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Large deformation diffeomorphic metric mapping of vector fields
Open accessAbstract
In large-deformation diffeomorphic metric mapping (LDDMM), the diffeomorphic matching of images are modeled as evolution in time, or a flow, of an associated smooth velocity vector field v controlling the evolution. The initial momentum parameterizes the whole geodesic and encodes the shape and form of the target image. Thus, methods such as principal component analysis (PCA) of the initial momentum leads to analysis of anatomical shape and form in target images without being restricted to small-deformation assumption in the analysis of linear displacements. We apply this approach to a study of dementia of the Alzheimer type (DAT). The left hippocampus in the DAT group shows significant shape abnormality while the right hippocampus shows similar pattern of abnormality. Further, PCA of the initial momentum leads to correct classification of 12 out of 18 DAT subjects and 22 out of 26 control subjects.
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Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
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CycleMorph: Cycle consistent unsupervised deformable image registration
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Metadata
- Authors
- Cao, Y., Miller, M. I., Winslow, R. L., & Younes, L.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2005-09
- Volume
- 24
- Issue
- 9
- Pages
- 1216-1230
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2145138920
- Cited by
- 180
- References
- 9
- FWCI
- 11.17
- Type
- journal article
- Language
- en
- Open access
- green
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