Add deconvolution and texture analysis notebooks for I2k 2023

[elevans]

This PR adds two new jupyter notebooks to the use case section in the PyImageJ documentation. These notebooks do the following:

• 3D Deconvolution
• GLCM/Haralick feature texture analysis

Both of these notebooks only need ImageJ2 and no legacy 🎉 so we don't need any changes to the code base. I also reorganized the use case section into sub sections:

• imagej-ops
• Integration
• Segmentation
• Other use cases

The goal is to use these notebooks in the upcoming I2K 2023 workshop.

[codecov]

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[ctrueden]

Wow! 🤩

For the GLCM notebook:

1. This is a super awesome notebook. It helps me too to understand Haralick features, which I never really understood before. It also helped me to see more things matplotlib can do. 😎
2. There are a few minor typos, misspellings, and formatting oddities, e.g. the "Slicing a net.imagej.DefaultDataset" comment not having the proper leading asterisk, due to Markdown. texutures, Promenence, Cluser, transistions, cotains, ... Might be fastest for someone who isn't you to just go through it with a fresh set of eyes and fix those little things.
3. I would suggest to name the crop regions differently than "crop_0" through "crop_5". It would be clearer to name them something like: cyto1, cyto2, cyto3, gag, bkg1, bkg2. Then explain why you chose these crops. Then finally at the end when they cluster up nicely, you don't have to spend so many words explaining what each crop represents, but only the explanation of why these areas measure in this way.

[gselzer]

I have a couple small suggestions, just about organization. Take or leave either of them:

1. In the Decon notebook, I think it would be really nice to display the original and deconvolved images side-by-side, so you can compare the difference more easily.
2. In the GLCM notebook, I'd suggest displaying the image right away - you talk about the image but unless I can immediately relate that to what I see in the image I immediately forget the details you listed off.

[bnorthan]

Hi @elevans

I would change the paragraph that says "this notebook utilizes the Richardson-Lucy Total Variation (RLTV) algorithm, which has significantly improved axial resolution over the standard RL algorithm"

The RLTV algorithm is used to limit noise, which can be a problem with the standard RL algorithm, however there is not alarge difference in 'resolution' between the standard and TV regularized version (as a side note resolution is a pretty nuanced topic and technically deconvolution improves 'contrast based' resolution methods, like the Rayleigh criteria, but does not restore out of band frequencies to a significant extent).

See below description and figure from my deconvolution/deep learning workshop. Even though the workshop used my opencl python version of RL, the figure linked below was actually generated a long time ago, for the 2015 ImageJ Conference, and used the version of RL from imagej-ops.

https://true-north-intelligent-algorithms.github.io/deconvolution-gpu-dl-course/20_deconvolution/0_intro_to_decon.html#richardson-lucy-with-total-variation-regularization-noisy-image

[elevans]

Thanks @bnorthan! I'll update my text to state that RTLV limits the noise that standard RL can amplify.

[hinerm]

Awesome stuff @elevans .. you taught me some stuff today! 😄