Open AccessQuantitative comparison of AIR, SPM, and the fully deformable model for atlas‐based segmentation of functional and structural MR images
Author(s) -
Wu Minjie,
Carmichael Owen,
LopezGarcia Pilar,
Carter Cameron S.,
Aizenstein Howard J.
Publication year - 2006
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20216
Subject(s) - artificial intelligence , computer science , segmentation , voxel , colocalization , pattern recognition (psychology) , computer vision , parametric statistics , image registration , statistical parametric mapping , atlas (anatomy) , signal (programming language) , image (mathematics) , mathematics , magnetic resonance imaging , neuroscience , anatomy , medicine , statistics , radiology , biology , programming language
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.