Gouillart Emmanuelle, Nunez-Iglesias Juan, van der Walt Stéfan
Surface du Verre et Interfaces, UMR 125 CNRS/Saint-Gobain, 93303 Aubervilliers, France.
Victorian Life Sciences Computation Initiative, University of Melbourne, Carlton, VIC Australia.
Adv Struct Chem Imaging. 2017;2(1):18. doi: 10.1186/s40679-016-0031-0. Epub 2016 Dec 7.
The exploration and processing of images is a vital aspect of the scientific workflows of many X-ray imaging modalities. Users require tools that combine interactivity, versatility, and performance. scikit-image is an open-source image processing toolkit for the Python language that supports a large variety of file formats and is compatible with 2D and 3D images. The toolkit exposes a simple programming interface, with thematic modules grouping functions according to their purpose, such as image restoration, segmentation, and measurements. scikit-image users benefit from a rich scientific Python ecosystem that contains many powerful libraries for tasks such as visualization or machine learning. scikit-image combines a gentle learning curve, versatile image processing capabilities, and the scalable performance required for the high-throughput analysis of X-ray imaging data.
图像的探索与处理是许多X射线成像模式科学工作流程的重要方面。用户需要具备交互性、多功能性和高性能的工具。scikit-image是一个用于Python语言的开源图像处理工具包,支持多种文件格式,并且与2D和3D图像兼容。该工具包提供了一个简单的编程接口,通过主题模块根据功能用途对函数进行分组,如图像恢复、分割和测量。scikit-image的用户受益于丰富的科学Python生态系统,该生态系统包含许多用于可视化或机器学习等任务的强大库。scikit-image结合了平缓的学习曲线、多功能的图像处理能力以及X射线成像数据高通量分析所需的可扩展性能。