Extended three-dimensional rotation invariant local binary patterns

This paper presents a new set of three-dimensional rotation invariant texture descriptors based on the well-known local binary patterns (LBPs). In the approach proposed here, we extend an existing three-dimensional LBP based on the region growing algorithm using existing features developed exquisitely for two-dimensional LBPs (pixel intensities and differences). We have conducted experiments on a synthetic dataset of three-dimensional randomly rotated texture images in order to evaluate the discriminatory power and the rotation invariant properties of our descriptors as well as those of other two-dimensional and three-dimensional texture descriptors. Our results demonstrate the effectiveness of the extended LBPs and improvements against other state-of-the-art hand-crafted three-dimensional texture descriptors on this dataset. Furthermore, we prove that the extended LBPs can be used in medical datasets to discriminate between MR images of oxygenated and non-oxygenated brain tissues of newborn babies. Display Omitted A new set of three-dimensional fully rotation invariant LBP descriptors is proposed.Proven utility of the third dimension in local binary patternsImprovements against other state-of-the-art 3D texture descriptorsApplication to a clinical dataset of susceptibility-weighted MR brain images