Reference : Transitively Consistent and Unbiased Multi-Image Registration Using Numerically Stabl...
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/23609
Transitively Consistent and Unbiased Multi-Image Registration Using Numerically Stable Transformation Synchronisation
English
Bernard, Florian mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)]
Thunberg, Johan mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Salamanca Mino, Luis mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Gemmar, Peter mailto [Trier University of Applied Sciences, Trier, GERMANY]
Hertel, Frank mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Goncalves, Jorge mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Husch, Andreas mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)]
2015
MIDAS Journal
Yes
MICCAI15 18th International Conference on Image Computing and Computer Assisted Interventions
from 5-9-2015 to 9-9-2015
[en] template construction ; multi-image registration ; groupwise registration ; transformation synchronisation ; multi-alignment
[en] Abstract. Transitive consistency of pairwise transformations is a desir- able property of groupwise image registration procedures. The transfor- mation synchronisation method [4] is able to retrieve transitively con- sistent pairwise transformations from pairwise transformations that are initially not transitively consistent. In the present paper, we present a numerically stable implementation of the transformation synchronisa- tion method for a ne transformations, which can deal with very large translations, such as those occurring in medical images where the coor- dinate origins may be far away from each other. By using this method in conjunction with any pairwise (a ne) image registration algorithm, a transitively consistent and unbiased groupwise image registration can be achieved. Experiments involving the average template generation from 3D brain images demonstrate that the method is more robust with re- spect to outliers and achieves higher registration accuracy compared to reference-based registration.
Researchers ; Students ; General public
http://hdl.handle.net/10993/23609
FnR ; FNR8864515 > Johan Thunberg > > Set Convergence in Nonlinear Multi-Agent Systems > 01/02/2015 > 31/01/2017 > 2014

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