Open source image analysis software toolboxes for microscopic applications
Modern microscopy allows for acquisition of images spanning in different spectral, spatial and temporal dimensions. Once acquired, these frequently huge images need to be condensed into several quantitative statements that can either support or falsify the initial research questions. This process of measurement and analysis cannot be performed nowadays without the use of specialized software toolboxes. These toolboxes make the backbone of a newly defined branch of bioinformatics denoted as bioimage informatics. This lecture will present several open source (OS) toolboxes for image analysis applicable to microscopic images.
It should be noted that the most popular OS image analysis toolboxes have extendable architecture, i.e. they can be customized and upgraded to suit the needs of a particular research project. ImageJ is an OS toolbox, written in Java, having a broad range of applications in microscopy, medical imaging, remote sensing and ecology. It has been evolved since 1997 by Wayne Raspband supported by an international group of contributors. ImageJ is endorsed by NIH, USA, and has become a de facto standard for image processing and feature extraction in the life sciences communities. ImageJ can be extended by 3rd party plugins for example to interface Matlab, R or Micro-Manager. One of the most popular plugin collections for microscopy is Fiji (http://fiji.sc), which also offers powerful image segmentation and visualization features. Fiji is maintained since 2008 and supported by the Max Planck Institute of Molecular Cell Biology and Genetics, Germany. Icy (http://icy.bioimageanalysis.org/) is a new OS toolbox based on Java and VTK developed in the Pasteur Institute, France. It supports the scripting languages JavaScript and Python and can interface Micro-Manager, ImageJ and Matlab. Icy provides flexible and interactive image and data visualization including a workflow designer. Finally, I will outline the possibilities to build analysis workflows with systems like KNIME and interface data managements systems like OME and LabIS.
References
D. Prodanov and K Verstreken (2012). Automated Segmentation and Morphometry of Cell and Tissue Structures. Selected Algorithms in ImageJ, in Molecular Imaging, B Schaller (Ed.), InTech, DOI: 10.5772/36729. http://www.intechopen.com/books/molecular-imaging/automated-segmentation-and-morphometry-of-cell-and-tissue-structures-selected-algorithms-in-imagej
J Schindelin, I Arganda-Carreras, E Frise, V Kaynig, M Longair, T Pietzsch, S Preibisch, C Rueden, S Saalfeld, B Schmid, J-Y Tinevez, DJ White, V Hartenstein, K Eliceiri, P Tomancak and A Cardona (2012) Fiji: an open-source platform for biological-image analysis, Nature Methods 9(7): 676-682
F. de Chaumont, S. Dallongeville, N. Chenouard, N. Herve, S. Pop, T. Provoost, V. Meas-Yedid, P. Pankajakshan, T. Lecomte, Y. Le Montagner, T. Lagache, A. Dufour, and J. C. Olivo-Marin (2012) Icy: an open bioimage informatics platform for extended reproducible research, Nat. Methods 9(7): 690-696
Keywords:
Bioimage analysis,
image processing,
light microscopy,
segmentation,
feature extraction,
workflow,
Data Mining and Machine Learning
Conference:
Imaging the brain at different scales: How to integrate multi-scale structural information?, Antwerp, Belgium, 2 Sep - 6 Sep, 2013.
Presentation Type:
Lecture
Topic:
Handling of light and magnetic resonance microscopy data sets
Citation:
Prodanov
D
(2013). Open source image analysis software toolboxes for microscopic applications.
Front. Neuroinform.
Conference Abstract:
Imaging the brain at different scales: How to integrate multi-scale structural information?.
doi: 10.3389/conf.fninf.2013.10.00005
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Received:
20 Jul 2013;
Published Online:
30 Aug 2013.
*
Correspondence:
Dr. Dimiter Prodanov, IMEC, EHS, Leuven, 3001, Belgium, Dimiter.Prodanov@imec.be