To facilitate education in image processing. Additionally, scientific research often requires custom modification of standard algorithms, further emphasizing the importance of open source.
PYTHON RASTER SCAN IMAGE ALGORITHM CODE
In the context of reproducible science, it is important to be able to inspect any source code used for algorithmic flaws or mistakes. Such algorithms are essential building blocks in many areas of scientific research, algorithmic comparisons and data exploration. To provide high quality, well-documented and easy-to-use implementations of common image processing algorithms. The rising popularity of Python as a scientific programming language, together with the increasing availability of a large eco-system of complementary tools, makes it an ideal environment in which to produce an image processing toolkit. This paper describes scikit-image, a collection of image processing algorithms implemented in the Python programming language by an active community of volunteers and available under the liberal BSD Open Source license.
PYTHON RASTER SCAN IMAGE ALGORITHM SOFTWARE
Exploring these rich data sources requires sophisticated software tools that should be easy to use, free of charge and restrictions, and able to address all the challenges posed by such a diverse field of analysis. Examples include DNA microarrays, microscopy slides, astronomical observations, satellite maps, robotic vision capture, synthetic aperture radar images, and higher-dimensional images such as 3-D magnetic resonance or computed tomography imaging.
In our data-rich world, images represent a significant subset of all measurements made.
scikit-image: image processing in Python. Cite this article van der Walt S, Schönberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Yu T, the scikit-image contributors. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. 8 Enthought, Inc., Austin, TX, USA DOI 10.7717/peerj.453 Published Accepted Received Academic Editor Shawn Gomez Subject Areas Bioinformatics, Computational Biology, Computational Science, Human-Computer Interaction, Science and Medical Education Keywords Image processing, Reproducible research, Education, Visualization, Open source, Python, Scientific programming Copyright © 2014 Van der Walt et al.
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