title: Incorporating prior knowledge into image registration.
abstract: The first step in the spatial normalization of brain images is usually to determine the affine transformation that best maps the image to a template image in a standard space. We have developed a rapid and automatic method for performing this registration, which uses a Bayesian scheme to incorporate prior knowledge of the variability in the shape and size of heads. We compared affine registrations with and without incorporating the prior knowledge. We found that the affine transformations derived using the Bayesian scheme are much more robust and that the rate of convergence is greater.
authors.affiliation: Wellcome Department of Cognitive Neurology, Institute of Neurology, London, WC1N 3BG, United Kingdom.
authors.forename: J
authors.initials: J
authors.lastname: Ashburner
authors.forename: P
authors.initials: P
authors.lastname: Neelin
authors.forename: D L
authors.initials: DL
authors.lastname: Collins
authors.forename: A
authors.initials: A
authors.lastname: Evans
authors.forename: K
authors.initials: K
authors.lastname: Friston
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001931:Brain Mapping; D002508:Cephalometry; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008432:Mathematical Computing