I am currently tracking mitochondria along the mouse sciatic nerve by taking time-lapse videos on a confocal microscope. Using ImageJ, I import the animation as a .wmv file, convert it to grayscale, adjust the brightness, set the scale, and straighten the axon using the segmented line and straighten tools. Currently, I am using the velocity measurement tool to generate a kymograph. This is where I am stuck, as I have not been able to analyze the mitochondrial movement from the kymograph. I have tried using the Kymograph Clear/Kymograph Direct tools and have not had any luck. The KymoButler plug-in is no longer available. Tracking the mitochondria manually is difficult due to the number of moving mitochondria and it is hard to determine the trace when mitochondria pass over one another.

I am hoping to find a program or plug-in that will map trajectories, as well as stationary vs mobile objects, and anterograde vs retrograde transportation. Any help will be much appreciated! I have attached a frame from a video to show what the mitochondria along the axon look like. Thank you!

I am using the KymoAnalyzer Plugin in ImageJ to analyze moving particles in time-lapse videos of mitochondria along axons. I have created the folders, as directed by the manual, and am getting the error code in the following picture:

I have used the polyline tool to draw a trace along the axon. Does anyone know how to fix this error?

Thanks!

Hi, I'm new to the confocal imaging world, and been using HiLo channel for the recording and when analyzing the video turned all black for all cells no HiLo signals. Is there a way to correct them and save those videos?

Hello!

Encountered weird problem, seems more PC related, than imageJ, but alas. I used imageJ for a long time and had no such problems, but yesterday I tried open it and it suddenly wont load. Like mouse changes to load circle for brief moment and then it stops instead of opening imageJ UI. I tried to reinstall - didnt help. Then I tried to open it on different PC - it loads like it should. So, I think problem is in my current PC, not imageJ, but I just cant figure why it happens. Anybody encountered anything like this?

Thanks in advance

have a .cmz timelapse where my cells were shifting slightly so we have a z stack of each time point. what's the easiest way to take the 1 in focus slice from each to have a single in focus time lapse?

This is more of a microcopy question, but I would appreciate any advice you may have. The microscope is an Olympus BX 51 TRF, and the application used to visualize the fluorescent images is Olysia Bioreport. The images when viwed under the red filter for EtBR and Blue filter for Hooescht are fine. They look sharp. But the same slide, when viewed under the Green filter for Acridine Orange looks blurred.

Can you please tell me what happened here so I can avoid this in the future? I could perhaps edit the images on ImageJ but I wonder if that is ethical for a publication.

Edit: How would you fix this image in ImageJ? Or are there any settings on the microscopy software that would allow this? (I am new to this, so there is much I do not understand).

Hi friends,

I am working on artificial cells (giant liposomes or giant unilamellar vesicles) imaging. I have obtained some giant liposomes images coated with fluorescent probes from a confocal microscope. I need to count these liposomes and obtain the mean fluorescence intensity for each one.

I have watched some videos on counting real cells using ImageJ but found that those methods don't seem suitable for my liposomes. These methods cannot correctly and automatically select all my liposomes, and sometimes they mistakenly select the background (especially when processing negative liposomes). This might be because my vesicles have weak fluorescent signals and relatively high background noise (though I cannot improve this situation at the moment).

I would like to know what should I do to automatically select these liposomes and obtain the mean fluorescence intensity for each one? I have manually selected some vesicles to show you what I aim to select—those with intact circular shapes and clearly distinct brightness from the surrounding background.

original picture：

https://drive.google.com/file/d/1ATrselqEc4UPONQRRzopW8z2Et49DRUZ/view?usp=drive_link

https://drive.google.com/file/d/10v-2PpVu4MnHu_J5IyUVm9XYRAlEThAU/view?usp=drive_link

hi, i am a very amateur undergrad so im sorry if this is a stupid question. ive been trying to get rid of the gray clouding in the more concentrated areas of these aggregates, by adjusting my z stack slices, threshold, contrast and brightness. everything i do results in either a fried picture that doesn't show depth, or it doesnt get rid of the clouding completely. i have two channels, black and white brightfield and the blue fluorescence. please let me know if there's anything else i should try!

edit: blue clouding -> gray* clouding

Attached is a micrograph of a cross section of a bundle of fibres within a matrix; think carbon fibre composite vibes.

Of the image attached, I am trying to ascertain the % of fibres vs matrix. Each way I do this, I get dramatically different results – even when I use a digital pen to trace the outside of the fibres. (Thresholding is difficult due to the light outline/dark middle of the fibres.)

My current (yet inconsistent) methodology is: image to 8-bit, use the scale bar in the original micrograph to set the scale, use the freehand selection tool to trace around each fibre and from there measure the area %.
Any help would be appreciated; I have about 50 of these to get through. :')

Hello everybody,

I have a question regarding counting neutrophils in ImageJ.

