Registration and Normalization

  • Klaus D. Toennies
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


Information about an object from different sources can be combined if a transformation allows mapping data from one source to data of the other source. In medical imaging, the two sources are image acquisition systems. If the two sources depict the same subject, this process is called registration. If they depict different subjects, it is called normalization. The mapping is a geometric transformation that accounts for different positioning of a patient in two image acquisition systems. Determining a registration or normalization transformation requires redundant information in the two images, a suitable restriction of acceptable transformations, and, for iterative schemes, a criterion that rates the quality of a given transformation. Various ways to compute a registration or normalization transformation from medical images will be discussed in this chapter.


Mutual Information Displacement Field Rigid Registration Iterative Close Point Algorithm Procrustes Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.Computer Science Department, ISGOtto-von-Guericke-Universität MagdeburgMagdeburgGermany

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