Title:
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A PARALLEL APPROACH FOR ALIGNMENT OF MULTIMODAL GRID-BASED DATA |
Author(s):
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Egor Dranischnikow , Elmar Schömer , Ulrich Schwanecke , Ralf Schulze , Dan Brüllmann |
ISBN:
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978-972-8924-97-3 |
Editors:
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Hans Weghorn and Pedro Isaías |
Year:
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2009 |
Edition:
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V I, 2 |
Keywords:
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Alignment, cone-beam data, multi-modal registration, parallel implementation, GPGPU. |
Type:
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Full Paper |
First Page:
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330 |
Last Page:
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337 |
Language:
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English |
Cover:
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Full Contents:
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click to dowload
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Paper Abstract:
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Multi-modal registration is still a big challenge in image processing. In this article we present a variation of the wellknown
fast Fourier transform (fft) accelerated methods for finding the alignment between two datasets, i.e. the rigid
transformation consisting of a rotation as well as a translation mapping all regions in both datasets belonging together in
an optimal manner. Our method can be applied to such multi-modal registration problems as computer tomography
(CT)/positron emission tomography (PET) matching, or matching CT data with data obtained by magnetic resonance
imaging (MRI).
We reformulate the alignment problem into an optimization problem concerning a metric measure. The particular form of
the proposed objective function can be exploited to fft-accelerate the translational part of the alignment-problem. Thus
the reduced problem can be solved for the three remaining degrees of freedom of the rotatory part using standard
optimizers, such as downhill-simplex or Powells method.
A further advantage of our approach is the straight forward parallelization of the objective functions computation. Our
implementation on a graphic processing unit (GPU) yielded a speedup factor between 5 and 25 depending on the size of
the data. The results show, that the application of a GPU can be highly rewarding for all fft accelerated algorithms. |
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