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
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.