Quantitative comparison of AIR, SPM, and the fully deformable model for atlas-based segmentation of functional and structural MR images
✍ Scribed by Minjie Wu; Owen Carmichael; Pilar Lopez-Garcia; Cameron S. Carter; Howard J. Aizenstein
- Publisher
- John Wiley and Sons
- Year
- 2006
- Tongue
- English
- Weight
- 252 KB
- Volume
- 27
- Category
- Article
- ISSN
- 1065-9471
No coin nor oath required. For personal study only.
✦ Synopsis
Abstract
Typical packages used for coregistration in functional image analyses include automated image registration (AIR) and statistical parametric mapping (SPM). However, both methods have limited‐dimension deformation models. A fully deformable model, which combines the piecewise linear registration for coarse alignment with demons algorithm for voxel‐level refinement, allows a higher degree of spatial deformation. This leads to a more accurate colocalization of the functional signal from different subjects and therefore can produce a more reliable group average signal. We quantitatively compared the performance of the three different registration approaches through a series of experiments and we found that the fully deformable model consistently produces a more accurate structural segmentation and a more reliable functional signal colocalization than does AIR or SPM. Hum Brain Mapp, 2006. © 2006 Wiley‐Liss, Inc.