Article · Wikipedia archive · Last revised Jun 8, 2026

Scikit-image

scikit-image is an open-source image processing library for the Python programming language. It includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Last revised
Jun 8, 2026
Read time
≈ 1 min
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259 w
Citations
6
Source
Wikipedia ↗
scikit-image
Original authorStéfan van der Walt
Initial releaseAugust 2009 (2009-08)
Stable release
0.26.01 / 20 December 2025 (20 December 2025)
Written inPython, Cython, and C.
Operating systemLinux, Mac OS X, Microsoft Windows
TypeLibrary for image processing
LicenseBSD License
Websitescikit-image.org
Repository

scikit-image (formerly scikits.image) is an open-source image processing library for the Python programming language.2 It includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more.3 It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Overview

The scikit-image project started as scikits.image, by Stéfan van der Walt. Its name stems from the notion that it is a "SciKit" (SciPy Toolkit), a separately-developed and distributed third-party extension to SciPy.4 The original codebase was later extensively rewritten by other developers. Of the various scikits, scikit-image as well as scikit-learn were described as "well-maintained and popular" in November 2012.5 Scikit-image has also been active in the Google Summer of Code.6

Implementation

scikit-image is largely written in Python, with some core algorithms written in Cython to achieve performance.

References

References

  1. "Release 0.26.0". 20 December 2025. Retrieved 4 April 2026.
  2. S van der Walt; JL Schönberger; J Nunez-Iglesias; F Boulogne; JD Warner; N Yager; E Gouillart; T Yu; the scikit-image contributors (2014). "scikit-image: image processing in Python". PeerJ. 2:e453: e453. arXiv:1407.6245. Bibcode:2014PeerJ...2..453V. doi:10.7717/peerj.453. PMC 4081273. PMID 25024921. {{cite journal}}: |author9= has generic name (help)
  3. Chiang, Eric (2014). "Image Processing with scikit-image".
  4. Dreijer, Janto. "scikit-image".
  5. Eli Bressert (2012). SciPy and NumPy: an overview for developers. O'Reilly. p. 43. ISBN 9781449361624.
  6. Birodkar, Vighnesh (2014). "GSOC 2014 – Signing Off".
External links