Prerequisites

Before starting this lesson, you should be familiar with:

Learning Objectives

After completing this lesson, learners should be able to:
• Explore and view the different dimensions image data can have.

Motivation

Apart from the X and Y dimensions, visible in the width and height of an image, image data can have additional dimensions. The most common additional dimensions include:

• the Z dimension, providing depth to image data,
• different channels, showing data recorded with different detectors or detector settings,
• the time dimension. Viewing multi-dimensional data can be challenging, because a computer monitor can only render a (multi-color) 2D representation. Therefore, it is important to know how to visualize and navigate through multi-dimensional data.

Concept map

graph TD XY("2D(XY)-image") --> ND("N-dimensional image") S("Z-slices") --> ND("N-dimensional image") C("Channels") --> ND("N-dimensional image") T("Time Points") --> ND("N-dimensional image")

Activities

Explore dimensions in a 3D image

• Open the 3D image xyz_8bit__chromsomes.tif.
• The data shows chromosomes wrapped around a spherical(!) nucleus; i.e. the data should look similar from all directions.
• View the data in XY, YZ, and XZ.
• Is the image calibrated?
• Observe that the rendering in XZ and YZ is deformed without taking the calibration into account.

Show activity for:

ImageJ GUI

• Open the 3D image xyz_8bit__chromosomes.tif.
• Observe that the image has 3 dimensions, X, Y, and Z.
• Explore the different dimensions using [ Image > Stacks > Orthogonal Views ] or [Ctrl-Shift-H].
• Explore the effect of wrong calibration:
• Open image properties: [ Image > Properties… ] or [Ctrl-Shift-P].
• Change all dimensions to `1 pixel`.
• Observe that the cell now appears as an oval in the `Orthogonal Views`.
• Reopen the image in order to have the correct calibration.

Napari GUI/Python

• Open the 3D image xyz_8bit__chromsomes.tif by dragging and dropping the file in a Napari viewer.
• The data shows chromosomes wrapped around a nucleus, but it does not appear spherical.
• View the data in XY, YZ, and XZ by rotating the stack (third button in the bottom left corner) and by activating the 3D-view option (second button in the bottom left corner).
• Observe that the image is not calibrated, because the XZ and YZ views are deformed and the chromosomes are wrapped around a nucleus that should be spherical but does not appear so in the image.
• To take the calibration into account, open the console (first button in the bottom left corner: `>_`) and type `viewer.layers[0].scale = (0.1500000, 0.0338542, 0.0338542)`. Observe the difference in the 3D view.

skimage napari

Explore dimensions in a 4D image (3D + channels)

• Open the 3D multi-channel image xyzc_8bit_beads_p_open.tif.
• Explore different options for rendering the channel dimension (e.g. gray-scale, color, composite, different layers).
• Check the properties of this image. Are the XYZ dimensions isotropic or anisotropic?

Show activity for:

ImageJ GUI

• Open the 3D multi-channel image xyzc_8bit_beads_p_open.tif.
• Use the sliders to explore different dimensions in the data, and observe that the image has 4 dimensions, X, Y, Z, and C (channel).
• Use [ Image > Adjust > Brightness/Contrast…] to adjust the display settings of the individual channels.
• Explore different viewing modes, using [ Image > Color > Channels Tool… ].
• Explore the voxel dimensions, using [ Image > Properties… ] or [Ctrl-Shift-P].
• Observe that the voxel dimensions are anisotropic.

