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Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer
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作者 Kai-Feng Yang Sheng-Jie Li +1 位作者 Jun Xu Yong-Bin Zheng 《World Journal of Gastrointestinal Surgery》 SCIE 2024年第6期1571-1581,共11页
BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the ... BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions. 展开更多
关键词 Colorectal cancer Synchronous liver metastasis Gray-level co-occurrence matrix Machine learning algorithm Prediction model
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Artificial intelligence on diabetic retinopathy diagnosis: an automatic classification method based on grey level co-occurrence matrix and naive Bayesian model 被引量:6
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作者 Kai Cao Jie Xu Wei-Qi Zhao 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2019年第7期1158-1162,共5页
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 metho... 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. 展开更多
关键词 GREY level co-occurrence matrix Bayesian textures artificial INTELLIGENCE receiver operating characteristiccurve DIABETIC RETINOPATHY
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Binary Image Steganalysis Based on Distortion Level Co-Occurrence Matrix 被引量:2
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作者 Junjia Chen Wei Lu +4 位作者 Yuileong Yeung Yingjie Xue Xianjin Liu Cong Lin Yue Zhang 《Computers, Materials & Continua》 SCIE EI 2018年第5期201-211,共11页
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 s... 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. 展开更多
关键词 Binary image steganalysis informational security embedding distortion distortion level map co-occurrence matrix support vector machine.
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3D Gray Level Co-Occurrence Matrix Based Classification of Favor Benign and Borderline Types in Follicular Neoplasm Images 被引量:1
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作者 Oranit Boonsiri Kiyotada Washiya +1 位作者 Kota Aoki Hiroshi Nagahashi 《Journal of Biosciences and Medicines》 2016年第3期51-56,共6页
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... 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. 展开更多
关键词 Thyroid Follicular Lesion 3D Gray Level co-occurrence matrix Random Ferest Classifier
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A Combination of Feature Selection and Co-occurrence Matrix Methods for Leukocyte Recognition System
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作者 Li Na Arlends Chris Bagus Mulyawan 《Journal of Software Engineering and Applications》 2012年第12期101-106,共6页
A leukocyte recognition system, as part of a differential blood counter system, is very important in hematology field. In this paper, the propose system aims to automatically classify the white blood cells (leukocytes... A leukocyte recognition system, as part of a differential blood counter system, is very important in hematology field. In this paper, the propose system aims to automatically classify the white blood cells (leukocytes) on a given microscopic image. The classifications of leukocytes are performed based on the combination of color and texture features of the blood cell images. The developed system classifies the leukocytes in one of the five categories (neutrophils, eosinophils, basophils, lymphocytes, and monocytes). In the preprocessing stage, the system starts with converting the microscopic images from Red Green Blue (RGB) color space to Hue Saturation Value (HSV) color space. Next, the system splits the Hue and Saturation features from the Value feature. For both Hue and Saturation features, the system processes their color information using the Feature Selection method and the Window Cropping method;while the Value feature is processed by its texture information using the Co-occurrence matrix method. The final recognition stage is performed using the Euclidean distance method. The combination of the Feature Selection and Co-occurrence Matrix methods gives the best overall recognition accuracies for classifying leukocyte images. 展开更多
关键词 LEUKOCYTE recognition WHITE BLOOD cell MICROSCOPIC image Feature selection co-occurrence matrix
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Material microstructures analyzed by using gray level Co-occurrence matrices 被引量:1
