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"The design of SuperElastix - a unifying framework for a wide range of image registration methodologies"

Floris Berendsen, Kasper Marstal, Stefan Klein and Marius Staring

Abstract

A large diversity of image registration methodologies has emerged from the research community. The scattering of methods over toolboxes impedes rigorous comparison to select the appropriate method for a given application. Toolboxes typically tailor their implementations to a mathematical registration paradigm, which makes internal functionality nonexchangeable. Subsequently, this forms a barrier for adoption of registration technology in the clinic. We therefore propose a unifying, role-based software design that can integrate a broad range of functional registration components. These components can be configured into an algorithmic network via a single high-level user interface. A generic component handshake mechanism provides users feedback on incompatibilities. We demonstrate the viability of our design by incorporating two paradigms from different code bases. The implementation is done in C++ and is available as open source. The progress of embedding more paradigms can be followed via https://github.com/kaspermarstal/SuperElastix.

 

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Copyright © 2016 by the authors. Published version © 2016 by . Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.

 

BibTeX entry

@inproceedings{Berendsen:2016,
author = "{Floris Berendsen and Kasper Marstal and Stefan Klein and Marius Staring}",
title = "{The design of SuperElastix - a unifying framework for a wide range of image registration methodologies}",
booktitle = "{International Workshop on Biomedical Image Registration (WBIR)}",
editor = "{Julia Schnabel and Kensaku Mori}",
address = "{Las Vegas, Nevada, USA}",
series = "{IEEE Conference on Computer Vision and Pattern Recognition Workshops}",
pages = "{498 - 506}",
month = "{July}",
year = "{2016}",
}

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