Textures have become widely adopted as an essential tool for lesion detection and classification through analysis of the lesion heterogeneities.In this study,higher order derivative images are being employed to combat...Textures have become widely adopted as an essential tool for lesion detection and classification through analysis of the lesion heterogeneities.In this study,higher order derivative images are being employed to combat the challenge of the poor contrast across similar tissue types among certain imaging modalities.To make good use of the derivative information,a novel concept of vector texture is firstly introduced to construct and extract several types of polyp descriptors.Two widely used differential operators,i.e.,the gradient operator and Hessian operator,are utilized to generate the first and second order derivative images.These derivative volumetric images are used to produce two angle-based and two vectorbased(including both angle and magnitude)textures.Next,a vector-based co-occurrence matrix is proposed to extract texture features which are fed to a random forest classifier to perform polyp classifications.To evaluate the performance of our method,experiments are implemented over a private colorectal polyp dataset obtained from computed tomographic colonography.We compare our method with four existing state-of-the-art methods and find that our method can outperform those competing methods over 4%-13%evaluated by the area under the receiver operating characteristics curves.展开更多
Texture features have played an essential role in the field of medical imaging for computer-aided diagnosis.The gray-level co-occurrence matrix(GLCM)-based texture descriptor has emerged to become one of the most succ...Texture features have played an essential role in the field of medical imaging for computer-aided diagnosis.The gray-level co-occurrence matrix(GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications.This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor.In this study,we first introduce a new parameter-stride,to explore the definition of GLCM.Then we propose three multi-scaling GLCM models according to its three parameters,(1)learning model by multiple displacements,(2)learning model by multiple strides(LMS),and(3)learning model by multiple angles.These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model.To further analyze the three parameters,we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas.Finally,the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model.LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.展开更多
基金This work was partially supported by the NIH/NCI,Nos.CA206171 and CA220004Dr.Lu was supported by the National Natural Science Foundation of China,No.81871424.
文摘Textures have become widely adopted as an essential tool for lesion detection and classification through analysis of the lesion heterogeneities.In this study,higher order derivative images are being employed to combat the challenge of the poor contrast across similar tissue types among certain imaging modalities.To make good use of the derivative information,a novel concept of vector texture is firstly introduced to construct and extract several types of polyp descriptors.Two widely used differential operators,i.e.,the gradient operator and Hessian operator,are utilized to generate the first and second order derivative images.These derivative volumetric images are used to produce two angle-based and two vectorbased(including both angle and magnitude)textures.Next,a vector-based co-occurrence matrix is proposed to extract texture features which are fed to a random forest classifier to perform polyp classifications.To evaluate the performance of our method,experiments are implemented over a private colorectal polyp dataset obtained from computed tomographic colonography.We compare our method with four existing state-of-the-art methods and find that our method can outperform those competing methods over 4%-13%evaluated by the area under the receiver operating characteristics curves.
基金This work was supported by the NIH/NCI,No.CA206171.
文摘Texture features have played an essential role in the field of medical imaging for computer-aided diagnosis.The gray-level co-occurrence matrix(GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications.This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor.In this study,we first introduce a new parameter-stride,to explore the definition of GLCM.Then we propose three multi-scaling GLCM models according to its three parameters,(1)learning model by multiple displacements,(2)learning model by multiple strides(LMS),and(3)learning model by multiple angles.These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model.To further analyze the three parameters,we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas.Finally,the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model.LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.
