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 relati...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.展开更多
Purpose–Theprecisesegmentation ofbraintumors isthe mostimportantandcrucialstepintheir diagnosis and treatment.Due to the presence of noise,uneven gray levels,blurred boundaries and edema around the brain tumor region...Purpose–Theprecisesegmentation ofbraintumors isthe mostimportantandcrucialstepintheir diagnosis and treatment.Due to the presence of noise,uneven gray levels,blurred boundaries and edema around the brain tumor region,the brain tumor image has indistinct features in the tumor region,which pose a problem for diagnostics.The paper aims to discuss these issues.Design/methodology/approach–In this paper,the authors propose an original solution for segmentation using Tamura Texture and ensemble Support Vector Machine(SVM)structure.In the proposed technique,124 features of each voxel are extracted,including Tamura texture features and grayscale features.Then,these features are ranked using the SVM-Recursive Feature Elimination method,which is also adopted to optimize the parameters of the Radial Basis Function kernel of SVMs.Finally,the bagging random sampling method is utilized to construct the ensemble SVM classifierbased on a weighted voting mechanism to classify the types of voxel.Findings–The experiments are conducted over a sample data set to be called BraTS2015.The experiments demonstrate that Tamura texture is very useful in the segmentation of brain tumors,especially the feature of line-likeness.The superior performance of the proposed ensemble SVM classifier is demonstrated by comparison with single SVM classifiers as well as other methods.Originality/value–The authors propose an original solution for segmentation using Tamura Texture and ensemble SVM structure.展开更多
基金Project(51722401)supported by the National Natural Science Foundation for Excellent Young Scholars of ChinaProject(FRF-TP-18-003C1)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(51734001)supported by the Key Program of National Natural Science Foundation of China
文摘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.
基金supported by the Natural Science Foundation of Hubei Province,China(No.2016CKC775)the Fundamental Research Funds for the Central Universities(No.CZY17028).
文摘Purpose–Theprecisesegmentation ofbraintumors isthe mostimportantandcrucialstepintheir diagnosis and treatment.Due to the presence of noise,uneven gray levels,blurred boundaries and edema around the brain tumor region,the brain tumor image has indistinct features in the tumor region,which pose a problem for diagnostics.The paper aims to discuss these issues.Design/methodology/approach–In this paper,the authors propose an original solution for segmentation using Tamura Texture and ensemble Support Vector Machine(SVM)structure.In the proposed technique,124 features of each voxel are extracted,including Tamura texture features and grayscale features.Then,these features are ranked using the SVM-Recursive Feature Elimination method,which is also adopted to optimize the parameters of the Radial Basis Function kernel of SVMs.Finally,the bagging random sampling method is utilized to construct the ensemble SVM classifierbased on a weighted voting mechanism to classify the types of voxel.Findings–The experiments are conducted over a sample data set to be called BraTS2015.The experiments demonstrate that Tamura texture is very useful in the segmentation of brain tumors,especially the feature of line-likeness.The superior performance of the proposed ensemble SVM classifier is demonstrated by comparison with single SVM classifiers as well as other methods.Originality/value–The authors propose an original solution for segmentation using Tamura Texture and ensemble SVM structure.
