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Proceedings Paper

Automated 3D nonlinear deformation procedure for determination of gross morphometric variability in human brain
Author(s): D. Louis Collins; Terence M. Peters; Alan C. Evans
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Paper Abstract

We describe an automated method to register MRI volumetric datasets to a digital human brain model. The technique employs 3D non-linear warping based on the estimation of local deformation fields using cross-correlation of invariant intensity features derived from image data. Results of the non-linear registration on a simple phantom, a complex brain phantom and real MRI data are presented. Anatomical variability is expressed with respect to the Talairach-like standardized brain-based coordinate system of the model. We show that the automated non-linear registration reduces the inter-subject variability of homologous points in standardized space by 15% over linear registration methods. A 3D variability map is shown.

Paper Details

Date Published: 9 September 1994
PDF: 11 pages
Proc. SPIE 2359, Visualization in Biomedical Computing 1994, (9 September 1994); doi: 10.1117/12.185178
Show Author Affiliations
D. Louis Collins, McConnell Brain Imaging Ctr. and McGill Univ. (Canada)
Terence M. Peters, McConnell Brain Imaging Ctr. and McGill Univ. (Canada)
Alan C. Evans, McConnell Brain Imaging Ctr. and McGill Univ. (Canada)


Published in SPIE Proceedings Vol. 2359:
Visualization in Biomedical Computing 1994
Richard A. Robb, Editor(s)

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