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Editore: VDM Verlag Dr. M�ller 2010-03-31, 2010
ISBN 10: 3639119541 ISBN 13: 9783639119541
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Intensity-based 2D-3D Medical Image Registration | Algorithms and Analysis | Daniel Russakoff | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2010 | VDM Verlag Dr. Müller | EAN 9783639119541 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.
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Aggiungi al carrelloKartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Russakoff DanielDaniel Russakoff received an A.B. in geophysics from Harvard University and his Ph.D. in computer science from Stanford. His research is in computer vision and pattern recognition in general, and medical image anal.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Intensity-based 2D-3D medical image registration is a special case of the pose estimation problem from computer vision with many applications in medicine. This work presents an overview of the 2D-3D intensity-based image registration problem in the medical domain as well as results from several methods developed to aid in its practice. In particular: 1) Light field rendering techniques from the graphics community are extended to rapidly generate digitally reconstructed radiographs (DRRs). 2) A full 2D-3D registration algorithm using light field DRRs is presented and validated against a real, clinical gold standard. 3) A new, hybrid similarity measure is presented that is a weighted combination of an intensity-based image similarity measure and a point-based measure incorporating a single fiducial marker. 4) Finally, a novel similarity measure called regional mutual information (RMI) is introduced. RMI is an extension of mutual information which incorporates spatial information in a principled way. The additional spatial information helps make its use as a similarity measure much more robust to initial misregistration than standard mutual information.