The ImageJ ecosystem: An open platform for biomedical image analysis

Technology in microscopy advances rapidly, enabling increasingly affordable, faster, and more precise quantitative biomedical imaging, which necessitates correspondingly more‐advanced image processing and analysis techniques. A wide range of software is available—from commercial to academic, special‐purpose to Swiss army knife, small to large—but a key characteristic of software that is suitable for scientific inquiry is its accessibility. Open‐source software is ideal for scientific endeavors because it can be freely inspected, modified, and redistributed; in particular, the open‐software platform ImageJ has had a huge impact on the life sciences, and continues to do so. From its inception, ImageJ has grown significantly due largely to being freely available and its vibrant and helpful user community. Scientists as diverse as interested hobbyists, technical assistants, students, scientific staff, and advanced biology researchers use ImageJ on a daily basis, and exchange knowledge via its dedicated mailing list. Uses of ImageJ range from data visualization and teaching to advanced image processing and statistical analysis. The software's extensibility continues to attract biologists at all career stages as well as computer scientists who wish to effectively implement specific image‐processing algorithms. In this review, we use the ImageJ project as a case study of how open‐source software fosters its suites of software tools, making multitudes of image‐analysis technology easily accessible to the scientific community. We specifically explore what makes ImageJ so popular, how it impacts the life sciences, how it inspires other projects, and how it is self‐influenced by coevolving projects within the ImageJ ecosystem. Mol. Reprod. Dev. 82: 518–529, 2015. © 2015 Wiley Periodicals, Inc.

[1]  S Inoué,et al.  Video image processing greatly enhances contrast, quality, and speed in polarization-based microscopy , 1981, The Journal of cell biology.

[2]  Konstantinos Konstantinides,et al.  The Khoros software development environment for image and signal processing , 1994, IEEE Trans. Image Process..

[3]  Bill Hibbard,et al.  VisAD: connecting people to computations and people to people , 1998, COMG.

[4]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[5]  Michael J Ackerman,et al.  Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit. , 2002, Studies in health technology and informatics.

[6]  Thomas Edward Dufresne,et al.  Quantification of regional fat volume in rat MRI , 2003, SPIE Medical Imaging.

[7]  Michael Unser,et al.  Complex wavelets for extended depth‐of‐field: A new method for the fusion of multichannel microscopy images , 2004, Microscopy research and technique.

[8]  Anne E Carpenter,et al.  CellProfiler: image analysis software for identifying and quantifying cell phenotypes , 2006, Genome Biology.

[9]  E. Meijering,et al.  Tracking in molecular bioimaging , 2006, IEEE Signal Processing Magazine.

[10]  R. Vale,et al.  μManager: Open Source Software for Light Microscope Imaging , 2007, Microscopy Today.

[11]  Tony J Collins,et al.  ImageJ for microscopy. , 2007, BioTechniques.

[12]  Thorsten Meinl,et al.  KNIME: The Konstanz Information Miner , 2007, GfKl.

[13]  Jan Kybic,et al.  bUnwarpJ : Consistent and Elastic Registration in ImageJ . Methods and Applications , 2008 .

[14]  Stephan Saalfeld,et al.  Globally optimal stitching of tiled 3D microscopic image acquisitions , 2009, Bioinform..

[15]  F. Frischknecht,et al.  Automated classification of Plasmodium sporozoite movement patterns reveals a shift towards productive motility during salivary gland infection , 2009, Biotechnology journal.

[16]  Benjamin Schmid,et al.  A high-level 3D visualization API for Java and ImageJ , 2010, BMC Bioinformatics.

[17]  Joachim M. Buhmann,et al.  Neuron geometry extraction by perceptual grouping in ssTEM images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  C. Rueden,et al.  Metadata matters: access to image data in the real world , 2010, The Journal of cell biology.

[19]  J. Douglas Armstrong,et al.  Bioinformatics Applications Note Systems Biology Simple Neurite Tracer: Open Source Software for Reconstruction, Visualization and Analysis of Neuronal Processes , 2022 .

[20]  Stefan Posch,et al.  Knowing What Happened - Automatic Documentation of Image Analysis Processes , 2011, ICVS.

[21]  Kevin W Eliceiri,et al.  NIH Image to ImageJ: 25 years of image analysis , 2012, Nature Methods.

[22]  Johannes E. Schindelin,et al.  TrakEM2 Software for Neural Circuit Reconstruction , 2012, PloS one.

[23]  Tobias Pietzsch,et al.  ImgLib2 - generic image processing in Java , 2012, Bioinform..

[24]  Hanchuan Peng,et al.  Visualization and Analysis of 3D Microscopic Images , 2012, PLoS Comput. Biol..

[25]  Tobias Pietzsch,et al.  ImgLib2—generic image processing in Java , 2012, Bioinform..

[26]  B. S. Manjunath,et al.  Biological imaging software tools , 2012, Nature Methods.

[27]  Nicolas Chenouard,et al.  Icy: an open bioimage informatics platform for extended reproducible research , 2012, Nature Methods.

[28]  Johannes E. Schindelin,et al.  Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.

[29]  Michael Unser,et al.  Snakes with Ellipse-Reproducing Property , 2011 .

[30]  Marcel Austenfeld,et al.  A Graphical User Interface for R in a Rich Client Platform for Ecological Modeling , 2012 .

[31]  Johannes E. Schindelin,et al.  OpenSPIM: an open-access light-sheet microscopy platform , 2013, Nature Methods.

[32]  Wilhelm Burger,et al.  Principles of Digital Image Processing , 2013, Undergraduate Topics in Computer Science.

[33]  Stephan Preibisch,et al.  OpenSPIM: an open-access light-sheet microscopy platform , 2013, Nature Methods.

[34]  David Johansson,et al.  Endrov: an integrated platform for image analysis , 2013, Nature Methods.