Commit 2fa2c2d3 authored by Christian Tischer's avatar Christian Tischer

Update workflow ideas

parent 32c71ba0
...@@ -11,9 +11,13 @@ ...@@ -11,9 +11,13 @@
## Why is this workshop very useful? ## Why is this workshop very useful?
Using ImageJ to teach bioimage analysis concepts has the great advantage that most people are already familiar with the ImageJ user interface. Thus, one can fully focus on bioimage analysis concepts and is not distracted by other technical hurdles that naturally arise when using a new software (or even a new programming language). Using the MorpholibJ plugin has the great advantage that basic important concepts like "connected components analysis" are explicitely executed (and not hidden inside some meta-functionality); also label images play a prominent role in MorphoLibJ. Moreover, MorpholibJ works both in 2D and 3D, has great documentation, and also offers many advanced functionalities that can be explored further on. Using ImageJ to teach bioimage analysis concepts has the great advantage that most people are already familiar with the ImageJ user interface. Thus, one can fully focus on bioimage analysis concepts and is not distracted by other technical hurdles that naturally arise when using a new software (or even a new programming language).
Following up the above conceptual teaching with bioimage analysis workflow building in KNIME is a very natural fit. The reason is that KNIME's image analysis nodes often literally have the same names as the menu entries in ImageJ and specificially as in MorphoLibJ. Thus, course participants will see the same concepts occuring a second time, which helps memorizing important concepts. The ImageJ user interface is very minimal, maximally focussing the attention on interactive exploration of the currently active image, and many image processing operations have an interactive preview functionality. Taken together these features also help to teach and learn image analysis concepts without much distraction.
Using the MorpholibJ ImageJ plugin has the advantage that basic important concepts like "connected components analysis" are explicitely executed as one single step (and not hidden inside some convenience meta-functionality that combine many concepts in one step). Moreover, MorpholibJ works both in 2D and 3D, has great documentation, and also offers many advanced functionalities that can be explored further on.
Following up the above conceptual teaching with bioimage analysis workflow building in KNIME is a very natural fit. The reason is that KNIME's image analysis nodes often literally have the same names as the menu entries in ImageJ and specificially as in MorphoLibJ. Thus, course participants will see the same concepts occuring a second time, which helps memorizing important concepts. Moreover, KNIME is a great tool for data science in general and thus definitely worth learning.
## Topics taught ## Topics taught
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