Robust 3D rotation invariant local binary pattern for volumetric texture classification

3D local binary pattern (LBP) shows significant performance in many domains such as solid textures analysis, face recognition and tumor detection. In recent years, rotation invariant 3D LBP texture descriptors have received increasing attention and several variants have been proposed. However, they are sensitive to the noise present in the image. In this paper, we propose an efficient rotation invariant texture descriptor known as robust extended 3D LBP (RELBP) for volumetric texture classification. Unlike the current 3D LBP framework, our descriptor uses the information of neighboring voxels to reduce noise. First, the 3D weighted average filter is employed to process each voxel in the image, in which the center voxel is replaced by the average local gray level based on weights. Besides, equidistant points on a sphere are sampled to construct a set of rotation invariant features. Our experiments demonstrate that the RELBP proposed here shows superior classification performance in texture classification tasks and our method is highly robust to image noise on benchmark datasets.