Multiresolution elastic matching
Computer Vision, Graphics, and Image Processing
Elements of information theory
Elements of information theory
A high performance computing approach to the registration of medical imaging data
Parallel Computing - Special double issue on biomedical applications
3D Multi-Modality Medical Image Registration Using Feature Space Clustering
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
Registration of 3D Intraoperative MR Images of the Brain Using a Finite Element Biomechanical Model
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Label space: a multi-object shape representation
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
Brain shift computation using a fully nonlinear biomechanical model
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
A unifying approach to registration, segmentation, and intensity correction
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Efficient population registration of 3d data
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
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Assessment of normal and abnormal anatomical variability requires a coordinate system enabling inter-subject comparison. We present a binary minimum entropy criterion to assess affine and nonrigid transformations bringing a group of subject scans into alignment. This measure is a data-driven measure allowing the identification of an intrinsic coordinate system of a particular group of subjects. We assessed two statistical atlases derived from magnetic resonance imaging of newborn infants with gestational age ranging from 24 to 40 weeks. Over this age range major structural changes occur in the human brain and existing atlases are inadequate to capture the resulting anatomical variability. The binary entropy measure we propose allows an objective choice between competing registration algorithms to be made.