Efficient nonrigid registration using ranked order statistics

Non-rigid image registration techniques are widely used in medical imaging applications. Due to high computational complexities of these techniques, finding appropriate registration method to both reduce the computation burden and increase the registration accuracy has become an intense area of research. In this paper we propose a fast and accurate nonrigid registration method for intra-modality volumetric images. Our approach exploits the information provided by an order statistics based segmentation method, to find the important regions for registration and use an appropriate sampling scheme to target those areas and reduce the registration computation time. A unique advantage of the proposed method is its ability to identify the point of diminishing returns and stop the registration process. Our experiments on registration of real lung CT images, with expert annotated landmarks, show that the new method is both faster and more accurate than the state of the art techniques, particularly for registration of images with large deformations.