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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 747-754 |
| Number of pages | 8 |
| Journal | Human Brain Mapping |
| Volume | 27 |
| Issue number | 9 |
| DOIs | |
| State | Published - Sep 2006 |
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Keywords
- Atlas-based segmentation
- Deformable model
- fMRI
- Image registration
ASJC Scopus subject areas
- Clinical Neurology
- Neuroscience(all)
- Radiological and Ultrasound Technology
Cite this
Quantitative comparison of AIR, SPM, and the fully deformable model for atlas-based segmentation of functional and structural MR images. / Wu, Minjie; Carmichael, Owen; Lopez-Garcia, Pilar; Carter, Cameron S; Aizenstein, Howard J.
In: Human Brain Mapping, Vol. 27, No. 9, 09.2006, p. 747-754.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Quantitative comparison of AIR, SPM, and the fully deformable model for atlas-based segmentation of functional and structural MR images
AU - Wu, Minjie
AU - Carmichael, Owen
AU - Lopez-Garcia, Pilar
AU - Carter, Cameron S
AU - Aizenstein, Howard J.
PY - 2006/9
Y1 - 2006/9
N2 - 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.
AB - 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.
KW - Atlas-based segmentation
KW - Deformable model
KW - fMRI
KW - Image registration
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UR - http://www.scopus.com/inward/citedby.url?scp=33748778481&partnerID=8YFLogxK
U2 - 10.1002/hbm.20216
DO - 10.1002/hbm.20216
M3 - Article
VL - 27
SP - 747
EP - 754
JO - Human Brain Mapping
JF - Human Brain Mapping
SN - 1065-9471
IS - 9
ER -