2014
DOI: 10.1002/hbm.22707
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Multi‐modal robust inverse‐consistent linear registration

Abstract: Registration performance can significantly deteriorate when image regions do not comply with model assumptions. Robust estimation improves registration accuracy by reducing or ignoring the contribution of voxels with large intensity differences, but existing approaches are limited to monomodal registration. In this work, we propose a robust and inverse-consistent technique for crossmodal, affine image registration. The algorithm is derived from a contextual framework of image registration. The key idea is to u… Show more

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Cited by 5 publications
(1 citation statement)
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“…Examples of such features include Gabor wavelet features [40], [41], local binary patterns [42], Haar-like features [43], and histograms of oriented gradients [44]. Results of an empirical study that compared several features for non-local means segmentation [45] indicates good results for features that compute image gradients and local entropies [46], [47]. …”
Section: Atlas-based Segmentationmentioning
confidence: 99%
“…Examples of such features include Gabor wavelet features [40], [41], local binary patterns [42], Haar-like features [43], and histograms of oriented gradients [44]. Results of an empirical study that compared several features for non-local means segmentation [45] indicates good results for features that compute image gradients and local entropies [46], [47]. …”
Section: Atlas-based Segmentationmentioning
confidence: 99%