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Titlebook: Biomedical Image Registration; 5th International Wo Benoît M. Dawant,Gary E. Christensen,Daniel Ruecke Conference proceedings 2012 Springer

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Consolidation of Axiomatic Set Theoryd applied to the original tensor images are compared to the deformed image without normalization for 11 different cases of mapping seven subjects (neonate through 2 years) to two different atlases. The method shows an improvement in DTI registration based on comparing the normalized fractional anisotropy values of major fiber tracts in the brain.
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3D Tensor Normalization for Improved Accuracy in DTI Tensor Registration Methodsd applied to the original tensor images are compared to the deformed image without normalization for 11 different cases of mapping seven subjects (neonate through 2 years) to two different atlases. The method shows an improvement in DTI registration based on comparing the normalized fractional anisotropy values of major fiber tracts in the brain.
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Automatic Detection of the Magnitude and Spatial Location of Error in Non-rigid Registrationeasure used. We show that our algorithm is capable of detecting the distribution and magnitude of registration error in a simulated case. Using real data, our algorithm also is able to identify registration error that is consistent with error that can be seen visually.
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Diffeomorphic Directly Manipulated Free-Form Deformation Image Registration via Vector Field Flowsent permitting, among other things, enforcement of stationary boundary conditions. In addition to theoretical and practical discussions of our contribution, we also describe its parallelized implementation as open source in the Insight Toolkit and perform an evaluation on publicly available brain data.
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