• Analyzing microtomography data...

Treffer: Analyzing microtomography data with Python and the scikit-image library.

Title:
Analyzing microtomography data with Python and the scikit-image library.
Authors:
Gouillart E; Surface du Verre et Interfaces, UMR 125 CNRS/Saint-Gobain, 93303 Aubervilliers, France., Nunez-Iglesias J; Victorian Life Sciences Computation Initiative, University of Melbourne, Carlton, VIC Australia., van der Walt S; Division of Applied Mathematics, Stellenbosch University, Stellenbosch, South Africa.
Source:
Advanced structural and chemical imaging [Adv Struct Chem Imaging] 2017; Vol. 2 (1), pp. 18. Date of Electronic Publication: 2016 Dec 07.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: SpringerOpen Country of Publication: Germany NLM ID: 101687126 Publication Model: Print-Electronic Cited Medium: Print ISSN: 2198-0926 (Print) Linking ISSN: 21980926 NLM ISO Abbreviation: Adv Struct Chem Imaging Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: Heidelberg : SpringerOpen, [2015]-[2020]
References:
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Contributed Indexing:
Keywords: 3D image; Image processing library; Python; Scikit-image
Entry Date(s):
Date Created: 20171117 Latest Revision: 20200929
Update Code:
20250114
PubMed Central ID:
PMC5660892
DOI:
10.1186/s40679-016-0031-0
PMID:
29142808
Database:
MEDLINE

Weitere Informationen

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.