proposed method utilized 3D gray level co-occurrence matrix (GLCM) and random forest classifier. It was applied to 22 data sets of FN images. Furthermore, the use of 3D GLCM was compared with 2D GLCM to evaluate the classification results. From experimental results, the proposed system achieved 95.45% of the classification. The use of 3D GLCM was better than 2D GLCM according to the accuracy of classification. Consequently, the proposed method probably helps a pathologist as a prescreening tool.

Material microstructures analyzed by using gray level Co-occurrence matrices

The mechanical properties of materials greatly depend on the microstructure morphology. The quantitative characterization of material microstructures is essential for the performance prediction and hence the material design. At present,the quantitative characterization methods mainly rely on the microstructure characterization of shape, size, distribution,and volume fraction, which related to the mechanical properties. These traditional methods have been applied for several decades and the subjectivity of human factors induces unavoidable errors. In this paper, we try to bypass the traditional operations and identify the relationship between the microstructures and the material properties by the texture of image itself directly. The statistical approach is based on gray level Co-occurrence matrix（GLCM）, allowing an objective and repeatable study on material microstructures. We first present how to identify GLCM with the optimal parameters, and then apply the method on three systems with different microstructures. The results show that GLCM can reveal the interface information and microstructures complexity with less human impact. Naturally, there is a good correlation between GLCM and the mechanical properties.

An Improved Deep Structure for Accurately Brain Tumor Recognition

Brain neoplasms are recognized with a biopsy,which is not commonly done before decisive brain surgery.By using Convolutional Neural Networks(CNNs)and textural features,the process of diagnosing brain tumors by radiologists would be a noninvasive procedure.This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure.The proposed model extracts Gray Level Co-occurrence Matrix(GLCM)textural features from MRI brain tumor images.Moreover,a deep neural network(DNN)model has been proposed to select the most salient features from the GLCM.Moreover,it manipulates the extraction of the additional high levels of salient features from a proposed CNN model.Finally,a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process.Two common datasets have been applied and tested,Br35H and FigShare datasets.The first dataset contains binary labels,while the second one splits the brain tumor into four classes;glioma,meningioma,pituitary,and no cancer.Moreover,several performance metrics have been evaluated from both datasets,including,accuracy,sensitivity,specificity,F-score,and training time.Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies.The proposed system has achieved about 98.22%accuracy value in the case of the Br35H dataset however,an accuracy of 98.01%has been achieved in the case of the FigShare dataset.

Hybrid Color Texture Features Classification Through ANN for Melanoma

Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

洪泽湖湿地纹理特征参数分析

应用纹理特征进行影像分类,关键在于纹理特征参数的确定。以洪泽湖湿地典型地区为研究对象,选择灰度共生矩阵进行纹理特征计算,探讨灰度共生矩阵窗口尺寸、移动步长、方向和纹理特征统计量对淡水湖泊湿地的区分能力;然后,利用纹理特征和地物光谱特征,结合决策树方法对研究区湿地及其他主要地类进行分类,并通过混淆矩阵进行精度评价。结果表明:研究区湿地分类中纹理特征的最佳窗口大小为3像元×3像元,方向为90°,步长为1个像元,纹理特征统计量组合为均值、熵和相关度;分类精度为83.24%,Kappa为0.788,其结果验证了纹理特征参数选择的科学性和合理性。

Pre-stack-texture-based reservoir characteristics and seismic facies analysis

Seismic texture attributes are closely related to seismic facies and reservoir characteristics and are thus widely used in seismic data interpretation.However,information is mislaid in the stacking process when traditional texture attributes are extracted from poststack data,which is detrimental to complex reservoir description.In this study,pre-stack texture attributes are introduced,these attributes can not only capable of precisely depicting the lateral continuity of waveforms between different reflection points but also reflect amplitude versus offset,anisotropy,and heterogeneity in the medium.Due to its strong ability to represent stratigraphies,a pre-stack-data-based seismic facies analysis method is proposed using the selforganizing map algorithm.This method is tested on wide azimuth seismic data from China,and the advantages of pre-stack texture attributes in the description of stratum lateral changes are verified,in addition to the method＇s ability to reveal anisotropy and heterogeneity characteristics.The pre-stack texture classification results effectively distinguish different seismic reflection patterns,thereby providing reliable evidence for use in seismic facies analysis.

