Multichannel images

Prerequisites
Before starting this lesson, you should be familiar with:

Digital image basics

Data types

Lookup tables

Learning Objectives
After completing this lesson, learners should be able to:

Understand/visualize different image channels.

Motivation

Typically, multichannel imaging involves using a fluorescence microscope equipped with multiple filter sets or detectors, each specific to a particular fluorophore’s emission wavelength. In fluorescence microscopy, fluorescence signal of different dyes (at different wavelengths) can be registered simultaneously to one set of image spatial coordinates. Each signal then represents one channel and this information can be used to study/analyze various cellular and molecular processes e.g. colocalization.

Concept map

graph TD F("Multichannel image") F --> C1("Channel 1") F --> C2("Channel 2") F --> CA(". . . .") F --> CN("Channel n")

Figure

Multichannel image. Example for three 2D (xy) channels. Left - Each individual image is a channel shown in blue, red and green lookup tables. Right - All channels overlaid to display a composite image. Note that the array shape of (x,y,c) is just an example of channel order. The order may vary depending upon the data structure used to read image

Activities

Open a sample multichannel image
- This is a 16-bits/channel composite color image of HeLa cells with red lysosomes, green mitochondria and blue nucleus. Image courtesy of Tony Collins, creator of the ImageJ for Microscopy collection of plugins at http://www.macbiophotonics.ca/imagej/
Explore how different channels can be viewed and selected
Learn to adjust look up tables and brightness/contrast settings
Learn to select channels and make an RGB image

Show activity for:

ImageJ GUI - Inspect/view channels

Use the slider bar under the image to select the channel to be processed/analyzed

The number of bar positions are equal to the number of channels

Note: The color of the subtitle changes when you switch to a different channel

Open the Channels tool using Image > Color > Channels Tool or by pressing shift-z to select/deselect channels to display

Use the B & C tool using Image > Adjust > Brightness/Contrast or press shift-c to adjust the brightness and contrast of the current channel. Note: The slider bar decides your active channel for changing brightness and contrast settings

Try changing the channel color by selecting a LUT from the Image > Lookup Tables menu

ImageJ GUI - Save channels as Tiff/RGB image

Open a sample multichannel image

Check the image data type using Image > Type. It is 16-bit. Convert it into RGB by selecting Image > Type > RGB Color

Use File > Save As > Tiff to save as a 48-bit TIFF

Now, reopen sample multichannel image

Split the channels using Image > Color > Split Channels

Select any two channels, adjust the brightness and contrast and change their look up table according to your own choice

Now merge the channels using Image > Color > Merge Channels... and select the channels in C1 and C2 fields and leave other channels set to *None*

Note: Keep these settings: [x] - Create composite, [] Keep source images, [] Ignore source LUTs

Convert this image to RGB using Image > Type > RGB Color

Use File > Save As > Tiff to save as a TIFF

Galaxy Napari - Inspect/view channels

Upload an image to Galaxy

Navigate to Galaxy

In the Tools panel on the left, click Upload Data.

Click the Paste/Fetch data button.

Paste the image URL: https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyc_16bit__hela-cells.tif and click the Start button.

After the upload finishes, click the Close button. The image will then be available in your Galaxy history.

Start the Napari Interactive Tool

In the Tools panel on the left, search for Run Napari interactive tool.

Select xyc_16bit__hela-cells.tif from the Images dropdown list.

Click the Run Tool button.

Once the Open link once it appears at the top of the page, click it. This will open Napari in a separate browser tab.

In the Napari tab, select File -> Open File(s), and choose the image xyc_16bit__hela-cells.tif from the “input” folder. The image will be displayed in Napari’s main window.

In the layer pane located at the bottom left, right-click the image and select Split RGB.

Experiment with adjusting the contrast of each channel.

Assessment

True of false?

In a multichannel image, each channel is a grayscale image that represents different data

Solution

True

Discuss with your neighbor

How can multichannel images be used to improve machine learning models for image/object classification?
Is RGB image always a 3-dimensional image?
What is a potential challenge when analyzing multichannel images?

Solution

By providing additional context and information that can be leveraged by the model

Not necessarily. In Fiji, one can have an RGB data type without alteration of the image array dimensions (still 2D for xy-images). However, in MALTAB and Python, for an RGB, an image array must be at least 3-dimensional

Correcting for crosstalk or bleed-through between channels

Follow-up material

Recommended follow-up modules:

Learn more: