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 busy, and you may be constrained as to how much (possibly unpublished) data 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
