# Merge, split and overlay channels in ImageJ (and make a composite figure)

> Merge channels in ImageJ with Image › Color › Merge Channels, split them with Split Channels, overlay with Add Image, and build a colour-blind-safe figure.

Published 2026-11-06 by LabCanvas team. https://www.labcanvas.com/blog/merge-channels-imagej

![ImageJ showing the two-channel sample as a green and magenta composite, with the Channels Tool set to Composite and its More menu open at Split Channels, Merge Channels and the channel colours](https://www.labcanvas.com/blog/imagej/merge-channels-imagej-channels-tool.webp)

To merge channels in ImageJ, open one image per channel and choose **Image › Color › Merge Channels…**: put each image in a colour slot (C2 green and C6 magenta for two channels), tick **Create composite** and **Ignore source LUTs**, and click OK. **Image › Color › Split Channels** does the reverse, and **Image › Overlay › Add Image…** lays one image over another with a chosen opacity.

A merge is for looking; numbers come from the separate channels. This guide shows the dialogs on a two-channel image, the one setting that silently overrides your colours, why an RGB merge must not be measured, and how to build a figure that colour-blind readers can read. All the commands here are in ImageJ 1.x, so they are the same in Fiji.

## Three words first: channel, composite, RGB

- A **channel** is one fluorescence signal (DAPI, GFP, a far-red dye) stored as its own grey image, usually 16-bit.
- A **composite** is ImageJ's multichannel image: every channel keeps its raw values, its lookup table (LUT, the colour it is drawn in) and its own brightness range, and the colours are added together on screen.
- An **RGB image** is a flat picture: 8 bits each of red, green and blue, made from whatever was on screen. Fine for a figure, wrong for measuring.

The worked example is a synthetic two-channel image: 384 × 384 pixels, 16-bit, 0.1 µm per pixel, with spots in each channel, some of them in both.

## How do I merge channels in ImageJ?

1. Open the single-channel images. They must have the same width and height; for a stack, the same number of slices.
2. Choose **Image › Color › Merge Channels…**.
3. Pick an image for each colour you need and leave the rest at *None*. For two channels, use **C2 (green)** and **C6 (magenta)**.
4. Tick **Create composite** to keep the raw 16-bit channels. Untick it and you get a 24-bit RGB image instead.
5. Tick **Keep source images** if you still want the single-channel windows afterwards.
6. Tick **Ignore source LUTs** if the images already have colours (for example after Split Channels). Without it, ImageJ keeps each image's own LUT and ignores the slot: when we put a red-LUT image in C2 and a green-LUT image in C6 with this box unticked, the merge came out red and green, not green and magenta.
7. Click **OK**, then set each channel's brightness in **Image › Adjust › Brightness/Contrast…** (move the channel slider between channels).

![The ImageJ Merge Channels dialog with its seven colour slots and options](https://www.labcanvas.com/blog/imagej/merge-channels-imagej-dialog.webp "Image › Color › Merge Channels… in ImageJ 1.54f, after splitting the two-channel sample: C1 red to C7 yellow, Create composite, Keep source images and Ignore source LUTs. For a green and magenta merge, choose the images in C2 and C6 and tick Ignore source LUTs.")

Three or more channels: the classic choice is blue for the nuclear stain (C3) with green and magenta for the two signals, or cyan, magenta and yellow. Beyond four channels, show the channels side by side rather than one crowded merge.

## How do I split channels in ImageJ?

Select the multichannel image and choose **Image › Color › Split Channels**. Each channel becomes its own window, named C1-name, C2-name and so on, with its original bit depth and values, so you can threshold or measure one channel without the others. An RGB image splits into three 8-bit images named (red), (green) and (blue).

The **Channels Tool** (**Image › Color › Channels Tool…**) is the control panel for a composite: the display mode, a checkbox per channel to hide or show it, and under **More »** the colours and the split, merge and RGB commands.

