The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Consistent landmark and intensity-based image registration
Abstract
Two new consistent image registration algorithms are presented: one is based on matching corresponding landmarks and the other is based on matching both landmark and intensity information. The consistent landmark and intensity registration algorithm produces good correspondences between images near landmark locations by matching corresponding landmarks and away from landmark locations by matching the image intensities. In contrast to similar unidirectional algorithms, these new consistent algorithms jointly estimate the forward and reverse transformation between two images while minimizing the inverse consistency error-the error between the forward (reverse) transformation and the inverse of the the reverse (forward) transformation. This reduces the ambiguous correspondence between the forward and reverse transformations associated with large inverse consistency errors. In both algorithms a thin-plate spline (TPS) model is used to regularize the estimated transformations. Two-dimensional (2-D) examples are presented that show the inverse consistency error produced by the traditional unidirectional landmark TPS algorithm can be relatively large and that this error is minimized using the consistent landmark algorithm. Results using 2-D magnetic resonance imaging data are presented that demonstrate that using landmark and intensity information together produce better correspondence between medical images than using either landmarks or intensity information alone.
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Cited by
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration
Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G. E., Collins, D. L., Gee, J., Hellier, P., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.037 · Cited by 2,227
Self-citationDeformable medical image registration: a survey
Sotiras, A., Davatzikos, C., & Paragios, N. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2265603 · Cited by 1,587
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Chen, J., Frey, E. C., He, Y., Segars, W. P., Li, Y., & Du, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102615 · Cited by 804
A Review of Geometric Transformations for Nonrigid Body Registration
Holden, M. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.904691 · Cited by 369
Integrating spatial configuration into heatmap regression based CNNs for landmark localization
Payer, C., Štern, D., Bischof, H., & Urschler, M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.03.007 · Cited by 365
Metadata
- Authors
- Johnson, H. J., & Christensen, G. E.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2002-05
- Volume
- 21
- Issue
- 5
- Pages
- 450-461
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2146497006
- Cited by
- 399
- References
- 2
- FWCI
- 18.72
- Type
- journal article
- Language
- en
- Open access
- closed
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