Image registration using a symmetric prior--in three dimensions.

Abstract:

:This paper describes a Bayesian method for three-dimensional registration of brain images. A finite element approach is used to obtain a maximum a posteriori estimate of the deformation field at every voxel of a template volume. The priors used by the MAP estimate penalize unlikely deformations and enforce a continuous one-to-one mapping. The deformations are assumed to have some form of symmetry, in that priors describing the probability distribution of the deformations should be identical to those for the inverses (i.e., warping brain A to brain B should not be different probablistically from warping B to A). A gradient descent algorithm is presented for estimating the optimum deformations.

journal_name

Hum Brain Mapp

journal_title

Human brain mapping

authors

Ashburner J,Andersson JL,Friston KJ

doi

10.1002/(sici)1097-0193(200004)9:4<212::aid-hbm3>3

keywords:

subject

Has Abstract

pub_date

2000-04-01 00:00:00

pages

212-25

issue

4

eissn

1065-9471

issn

1097-0193

pii

10.1002/(SICI)1097-0193(200004)9:4<212::AID-HBM3>3

journal_volume

9

pub_type

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