elastix: A Toolbox for Intensity-Based Medical Image Registration. Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), different time points (e.g., follow-up scans), and/or different subjects (in case of population studies). A large number of methods for image registration are described in the literature. Unfortunately, there is not one method that works for all applications. We have therefore developed elastix, a publicly available computer program for intensity-based medical image registration. The software consists of a collection of algorithms that are commonly used to solve medical image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. The command-line interface enables automated processing of large numbers of data sets, by means of scripting. The usage of elastix for comparing different registration methods is illustrated with three example experiments, in which individual components of the registration method are varied

References in zbMATH (referenced in 14 articles )

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  1. Max Langer, Yuhe Zhang, Diogo Figueirinhas, Jean-Baptiste Forien, Claire Mouton, Rajmund Mokso, Pablo Villanueva-Perez: PyPhase - a Python package for X-ray phase imaging (2020) arXiv
  2. Dölz, Jürgen; Gerig, Thomas; Lüthi, Marcel; Harbrecht, Helmut; Vetter, Thomas: Error-controlled model approximation for Gaussian process morphable models (2019)
  3. Li, Y.; Sixou, B.; Peyrin, F.: Nonconvex mixed TV/Cahn-Hilliard functional for super-resolution/segmentation of 3D trabecular bone images (2019)
  4. Mang, Andreas; Gholami, Amir; Davatzikos, Christos; Biros, George: CLAIRE: a distributed-memory solver for constrained large deformation diffeomorphic image registration (2019)
  5. Pirpinia, Kleopatra; Bosman, Peter A. N.; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja: Evolutionary machine learning for multi-objective class solutions in medical deformable image registration (2019)
  6. Hao, Rui; Qiang, Yan; Yan, Xiaofei: Juxta-vascular pulmonary nodule segmentation in PET-CT imaging based on an LBF active contour model with information entropy and joint vector (2018)
  7. Richard Beare; Bradley Lowekamp; Ziv Yaniv: Image Segmentation, Registration and Characterization in R with SimpleITK (2018) not zbMATH
  8. Lee, Yin Tat; Lam, Ka Chun; Lui, Lok Ming: Landmark-matching transformation with large deformation via (n)-dimensional quasi-conformal maps (2016)
  9. Mang, Andreas; Biros, George: Constrained (H^1)-regularization schemes for diffeomorphic image registration (2016)
  10. Gao, Yi; Zhu, Liangjia; Cates, Joshua; MacLeod, Rob S.; Bouix, Sylvain; Tannenbaum, Allen: A Kalman filtering perspective for multiatlas segmentation (2015)
  11. Kiechle, Martin; Habigt, Tim; Hawe, Simon; Kleinsteuber, Martin: A bimodal co-sparse analysis model for image processing (2015)
  12. Jud, Christoph; Lüthi, Marcel; Albrecht, Thomas; Schönborn, Sandro; Vetter, Thomas: Variational image registration using inhomogeneous regularization (2014)
  13. Platero, Carlos; Tobar, M. Carmen: A multiatlas segmentation using graph cuts with applications to liver segmentation in CT scans (2014)
  14. Liu, Peng; Eberhardt, Benjamin; Wybranski, Christian; Ricke, Jens; Lüdemann, Lutz: Nonrigid 3D medical image registration and fusion based on deformable models (2013)