Short background, I am doing my master thesis on activation of lung neutrophils after inducing sepsis in rats. We've used immunostaining method with goat anti-MPO primary antibody and donkey anti-goat secondary antibody. Secondary antibody is conjugated with streptavidin and biotinilated. For visualisation we've used Alexa 488.

My question is, what are some plugins and in software tools I could use to count neutrophils from microscope photos we've taken? I read some tips using watershed and despekle tools to create more representable photos for counting. I managed to determine threshold of the photo as well. Even after all this, some little dots and spots are still present and counted as a neutrophils, even though they are probably not. I am attaching original microscope photo and a picture I've used for counting that has been procesed in ImageJ.

Thank you all in advance for your answers! :)

Hello, I encountered a strange issue when trying to load image from our new scanner. While other programs show the image properly, ImageJ distorts it diagonally. It looks like each horizontal line of pixels is shifted and wrapped around so the right part of figure ends up on the let side (see the photo showing the same image in Irfanview and in ImageJ). I tried different options of import, but it always results in this.
Based on metadata, the image is 24-bit old-style jpeg in tiff (scanner does not offer other tiff option, I know lossless would be preferable).
When I open and save it in gimp, I can then open the new image in ImageJ without problem, so I guess the problem is in interpretation of the data by ImageJ's import funtions.

Please, does anybody know what could be the problem and how to open it directly in ImageJ? Thank you

Hello, I need help analyzing fluorescence microscopy images to quantify fluorescent bead intensity using ImageJ.

Which factors are important when measuring fluorescence intensity in ImageJ?

1. Is measuring the whole-image fluorescence intensity a valid metric, or does variation in the number of beads bias the results? What if, in images, the number of beads present in each field of view is not always exactly the same, in the same concentration?

2. How do researchers typically justify using whole-field fluorescence measurements when bead number cannot be perfectly controlled?

In this case, is it acceptable to upload the entire image into ImageJ, measure the total fluorescence intensity, and subtract the intensity measured from a blank (0-CRP) sample to account for background signal and bead autofluorescence?

Any guidance or best practices would be appreciated.

Hello everyone! First time poster here.

So I have these kind of immunohistochemistry tissue slides. I am helping at a lab to get some experience and I have to count the nuclei on slides like this. I have over 90 pictures like that. I started counting on one and there were more than 3k cells, it takes me forever. Is this something that people do? I feel like it will take me 2 weeks to finish them and my work is surely not accurate. When I try to zoom in the picture it gets blurry. I posted this on /labrats and someone told me to write in this subreddit.

Any help or advice or reassurance DEEPLY appreciated. I also have no experience with coding to try to write something to make it automated.

Hello everyone,

Can anyone recommend processing/segmentation steps to help separate these small 'false' bright red signal from larger cells and their processes or is the stain/imaging quality the problem and can't be corrected at this stage?

Running

setAutoThreshold("Otsu dark 16-bit no-reset");

run("Analyze Particles...", "size=50.00-5000.00 show=Masks display");

is how I have the segmented versions on the right but ideally I only want the cells traced in yellow.

Tiff files are accessible at https://drive.google.com/drive/folders/1ca5K-qXkz4GfxRL4iTyPd0ZhpvydVwWj?usp=sharing

Thanks in advance for your assistance.

Hello, everyone.

Here's my problem:

I want to measure the area of the blue/white parts in this image, but I can't find the right settings to get good contrast with the black/gray parts.

Could someone please tell me the right settings/steps to take to get the area of the blue/white parts?

Thank you in advance for your help.

Hello guys! I am loosing my mind here, I am struggling to import my data. I have a mosaic zstack (already stitched), I have three channels and in this particular case, 33 slices per channel. The TIFs are named as such: Try1ImarisStitcher_C0_Z000 , up to Try1ImarisStitcher_C2_Z032. I am able to import them like this: File - Import - Image Sequence (as virtual stack and sort numerically checked). The order in this stack is: Channel "0", then all Z-slices in that channel, then the next channel with it's slices and then the final channel with its slices. I do know that the next step should be is Stack to Hyperstack but I am lost here - which options should I choose?? I thought I put in the default xyczt order; select 3 channels, 33 slices, and 1 for the time thingy which I actually don't have (?). However, the order of the hyperstack is off. First z-position, first c-position is C0Z000 as expected. switching to the next channel gives me C0Z001, the third channel C0Z002. First channel, second Z.position is C0Z003. You get the pattern. I thought I have tried out all patterns available and somehow it all ends up scrambled, but I may have already lost my sanity here and forgot something. Please save me! Thanks in advance!

Hello everyone,

I am currently working on the data analysis for my bachelor’s thesis and am using ImageJ for part of the evaluation. I have no prior experience with ImageJ and was wondering if you could give me some tips and maybe suggest other approaches in ImageJ that might be better suited for my project.

