Texture Analysis for Evaluating Image Registration

Accurate image registration is essential for both cross-sectional and longitudinal MR studies. In longitudinal studies aligning same contrast intra-subject images, which are the focus of our work, registration is assumed to be a rigid body problem. This assumption is questionable due to global and local changes in brain volume either due to hydration or atrophy. Consequently, misregistration at the voxel level may occur in these studies, which might lead to subject data being discarded. This misregistration is evident particularly around the cortex. Visual inspection of images is used to determine registration accuracy. While this approach is suitable for assessing alignment of landmark structures, it fails to capture the millimteric or sub-millimetric misregistrations. Automatic metrics can precisely estimate the overall performance of a given registration algorithm by employing an evaluation database. However, to estimate the registration accuracy of a given pair of images, such metrics are unsuitable. In this work we propose texture analysis of a subtraction image to evaluate the registration accuracy of a given pair of same contrast, intra-subject images. Once registered, images are intensity normalized, blurred and subtracted. In the event of registration errors, or violation of the rigid body assumption, the subtraction images have artifacts. The texture features of these artifacts are different from the artifact-free (clean) areas of the subtraction images. Using a texture-based classifier, artifact areas in the subtraction images that indicate failed registration are identified. In addition to determining if the registration has failed, our approach can identify the specific locations of misregistration, which can be corrected, leading to a more inclusive subject data.