a very clear tutorial thank you very much! I had a small question, I was wondering whether it's possible to save somehow the image at 3:38 (image with the yellow dots) so that I can put up this as my counting strategy for the thesis.. i tried saving but I get the image without these yellow dots. I would be grateful for your help :)
This has been a really helpful tutorial. I am trying to quantify LC3 puncta in cells after serum starvation in WT cell lines and some knockouts. How to rule out the false positive in the control cells which gives are mostly without an puncta?
Hello @AbhishekBalmik-eu3nr. You basically should process your images the same way for both the control and positive cells. Setting a size range for puncta to be included is also a good idea...again, this setting should be the same for both kinds of images.
Loved the tutorial, the special effects, and most of all the sound track.. which made the tutorial as if we were going though a new adventure... would love to know the name of the track if that is okay with you! And above all, thank you so much for your work!
One question, should we use the same values for Brightness and Contrast and threshold if we want to compare between images (to make sure ImageJ is counting the same type of features in the pictures)? Thank you so much for making this video! Nice work!
Hello @artctx. For visual comparison between images, using the same brightness/contrast settings is good…but not before thresholding. You could use the same threshold settings to count objects in your images….as long as the image acquisition settings are the same.
Hi Akanksha. I’m happy to help. Difference of Gaussians is a common feature enhancement method in image processing. To quote Pete Bankhead: “Suppose we apply one Gaussian filter to reduce small structures. Then we apply a second Gaussian filter, bigger than the first, to a duplicate of the image. This will remove even more structures, while still preserving the largest features in the image. But if we finally subtract this second filtered image from the first, we are left with an image that contains the information that 'falls between' the two smoothing scales we used. This process is called difference of Gaussians (DoG) filtering, and it is especially useful for detecting small spots or as an alternative to the gradient magnitude for enhancing edges.” (bioimagebook.github.io/chapters/appendices/macros/macro_dog.html)
It is nice explanation but when I use count mask option the result image I got is blurry type... Nor perfect particles where seen... Is that correct,?? I could not find how to do it. I use gussain filter of 3 and gussian filter 5 . Substract 3-5...
Hi Rupa (@rupachowdhury6164). If you use a larger gaussian blur filter, the resulting image will of course look more blurry. However, when you thrreshold the image from the subtraction and count masks (from Analyze Particles), the mask will just show the segmented objects. If you don't see the objects, change the LUT to glasbey-on-dark. You should be able to see the ROIs in the ROI Manager.
Thank you very much for those nice videos. I am working on Parkinson disease model and wanna count the number and the size of alpha synuclein aggregations. Is this method valid in my case? Of note, I always get some non-specific staining over the nucleus. If it is applicable I can send a sample.
Hi @Mohammad Khashan. I think you can try the DoG filter. If it doesn’t work for your images, there should be another way to quantify alpha synuclein aggregates.
What a clear and excellent tutorial, thank you so much!
I’m glad you like it. Thanks for your support.
a very clear tutorial thank you very much! I had a small question, I was wondering whether it's possible to save somehow the image at 3:38 (image with the yellow dots) so that I can put up this as my counting strategy for the thesis.. i tried saving but I get the image without these yellow dots. I would be grateful for your help :)
Hi Maha. I just took a screenshot of the image in the video. Do you just want the image or the whole screen showing the ROI Manager as well?
If you go to my 'Community' tab, I posted the screenshots there.
This has been a really helpful tutorial. I am trying to quantify LC3 puncta in cells after serum starvation in WT cell lines and some knockouts. How to rule out the false positive in the control cells which gives are mostly without an puncta?
Hello @AbhishekBalmik-eu3nr. You basically should process your images the same way for both the control and positive cells. Setting a size range for puncta to be included is also a good idea...again, this setting should be the same for both kinds of images.
Loved the tutorial, the special effects, and most of all the sound track.. which made the tutorial as if we were going though a new adventure... would love to know the name of the track if that is okay with you! And above all, thank you so much for your work!
Hi @visnuc. Thanks for all your positive comments. The track is called "Outside the Box" by Patrick Patrikios. I'm glad you enjoyed watching.
Again, many thanks! @@johanna.m.dela-cruz
One question, should we use the same values for Brightness and Contrast and threshold if we want to compare between images (to make sure ImageJ is counting the same type of features in the pictures)?
Thank you so much for making this video! Nice work!
Hello @artctx. For visual comparison between images, using the same brightness/contrast settings is good…but not before thresholding. You could use the same threshold settings to count objects in your images….as long as the image acquisition settings are the same.
@@johanna.m.dela-cruz Thank you for the feedback!
Thank you so much for this video. Its a great help. I would really appreciate if you provide some references regarding this method.
Hi Akanksha. I’m happy to help. Difference of Gaussians is a common feature enhancement method in image processing. To quote Pete Bankhead: “Suppose we apply one Gaussian filter to reduce small structures. Then we apply a second Gaussian filter, bigger than the first, to a duplicate of the image. This will remove even more structures, while still preserving the largest features in the image. But if we finally subtract this second filtered image from the first, we are left with an image that contains the information that 'falls between' the two smoothing scales we used. This process is called difference of Gaussians (DoG) filtering, and it is especially useful for detecting small spots or as an alternative to the gradient magnitude for enhancing edges.” (bioimagebook.github.io/chapters/appendices/macros/macro_dog.html)
Very cool! Thanks for your video. Is it possible to create a macro automatically get the foci's ROI?
Hello! Yes, a macro would definitely make things faster.
thank you so much! that was exactly what I needed
Glad you found this useful. Thanks for watching, @judith.
It is nice explanation but when I use count mask option the result image I got is blurry type... Nor perfect particles where seen... Is that correct,?? I could not find how to do it. I use gussain filter of 3 and gussian filter 5 . Substract 3-5...
Hi Rupa (@rupachowdhury6164). If you use a larger gaussian blur filter, the resulting image will of course look more blurry. However, when you thrreshold the image from the subtraction and count masks (from Analyze Particles), the mask will just show the segmented objects. If you don't see the objects, change the LUT to glasbey-on-dark. You should be able to see the ROIs in the ROI Manager.
Thank you very much for those nice videos. I am working on Parkinson disease model and wanna count the number and the size of alpha synuclein aggregations. Is this method valid in my case?
Of note, I always get some non-specific staining over the nucleus. If it is applicable I can send a sample.
Hi @Mohammad Khashan. I think you can try the DoG filter. If it doesn’t work for your images, there should be another way to quantify alpha synuclein aggregates.
Hi can you able to do it?
Brilliant. Thanks again.
My pleasure.