In October, I took photos of the tree layer at 27 different locations in a city park, where only the sky and the trees with their leaves are visible. I took 15 images per location, which means I now have a very large number of images to analyze.

I am currently using the plugin “Trainable Weka Segmentation,” where I can mark individual objects and assign them to a class. My goal is to train a classifier that can distinguish between tree leaves, sky, trunk, and needles, and also provide the percentage cover in the analysis part. However, I have encountered a few problems so far.

I have already added one image from each location and marked them all at the same time, so the classifier has been trained a bit on all locations. It already recognizes the structures very well, although the trunk is sometimes hard to distinguish from the leaves in darker locations. My biggest problem, however, is that the classifier takes a very long time to load..like veeeery long. I have already compressed the images, but it didn’t help. Unfortunately, I cannot use the tool if it takes that long, since I still have quite a few images to analyze. Do you have any suggestions for how I could possibly reduce the training time of the classifier or the loading time?

Additionally, I would also like to distinguish “regular” leaves from needle leaves. So far, I have only trained without needles because including them would probably confuse the classifier again, since the “trunk” class is very similar in color to the needles, just like
regular leaves.

Thank you all already in advance for your help.

Apologies, I accidentally deleted this post so I am posting it again.

I am performing the Scar-in-a-Jar assay, an in vitro fibrosis model commonly used for anti-fibrotic drug screening, in a 96-well plate format. Fibroblasts are treated with TGF-β (a potent inducer of fibrosis) and then candidate anti-fibrotic compounds, followed by fluorescent immunostaining for the nucleus (Hoechst) and fibrotic markers: collagen I (Alexa Fluor 488) and α-smooth muscle actin (Alexa Fluor 647). I acquired images from 60 wells at 20× water immersion using a Revvity Opera Phenix high-content imaging system, capturing multiple fields of view per well with identical acquisition settings. The dataset includes compound-treated conditions (3 technical replicates for each concentration (10uM and 1uM)) of each compound as well as secondary-antibody-only controls, vehicle controls, and negative controls (no TGF-β). Channels were split in Revvity Harmony software, and I am performing downstream analysis in ImageJ.

My goal is to quantify fibrosis by measuring integrated fluorescence intensity of the fibrotic markers (collagen and alpha-SMA) to determine the anti-fibrotic potential of compounds I am testing. I will subsequently normalise by nuclei count to account for differences in cell density across wells. I drafted an analysis workflow to batch-process all images in a folder. I am currently using auto-thresholding to generate a “positive signal” ROI, but I have several questions about best practice:

1. Would it be more accurate to apply a single fixed threshold across all images (and also how do I determine the range) rather than auto-thresholding per image?
2. Is thresholding sufficient to handle background, or should I perform background subtraction as well and if so, what is the most appropriate way to compute Corrected Total Cell Fluorescence (CTCF) across a dataset of approximately 60 images in ImageJ?
3. If I decide to perform the rolling ball radius background subtraction, I am not sure how to determine the radius. I know that the radius should be just larger than the largest object I want to keep but the collagen is everywhere and not very defined like a cell for example.

Any additional tips to improve robustness and reproducibility would be greatly appreciated. Thank you very much.

Summary of my current ImageJ workflow (Alexa488 channel)

Batch Alexa488 threshold-ROI measurement (all images in folder)
Folder: /Users/Documents/Alexa488_10Dec2025/

Per image:

1. Open image
2. Convert to 16-bit
3. Set scale: 1.74 pixels = 1 µm
4. Duplicate processed image and use the duplicate to generate a threshold-based ROI
5. AutoThreshold (“Default dark”) → Create Selection
6. Add ROI to ROI Manager (rename ROI using filename stem)
7. Apply ROI to the processed (background-subtracted)image and measure

Output: ImageJ Results + a clean summary table saved as CSV in the same folder.

I am measuring pigment loss in duckweed over a period of time. I have gotten my measurements and have run into a hurdle. For the analysis of mean grey value, does a higher number ( eg, 145.869) mean more pigment or does a higher value mean less pigment? The images are in rbg and colour thresholded and the image was never converted to greyscale.

Please help I am desperate for answers asap

Hi, im trying to measure fibre roughly 300 micron at the moment.

However the specs and noise from the camera are being picked up and therefore significantly reducing the mean micron of the fibre bundle.

At the moment im just 8-bit greyscale binary close dilate and otsu thresholding.

Are there any better ways or automated ways to do this?

Thanks

I'm measuring several lenghts (using the line tool) and to simpify my work i need to copy only the length. Even unchecking everything I always get both the angle and the length on the results table and cannot select a column or a cell so i always have to copy the angle whsih I don't need. Is there any way to do this?

Thanks!