Dear Brian,
of course I'm fully with you regarding the problem of generalization
that is one of the greatest hurdles when trying to help people who need
to process or analyze a batch of more or less similar images but who
post or are allowed to post only a single or a few images that often are
not really representative for the batch.
If "Curtis's result is almost there", why then don't we see the result?
I doubt that the approach is fully automatic as long as I can't
reproduce it or in other words: The proof of the pudding is...
Regards
Herbie
:::::::::::::::::::::::::::::::::::::::::::
Am 09.03.24 um 13:32 schrieb Brian Northan:
Hi again
Herbie says "why then don't we see any results that are fully automatically
obtained from your sample image"
Curtis's result is almost there, and Curtis suggests "And filter out
results that aren't close to the expected size, or aren't at the
correct (X,Y) coordinates to be one of the petal shapes."
With a little bit more work I'm confident you could solve the one sample
image by tuning label filter and merge rules, but as I mentioned I'd
hesitate to make conclusions from that one image without being able to test
on a separate 'validation' set, for which we have not trained the pixel
classifier on, or tuned the label modification rules on.
Brian
On Sat, Mar 9, 2024 at 7:03 AM Brian Northan <[email protected]> wrote:
Hi Anu
There are many similar problems on the Image.sc message board, and both
classical and advanced AI methods are often shown to be good at solving the
one image the researcher shares with the community. What is often lacking
is feedback on how the solution(s) suggested, work on the entire set of
images the researcher needs to process.
I really like Curtis's approach. However the question is how will this
work on the other 50 or more images? It will depend on the variation in
the image set. As Herbie points out, we have no idea how Curtis's method
(or other solution) will work on the entire set.
I hesitate to ask researchers to share more data, as I realize you are
busy, and you may be constrained as to how much (possibly unpublished) data
you are able to release publicly.
However if it is at all possible to share more data (5-10 examples) it
would really help in assessing whether proposed solutions generalize to
your entire image set.
At this point I agree that it may be faster to manually do it. However,
assessing the potential of automation on this problem is still valuable,
perhaps you will have to do another batch of images in a few months or
something, and the insights may help others facing similar problems.
Brian
On Sat, Mar 9, 2024 at 6:11 AM Herbie <[email protected]> wrote:
Greetings Anu,
because you called my suggestion as being your "last option" I should
like to remark that this idea is exactly *contrary* to what I've
suggested.
My prognosis is, that you will invest a lot of time with learning and
trying various (advanced) approaches and then realize that none of them
works fully automatically, i.e. will need additional manual
interventions that again take time. In the end you may realize that the
better (quicker) approach would have been to start immediately with the
manual segmentation of your 50 images.
Last but not least, if things would be so easy and economic with using
(advanced) approaches, why then don't we see any results that are fully
automatically obtained from your sample image?
Good luck
Herbie
::::::::::::::::::::::::::::::::::::::::
Am 08.03.24 um 19:01 schrieb anusuya pal:
Thanks so much everyone for suggesting so many ways. Thanks, Herbie,
yes,
that's the last option I thought. :-)
I really like Ankit's proposal as it's very much automated. The idea
given
by Michael -- I am kind of doing that for finding the spacing between
the
consecutive petal like patterns. But, that doesn't give me a good
estimate
for all of my images, as it is just one type of pattern.
I also like Curtis's idea, I need to play with that as suggested for the
various patterns to see which one works the best.
I really appreciate your valuable time and suggestions.
Thanks,
Anu
On Saturday, March 9, 2024, Curtis Rueden <[email protected]>
wrote:
Hi Anu,
I think your segmentation can be automated, but it is a bit tricky.
Here is
a quick attempt I made:
1. Labkit - https://imagej.net/plugins/labkit/
This is a machine-learning based pixel classification, where you do
manual
painting over the different areas of your image. Then train it, and
paint
again over the parts it got wrong. Repeat until it learns well how
things
should be.
Here is how it looked for me after I did this process back and forth a
couple of times:
[image: labkit-small.png]
As you can see, it is not perfect, but it gets close enough that you
can
then do additional steps afterward to extract the information you want.
Then, you can save the classifier and apply it to as many other similar
images as you want.
Note that Labkit (at least in my hands today) has an annoying bug where
after running the classifier (Ctrl+Shift+T), the pencil tool sometimes
stops being able to paint lines until you click (or Alt+Tab) away from
the
Labkit window and then back.
2. Export probability map to ImageJ
This gets you back to a regular image window, which you can then
manipulate
with other plugins.
You might always want to save this image to a TIFF file now, since it
will
serve as a good starting point for further experimentation.
3. Smooth the image to reduce noise. I used the Kuwahara filter. But it
didn't want to run on a 32-bit multichannel image, so I had to first
run
Image > Type > 8-bit and then Duplicate only the first slice of the
image.
The easiest way to run it is to type "kuwa" into the search bar of
Fiji.
After running this filter with a smoothing window of 5, my image looked
like this:
[image: smoothed-small.png]
4. Do the actual segmentation with the Morphological Segmentation
plugin,
part of MorphoLibJ. https://imagej.net/plugins/morpholibj
For this plugin you will need to enable the IJPB-plugins update site
via
Help > Update..., "Manage Update Sites" button, in Fiji.
I left the input image as Border Image, changed Tolerance to 30,
clicked
Run, and then changed the Results Display to "Catchment basins". Here
is
what that looked like:
[image: morpholibj-small.png]
As you can see, it erroneously bisected two of the regions on the
bottom
half, as well as one on the top half, but it got most of then right.
5. You could then click "Create image" to make another image and
measure
the number of pixels of each color to get the size of each region. And
filter out results that aren't close to the expected size, or aren't
at the
correct (X,Y) coordinates to be one of the petal shapes.
I would also suggest to give CellPose a try—I did not try it, but it
does
very well on a wide variety of input images.
You might get better answers on https://forum.image.sc rather than
here,
since the state-of-the-art for segmenting scientific images has
changed a
lot in recent years and there are many more powerful tools than
classical
ImageJ-based segmentation now.
Regards,
Curtis
--
Curtis Rueden
Software architect, LOCI/Eliceiri lab - https://uw-loci.github.io/
ImageJ2 lead, Fiji maintainer - https://imagej.net/people/ctrueden
Have you tried the Image.sc Forum? https://forum.image.sc/
On Fri, Mar 8, 2024 at 1:48 AM anusuya pal <[email protected]>
wrote:
Dear all,
I want to measure the area of the flower-like patterns as shown in the
image. I can do it manually, but I have more than 50 images. Do you
have
any suggestions for doing it automatically?
Thanks for your help,
Anu
--
ImageJ mailing list: http://imagej.nih.gov/ij/list.html
--
ImageJ mailing list: http://imagej.nih.gov/ij/list.html
--
ImageJ mailing list: http://imagej.nih.gov/ij/list.html
--
ImageJ mailing list: http://imagej.nih.gov/ij/list.html
--
ImageJ mailing list: http://imagej.nih.gov/ij/list.html
--
ImageJ mailing list: http://imagej.nih.gov/ij/list.html
