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      scikit-image: image processing in Python

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          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.

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          NIH Image to ImageJ: 25 years of image analysis.

          Lynda C. Schneider, Wayne S Rasband (2013)
          For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            Python for Scientific Computing

            Travis E. Oliphant (2007)
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              The NumPy array: a structure for efficient numerical computation

              Gael Varoquaux, S. Chris Colbert, Stefan van der Walt (2011)
              In the Python world, NumPy arrays are the standard representation for numerical data. Here, we show how these arrays enable efficient implementation of numerical computations in a high-level language. Overall, three techniques are applied to improve performance: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. We first present the NumPy array structure, then show how to use it for efficient computation, and finally how to share array data with other libraries.
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                Author and article information

                Contributors
                Stéfan van der Walt
                Journal
                Journal ID (nlm-ta): PeerJ
                Journal ID (iso-abbrev): PeerJ
                Journal ID (pmc): PeerJ
                Journal ID (publisher-id): PeerJ
                Title: PeerJ
                Publisher: PeerJ Inc. (San Francisco, USA )
                ISSN (Electronic): 2167-8359
                Publication date (Electronic): 19 June 2014
                Publication date Collection: 2014
                Volume: 2
                Electronic Location Identifier: e453
                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
                Article
                Publisher ID: 453
                DOI: 10.7717/peerj.453
                PMC ID: 4081273
                PubMed ID: 25024921
                SO-VID: 0732c860-3263-4c79-a874-399d1a852efd
                Copyright © © 2014 Van der Walt et al.
                License:

                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. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                Date received : 2 April 2014
                Date accepted : 4 June 2014
                Funding
                Funded by: NIH F30DK098832
                Portions of the research reported in this publication were supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under award number F30DK098832. Portions of the research reported in this paper were supported by the Victorian Life Sciences Computation Initiative. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Bioinformatics
                Subject: Computational Biology
                Subject: Computational Science
                Subject: Human–Computer Interaction
                Subject: Science and Medical Education

                Keywords: image processing,reproducible research,education,visualization,open source,python,scientific programming
                Data availability:
                Keywords: image processing, reproducible research, education, visualization, open source, python, scientific programming

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