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作者 胡延苏 王志军 +2 位作者 樊晓光 李俊杰 高昂 《Chinese Physics B》 SCIE EI CAS CSCD 2017年第9期483-490,共8页
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 ... 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. 展开更多
关键词 microstructures quantitative characterization mechanical properties gray level co-occurrence matrix
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Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma 被引量:1
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作者 Chao Lu Zhi-Xiang Xing +4 位作者 Xi-Gang Xia Zhi-Da Long Bo Chen Peng Zhou Rui Wang 《World Journal of Gastrointestinal Oncology》 SCIE 2023年第7期1241-1252,共12页
BACKGROUND There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma(PHC).Previous reports have shown that over 10%of patients with PHC exp... BACKGROUND There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma(PHC).Previous reports have shown that over 10%of patients with PHC experience postoperative pulmonary infections.Thus,it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC.AIM To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management.METHODS We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery.Radiomics data were selected for statistical analysis,and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables.We then developed a pulmonary infection prediction model using three different models:An artificial neural network model;a random forest model;and a generalized linear regression model.Finally,we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses.RESULTS Among the 505 patients,86 developed a postoperative pulmonary infection,resulting in an incidence rate of 17.03%.Based on the gray-level co-occurrence matrix,we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models.Among these,energy,contrast,the sum of squares(SOS),the inverse difference(IND),mean sum(MES),sum variance(SUV),sum entropy(SUE),and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models.The random forest model algorithm,in combination with IND,SOS,MES,SUE,SUV,and entropy,demonstrated the highest prediction efficiency in both the training and internal verification sets,with areas under the curve of 0.823 and 0.801 and a 95%confidence interval of 0.766-0.880 and 0.744-0.858,respectively.The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95%confidence intervals of 0.677-0.791 and 0.766-0.864,respectively.CONCLUSION Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND,SOS,MES,SUE,SUV,energy,and entropy.The prediction model in this study based on diffusion-weighted images,especially the random forest model algorithm,can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy,providing valuable guidance for postoperative management. 展开更多
关键词 Primary hepatic carcinoma Pulmonary infection Gray-level co-occurrence matrix Machine learning PREDICTION
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Automatic Identification of Butterfly Species Based on Gray-Level Co-occurrence Matrix Features of Image Block 被引量:3
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作者 XUE Ankang LI Fan XIONG Yin 《Journal of Shanghai Jiaotong university(Science)》 EI 2019年第2期220-225,共6页
In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of... In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of science but also important to the agricultural production and the environment. Texture as a notable feature is widely used in digital image recognition technology; for describing the texture, an extremely effective method, graylevel co-occurrence matrix(GLCM), has been proposed and used in automatic identification systems. However,according to most of the existing works, GLCM is computed by the whole image, which likely misses some important features in local areas. To solve this problem, this paper presents a new method based on the GLCM features extruded from three image blocks, and a weight-based k-nearest neighbor(KNN) search algorithm used for classifier design. With this method, a butterfly classification system works on ten butterfly species which are hard to identify by shape features. The final identification accuracy is 98%. 展开更多
关键词 automatic identification butterfly species gray-level co-occurrence matrix(GLCM) features of image block
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An Improved Deep Structure for Accurately Brain Tumor Recognition
