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MiToBo - A Microscope Image Analysis Toolbox: v1.7

mitobo software on GitHub in support of 'MiToBo - A Toolbox for Image Processing and Analysis'
Authors: Moeller, Birgit; Posch, Stefan; Glass, Markus; Misiak, Danny;

MiToBo - A Microscope Image Analysis Toolbox: v1.7

Abstract

The Microscope Image Analysis Toolbox MiToBo is a Java toolbox and library of basic, intermediate and advanced image processing and analysis algorithms and tools. The toolbox for instance offers various filters (e.g., Gauss and Gabor filters, rank operators, vesselness filters), image enhancement approaches, image transformations (e.g., wavelets) and segmentation algorithms (e.g., thresholding, component labeling, active contours). In addition it ships with several advanced tools mainly dedicated to specific tasks in biomedical or biological image analysis, but not restricted to these fields. Examples are tools for DAPI stained nuclei detection, neurite segmentation, cell segmentation and tracking, and particle detection. MiToBo builds upon and extends the widely used image processing application ImageJ (http://imagej.nih.gov/ij/). All of its operators can be run from within ImageJ, but stand-alone usage or using MiToBo as a library for your own development is also possible. Finally, since MiToBo is based on Alida (http://www.informatik.uni-halle.de/alida), an advanced library for integrated development of data analysis applications, it offers a user- and programmer friendly framework for developing new image processing and analysis algorithms including automatic generation of user interfaces and a graphical workflow editor.

The MiToBo distribution is available in two versions. The basic version (file mitobo-1.7-bin.zip) contains MiToBo with all its operators, algorithms, start scripts and documentation while the extended version (file mitobo-plugins-1.7-bin.zip) also includes ImageJ plugins for directly integrating and running MiToBo from within ImageJ.

Keywords

Java Library, Image Processing, Image Analysis

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citations
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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