Multichannel images

Prerequisites

Before starting this lesson, you should be familiar with:

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


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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



Assessment

True of false?

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

Solution

  1. True

Discuss with your neighbor

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

Solution

  1. By providing additional context and information that can be leveraged by the model
  2. 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
  3. Correcting for crosstalk or bleed-through between channels

Explanations




Follow-up material

Recommended follow-up modules:

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