![The ImageJ Channels Tool beside a green and magenta composite, with its More menu](https://www.labcanvas.com/blog/imagej/merge-channels-imagej-channels-tool.webp "Image › Color › Channels Tool… on the sample as a composite. 1 Composite, Color or Grayscale display; 2 show or hide each channel; 3 More »: 4 Split Channels and Merge Channels…, 5 a colour for the current channel, here channel 1 Green and channel 2 Magenta.")

| Display mode | What you see |
|---|---|
| **Composite** | All ticked channels added together, each in its colour |
| **Color** | One channel at a time, in its colour |
| **Grayscale** | One channel at a time, in grey (best for judging faint signal) |

## How do I overlay one image on another?

**Image › Overlay › Add Image…** draws a second image on top of the current one as an overlay. Typical uses: a fluorescence channel on a brightfield or phase image, or an inset. Pick the image, its position (X and Y of its top-left corner, in pixels) and an **Opacity**: 100 hides what is underneath, 50 shows both. **Zero transparent** lets the background show through wherever the added image is 0.

![The ImageJ Add Image overlay dialog](https://www.labcanvas.com/blog/imagej/merge-channels-imagej-add-image.webp "Image › Overlay › Add Image… in ImageJ 1.54f: the image to add, its X and Y location, the opacity 0–100% and Zero transparent.")

An overlay is a drawing on top of the image, not part of its pixels: measurements ignore it, and it is saved only in TIFF files. **Image › Overlay › Flatten** makes an RGB copy with the overlay burned in, for a figure; **Image › Overlay › Remove Overlay** takes it off.

If both images are fluorescence channels of the same field, a merge (above) is better than an overlay: it keeps both channels measurable.

## Why measure on the separate channels, not the RGB merge?

**Image › Color › Stack to RGB** (or **Image › Type › RGB Color**) turns a composite into the picture you see. Two things happen on the way: each channel is squeezed into 0–255, and the squeeze follows the display range you happened to set. The same raw data then give different numbers.

We measured channel 2 of the sample both ways:

| Channel 2, whole image | Mean | Pixels at the top value |
|---|---|---|
| Raw 16-bit channel | 352.6 | none (maximum 5,469) |
| RGB merge, display set by Enhance Contrast (0.35% saturated), green component | 21.7 | 272 at 255 |
| The same merge after halving the display maximum | 42.2 | 3,253 at 255 |

Nothing about the sample changed between the last two rows; only the display did, and the mean doubled while twelve times as many pixels were clipped. Measure intensities, ratios and colocalisation on the raw channels (in the composite, with the ROI Manager's Multi Measure, or after Split Channels), and keep RGB for the figure. The [ROI Manager guide](https://www.labcanvas.com/blog/imagej-roi-manager) shows how to measure the same regions on every channel.

## Which colours are colour-blind safe?

About 1 in 12 men and 1 in 200 women have red-green colour vision deficiency. For them, a red and green merge is a picture in one colour: the two channels and their overlap look the same. Green and magenta keep the channels apart, and overlap still shows as white.

![The two channels in grey, a red and green merge and a green and magenta merge, and both merges as seen with deuteranopia](https://www.labcanvas.com/blog/imagej/merge-channels-imagej-colour-blind.webp "The sample in ImageJ: each channel in grey, then merged red and green left and green and magenta right, exported with Stack to RGB. Bottom row: the same merges with deuteranopia simulated Machado et al. 2009. Red and green collapse to one yellow; green and magenta stay two colours.")

- Two channels: **green and magenta**.
- Three channels: green, magenta and blue (blue is dim on screen, so use it for the nuclear stain), or cyan, magenta and yellow.
- Always show each channel alone **in grey** beside the merge: the eye judges grey far better than colour, and faint signal is easier to see.
- Check the finished figure with the [colour-blindness checker](https://www.labcanvas.com/tools/scientific-figure-color-blindness-checker), and see [LUTs in ImageJ](https://www.labcanvas.com/blog/imagej-lut) for false-colour tables such as Fire and Viridis.

