Elastic Model Based Non-rigid Registration Incorporation Statistical Shape Information
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Object Class Recognition Using Multiple Layer Boosting with Heterogeneous Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistically-Constrained High-Dimensional Warping Using Wavelet-Based Priors
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Learning best features for deformable registration of MR brains
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Learning best features and deformation statistics for hierarchical registration of MR brain images
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
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This paper presents a general learning framework for non-rigid registration of MR brain images. Given a set of training MR brain images, three major types of information are particularly learned, and further incorporated into a HAMMER registration algorithm for improving the performance of registration. First, the best features are learned from different types of local image descriptors for each part of brain, thereby the learned best features are consistent on the correspondence points across individual brains, but different on non-correspondence points. Moreover, the statistics of selected best features is learned from the training samples, and used to guide the feature matching during the image registration. Second, in order to avoid the local minima in the registration, the points hierarchically selected to drive image registration are determined by the learned consistency and distinctiveness of their respective best features. Third, deformation fields are adaptively represented by B-splines, with more control points placed on the regions with large shape variations across individual brains or on the regions with consistent and distinctive best features. Also, the statistics of B-splines based deformations is captured and used to regularize the brain registration. Finally, by incorporating all learned information into HAMMER registration framework, promising results are obtained on both real and simulated data.