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作者 Mohamed Maher Ata Reem N.Yousef +1 位作者 Faten Khalid Karim Doaa Sami Khafaga 《Computer Systems Science & Engineering》 SCIE EI 2023年第8期1597-1616,共20页
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 radiolo... 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. 展开更多
关键词 Brain tumor convolutional neural network gray level co-occurrence matrix NONINVASIVE FigShare dataset Br35H dataset
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Hybrid Color Texture Features Classification Through ANN for Melanoma
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作者 Saleem Mustafa Arfan Jaffar +3 位作者 Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani Ahmed Alghamdi 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期2205-2218,共14页
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 ar... 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. 展开更多
关键词 Gray level co-occurrence matrix local binary pattern artificial neural networks support vector machines COLOR skin cancer dermoscopic
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基于灰度-梯度共生矩阵的焊缝缺陷聚类分析 被引量:7
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作者 戴光 崔巍 +1 位作者 张颖 王学增 《中国安全科学学报》 CAS CSCD 北大核心 2013年第3期79-85,共7页
为识别焊缝不同类型缺陷,以焊缝缺陷的漏磁检测(MFL)图像为研究对象,提出基于灰度-梯度共生矩阵(GGCM)和聚类分析的焊缝缺陷识别方法。将采集的三维MFL信号转换为二维灰度图像,利用GGCM直接提取焊缝3种状态下(焊缝无缺陷、焊道上分布圆... 为识别焊缝不同类型缺陷,以焊缝缺陷的漏磁检测(MFL)图像为研究对象,提出基于灰度-梯度共生矩阵(GGCM)和聚类分析的焊缝缺陷识别方法。将采集的三维MFL信号转换为二维灰度图像,利用GGCM直接提取焊缝3种状态下(焊缝无缺陷、焊道上分布圆柱体缺陷、焊道上分布矩形槽缺陷)MFL图像的特征信息。结合2种聚类算法,用层次聚类法选取评述焊缝图像的特征量,用k-均值聚类方法分析这些特征量,并以可视化图形显示聚类结果。结果表明:根据GGCM提取的特征量的聚类分析结果,焊缝典型缺陷的识别率大于96%。 展开更多
关键词 焊缝缺陷 漏磁检测(MFL)图像 灰度-梯度共生矩阵(ggcm) 层次聚类法 k均值聚类法
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基于BoF模型的多特征融合纹理图像分类 被引量:6
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作者 汪宇玲 黎明 +2 位作者 李军华 张聪炫 陈昊 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2018年第9期1869-1877,共9页
针对特征词袋(BoF)模型缺乏空间和几何信息,对纹理图像内容表达不明显等问题,提出一种基于BoF模型的多特征融合纹理分类算法。将灰度梯度共生矩阵(GGCM)和尺度不变特征转换(SIFT)融合特征作为纹理图像的区域特征描述,通过动态权重鉴别... 针对特征词袋(BoF)模型缺乏空间和几何信息,对纹理图像内容表达不明显等问题,提出一种基于BoF模型的多特征融合纹理分类算法。将灰度梯度共生矩阵(GGCM)和尺度不变特征转换(SIFT)融合特征作为纹理图像的区域特征描述,通过动态权重鉴别能量分析进行最优参数特征选择,并用BoF量化纹理特征,使用支持向量机对图像进行训练和预测,得出分类结果。实验结果表明,本文算法对有旋转扭曲的纹理、边缘模糊纹理、有光照变化的纹理及杂乱纹理等均能取得较好的分类效果,相对于传统BoF模型及凹凸划分(CCP)方法等算法在UIUC纹理库上的分类正确率均有不同程度的提高,平均分类正确率分别提高12. 8%和7. 9%,说明本文算法针对纹理图像分类具有较高的精度和较好的鲁棒性。 展开更多
关键词 纹理分类 多特征融合 特征词袋(BoF) 灰度梯度共生矩阵(ggcm) 尺度不变特征转换(SIFT)
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基于模糊方法的尿沉渣有形成分聚类分析 被引量:1
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作者 张颖超 魏宇璋 《控制工程》 CSCD 北大核心 2009年第2期203-206,共4页
以往的应用于尿沉渣有形成分图像识别的方法一般是对通过尿沉渣个体的形态特征的提取进行一个一个的识别。在对尿沉渣有形成分分割以后,首先通过构造灰度-梯度共生矩阵来获得尿沉渣有形成分的纹理特性参数矩阵,再从由此矩阵变换得到传... 以往的应用于尿沉渣有形成分图像识别的方法一般是对通过尿沉渣个体的形态特征的提取进行一个一个的识别。在对尿沉渣有形成分分割以后,首先通过构造灰度-梯度共生矩阵来获得尿沉渣有形成分的纹理特性参数矩阵,再从由此矩阵变换得到传递闭包矩阵中找出各个等级的阈值λ。选择具有最大F-统计量的划分聚类方案。利用纹理特征聚类不但高效率地得到各成分的数量比例,而且比用形态特征聚类有较高的准确度。 展开更多
关键词 灰度-梯度共生矩阵 纹理 传递闭包矩阵 F-统计量
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Pre-stack-texture-based reservoir characteristics and seismic facies analysis 被引量:3
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作者 宋承云 刘致宁 +2 位作者 蔡涵鹏 钱峰 胡光岷 《Applied Geophysics》 SCIE CSCD 2016年第1期69-79,219,共12页
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 tradit... 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. 展开更多
关键词 Pre-stack texture attributes reservoir characteristic seismic facies analysis SOM clustering gray level co-occurrence matrix
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洪泽湖湿地纹理特征参数分析 被引量:13
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作者 张楼香 阮仁宗 夏双 《国土资源遥感》 CSCD 北大核心 2015年第1期75-80,共6页
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... 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。 展开更多
关键词 洪泽湖湿地 纹理特征 窗口尺寸 移动步长和方向 灰度共生矩阵 GRAY level co-occurrence matrix(GLCM)
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基于多特征的金属断口图像疲劳条带分割 被引量:1
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作者 梁欣 黎明 冷璐 《计算机仿真》 CSCD 北大核心 2014年第4期384-388,429,共6页
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... 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。 展开更多
关键词 疲劳条带分割 金属断口图像 纹理特征 灰度共生矩阵 小波包变换 GRAY level co-occurrence matrix (GLCM) wavelet packet transform (WPT)
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Coal–rock interface detection on the basis of image texture features 被引量:20
