The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Spatial Transformation and Registration of Brain Images Using Elastically Deformable Models
Open accessAbstract
The development of algorithms for the spatial transformation and registration of tomographic brain images is a key issue in several clinical and basic science medical applications, including computer-aided neurosurgery, functional image analysis, and morphometrics. This paper describes a technique for the spatial transformation of brain images, which is based on elastically deformable models. A deformable surface algorithm is used to find a parametric representation of the outer cortical surface and then to define a map between corresponding cortical regions in two brain images. Based on the resulting map, a three-dimensional elastic warping transformation is then determined, which brings two images into register. This transformation models images as inhomogeneous elastic objects which are deformed into registration with each other by external force fields. The elastic properties of the images can vary from one region to the other, allowing more variable brain regions, such as the ventricles, to deform more freely than less variable ones. Finally, the framework of prestrained elasticity is used to model structural irregularities, and in particular the ventricular expansion occurring with aging or diseases, and the growth of tumors. Performance measurements are obtained using magnetic resonance images.
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Citations
Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Statistical parametric maps in functional imaging: A general linear approach
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. ‐., Frith, C. D., & Frackowiak, R. S. J. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020402 · Cited by 9,688
Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space
Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199403000-00005 · Cited by 3,431 · cited 2×
“Pennsylvania [8] [9] [10], Washington University [11] [12], and The spatial transformation of images plays an important Montreal Neurological Institute [13].”
Method
Cited by
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
Cortical Surface-Based Analysis
Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147
Deformable 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
“Davatzikos [18] first used geometric characteristics to establish a mapping between the cortical surfaces.”
MethodSelf-citationUnsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Dalca, A. V., Balakrishnan, G., Guttag, J., & Sabuncu, M. R. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.07.006 · Cited by 467
Metadata
- Authors
- Davatzikos, C.
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 1997-05
- Volume
- 66
- Issue
- 2
- Pages
- 207-222
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W2065646479
- Cited by
- 375
- References
- 3
- FWCI
- 17.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
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