: Multi-subject Registration for Unbiased Statistical Atlas Construction

This paper introduces a new similarity measure designed to bring a population of segmented subjects into alignment in a common coordinate system. Our metric aligns each subject with a hidden probabilistic model of the common spatial distribution of anatomical tissues, estimated using STAPLE. Our approach does not require the selection of a subject of the population as a "target subject", nor the identification of "stable" landmarks across subjects. Rather, the approach determines automatically from the data what the most consistent alignment of the joint data is, subject to the particular transformation family used to align the subjects. The computational cost of joint simultaneous registration of the population of subjects is small due to the use of an efficient gradient estimate used to solve the optimization transform aligning each subject. The efficacy of the approach in constructing an unbiased statistical atlas was demonstrated by carrying out joint alignment ...

• Type: Conference
• Year: 2004
• Where: MICCAI
• Authors: Mathieu De Craene, Aloys du Bois d'Aische, Benoît Macq, Simon K. Warfield
• Keywords: Affine Transformation Model, Joint Alignment, Medical Imaging, Mesh Transformation Model, MICCAI 2004