Title |
Analyzing microtomography data with Python and the scikit-image library
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Published in |
Advanced Structural and Chemical Imaging, December 2016
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DOI | 10.1186/s40679-016-0031-0 |
Pubmed ID | |
Authors |
Emmanuelle Gouillart, Juan Nunez-Iglesias, Stéfan van der Walt |
Abstract |
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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Country | Count | As % |
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Brazil | 1 | 11% |
Germany | 1 | 11% |
India | 1 | 11% |
France | 1 | 11% |
Chile | 1 | 11% |
United States | 1 | 11% |
Unknown | 3 | 33% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 5 | 56% |
Scientists | 4 | 44% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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France | 1 | <1% |
Unknown | 127 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 35 | 27% |
Researcher | 21 | 16% |
Student > Master | 17 | 13% |
Professor | 5 | 4% |
Student > Bachelor | 5 | 4% |
Other | 16 | 13% |
Unknown | 29 | 23% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 18 | 14% |
Engineering | 18 | 14% |
Materials Science | 15 | 12% |
Physics and Astronomy | 11 | 9% |
Earth and Planetary Sciences | 7 | 5% |
Other | 21 | 16% |
Unknown | 38 | 30% |