The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Brain Functional Localization: A Survey of Image Registration Techniques
Abstract
Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610
Nonrigid registration using free-form deformations: application to breast mr images
Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.796284 · Cited by 5,265
Multimodality image registration by maximization of mutual information
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.563664 · Cited by 4,547
A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data
Eickhoff, S. B., Stephan, K. E., Mohlberg, H., Grefkes, C., Fink, G. R., Amunts, K., & Zilles, K. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.034 · Cited by 4,033
Image matching as a diffusion process: an analogy with maxwell’s demons
Thirion, J.-P. (1998) · Medical Image Analysis · doi:10.1016/s1361-8415(98)80022-4 · Cited by 2,445
Variational Methods for Multimodal Image Matching
Hermosillo, G., Chefd'Hotel, C., & Faugeras, O. (2002) · International Journal of Computer Vision · doi:10.1023/a:1020830525823 · Cited by 388
Three-dimensional statistical analysis of sulcal variability in the human brain
Thompson, P. M., Schwartz, C., Lin, R. T., Khan, A. A., & Toga, A. W. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-13-04261.1996 · Cited by 346
Validation of medical image processing in image-guided therapy
Jannin, P., Fitzpatrick, J. M., Hawkes, D. J., Pennec, X., Shahidl, R., & Vannier, M. W. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.806568 · Cited by 177
Cited by
Elastix: a toolbox for intensity-based medical image registration
Klein, S., Staring, M., Murphy, K., Viergever, M. A., & Pluim, J. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2035616 · Cited by 4,311
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
Fast free-form deformation using graphics processing units
Modat, M., Ridgway, G. R., Taylor, Z. A., Lehmann, M., Barnes, J., Hawkes, D. J., Fox, N. C., & Ourselin, S. (2010) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2009.09.002 · Cited by 1,085
Fast and robust multi-atlas segmentation of brain magnetic resonance images
Lötjönen, J. M., Wolz, R., Koikkalainen, J. R., Thurfjell, L., Waldemar, G., Soininen, H., & Rueckert, D. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.026 · Cited by 391
Ghosh, S. S., Kakunoori, S., Augustinack, J., Nieto-Castanon, A., Kovelman, I., Gaab, N., Christodoulou, J. A., Triantafyllou, C., Gabrieli, J. D. E., & Fischl, B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.075 · Cited by 281
Metadata
- Authors
- Gholipour, A., Kehtarnavaz, N., Briggs, R., Devous, M., & Gopinath, K.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2007-04
- Volume
- 26
- Issue
- 4
- Pages
- 427-451
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2153628642
- Cited by
- 274
- References
- 11
- FWCI
- 32.73
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Brain Functional Localization: A Survey of Image Registration Techniques. https://app.cochat.ai/papers/10.1109/tmi.2007.892508
- The paper
- Gholipour, A., Kehtarnavaz, N., Briggs, R., Devous, M., & Gopinath, K. (2007). Brain Functional Localization: A Survey of Image Registration Techniques. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2007.892508
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
