Automated 3D nonlinear deformation procedure for determination of gross morphometric variability in human brain

作者：D. Louis Collins, Terence M. Peters, Alan Charles Evans · 发表于：Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 年份：1994 · DOI：10.1117/12.185178 · 被引用次数：79 · 研究领域：Medical Image Segmentation Techniques、Advanced Neuroimaging Techniques and Applications、Advanced MRI Techniques and Applications

We describe an automated method to register MRI volumetric datasets to a digital human brain model. The technique employs 3D non-linear warping based on the estimation of local deformation fields using cross-correlation of invariant intensity features derived from image data. Results of the non-linear registration on a simple phantom, a complex brain phantom and real MRI data are presented. Anatomical variability is expressed with respect to the Talairach-like standardized brain-based coordinate system of the model. We show that the automated non-linear registration reduces the inter-subject variability of homologous points in standardized space by 15% over linear registration methods. A 3D variability map is shown.