itk-elastix: Medical image registration in Python
Konstantinos Ntatsis, Niels Dekker, Viktor van der Valk, Tom Birdsong, Dženan Zukić, Stefan Klein, Marius Staring, 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.