## Making the composite figure in ImageJ

1. Set the brightness of each channel once (**Image › Adjust › Brightness/Contrast…**), with the same settings for every image you will compare. Write the ranges down.
2. Add a scale bar: **Analyze › Tools › Scale Bar…** (see [scale bars and Set Scale](https://www.labcanvas.com/blog/imagej-scale-bar-set-scale)).
3. For the merge: **Image › Color › Stack to RGB**. For each channel in grey: switch the Channels Tool to **Grayscale**, show one channel and use **Image › Duplicate…** then **Image › Type › RGB Color**, or split and convert each.
4. **Image › Stacks › Images to Stack**, then **Image › Stacks › Make Montage…** puts the panels side by side (or lay them out in a figure editor).
5. Save as TIFF or PNG, never JPEG: compression smears the colours of small spots.

## Pitfalls

- **Ignore source LUTs left unticked.** The merge keeps the images' old colours, whatever slots you chose.
- **Different display ranges between conditions.** If control and treated were adjusted separately, the merge shows a difference the data may not have. Use the same ranges (Brightness/Contrast › **Set**, with **Propagate to all other open images**) and say so in the legend.
- **Saturated merges.** Pushing the contrast until the spots are solid white hides real differences; keep the brightest structures just below the top.
- **Yellow is not colocalisation.** Overlap in a merge depends on the display ranges. Measure it with Pearson's and Manders' coefficients; see [colocalisation analysis](https://www.labcanvas.com/blog/colocalization-analysis-pearson-manders).
- **Channel shift.** Different wavelengths can be offset by a pixel or two (chromatic aberration). Image a multicolour bead and check the merge before you trust overlap.
- **Measuring the RGB.** See above: always measure the raw channels.

## Reporting it

> Channels were merged in [Fiji (ImageJ 1.54)] as a composite ([GFP] in green, [mCherry] in magenta) with Image › Color › Merge Channels; the same display ranges were applied to all conditions ([GFP 120–3,600; mCherry 90–3,200]). Single channels are shown in grey. Intensities were measured on the raw 16-bit channels, not on the RGB images.

Cite ImageJ (Schneider et al. 2012) or Fiji (Schindelin et al. 2012).

## An easier way: channels and figures in LabCanvas

[LabCanvas Image Analysis](https://www.labcanvas.com/analysis.html) shows, measures and sends the channels of a multichannel TIFF to a figure in the browser, with nothing to install. It opens an ImageJ hyperstack or OME-TIFF as one image with its channels.

1. Click **Open an image** and choose the multichannel TIFF, or the two-channel sample on the start screen.
2. In **Image › Display**, choose **Composite**, **One channel** or **Gray**, and give each channel a lookup table (**Green** and **Magenta** for two channels); **Auto** or the minimum and maximum set the brightness. Display changes only the picture.
3. **File › Add channels to a figure** (or **Each channel and the merge**) sends every channel and the merge, side by side with the scale bar, to the figure, slide or poster open in another tab; tick **Grey** for the single channels in grey.
4. To measure across channels, use **Measure › Colocalisation**, or **Measure** with **All channels** for one row per ROI per channel.

:::grid
![Line intensity profile in LabCanvas Image Analysis](https://www.labcanvas.com/analysis-examples/profile.webp "The two-channel sample in LabCanvas, shown as a green and magenta composite with Image › Display. A 28.56 µm line 3 px wide gives one profile per channel from the raw values: a spot in both channels has peaks in both curves.")
![Colocalisation in LabCanvas Image Analysis](https://www.labcanvas.com/analysis-examples/coloc.webp "Colocalisation: Pearson's r 0.529; above the Costes thresholds tM1 0.219 and tM2 0.214 7,169 colocalised pixels, in yellow.")
:::

Why it is a good choice for multichannel figures:

- **Nothing to install**: it runs in the browser on any computer or tablet.
- **The RGB trap cannot happen**: every measurement, threshold and assay reads the raw channel values, whatever the display.
- **Channels and merge into a figure in one step**, with the scale bar, instead of Stack to RGB, Images to Stack and Make Montage.
- **Colocalisation on the raw values**: Pearson's r and Manders' coefficients with Costes thresholds.
- **Free with a LabCanvas account**: adding to a figure, measuring, Copy and Send to Graphs; downloading files (PNG, TIFF, CSV) is part of LabCanvas Pro.

