replace to links in the markdown: from G-Drive to GitHub

Hi Christian,

How would you like to be cited? could you please add a license so people can use your work.
Thanks a lot for all the great materials.

Paula

find data, where the colocalization increase after some treatment.

open a czi file and show that you also have color sliders a.s.o.
can be part of the handling multi-color images practical

svfsdfg

Add teaching material for this:
https://imagej.net/MorphoLibJ#Application_to_grayscale_images
This should definitely be in some advanced section.
Example could be: finding the large bright region in Eugenes data.

@imagejan @ssgpers

if you have some opinions here that would be also helpful:

https://github.com/tischi/imagej-courses/blob/master/lectures/cellprofiler-pro-con.md#cellprofiler
https://github.com/tischi/imagej-courses/blob/master/lectures/fiji-pro-con.md#the-pros-and-cons-of-fiji

simplify the filenames in the colocalisation folder; remove unnecessary files

after some teaching let students find different ways of counting the number of nuclei in an image

• smooth => count number of local maxima
  • Blur > Find Maxima
• binarise => count number of connected componets
  • Adjust threshold > Analyze particles

• raw data => pre-processing => thresholding => object segmentation => object based measurements
• intensity measurements: background subtraction (local or global)

Facts to be demonstrated

• saturation cannot be fixed. impossible to determine number of objects (and to quantify intensities)
• intensity offsets are normal; they usually are not a problem

Contents

• distance transform
  • example: nuclear periphery quantification
• macro recording of complex workflows

Formative assessments

• outcomes of convolution filters on some data
• assessing by etherpad

@imagejan @ssgpers

Suggestions as to what to add there?

https://github.com/tischi/imagej-courses/blob/master/lectures/recommended-reading.md#recommended-literature-on-bioimage-analysis

...

• point out image.sc forum with an example of what one could search for
• point out google search with an example of a search
• make a list of useful imagej learning links

Meta

• etherpad is nice

Levels

• novice (no mental model of domain, can't ask domain questions)
• competent practitioner (can ask domain questions)
• expert

also find out what it is doing

Think of a little project that would take students a few hours.
This could/should be done after the learned already "enough".

Practicals to add

Find particles inside cells and measure their properties; identify parent cell by measuring particle intensities in label mask

3D objects..something with the new 3D manager

Local Image Filtering

• explain convolution filtering
• practical where different filters are compared: edge preserving, filter size, ...
• probably use the autophagosome data

Automated Global Thresholding: Using the image histogram

• show different images and their histograms side-by-side
• discuss the case where there are no objects
• discuss that most algorithms assume a certain fraction of the image occupied with objects. this is a problem if this varies a lot, for example detecting nuclei (show examples)

Automated Local Thresholding: Using local mathematics

Smoothing improves thresholding

• add this to the autophagosome workflow by adding some noise

Local Background Subtraction

• try subtracting a median filtered opening from a pure noise image => does the mean background value become zero?

Workflow:

• Tophat to remove nucleus and cell edges
• LoG filter
• Find maxima
• Dilate maxima
  • fixed distance
  • using intensity image
• Watershed touching maxima