Deformation Robust Texture Features for Polyp Classification via CT Colonography

In this article, we introduce a deformation independent model to solve the shape and posture changing issue for polyp characterization in computer-aided diagnosis (CADx) via CT colonography. After volumetric data parameterization in a four-dimensional space, the first fundamental form (FFF) is employed to construct the polyp model which contains several excellent properties such as locality, symmetry, orientation robustness, shift and isometric invariance. In consideration of the scaling effects, gray level co-occurrence matrix (GLCM) is utilized to remove the scaling factor and extract texture descriptors. As a symmetrical square tensor, however, it is difficult to put the FFF into GLCM directly. To solve this problem, we perform matrix decomposition on FFF to extract its eigenvalues and eigenvectors which are used to construct three metric images as the input of GLCM. Then Haralick measures extracted from GLCM are applied to construct texture descriptors which are fed to a random forest classifier to perform polyp classification. Experiments show that the proposed method obtains an encouraging classification performance with area under the curve of receiver operating characteristics (AUC score) of 95.3% which is a significant improvement comparing with five existing methods.