Multi-modal image set registration and atlas formation

In this paper, we present a Bayesian framework for both generating inter-subject large deformation transformations between two multi-modal image sets of the brain and for forming multi-class brain atlases. In this framework, the estimated transformations are generated using maximal information about...

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Bibliographic Details
Published in:Medical image analysis 2006-06, Vol.10 (3), p.440-451
Main Authors: Lorenzen, Peter, Prastawa, Marcel, Davis, Brad, Gerig, Guido, Bullitt, Elizabeth, Joshi, Sarang
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Atlas formation
Brain - anatomy & histology
Computational anatomy
Databases, Factual
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Information Storage and Retrieval - methods
Information theory
Inverse consistent registration
Magnetic Resonance Imaging - methods
Medical image analysis
Multi-modal image set registration
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity
Subtraction Technique
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Summary:In this paper, we present a Bayesian framework for both generating inter-subject large deformation transformations between two multi-modal image sets of the brain and for forming multi-class brain atlases. In this framework, the estimated transformations are generated using maximal information about the underlying neuroanatomy present in each of the different modalities. This modality independent registration framework is achieved by jointly estimating the posterior probabilities associated with the multi-modal image sets and the high-dimensional registration transformations mapping these posteriors. To maximally use the information present in all the modalities for registration, Kullback–Leibler divergence between the estimated posteriors is minimized. Registration results for image sets composed of multi-modal MR images of healthy adult human brains are presented. Atlas formation results are presented for a population of five infant human brains.
ISSN:1361-8415
1361-8423
DOI:10.1016/j.media.2005.03.002