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21 March 2014 A symmetric block-matching framework for global registration
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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.
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Marc Modat, David M. Cash, Pankaj Daga, Gawin P. Winston, John S. Duncan, and Sébastien Ourselin "A symmetric block-matching framework for global registration", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341D (21 March 2014);

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