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作者 Sun Jiping Su Bo 《International Journal of Mining Science and Technology》 SCIE EI 2013年第5期681-687,共7页
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... 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. 展开更多
关键词 Coal–rock interface detection TEXTURE Gray level co-occurrence matrix Feature selection Fisher discriminant method Cross-validation
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Case study on the extraction of land cover information from the SAR image of a coal mining area 被引量:11
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作者 HU Zhao-ling LI Hai-quan DU Pei-jun 《Mining Science and Technology》 EI CAS 2009年第6期829-834,共6页
In this study,analyses are conducted on the information features of a construction site,a cornfield and subsidence seeper land in a coal mining area with a synthetic aperture radar (SAR) image of medium resolution. Ba... In this study,analyses are conducted on the information features of a construction site,a cornfield and subsidence seeper land in a coal mining area with a synthetic aperture radar (SAR) image of medium resolution. Based on features of land cover of the coal mining area,on texture feature extraction and a selection method of a gray-level co-occurrence matrix (GLCM) of the SAR image,we propose in this study that the optimum window size for computing the GLCM is an appropriate sized window that can effectively distinguish different types of land cover. Next,a band combination was carried out over the text feature images and the band-filtered SAR image to secure a new multi-band image. After the transformation of the new image with principal component analysis,a classification is conducted selectively on three principal component bands with the most information. Finally,through training and experimenting with the samples,a better three-layered BP neural network was established to classify the SAR image. The results show that,assisted by texture information,the neural network classification improved the accuracy of SAR image classification by 14.6%,compared with a classification by maximum likelihood estimation without texture information. 展开更多
关键词 SAR image gray-level co-occurrence matrix texture feature neural network classification coal mining area
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Decision tree and deep learning based probabilistic model for character recognition 被引量:6
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作者 A.K.Sampath Dr.N.Gomathi 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第12期2862-2876,共15页
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, i... 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. 展开更多
关键词 GREY level co-occurrence matrix FEATURE HISTOGRAM oriented GABOR gradient FEATURE hybrid CLASSIFIER holoentropy enabled decision tree CLASSIFIER
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Liver fibrosis identification based on ultrasound images captured under varied imaging protocols 被引量:4
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作者 曹桂涛 施鹏飞 胡兵 《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》 SCIE EI CAS CSCD 2005年第11期1107-1114,共8页
Diagnostic ultrasound is a useful and noninvasive method in clinical medicine. Although due to its qualitative, sub- jective and experience-based nature, ultrasound image interpretation can be influenced by image cond... Diagnostic ultrasound is a useful and noninvasive method in clinical medicine. Although due to its qualitative, sub- jective and experience-based nature, ultrasound image interpretation can be influenced by image conditions such as scanning frequency and machine settings. In this paper, a novel method is proposed to extract the liver features using the joint features of fractal dimension and the entropies of texture edge co-occurrence matrix based on ultrasound images, which is not sensitive to changes in emission frequency and gain. Then, Fisher linear classifier and support vector machine are employed to test a group of 99 in-vivo liver fibrosis images from 18 patients, as well as other 273 liver images from 18 normal human volunteers. 展开更多
关键词 Liver fibrosis TEXTURE co-occurrence matrix Fisher classifier Support vector machine
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