New feature points based on geometric invariants for 3D image registration
International Journal of Computer Vision
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Average brain models: a convergence study
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Localization of 3D Anatomical Point Landmarks in 3D Tomographic Images Using Deformable Models
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
International Journal of Computer Vision
Image Based Regression Using Boosting Method
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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
Detecting mutually-salient landmark pairs with MRF regularization
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Laplacian eigenmaps manifold learning for landmark localization in brain MR images
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Spline-Based probabilistic model for anatomical landmark detection
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A log-euclidean framework for statistics on diffeomorphisms
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Hi-index | 0.00 |
The localization of clinically important points in brain images is crucial for many neurological studies. Conventional manual landmark annotation requires expertise and is often time-consuming. In this work, we propose an automatic approach for interest point localization in brain image using landmark-annotated atlas (LAA). The landmark detection procedure is formulated as a problem of finding corresponding points of the atlas. The LAA is constructed from a set of brain images with clinically relevant landmarks annotated. It provides not only the spatial information of the interest points of the brain but also the optimal features for landmark detection through a learning process. Evaluation was performed on 3D magnetic resonance (MR) data using cross-validation. Obtained results demonstrate that the proposed method achieves the accuracy of ∼ 2 mm, which outperforms the traditional methods such as block matching technique and direct image registration. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 145–152, 2012 © 2012 Wiley Periodicals, Inc.