SimpleElastix: A User-Friendly, Multi-lingual Library for Medical Image Registration

In this paper we present SimpleElastix, an extension of SimpleITK designed to bring the Elastix medical image registration library to a wider audience. Elastix is a modular collection of robust C++ image registration algorithms that is widely used in the literature. However, its command-line interface introduces overhead during prototyping, experimental setup, and tuning of registration algorithms. By integrating Elastix with SimpleITK, Elastix can be used as a native library in Python, Java, R, Octave, Ruby, Lua, Tcl and C# on Linux, Mac and Windows. This allows Elastix to intregrate naturally with many development environments so the user can focus more on the registration problem and less on the underlying C++ implementation. As means of demonstration, we show how to register MR images of brains and natural pictures of faces using minimal amount of code. SimpleElastix is open source, licensed under the permissive Apache License Version 2.0 and available at https://github.com/kaspermarstal/SimpleElastix.

[1]  Kathy Sierra,et al.  Head First Design Patterns , 2004 .

[2]  Elena Casiraghi,et al.  Liver segmentation from computed tomography scans: A survey and a new algorithm , 2009, Artif. Intell. Medicine.

[3]  B C Stoel,et al.  Towards local progression estimation of pulmonary emphysema using CT. , 2014, Medical physics.

[4]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[5]  Klaus J. Kirchberg,et al.  Robust Face Detection Using the Hausdorff Distance , 2001, AVBPA.

[6]  Stefan Klein,et al.  Groupwise image registration of multimodal head-and-neck images , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[7]  Satrajit S. Ghosh,et al.  Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python , 2011, Front. Neuroinform..

[8]  Luis Ibáñez,et al.  The Design of SimpleITK , 2013, Front. Neuroinform..

[9]  N. Schwenzer,et al.  A strategy for multimodal deformable image registration to integrate PET/MR into radiotherapy treatment planning , 2013, Acta oncologica.

[10]  Carlos Ortiz-de-Solorzano,et al.  Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data , 2009, IEEE Transactions on Medical Imaging.

[11]  Luis Ibanez,et al.  The ITK Software Guide Book 1: Introduction and Development Guidelines (Volume 1) , 2015 .

[12]  Josien P. W. Pluim,et al.  Evaluation of optimization methods for intensity-based 2D-3D registration in x-ray guided interventions , 2011, Medical Imaging.

[13]  Darrel C. Ince,et al.  The case for open computer programs , 2012, Nature.

[14]  Daniel Rueckert,et al.  Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest , 2008, NeuroImage.

[15]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[16]  G. Houston,et al.  Diagnostic classification of arterial spin labeling and structural MRI in presenile early stage dementia , 2014, Human brain mapping.

[17]  Stefan Klein,et al.  Automated Brain Structure Segmentation Based on Atlas Registration and Appearance Models , 2012, IEEE Transactions on Medical Imaging.

[18]  Stefan Klein,et al.  Nonrigid registration of dynamic medical imaging data using nD + t B-splines and a groupwise optimization approach , 2011, Medical Image Anal..

[19]  Nico Karssemeijer,et al.  Automated localization of breast cancer in DCE-MRI , 2015, Medical Image Anal..

[20]  Josien P. W. Pluim,et al.  Free-form image registration regularized by a statistical shape model: application to organ segmentation in cervical MR , 2013, Comput. Vis. Image Underst..

[21]  Timothée Poisot Best publishing practices to improve user confidence in scientific software , 2015 .

[22]  Alexander Hammers,et al.  Three‐dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe , 2003, Human brain mapping.