Abstract
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 the 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.
Concepts, notions and definitions introduced in this chapter
- Rigid and nonrigid registration
- Normalization
- Similarity criteria: average distance, Procrustes distance, intensity difference, correlation and covariance, stochastic and deterministic sign change, mutual information
- Analytic solution for rigid registration
- Constraints for iterative registration: elasticity, viscosity, splines, similarity of displacement vectors
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- 1.
Sometimes the square root is not taken.
- 2.
Different similarity measures in 2d to 3d registration have been compared in Penney et al. (1998).
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Toennies, K.D. (2012). Registration and Normalization. In: Guide to Medical Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-2751-2_10
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