Modules:LinearRegistration-Documentation-3.4

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Linear Registration

Linear Registration

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General Information

Module Type & Category

Type: CLI

Category: Registration

Authors, Collaborators & Contact

• Author: Daniel Blezek
• Contact: daniel.blezek at gmail.com

Module Description

This command line module implements a registration algorithm based on the Mattes mutual information registration metric. The transformation mapping the moving image to the fixed image consists of 3 translations and 3 rotations. Thus only rigid body transformations are permitted. Both the fixed and moving images may be optionally smoothed before registration. The module optionally breaks the optimization into multiple stages, each with a different learning rate and number of iterations.

Usage

Examples, Use Cases & Tutorials

• This module is often used to align images of the same subject acquired at different times.
• The Mattes mutual information metric is suitable for aligning images of the same or different modalities.
• The rigid body transformation allows a limited degree of deformation and frequently is used as a pre-processing step for higher order transformations such as the BSpline and Demons.

Quick Tour of Features and Use

List all the panels in your interface, their features, what they mean, and how to use them. For instance:

• Input panel:
• Parameters panel:
• Output panel:
• Viewing panel:

Development

Dependencies

No other modules are required for this module.

Limitations

The module uses an itkOrientImageFilter to realign the fixed and moving images to axial before registration. This is required due to limitations in ITK's handling of non-axial images in filters used by registration.

Known bugs

None. To report bugs, follow this link to the Slicer3 bug tracker.

Usability issues

Follow this link to the Slicer3 bug tracker. Please select the usability issue category when browsing or contributing.

Source code & documentation

Source Code: C++ Source and XML Description

Documentation:

More Information

Acknowledgment

This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from National Centers for Biomedical Computing.

References

D. Mattes, D.R. Haynor, H. Vesselle, T.K. Lewellen, and W. Eubank. PET-CT image registration in the chest using free-form deformations. IEEE Transactions on Medical Imaging, 22(1):120–128, 2003.