Currently, this works with up to 7 or 8 channels (depending on software version an extra one was added recently). ImageJ has three main modes for displaying multichannel images, accessible from the drop-down menu of the Channels Tool dialog:Ĭomposite – Merge some or all the channels together for display. To be more precise, a ‘composite image’ in this context is a multichannel image that is displayed using a particular display mode. The question might be a little bit confusing because ‘composite’ and ‘multichannel’ do not quite mean the same thing, although they are sometimes used interchangeably with ImageJ. If you run the Channels Tool… command for an RGB image and try to do anything, ImageJ will askĪgreeing to the prompt will convert the RGB image to a multichannel image. One of the most useful commands that requires some explanation is Image ‣ Color ‣ Channels Tool… Shift+ Z. Some of these, such as Image ‣ Color ‣ Split channels and Image ‣ Color ‣ Merge channels are quite intuitive, and work for both RGB and multichannel images. There are several useful commands for working with color channels under Image ‣ Color ‣. Image with Overlay looks colorful, but the colors all come from the overlay – not from the image being RGB. We will meet z-slices in the next chapter. This is because the slider isn’t for channels, but rather for z-slices. Don’t expect too much consistency in naming: always check the types!įly Brain has a slider, but it doesn’t have a C label. For ImageJ, that’s not really an RGB image, it’s a multichannel image. HeLa Cells (48-bit RGB) is not treated as having the RGB type… and ImageJ doesn’t have a ‘48-bit’ type either! Here, 48-bit RGB means that we actually have three 16-bit channels with red, green and blue LUTs. When it comes to measuring an RGB image, the enigmatic statement above that ImageJ ‘converts the image’ is explained (in more detail than you might want) at the end. It is not possible to change LUTs per channel Image ‣ Adjust ‣ Brightness/Contrast… can only be used to adjust the brightness & contrast for all channels simultaneously It is possible to change LUTs independently for each channelĪnalyze ‣ Measure converts the image to have a single 8-bit channel before making measurementsĪnalyze ‣ Histogram creates a histogram after converting the image as above, but also provides an RGB button to also view separate histograms for the red, green and blue channels Image ‣ Adjust ‣ Brightness/Contrast… can be used to adjust the brightness & contrast for the active channel The distinction is really important when it comes to measurements and histograms, because the behavior between RGB and multichannel images is subtly different:Īnalyze ‣ Measure uses the active channel only, based on the C sliderĪnalyze ‣ Histogram uses either uses the active channel only or all channels, after prompting the user to choose The colors used to display the image are identical, but other aspects of the image window indicate that the images themselves are quite different. 32 A multichannel image (left) and RGB image (right). The Image ‣ Type ‣ will be 8-bit, 16-bit or 32-bit.įig = create_figure ( figsize = ( 8, 4 )) show_image ( 'images/neuron-composite.png', pos = 121 ) show_image ( 'images/neuron-rgb.png', pos = 122 ) glue_fig ( 'fig_color_neuron', fig )įig. Multichannel images that aren’t RGB are recognizable because they have an extra slider at the bottom, with a small C label, which is used to switch the ‘active’ channel. When moving the cursor over the image, there are three values shown in the status bar (e.g. The text RGB appears at the top of the image window There are several ways to recognize that an image has the type ‘RGB’: ImageJ treats RGB as a special image type, even though it is still 8-bit unsigned integer. This section explains the ideas, including common areas of confusion. ImageJ has good support for both RGB and multichannel images, but the behavior is not always obvious. append ( './././' ) from helpers import * from matplotlib import pyplot as plt from myst_nb import glue import numpy as np from scipy import ndimage Introduction #
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