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 journalArticle

53 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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