Analyzing microtomography data with Python and the scikit-image library.
Identifieur interne : 001287 ( PubMed/Corpus ); précédent : 001286; suivant : 001288Analyzing microtomography data with Python and the scikit-image library.
Auteurs : Emmanuelle Gouillart ; Juan Nunez-Iglesias ; Stéfan Van Der WaltSource :
- Advanced structural and chemical imaging [ 2198-0926 ] ; 2017.
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.
DOI: 10.1186/s40679-016-0031-0
PubMed: 29142808
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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.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">29142808</PMID><DateCreated><Year>2017</Year><Month>11</Month><Day>16</Day></DateCreated><DateRevised><Year>2017</Year><Month>11</Month><Day>16</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">2198-0926</ISSN><JournalIssue CitedMedium="Print"><Volume>2</Volume><Issue>1</Issue><PubDate><Year>2017</Year></PubDate></JournalIssue><Title>Advanced structural and chemical imaging</Title><ISOAbbreviation>Adv Struct Chem Imaging</ISOAbbreviation></Journal><ArticleTitle>Analyzing microtomography data with Python and the scikit-image library.</ArticleTitle><Pagination><MedlinePgn>18</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s40679-016-0031-0</ELocationID><Abstract><AbstractText>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. 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