LabCanvas does not merge separate single-channel files into one image: if your microscope saves one file per channel, merge them in ImageJ or Fiji (above) and save the composite as TIFF, or export a multichannel TIFF from the acquisition software. Fiji is also the tool for files in a maker's own format (.czi, .nd2, .lif). [Open Image Analysis](https://www.labcanvas.com/analysis.html) and try the two-channel sample. ImageJ and Fiji are independent open-source projects; LabCanvas is not affiliated with them.

## References

- Machado GM, Oliveira MM, Fernandes LAF (2009). A physiologically-based model for simulation of color vision deficiency. *IEEE Transactions on Visualization and Computer Graphics* 15, 1291–1298. [doi:10.1109/TVCG.2009.113](https://doi.org/10.1109/TVCG.2009.113)
- Schindelin J, Arganda-Carreras I, Frise E, et al. (2012). Fiji: an open-source platform for biological-image analysis. *Nature Methods* 9, 676–682. [doi:10.1038/nmeth.2019](https://doi.org/10.1038/nmeth.2019)
- Schneider CA, Rasband WS, Eliceiri KW (2012). NIH Image to ImageJ: 25 years of image analysis. *Nature Methods* 9, 671–675. [doi:10.1038/nmeth.2089](https://doi.org/10.1038/nmeth.2089)

## Frequently asked questions

### How do I merge channels in ImageJ?

Open the single-channel images (same width and height), choose Image › Color › Merge Channels…, pick an image for each colour (C1 red, C2 green, C3 blue, C4 gray, C5 cyan, C6 magenta, C7 yellow), tick Create composite and click OK. Tick Ignore source LUTs if the images already have colours, or their own colours win.

### How do I split channels in ImageJ?

Select the multichannel image and choose Image › Color › Split Channels. A composite or hyperstack becomes one image per channel (C1-name, C2-name and so on) with its original values; an RGB image becomes three 8-bit images named (red), (green) and (blue).

### How do I overlay two images in ImageJ?

Select the bottom image and choose Image › Overlay › Add Image…, pick the image to lay on top, set its X and Y position and an Opacity such as 50%, and click OK. The overlay is only drawn on screen; Image › Overlay › Flatten makes an RGB copy with it burned in.

### Should I measure intensity on the merged RGB image?

No. An RGB merge has 8 bits per colour and its values depend on the brightness and contrast you set before converting, so the same data can give very different numbers. Measure each channel in the original image or the composite, and use RGB only for the figure.

### Which colours should I use for a two-channel merge?

Green and magenta. Where the two overlap the merge is white, and people with red-green colour blindness (about 1 in 12 men) can still tell the channels apart, which they cannot with red and green. For three channels, add blue or show each channel in grey beside the merge.

### What is the difference between a composite and an RGB image in ImageJ?

A composite keeps every channel at its original bit depth (for example 16-bit), each with its own lookup table and display range, so you can still measure it. An RGB image is a flat 24-bit picture of what was on screen. Image › Color › Stack to RGB or Image › Type › RGB Color turns a composite into RGB.

## Related links

- [ROI Manager in ImageJ](https://www.labcanvas.com/blog/imagej-roi-manager): Measure the same ROIs on every channel
- [Colocalisation: Pearson vs Manders](https://www.labcanvas.com/blog/colocalization-analysis-pearson-manders): Measure overlap, not yellow pixels
- [LUTs in ImageJ](https://www.labcanvas.com/blog/imagej-lut): Which lookup table to use
- [Colour-blindness checker for figures](https://www.labcanvas.com/tools/scientific-figure-color-blindness-checker): See your figure as colour-blind readers do
- [Figure panel maker](https://www.labcanvas.com/tools/figure-panel-maker): Lay out channels and merge as panels
- [Image Analysis: the complete guide](https://www.labcanvas.com/blog/image-analysis-complete-guide): Every tool and setting in the app