itk-elastix: Python医学图像配准

K. Ntatsis, Niels Dekker, Viktor van der Valk, Tom Birdsong, Dženan Zukić, S. Klein, M. Staring, Matthew Mccormick
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引用次数: 0

摘要

-图像配准在理解2D和3D科学成像数据集中发生的变化方面起着至关重要的作用。配准包括通过优化相关的图像相似性度量来找到一个空间转换,使一个图像与另一个图像对齐。在本文中,我们介绍了itk-elastix,一个用户友好的Python包装成熟的elastix注册工具箱。这个开源工具支持刚性、仿射和b样条可变形配准,使其适用于各种成像数据集。利用itk-elastix的模块化设计,用户可以有效地配置和比较不同的配准方法,并将这些方法嵌入到图像分析工作流程中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
itk-elastix: Medical image registration in Python
—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 de-sign of itk-elastix , users can efficiently configure and compare different registration methods, and embed these in image analysis workflows.
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