*Jan Modersitzki*

- Published in print:
- 2003
- Published Online:
- September 2007
- ISBN:
- 9780198528418
- eISBN:
- 9780191713583
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198528418.003.0007
- Subject:
- Mathematics, Applied Mathematics

This chapter summarizes the techniques discussed so far in this book. The techniques are all based on the minimization of a certain distance measure, and the distance measure is based on image ...
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This chapter summarizes the techniques discussed so far in this book. The techniques are all based on the minimization of a certain distance measure, and the distance measure is based on image features or directly on image intensities. Image features can be user supplied (e.g., landmarks) or may be deduced automatically from the image intensities (e.g., principal axes). Typical examples of intensity-based distance measures are the sum of squared differences, correlation or mutual information. For all proposed techniques, the transformation is parametric, i.e., it can be expanded in terms of some parameters and basis functions. The desired transformation is a minimizer of the distance measure in the space spanned by the basis functions. The minimizer can be obtained from algebraic equations or by applying appropriate optimization tools.Less

This chapter summarizes the techniques discussed so far in this book. The techniques are all based on the minimization of a certain distance measure, and the distance measure is based on image features or directly on image intensities. Image features can be user supplied (e.g., landmarks) or may be deduced automatically from the image intensities (e.g., principal axes). Typical examples of intensity-based distance measures are the sum of squared differences, correlation or mutual information. For all proposed techniques, the transformation is parametric, i.e., it can be expanded in terms of some parameters and basis functions. The desired transformation is a minimizer of the distance measure in the space spanned by the basis functions. The minimizer can be obtained from algebraic equations or by applying appropriate optimization tools.

*Jan Modersitzki*

- Published in print:
- 2003
- Published Online:
- September 2007
- ISBN:
- 9780198528418
- eISBN:
- 9780191713583
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198528418.003.0006
- Subject:
- Mathematics, Applied Mathematics

This chapter investigates the question of how to find an optimal linear transformation based on a distance measure. Popular choices for distance measures such as the sum of squared differences, ...
More

This chapter investigates the question of how to find an optimal linear transformation based on a distance measure. Popular choices for distance measures such as the sum of squared differences, correlation, and mutual information are discussed. Particular attention is paid to the differentiability of the distance measures. The desired transformation is restricted to a parameterizable space, and as such can be expanded in terms of a linear combination of some basis functions. The registration task is considered as an optimization problem, where the objective is to find the optimal coefficient in the expansion while minimizing the distance measure. The well-known Gauss-Newton method is described and used for numerical optimization. Different examples are used to identify similarities and differences of the distance measures.Less

This chapter investigates the question of how to find an optimal linear transformation based on a distance measure. Popular choices for distance measures such as the sum of squared differences, correlation, and mutual information are discussed. Particular attention is paid to the differentiability of the distance measures. The desired transformation is restricted to a parameterizable space, and as such can be expanded in terms of a linear combination of some basis functions. The registration task is considered as an optimization problem, where the objective is to find the optimal coefficient in the expansion while minimizing the distance measure. The well-known Gauss-Newton method is described and used for numerical optimization. Different examples are used to identify similarities and differences of the distance measures.