TY - GEN
T1 - Simultaneous geometric - Iconic registration
AU - Sotiras, Aristeidis
AU - Ou, Yangming
AU - Glocker, Ben
AU - Davatzikos, Christos
AU - Paragios, Nikos
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79960495952&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15745-5_83
DO - 10.1007/978-3-642-15745-5_83
M3 - Conference contribution
C2 - 20879374
AN - SCOPUS:79960495952
SN - 3642157440
SN - 9783642157448
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 676
EP - 683
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
PB - Springer Verlag
ER -