基于多特征的金属断口图像疲劳条带分割

疲劳条带是疲劳断口典型的微观特征,分割是对金属断口图像进行定量分析以反推疲劳寿命和疲劳应力的重要环节。由于断裂过程中的复杂性使得实际断口多表现为多样性的混合形态,且不同区域的疲劳条带周期差别很大,使得疲劳条带纹理区域和纹理边缘的准确定位成为分割的一大难点。传统单一纹理特征对这类复杂的自然纹理分割准确性低。通过分析断口的自然纹理特性,提出结合灰度共生矩阵和小波包变换,采用多特征对断口图像的疲劳条带进行准确分割,从而发挥了时域和频域两类特征的双重优势。实验结果表明,改进的多特征方法对疲劳条带自动分割精度优于传统方法。

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.

Remote Sensing Image Classification Algorithm Based on Texture Feature and Extreme Learning Machine

With the development of satellite technology,the satellite imagery of the earth’s surface and the whole surface makes it possible to survey surface resources and master the dynamic changes of the earth with high efficiency and low consumption.As an important tool for satellite remote sensing image processing,remote sensing image classification has become a hot topic.According to the natural texture characteristics of remote sensing images,this paper combines different texture features with the Extreme Learning Machine,and proposes a new remote sensing image classification algorithm.The experimental tests are carried out through the standard test dataset SAT-4 and SAT-6.Our results show that the proposed method is a simpler and more efficient remote sensing image classification algorithm.It also achieves 99.434%recognition accuracy on SAT-4,which is 1.5%higher than the 97.95%accuracy achieved by DeepSat.At the same time,the recognition accuracy of SAT-6 reaches 99.5728%,which is 5.6%higher than DeepSat’s 93.9%.

A seismic texture coherence algorithm and its application

The first generation coherence algorithm （the C1 algorithm） that calculates the coherence of seismic data in-line and cross-line was developed using statistical cross-correlation theory, and it has the limitation that the technique can only be applied to horizons. Based on the texture technique, the texture coherence algorithm uses seismic information in different directions and differences among multiple traces. It can not only calculate seismic coherence in in-line and cross-line directions but also in all other directions. In this study, we suggested first an optimization method and a criterion for constructing the gray level co-occurrence matrix of the seismic texture coherence algorithm. Then the co-occurrence matrix was prepared to evaluate differences among multiple traces. Compared with the C1 algorithm, the seismic texture coherence algorithm suggested in this paper is better than the C1 in its information extraction and application. Furthermore, it implements the multi-direction information fusion and it, also has the advantage of simplicity and effectiveness, and improves the resolution of the seismic profile. Application of the method to field data shows that the texture coherence attribute is superior to that of C 1 and that it has merits in identification of faults and channels.

An Efficient Deep Learning-based Content-based Image Retrieval Framework

The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provides efficient image retrieval for the given query image.

Temperature Calculation of Pellet Rotary Kiln Based on Texture

In order to improve the quality of clinker produced by pellet rotary kiln, flame temperature that it is a very important factor of affecting on the quality of clinker is studied. The flame images collected from pellet rotary kiln are decomposed into three gray images by the method of RGB, so we can get more information of flame. Taking advantage of gray level co-occurrence matrix, the monitoring model for flame temperature based on image texture is established with RGB channels. In order to test the universality of the algorithm, candle flame temperature is detected by this method. The maximum error of the model is less than 3%.

Multi-source Remote Sensing Image Registration Based on Contourlet Transform and Multiple Feature Fusion

Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.

Landform classification based on optimal texture feature extraction from DEM data in Shandong Hilly Area, China

Texture and its analysis methods are crucial for image feature extraction and classification. Digital elevation model (DEM) is the most important data source of digital terrain analysis and landform classification, and considerable research values are gained from texture feature extraction and analysis from DEM data. In this research, on the basis of optimal texture feature extraction, the hilly area in Shandong, China, was selected as the study area, and DEM data with a resolution of 500 m were used as the experimental data for landform classification. First, second-order texture measures and texture image were extracted from DEM data by using a gray level cooccurrence matrix (GLCM). Second, the variation characteristics of each texture measure were analyzed, and the optimal feature parameters, such as direction, gray level, and texture window, were determined. Meanwhile, the texture feature value, combined with maximum information, was calculated, and the multiband texture image was obtained by resolving three optimal texture measure images. Finally, a support vector machine (SVM) method was adopted to classify landforms on the basis of the multiband texture image. Results indicated that the texture features of DEM data can be sufficiently represented and measured via the quantitative GLCM method. However, the feature parameters during the texture feature value calculation required further optimization. Based on the image texture from DEM data, efficient classification accuracy and ideal classification effect were achieved.

