Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory
Elements of information theory
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Multi-Modality Image Registration Maximization of Mutual Information
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Hierarchical adaptive local affine registration for respiratory motion estimation from 3-D MRI
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Registering histological and MR images of prostate for image-based cancer detection
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
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In this paper we present an enhanced method for non-rigid registration of volumetric multi-modal images using Mutual Information (MI). Based on a hierarchical subdivision scheme, the non-rigid matching problem is decomposed into numerous rigid registrations of subimages of decreasing size. A thorough investigation revealed limitations of this approach, caused by a peculiar behavior of MI when applied to regions covering only a limited number of image pixels. We examine and explain the loss of MI's statistical consistency along the hierarchical subdivision. We also propose to use information theoretical measures to identify the problematic regions in order to overcome the MI drawbacks. This does not only improve the accuracy and robustness of the registration, but also can be used as a very efficient stopping criterion for the further subdivision of nodes in the hierarchy, which drastically reduces the computational costs of the entire registration procedure.