 |
      |
 |
| |
"itk-elastix: Medical image registration in Python"
Konstantinos Ntatsis, Niels Dekker, Viktor van der Valk, Tom Birdsong, Dzenan Zukic, Stefan Klein, Marius Staring and Matthew McCormick
Abstract
Image registration plays a vital role in understanding changes that occur in 2D and 3D scientific imaging datasets. Registration involves finding a spatial transformation that aligns one image to another by optimizing relevant image similarity metrics. In this paper, we introduce itk-elastix, a user-friendly Python wrapping of the mature elastix registration toolbox. The open-source tool supports rigid, affine, and B-spline deformable registration, making it versatile for various imaging datasets. By utilizing the modular design of itk-elastix, users can efficiently configure and compare different registration methods, and embed these in image analysis workflows.
|
| |
Download |
| PDF (5 pages, 874 kB) |
|
| From publisher |
link
|
|
Copyright © 2023 by the authors.
Published version © 2023 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{Ntatsis:2023, |
| author |
= {Ntatsis, Konstantinos and Dekker, Niels and van der Valk, Viktor and Birdsong, Tom and Zukic, Dzenan and Klein, Stefan and Staring, Marius and McCormick, Matthew}, |
| title |
= {itk-elastix: Medical image registration in Python}, |
| booktitle |
= {Proceedings of the 22nd Python in Science Conference}, |
| editor |
= {Agarwal, Meghann and Calloway, Chris and Niederhut, Dillon}, |
| pages |
= {101 - 105}, |
| month |
= {July}, |
| year |
= {2023}, |
| } |
|
|
|
|
| last modified: 14-08-2023 |
| | webmaster |
| | Copyright 2004-2024 © by Marius Staring |
|