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.
