title: Hierarchical estimation of a dense deformation field for 3-D robust registration.
abstract: A new method for medical image registration is formulated as a minimization problem involving robust estimators. We propose an efficient hierarchical optimization framework which is both multiresolution and multigrid. An anatomical segmentation of the cortex is introduced in the adaptive partitioning of the volume on which the multigrid minimization is based. This allows to limit the estimation to the areas of interest, to accelerate the algorithm, and to refine the estimation in specified areas. At each stage of the hierarchical estimation, we refine current estimate by seeking a piecewise affine model for the incremental deformation field. The performance of this method is numerically evaluated on simulated data and its benefits and robustness are shown on a database of 18 magnetic resonance imaging scans of the head.
authors.affiliation: IRISA/INRIA, Rennes, France.
authors.forename: P
authors.initials: P
authors.lastname: Hellier
authors.forename: C
authors.initials: C
authors.lastname: Barillot
authors.forename: E
authors.initials: E
authors.lastname: Mémin
authors.forename: P
authors.initials: P
authors.lastname: Pérez
mesh_terms: D003198:Computer Simulation / Q000145:classification / Q000706:statistics & numerical data*; D003720:Densitometry; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000145:classification / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data; D019047:Phantoms, Imaging; D012680:Sensitivity and Specificity; D012985:Software Design