The registration of MR images using multiscale robust methods |
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| Institution: | 1. Instituto de Investigación en Ciencias Biomédicas, Universidad Ricardo Palma, Lima, Perú;2. Facultad de Medicina, Universidad Cesar Vallejo, Filial Piura, Piura, Perú;3. Facultad de Medicina Humana, Universidad Nacional San Luis Gonzaga, Ica, Perú;4. Escuela de Medicina Humana, Universidad Continental, Huancayo, Perú;5. Escuela de Posgrado, Universidad Privada Antenor Orrego, Trujillo, Perú;1. Alentejo Biotechnology Center for Agriculture and Agro-Food (CEBAL), Polytechnic Institute of Beja (IPBeja), 7801-908, Beja, Portugal;2. MED – Mediterranean Institute for Agriculture, Environment and Development, CHANGE - Global Change and Sustainability Institute, CEBAL, 7801-908, Beja, Portugal;1. Faculty of Engineering, China University of Geosciences (Wuhan), Wuhan, Hubei, 430074, China;2. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, Hubei, 430071, China;1. Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom;2. German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany;3. Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile;4. Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile;5. Department of Biomedical Magnetic Resonance, Institute of Experimental Physics, Otto von Guericke University, Magdeburg, Germany;6. Center for Behavioural Brain Sciences, Magdeburg, Germany;7. Leibniz Institute for Neurobiology, Magdeburg, Germany;1. Faculty of Electrical Engineering, West Pomeranian University of Technology in Szczecin, ul. Sikorskigo 37, Szczecin 70-313, Poland;2. Faculty of Civil and Environmental Engineering, West Pomeranian University of Technology in Szczecin, Al. Piastów 50, 71-311 Szczecin, Poland;1. The Children''s Hospital of Philadelphia, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA;2. Department of Otolaryngology, Beijing Children''s Hospital, Beijing, China;3. Department of Pulmonary Medicine, Beijing Children''s Hospital, Beijing, China |
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| Abstract: | Acquisition of MR images involves their registration against some prechosen reference image. Motion artifacts and misregistration can seriously flaw their interpretation and analysis. This article provides a global registration method that is robust in the presence of noise and local distortions between pairs of images. It uses a two-stage approach, comprising an optional Fourier phase-matching method to carry out preregistration, followed by an iterative procedure. The iterative stage uses a prescribed set of registration points, defined on the reference image, at which a robust nonlinear regression is computed from the squared residuals at these points. The method can readily accommodate general linear, or even nonlinear, registration transformations on the images. The algorithm was tested by recovering the registration transformation parameters when a 256 × 256 pixel T21-weighted human brain image was scaled, rotated, and translated by prescribed amounts, and to which different amounts of Gaussian noise had been added. The results show subpixel accuracy of recovery when no noise is present, and graceful degradation of accuracy as noise is added. When 40% noise is added to images undergoing small shifts, the recovery errors are less than 3 pixels. The same tests applied to the Woods algorithm gave slightly inferior accuracy for these images, but failed to converge to the correct parameters in some cases of large-scale-shifted images with 10% added noise. |
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