scikit-image: image processing in Python

Stéfan van der Walt; Johannes L Schönberger; Juan Nunez-Iglesias; François Boulogne; Joshua D Warner; Neil Yager; Emmanuelle Gouillart; Tony Yu; scikit-image contributors

doi:10.7717/peerj.453

scikit-image: image processing in Python

PeerJ. 2014 Jun 19:2:e453. doi: 10.7717/peerj.453. eCollection 2014.

Authors

Stéfan van der Walt¹, Johannes L Schönberger², Juan Nunez-Iglesias³, François Boulogne⁴, Joshua D Warner⁵, Neil Yager⁶, Emmanuelle Gouillart⁷, Tony Yu⁸; scikit-image contributors

Affiliations

¹ Stellenbosch University , Stellenbosch , South Africa.
² Department of Computer Science, University of North Carolina at Chapel Hill , Chapel Hill, NC , USA.
³ Victorian Life Sciences Computation Initiative , Carlton, VIC , Australia.
⁴ Department of Mechanical and Aerospace Engineering, Princeton University , Princeton, NJ , USA.
⁵ Department of Biomedical Engineering, Mayo Clinic , Rochester, MN , USA.
⁶ AICBT Ltd , Oxford , UK.
⁷ Joint Unit, CNRS/Saint-Gobain , Cavaillon , France.
⁸ Enthought, Inc. , Austin, TX , USA.

Abstract

scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.

Keywords: Education; Image processing; Open source; Python; Reproducible research; Scientific programming; Visualization.

Grants and funding

F30 DK098832/DK/NIDDK NIH HHS/United States