Quantitative comparison of AIR, SPM, and the fully deformable model for atlas-based segmentation of functional and structural MR images

Minjie Wu, Owen Carmichael, Pilar Lopez-Garcia, Cameron S Carter, Howard J. Aizenstein

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

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 languageEnglish (US)
Pages (from-to)747-754
Number of pages8
JournalHuman Brain Mapping
Volume27
Issue number9
DOIs
StatePublished - Sep 2006

Keywords

  • Atlas-based segmentation
  • Deformable model
  • fMRI
  • Image registration

ASJC Scopus subject areas

  • Clinical Neurology
  • Neuroscience(all)
  • Radiological and Ultrasound Technology

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