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

Title:
Analyzing microtomography data with Python and the scikit-image library
Contributors:
Surface du Verre et Interfaces (SVI), Centre National de la Recherche Scientifique (CNRS), University of Melbourne, Stellenbosch University
Source:
Advanced Structural and Chemical Imaging. 2:18-18
Publisher Information:
HAL CCSD; Springer, 2017.
Publication Year:
2017
Collection:
collection:UPMC
collection:CNRS
collection:SVI
collection:SORBONNE-UNIVERSITE
collection:SU-SCIENCES
collection:SU-SCI
collection:SU-TI
collection:ALLIANCE-SU
Original Identifier:
HAL: hal-01448271
Document Type:
Zeitschrift article<br />Journal articles
Language:
English
ISSN:
2198-0926
Relation:
info:eu-repo/semantics/altIdentifier/doi/10.1186/s40679-016-0031-0
DOI:
10.1186/s40679-016-0031-0
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.hal.01448271v1
Database:
HAL

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 2-D and 3-D 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.