Learning-based deformable registration of MR brain images

This paper presents a learning-based method for deformable registration of magnetic resonance (MR) brain images. There are two novelties in the proposed registration method. First, a set of best-scale geometric features are selected for each point in the brain, in order to facilitate correspondence...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2006-09, Vol.25 (9), p.1145-1157
Main Authors: Wu, Guorong, Qi, Feihu, Shen, Dinggang
Format: Article
Language:English
Subjects:
Algorithms
Anatomical structure
Artificial Intelligence
Best features
best scale selection
Biomedical imaging
Biomedical measurements
Brain - anatomy & histology
Brain modeling
Computer science
consistency measurement
deformable registration
feature-based registration
hierarchical registration
Humans
Image color analysis
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image registration
Information Storage and Retrieval - methods
Learning systems
learning-based method
Magnetic resonance
Magnetic Resonance Imaging - instrumentation
Magnetic Resonance Imaging - methods
Medical simulation
Pattern Recognition, Automated - methods
Phantoms, Imaging
Reproducibility of Results
saliency measurement
Sensitivity and Specificity
Subtraction Technique
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Summary:This paper presents a learning-based method for deformable registration of magnetic resonance (MR) brain images. There are two novelties in the proposed registration method. First, a set of best-scale geometric features are selected for each point in the brain, in order to facilitate correspondence detection during the registration procedure. This is achieved by optimizing an energy function that requires each point to have its best-scale geometric features consistent over the corresponding points in the training samples, and at the same time distinctive from those of nearby points in the neighborhood. Second, the active points used to drive the brain registration are hierarchically selected during the registration procedure, based on their saliency and consistency measures. That is, the image points with salient and consistent features (across different individuals) are considered for the initial registration of two images, while other less salient and consistent points join the registration procedure later. By incorporating these two novel strategies into the framework of the HAMMER registration algorithm, the registration accuracy has been improved according to the results on simulated brain data, and also visible improvement is observed particularly in the cortical regions of real brain data
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2006.879320