title: Simultaneous geometric--iconic registration.
abstract: In this paper, we introduce a novel approach to bridge the gap between the landmark-based and the iconic-based voxel-wise registration methods. The registration problem is formulated with the use of Markov Random Field theory resulting in a discrete objective function consisting of thee parts. The first part of the energy accounts for the iconic-based volumetric registration problem while the second one for establishing geometrically meaningful correspondences by optimizing over a set of automatically generated mutually salient candidate pairs of points. The last part of the energy penalizes locally the difference between the dense deformation field due to the iconic-based registration and the implied displacements due to the obtained correspondences. Promising results in real MR brain data demonstrate the potentials of our approach.
authors.affiliation: Laboratoire MAS, Ecole Centrale de Paris, France. aristeidis.sotiras@ecp.fr
authors.forename: Aristeidis
authors.initials: A
authors.lastname: Sotiras
authors.forename: Yangming
authors.initials: Y
authors.lastname: Ou
authors.forename: Ben
authors.initials: B
authors.lastname: Glocker
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
authors.forename: Nikos
authors.initials: N
authors.lastname: Paragios
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique