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Open Microscopy Environment and FindSpots: integrating image informatics with quantitative multidimensional image analysis
Author(s) -
David Schiffmann,
Dina Dikovskaya,
Paul L. Appleton,
Ian P. Newton,
Douglas A. Creager,
Chris Allan,
Inke Näthke,
I. Goldberg
Publication year - 2006
Publication title -
biotechniques
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.617
H-Index - 131
eISSN - 1940-9818
pISSN - 0736-6205
DOI - 10.2144/000112224
Subject(s) - computer science , segmentation , computer vision , image segmentation , artificial intelligence , digital microscope , image processing , microscope , microscopy , digital image , digital image processing , image (mathematics) , optics , physics
Biomedical research and drug development increasingly involve the extraction of quantitative data from digital microscope images, such as those obtained using fluorescence microscopy. Here, we describe a novel approach for both managing and analyzing such images. The Open Microscopy Environment (OME) is a sophisticated open-source scientific image management database that coordinates the organization, storage, and analysis of the large volumes of image data typically generated by modern imaging methods. We describe FindSpots, a powerful image-analysis package integrated in OME that will be of use to those who wish to identify and measure objects within microscope images or time-lapse movies. The algorithm used in FindSpots is in fact only one of many possible segmentation (object detection) algorithms, and the underlying data model used by OME to capture and store its results can also be used to store results from other segmentation algorithms. In this report, we illustrate how image segmentation can be achieved in OME using one such implementation of a segmentation algorithm, and how this output subsequently can be displayed graphically or processed numerically using a spreadsheet.

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