jicbioimage: a tool for automated and reproducible bioimage analysis

There has been steady improvement in methods for capturing bioimages. However analysing these images still remains a challenge. The Python programming language provides a powerful and flexible environment for scientific computation. It has a wide range of supporting libraries for image processing bu...

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Bibliographic Details
Published in:PeerJ (San Francisco, CA) CA), 2016-11, Vol.4, p.e2674-e2674, Article e2674
Main Authors: Olsson, Tjelvar S G, Hartley, Matthew
Format: Article
Language:English
Subjects:
Algorithms
Automation
Bioimage analysis
Bioinformatics
Biology
Cell Biology
Computational Biology
Computational Science
Data processing
Ecosystem biology
Ecosystems
Engineering
Exploration
Human-Computer Interaction
Image processing
Library collections
Microscopy
Provenance
Rapid iteration
Reproducibility
Researchers
Software
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Summary:There has been steady improvement in methods for capturing bioimages. However analysing these images still remains a challenge. The Python programming language provides a powerful and flexible environment for scientific computation. It has a wide range of supporting libraries for image processing but lacks native support for common bioimage formats, and requires specific code to be written to ensure that suitable audit trails are generated and analyses are reproducible. Here we describe the development of a Python tool that: (1) allows users to quickly view and explore microscopy data; (2) generate reproducible analyses, encoding a complete history of image transformations from raw data to final result; and (3) scale up analyses from initial exploration to high throughput processing pipelines, with a minimal amount of extra effort. The tool, jicbioimage, is open source and freely available online at http://jicbioimage.readthedocs.io.
ISSN:2167-8359
2167-8359
DOI:10.7717/peerj.2674