Commit 32c71ba0 authored by Christian Tischer's avatar Christian Tischer
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Add workshops and modules

parent af5e72cf
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* Check where the lowest pixel indices are in the displayed image:
* Most commonly: Upper left corner, which is different to conventional coordinate systems.
TODO: add animated-histogram.gif
## Image calibration
......@@ -86,6 +87,8 @@ brightness = ( value - min ) / ( max - min )
contrast = max - min
TODO: add animated image contrast demo from twitter
### Activity
* Open image: xy_8bit__nuclei_noisy_different_intensity.tif
## Recap
Take few sheets of empty (A4) paper.
Work in pairs of two.
Work in pairs of two or three.
* Draw a typical image analysis workflow: From intensity image to objects shape table.
* Write down a few (e.g., two) noteworthy facts about:
* Pixel data types
* Label images
* Intensity measurements
* Object shape measurements
* Write down answers to below questions (there can be multiple answers for some questions):
* How can you split touching objects?
* What can you use a distance map for?
* How can you split touching objects?
* What can you use a distance map for?
* What can you do to segment spots in prescence of uneven background signal?
* What can you do to remove small objects from a binary image?
# Basics of Image Analysis: Concepts and Workflows
# Basics of Bioimage Analysis: Concepts (MorpholibJ) and Workflows (KNIME)
## Duration
2-4 days
## Schedule
1-2 days: Learn concepts of bioimage analysis, using MorphoLibJ (ImageJ Plugin)
1-2 days: Build bioimage analysis workflows, using KNIME.
## 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.
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.
## Topics taught
The number of topics can be variied, depending on whether this course should be taught in 2,3, or 4 days.
- Image data types
- Image segmentation by thresholding
- Manual
- Automated?
- Binary images
- Connected component analysis
- Label masks
- Object shape measurements
- Object intensity measurements
- Convolutional filters
- Rank filters
- Applied on binary images
- Applied on grayscale images
- Local background subtraction algorithms
- Distance transform
- Object splitting by watershed
- Intensity based watershed
- Distance map based watershed
- Machine learning based image segmentation
## Notes
- Running this workshop it is useful to fine-tune what exactly is taught in the two parts, in order to maximise the overlap of bioimage analysis concepts.
- ImageJ macro programming is on purpose not taught, because it would dilute the teaching of image analysis concepts with (very distracting) programming tasks.
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