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Hierarchical multimodal image registration based on adaptive local mutual information.
- Source :
-
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention [Med Image Comput Comput Assist Interv] 2010; Vol. 13 (Pt 2), pp. 643-51. - Publication Year :
- 2010
-
Abstract
- We propose a new, adaptive local measure based on gradient orientation similarity for the purposes of multimodal image registration. We embed this metric into a hierarchical registration framework, where we show that registration robustness and accuracy can be improved by adapting both the similarity metric and the pixel selection strategy to the Gaussian blurring scale and to the modalities being registered. A computationally efficient estimation of gradient orientations is proposed based on patch-wise rigidity. We have applied our method to both rigid and non-rigid multimodal registration tasks with different modalities. Our approach outperforms mutual information (MI) and previously proposed local approximations of MI for multimodal (e.g. CT/MRI) brain image registration tasks. Furthermore, it shows significant improvements in terms of mTRE over standard methods in the highly challenging clinical context of registering pre-operative brain MRI to intra-operative US images.
- Subjects :
- Humans
Image Enhancement methods
Reproducibility of Results
Sensitivity and Specificity
Algorithms
Brain anatomy & histology
Echoencephalography methods
Image Interpretation, Computer-Assisted methods
Magnetic Resonance Imaging methods
Pattern Recognition, Automated methods
Subtraction Technique
Subjects
Details
- Language :
- English
- Volume :
- 13
- Issue :
- Pt 2
- Database :
- MEDLINE
- Journal :
- Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
- Publication Type :
- Academic Journal
- Accession number :
- 20879370
- Full Text :
- https://doi.org/10.1007/978-3-642-15745-5_79