Abstract

Although numerous methods to register brains of different individuals have been proposed, no work has been done, as far as we know, to evaluate and objectively compare the performances of different nonrigid (or elastic) registration methods on the same database of subjects. In this paper, we propose an evaluation framework, based on global and local measures of the relevance of the registration. We have chosen to focus more particularly on the matching of cortical areas, since intersubject registration methods are dedicated to anatomical and functional normalization, and also because other groups have shown the relevance of such registration methods for deep brain structures. Experiments were conducted using 6 methods on a database of 18 subjects. The global measures used show that the quality of the registration is directly related to the transformation's degrees of freedom. More surprisingly, local measures based on the matching of cortical sulci did not show significant differences between rigid and non rigid methods.

Keywords

Image registrationArtificial intelligenceComputer scienceNormalization (sociology)Spatial normalizationMatching (statistics)Computer visionFocus (optics)Transformation (genetics)Pattern recognition (psychology)MathematicsImage (mathematics)StatisticsVoxel

MeSH Terms

AdultBrainCerebral CortexDatabasesFactualHumansImage InterpretationComputer-AssistedImagingThree-DimensionalMagnetic Resonance ImagingMalePattern RecognitionAutomatedReproducibility of ResultsRetrospective StudiesSensitivity and SpecificitySignal ProcessingComputer-AssistedSingle-Blind MethodSubtraction Technique

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Publication Info

Year
2003
Type
article
Volume
22
Issue
9
Pages
1120-1130
Citations
202
Access
Closed

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202
OpenAlex
8
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150
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Cite This

Pierre Hellier, Christian Barillot, Isabelle Corouge et al. (2003). Retrospective evaluation of intersubject brain registration. IEEE Transactions on Medical Imaging , 22 (9) , 1120-1130. https://doi.org/10.1109/tmi.2003.816961

Identifiers

DOI
10.1109/tmi.2003.816961
PMID
12956267

Data Quality

Data completeness: 81%