Effective Crowd Anomaly Detection Through Spatio-temporal Texture Analysis

Abnormal crowd behaviors in high density situations can pose great danger to public safety. Despite the extensive installation of closed-circuit television(CCTV) cameras, it is still difficult to achieve real-time alerts and automated responses from current systems. Two major breakthroughs have been reported in this research. Firstly, a spatial-temporal texture extraction algorithm is developed. This algorithm is able to effectively extract video textures with abundant crowd motion details. It is through adopting Gaborfiltered textures with the highest information entropy values. Secondly, a novel scheme for defining crowd motion patterns(signatures)is devised to identify abnormal behaviors in the crowd by employing an enhanced gray level co-occurrence matrix model. In the experiments, various classic classifiers are utilized to benchmark the performance of the proposed method. The results obtained exhibit detection and accuracy rates which are, overall, superior to other techniques.

Remote Sensing Estimation of Forest Canopy Density Combined with Texture Features

The development of high-resolution remote sensing imaging technology provides a new way to the large-scale estimation of forest canopy density. The traditional inversion methods for canopy density only use spectral or topographical features of remote sensing images.However,due to the existence of the different thing with same spectrum and the same thing with different spectrum phenomena,it is difficult to improve the estimation accuracy of canopy density.Based on spectrum and other traditional features,this paper combines texture features of remote sensing images to estimate canopy density.Firstly,the gray level co-occurrence matrix (GLCM) texture features are computed using objectbased method.Then,the principal component analysis (PCA) method is applied in correlation analysis and dimension reduction of texture features.Finally, spectrum and topographical features together with texture features are introduced into stepwise regression model to estimate canopy density.The experimental results showed that compared with the traditional method only based on spectrum or topographical features,the method combined with texture features greatly improved the estimation accuracy.The coefficient of determination(adjusted R^2 ) increased from 0.737 to 0.805.The estimation accuracy increased from 81.03%to 84.32%.

High-resolution Remote Sensing of Textural Images for Tree Species Classification

Remote sensing images show a very promising perspective for distinguishing tree species,especially those with the very high resolution ranging from 1 to 4 m.However,the traditional methodology for classifying land cover types,solely depending on spectral features,while texture and other spatial information are neglected, has the weakness such as inadequately utilization of information,low accuracies of classification,etc. Considering to the texture differences among forest species,it is more important for spatial information description of high-resolution remote sensing image to improve the precision of textural features choosing.In this study,the factors to influence the nine textural features choosing were analyzed and the results showed that the moving window size was the main factor to affect the obtaining processes of textural features based on the gray level co-occurrence matrix(GLCM) method,and the imagery was then classified combining the maximum likelihood classification(MLC) method with the original spectral values and texture features.First,this study utilized a correlation analysis of the images from a principal component analysis.Second,through multiple information sources,including textual features derived from the data.For the high-resolution remote sensing image, the most proper moving window size was determined from 3×3 to 31×31.Classification of the major tree species throughout the study area (the SunYat-Sen Mausoleum in Nanjing) was undertaken using the MLC.Third,to aid forest research,classification accuracy was improved using the GLCM.According to correlations among textures and richness of the data,GLCM provided the best window size and textural parameters. Results indicated that the texture characteristics were add in the spectral characteristics to improve the precision of the results of the classification, 19×19 window for best window.The total precision can reach 66.322 6%,Kappa coefficient is 0.584 0.Each tree species has greatly improved accuracies of the classification.By the calculation of R^2 values,the textural features of mean, homogeneity and correlation were chosen to be best combination for the size of 19×19 and the combination of skewness,homogeneity and mean was considered the most properly for the moving window size 19×19.Precision assessment of different textural combinations showed that VA,HO, CR combination with optimal moving window size (from 3×3 to 31×31) could evidently improve the classification precision for high-resolution remote sensing image.And the combination of mean,homogeneity,skewness,and contrast texture factors correlation can effectively reduce data redundancy,which obtained the similar results.In the texture features,the mean is the most important factor and impacts the classification of the tree species.This method could solve problems of forestry type classification,tree species classification,etc.It is much better than traditional method of based on pixel values.This procedure effectively reduced data redundancy and could assist in tree species classification.