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ImageJ Macro Programming for Biological Image Analysis

Learn how to write ImageJ macros for the automation of image analysis tasks. For each topic there is a short presentation followed by a small exercise that has to be solved by the participants. Ideally you have already used ImageJ before but you know nothing or very little about programming. If this is not the case you might still find parts of the workshop interesting. Most of the workshop is available in the form of macro toolsets that you can install in FIJI. The macro toolsets provide predefined parts of the code that the student has to complete in order to solve an exercise.

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μManager is a software package for control of automated microscopes.

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Collaborative Annotation Toolkit for Massive Amounts of Image Data

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The application Bio7 is an integrated development environment for ecological modelling and contains powerful tools for model creation, scientific image analysis and statistical analysis. The application itself is based on an RCP-Eclipse-Environment (Rich-Client-Platform) which offers a huge flexibility in configuration and extensibility because of its plug-in structure and the possibility of customization.

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Reproducing an experiment doesn’t stop at the bench when images are concerned. Icy is an open source bioimaging software package that aims to provide a framework for authors to share, and others to reproduce, research once the sample hits the microscope. Icy was released in April 2011 and is being developed at the Quantitative Image Analysis Unit at the Pasteur Institute in France by Jean-Christophe Olivo-Marin and his team.

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Fiji is just ImageJ: a distribution of ImageJ (and ImageJ2) together with Java, Java 3D and a lot of plugins organized into a coherent menu structure. The main focus of Fiji is to assist research in life sciences. It is a free, open-source, community-driven project.

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CellProfiler is designed to enable biologists without training in computer vision or programming to quantitatively measure cell or whole-organism phenotypes from thousands of images automatically. The researcher creates an analysis pipeline from modules that find cells and cell compartments, measure features of those cells to form a rich, quantitative dataset that characterizes the imaged site in all of its heterogeneity.

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