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.

Artificial intelligence on diabetic retinopathy diagnosis: an automatic classification method based on grey level co-occurrence matrix and naive Bayesian model

AIM: To develop an automatic tool on screening diabetic retinopathy(DR) from diabetic patients.METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix method and trained a Bayesian model based on these textures. The receiver operating characteristic(ROC) curve was used to estimate the sensitivity and specificity of the Bayesian model.RESULTS: A total of 1000 eyes fundus images from diabetic patients in which 298 eyes were diagnosed as DR by two ophthalmologists. The Bayesian model was trained using four extracted textures including contrast, entropy, angular second moment and correlation using a training dataset. The Bayesian model achieved a sensitivity of 0.949 and a specificity of 0.928 in the validation dataset. The area under the ROC curve was 0.938, and the 10-fold cross validation method showed that the average accuracy rate is 93.5%.CONCLUSION: Textures extracted by grey level cooccurrence can be useful information for DR diagnosis, and a trained Bayesian model based on these textures can be an effective tool for DR screening among diabetic patients.

Binary Image Steganalysis Based on Distortion Level Co-Occurrence Matrix

In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security.In most state-of-the-art binary image steganographic schemes,they always find out the flippable pixels to minimize the embedding distortions.For this reason,the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain.However,the distortion maps can be calculated for cover and stego images and the difference between them is significant.In this paper,a novel binary image steganalytic scheme is proposed,which is based on distortion level co-occurrence matrix.The proposed scheme first generates the corresponding distortion maps for cover and stego images.Then the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego images.Finally,support vector machine,based on the gaussian kernel,is used to classify the features.Compared with the prior steganalytic methods,experimental results demonstrate that the proposed scheme can effectively detect stego images.

Design on AM-OLED display control ASIC with high gray scale levels

The paper puts forward a method on controlling the AM-OLED panel to display image with high gray scale levels. It also gives an ASIC design sample to implement this method. A twenty sub-fields scan scheme has been taken into use in the chip to display 256 gray scale levels on a QVGA resolution AM-OLED display screen. The functions of image scaling and rotating have also been implemented for multiply application. The simulation and chip test result show that the chip design has met the design requirements.

Assessment of Severity Level for Diabetic Macular Oedema Using Machine Learning Algorithms

The macula is an imperative part present in our human visual system which is most responsible for clear and colour vision. For the people suffering from diabetes, the various parts of the body including the retina of the eye are affected. These retinal damages cause swelling and other abnormalities nearby macula. The pathologies in macula due to diabetes are called Diabetic Macular oEdema (DME). It affects patients’ vision that may lead to vision loss. It can be overcome by advance identification of causes for swelling. The major causes for the swelling are neovascularization and other abnormalities occurring in the blood vessels nearby the macula. The aim of this work is to avoid vision loss by detecting the presence of abnormalities in macula in advance. The pathologies present in the abnormal images are detected by image segmentation technique viz. Fuzzy K-means algorithm. The classification is done by two different classifiers namely Cascade Neural Network and Partial Least Square which are employed to identify whether the image is normal or abnormal. The results of both the classifiers are compared with respect to classifier accuracy, sensitivity and specificity. The classifier accuracies of Cascade Neural Network and Partial Least Square are 96.84% and 94.36%, respectively. The information about the severity of the disease and the localization of pathologies are very useful to the ophthalmologist for diagnosing the disease and apply proper treatments to the patients to avoid the formation of any lesion and prevent vision loss.

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.

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.

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.

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。

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。

Decision tree and deep learning based probabilistic model for character recognition

One of the most important methods that finds usefulness in various applications, such as searching historical manuscripts, forensic search, bank check reading, mail sorting, book and handwritten notes transcription, is handwritten character recognition. The common issues in the character recognition are often due to different writing styles, orientation angle, size variation(regarding length and height), etc. This study presents a classification model using a hybrid classifier for the character recognition by combining holoentropy enabled decision tree(HDT) and deep neural network(DNN). In feature extraction, the local gradient features that include histogram oriented gabor feature and grid level feature, and grey level co-occurrence matrix(GLCM) features are extracted. Then, the extracted features are concatenated to encode shape, color, texture, local and statistical information, for the recognition of characters in the image by applying the extracted features to the hybrid classifier. In the experimental analysis, recognition accuracy of 96% is achieved. Thus, it can be suggested that the proposed model intends to provide more accurate character recognition rate compared to that of character recognition techniques used in the literature.

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.

Fuzzy-Rough Feature Selection for Mammogram Classification

Feature selection（FS） refers to the process of selecting those input attributes that are most predictive of a given outcome. Unlike other dimensionality reduction methods,feature selectors preserve the original meaning of the features after reduction. The benefits of FS are twofold：it considerably decreases the running time of the induction algorithm,and increases the accuracy of the resulting model. This paper analyses the FS process in mammogram classification using fuzzy logic and rough set theory. Rough set and fuzzy logic based Quickreduct algorithms are applied for the FS from the features extracted using gray level co-occurence matrix（GLCM） constructed over the mammogram region. The predictive accuracy of the features is tested using NaiveBayes,Ripper,C4.5,and ant-miner algorithms. The results show that the ant-miner produces significant result comparing with others and the number of features selected using fuzzy-rough quick reduct algorithm is minimum,too.

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.

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.

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.