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Efficient and Robust Model-to-Image Alignment using 3D Scale-Invariant Features

Institution:
Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: mt@bwh.harvard.edu.
Publisher:
Elsevier Science
Publication Date:
Apr-2013
Journal:
Med Image Anal
Volume Number:
17
Issue Number:
3
Pages:
271-82
Citation:
Med Image Anal. 2013 Apr;17(3):271-82.
Links:
http://dx.doi.org/10.1016/j.media.2012.11.002
http://www.na-mic.org/publications/item/view/2267
PubMed ID:
23265799
PMCID:
PMC3606671
Keywords:
3D scale-invariant feature, Orientation assignment, Feature Descriptor, Probabilistic model, Image alignment
Appears in Collections:
NAC, SLICER, SPL
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
R01 HD057963/HD/NICHD NIH HHS/United States
Generated Citation:
Toews M., Wells III W.M. Efficient and Robust Model-to-Image Alignment using 3D Scale-Invariant Features. Med Image Anal. 2013 Apr;17(3):271-82. PMID: 23265799. PMCID: PMC3606671.
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This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down.
The Feature Extractor Code is available for download.

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