A symmetric block-matching framework for global registration Marc Modat a,b , David M. Cash a,b , Pankaj Daga a , Gawin P. Winston c , John S. Duncan c and S´ ebastien Ourselin a,b a Centre for Medical Image Computing, University College London, UK. b Dementia Research Centre, Institute of Neurology, WC1N 3BG, University College London, UK. c Department of Clinical and Experimental Epilepsy, WC1N 3BG, University College London, UK. ABSTRACT Most registration algorithms suffer from a directionality bias that has been shown to largely impact on subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of non-linear registration but little work has been done in the context of global registration. We propose a symmetric approach based on a block-matching technique and least trimmed square regression. The proposed method is suitable for multi-modal registration and is robust to outliers in the input images. The symmetric framework is compared to the original asymmetric block-matching technique, outperforming it in terms accuracy and robustness. 1. INTRODUCTION Medical image registration is core to many image analysis pipelines. It consists of bringing two or more images into spatial alignment, often mapping one image into the space of another. However, recent studies have highlighted that this directionality in the registration can create a bias in analyses. 1–3 Several approaches have been proposed to address this issue in the context of non-linear registration. Christensen and Johnson 4 jointly optimise forward and backward transformations while minimising an inverse-consistency error criterion. Avants et al., 5 Vercauteren et al. 6 and others optimise the transfor- mation parameters in a mid-point image or a set of intermediate images. Little has however been proposed to remove directionality bias in the case of global registration. Reuter et al. 7 developed a global registration technique based on robust statistic optimisation performed in a mid-point space. This approach minimises the residual differences between input images while rejecting outliers. A linear intensity scaling is used in order to increase the robustness of the algorithm and deal with different ranges of intensity within the same modality or pulse-sequence. The proposed approach for robust and symmetric registration differs from Reuter et al. in two main aspects. First, we used a block-matching approach 8 to establish the spatial correspondences, where the normalised cross-correlation is used as a measure of similarity. Due to the small dimension of the blocks under consideration, the proposed approach is suitable for multimodal registration cases and is robust in the presence of outliers. 9 The second main difference is that joint forward and backward transformation parameters are simultaneously calculated rather than using a mid-point. This removes the need to discretise the transformed input images into an average space, which can be problematic when input images have different fields of view and resolution, often the case in multimodal registration. We evaluated the accuracy and robustness of our symmetric block-matching based approach using several databases of synthetic MR images, MR longitudinal studies, multimodal studies of MRI, PET and CT scans, and pairs of pre-operative and intraoperative MR. Medical Imaging 2014: Image Processing, edited by Sebastien Ourselin, Martin A. Styner, Proc. of SPIE Vol. 9034, 90341D · © 2014 SPIE CCC code: 1605-7422/14/$18 · doi: 10.1117/12.2043652 Proc. of SPIE Vol. 9034 90341D-1 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 03/19/2015 Terms of Use: http://spiedl.org/terms