Volumetric Image Registration From Invariant Keypoints

We present a method for image registration based on 3D scale- and rotation-invariant keypoints. The method extends the scale invariant feature transform (SIFT) to arbitrary dimensions by making key modifications to orientation assignment and gradient histograms. Rotation invariance is proven mathema...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on image processing 2017-10, Vol.26 (10), p.4900-4910
Main Authors: Rister, Blaine, Horowitz, Mark A., Rubin, Daniel L.
Format: Article
Language:English
Subjects:
3D SIFT
Affine transformations
Biomedical imaging
Brain
Computed tomography
computed tomography (CT)
Computer simulation
Computer vision
Head
Histograms
Image registration
Invariants
Lesions
Libraries
Magnetic resonance imaging
magnetic resonance imaging (MRI)
Mathematical analysis
medical image registration
Medical imaging
Multiple sclerosis
Registration
Tensile stress
Three-dimensional displays
Transformations (mathematics)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present a method for image registration based on 3D scale- and rotation-invariant keypoints. The method extends the scale invariant feature transform (SIFT) to arbitrary dimensions by making key modifications to orientation assignment and gradient histograms. Rotation invariance is proven mathematically. Additional modifications are made to extrema detection and keypoint matching based on the demands of image registration. Our experiments suggest that the choice of neighborhood in discrete extrema detection has a strong impact on image registration accuracy. In head MR images, the brain is registered to a labeled atlas with an average Dice coefficient of 92%, outperforming registration from mutual information as well as an existing 3D SIFT implementation. In abdominal CT images, the spine is registered with an average error of 4.82 mm. Furthermore, keypoints are matched with high precision in simulated head MR images exhibiting lesions from multiple sclerosis. These results were achieved using only affine transforms, and with no change in parameters across a wide variety of medical images. This paper is freely available as a cross-platform software library.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2017.2722689