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IQM: An Extensible and Portable Open Source Application for Image and Signal Analysis in Java

Image and signal analysis applications are substantial in scientific research. Both open source and commercial packages provide a wide range of functions for image and signal analysis, which are sometimes supported very well by the communities in the corresponding fields. Commercial software package...

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Detalles Bibliográficos
Autores principales: Kainz, Philipp, Mayrhofer-Reinhartshuber, Michael, Ahammer, Helmut
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4303421/
https://www.ncbi.nlm.nih.gov/pubmed/25612319
http://dx.doi.org/10.1371/journal.pone.0116329
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author Kainz, Philipp
Mayrhofer-Reinhartshuber, Michael
Ahammer, Helmut
author_facet Kainz, Philipp
Mayrhofer-Reinhartshuber, Michael
Ahammer, Helmut
author_sort Kainz, Philipp
collection PubMed
description Image and signal analysis applications are substantial in scientific research. Both open source and commercial packages provide a wide range of functions for image and signal analysis, which are sometimes supported very well by the communities in the corresponding fields. Commercial software packages have the major drawback of being expensive and having undisclosed source code, which hampers extending the functionality if there is no plugin interface or similar option available. However, both variants cannot cover all possible use cases and sometimes custom developments are unavoidable, requiring open source applications. In this paper we describe IQM, a completely free, portable and open source (GNU GPLv3) image and signal analysis application written in pure Java. IQM does not depend on any natively installed libraries and is therefore runnable out-of-the-box. Currently, a continuously growing repertoire of 50 image and 16 signal analysis algorithms is provided. The modular functional architecture based on the three-tier model is described along the most important functionality. Extensibility is achieved using operator plugins, and the development of more complex workflows is provided by a Groovy script interface to the JVM. We demonstrate IQM’s image and signal processing capabilities in a proof-of-principle analysis and provide example implementations to illustrate the plugin framework and the scripting interface. IQM integrates with the popular ImageJ image processing software and is aiming at complementing functionality rather than competing with existing open source software. Machine learning can be integrated into more complex algorithms via the WEKA software package as well, enabling the development of transparent and robust methods for image and signal analysis.
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spelling pubmed-43034212015-01-30 IQM: An Extensible and Portable Open Source Application for Image and Signal Analysis in Java Kainz, Philipp Mayrhofer-Reinhartshuber, Michael Ahammer, Helmut PLoS One Research Article Image and signal analysis applications are substantial in scientific research. Both open source and commercial packages provide a wide range of functions for image and signal analysis, which are sometimes supported very well by the communities in the corresponding fields. Commercial software packages have the major drawback of being expensive and having undisclosed source code, which hampers extending the functionality if there is no plugin interface or similar option available. However, both variants cannot cover all possible use cases and sometimes custom developments are unavoidable, requiring open source applications. In this paper we describe IQM, a completely free, portable and open source (GNU GPLv3) image and signal analysis application written in pure Java. IQM does not depend on any natively installed libraries and is therefore runnable out-of-the-box. Currently, a continuously growing repertoire of 50 image and 16 signal analysis algorithms is provided. The modular functional architecture based on the three-tier model is described along the most important functionality. Extensibility is achieved using operator plugins, and the development of more complex workflows is provided by a Groovy script interface to the JVM. We demonstrate IQM’s image and signal processing capabilities in a proof-of-principle analysis and provide example implementations to illustrate the plugin framework and the scripting interface. IQM integrates with the popular ImageJ image processing software and is aiming at complementing functionality rather than competing with existing open source software. Machine learning can be integrated into more complex algorithms via the WEKA software package as well, enabling the development of transparent and robust methods for image and signal analysis. Public Library of Science 2015-01-22 /pmc/articles/PMC4303421/ /pubmed/25612319 http://dx.doi.org/10.1371/journal.pone.0116329 Text en © 2015 Kainz et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Kainz, Philipp
Mayrhofer-Reinhartshuber, Michael
Ahammer, Helmut
IQM: An Extensible and Portable Open Source Application for Image and Signal Analysis in Java
title IQM: An Extensible and Portable Open Source Application for Image and Signal Analysis in Java
title_full IQM: An Extensible and Portable Open Source Application for Image and Signal Analysis in Java
title_fullStr IQM: An Extensible and Portable Open Source Application for Image and Signal Analysis in Java
title_full_unstemmed IQM: An Extensible and Portable Open Source Application for Image and Signal Analysis in Java
title_short IQM: An Extensible and Portable Open Source Application for Image and Signal Analysis in Java
title_sort iqm: an extensible and portable open source application for image and signal analysis in java
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4303421/
https://www.ncbi.nlm.nih.gov/pubmed/25612319
http://dx.doi.org/10.1371/journal.pone.0116329
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