用Python和scikit-image库分析微层析成像数据

IF 3.56 Q1 Medicine
Emmanuelle Gouillart, Juan Nunez-Iglesias, Stéfan van der Walt
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引用次数: 24

摘要

图像的探索和处理是许多x射线成像模式的科学工作流程的一个重要方面。用户需要兼具交互性、多功能性和性能的工具。scikit-image是Python语言的开源图像处理工具包,支持多种文件格式,并与2D和3D图像兼容。该工具包公开了一个简单的编程接口,其中的主题模块根据其用途对功能进行分组,例如图像恢复、分割和测量。scikit-image用户受益于丰富的科学Python生态系统,其中包含许多用于可视化或机器学习等任务的强大库。scikit-image结合了温和的学习曲线,通用的图像处理能力,以及高通量x射线成像数据分析所需的可扩展性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Analyzing microtomography data with Python and the scikit-image library

Analyzing microtomography data with Python and the scikit-image library

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.

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来源期刊
Advanced Structural and Chemical Imaging
Advanced Structural and Chemical Imaging Medicine-Radiology, Nuclear Medicine and Imaging
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