Napari GUI/Python

• Open the 3D multi-channel image xyzc_8bit_beads_p_open.tif by dragging and dropping it in the Napari viewer.
• Note that the channel and z-dimension both appear as sliders.
• In order to see the different channels of the image simultaneously, the two channels have to be on different layers. One way to achieve this is the following:
1. Open the Napari console.
2. Observe the ‘shape’ of the data by typing `viewer.layers[0].data.shape`. You should get `(41, 2, 297, 284)`. This means that the dimensions in the data are in the order ZCYX since we have two channels.
3. Swap the Z and C dimension with numpy:
• `import numpy as np`
• `viewer.layers[0].data = np.transpose(viewer.layers[0].data, (1, 0, 2, 3))`
• You can now split the stack by right-mouse clicking on the image layer and selecting ‘Split Stack’.
• Observe that you now have two layers, one for each channel, that have distinct colors in the Napari viewer.
4. The image is not calibrated. To take the calibration into account, you can specify it like this: `viewer.layers[0].scale = (0.1000000, 0.0222057, 0.0222057)` and `viewer.layers[1].scale = (0.1000000, 0.0222057, 0.0222057)`. It is also possible to use the AICSImage plugin to read in the calibration metadata (see the Spatial Calibration module).

skimage napari

Explore dimensions in a 4D image (3D + time)

• Open image: xyzt_8bit__starfish_chromosomes.tif
• Inspect the different axis and the values they can take
• If required change the axes (e.g. swap `Z` and `T`) so that it correctly renders

Show activity for:

skimage napari

Explore a 5D image (3D + channels + time)

Explore various visualization options for the different dimensions.

Examples images:

Show activity for:

ImageJ GUI

• Open xyzct_16bit__metaphase_eb3_cenpa.tif.
• Use the sliders to explore different dimensions in the data, and observe that the image has 5 dimensions, X, Y, Z, C (channel), and T (Time).
• Use [ Image > Adjust > Brightness/Contrast…] to adjust the display settings of the individual channels.
• Explore different viewing modes, using [ Image > Color > Channels Tool… ].

Napari GUI/Python

• Open xyzct_16bit__metaphase_eb3_cenpa.tif by dragging and dropping it in Napari.
• The channel, Z, and time dimensions appear as sliders
• Check the order of the dimensions by opening the Napari console and typing `viewer.layers[0].data.shape`. This should return `(6, 151, 2, 84, 79)`, corresponding to time, Z, channel, Y, X, respectively.
• Note that the display of the dimensions in Napari is in reverse order: by default, the last two dimensions (typically Y and X) form a 2D image on the screen, and each additional dimension appears as a slider, in the same reverse order (channel, Z, time) from top to bottom.
• Note that if you switch display mode from 2D to 3D, the channel dimension is the first in line, so this becomes the third dimension in the viewer (and not the Z dimension, as you might have expected).
• In order to see the data in 3D space, we need to reorder the image dimensions, such that the Z dimension becomes the third-last dimension. Do the following in the console:
• `import numpy as np`
• Reorder the dimensions: `viewer.layers[0].data = np.transpose(viewer.layers[0].data, (2, 0, 1, 3, 4))`. This will change the order of the dimensions to channel, time, Z, Y, X.
• Note that if you change the viewer from 2D to 3D, you will now see the image in 3D space.
• In order to see the two channels simultaneously in different colors, we need to split the stack. Right mouse click on the layer and selecting ‘Split Stack’. You can also do so with the commands `viewer.add_image(viewer.layers[0].data[0])` and `viewer.add_image(viewer.layers[0].data[1])`.

Assessment

Fill in the blanks with slices, anisotropic, deformed, stack

1. A set of 2D ____ placed on top of each other form a 3D ____.
2. An ____ voxel size can cause the image to appear ____ when viewing it at an angle.

Solution

1. 2D slices placed on top of each other from a 3D stack.
2. An anisotropic voxel size can cause the image to appear deformed when viewing at a certain angle.

True or False

1. Isotropic image data has voxels of equal XYZ dimensions.
2. Image data can have up to 5 dimensions.

Solution

1. True
2. False - While the dimensions X, Y, Z, channel and time are the most common dimensions, there is nothing that prevents an image from having additional dimensions. In medical imaging, additional dimensions can be used to hold information such as the age of a patient, or physiological parameters like heart rate. In astronomy, images of the universe may also have additional dimensions, such as light polarization.

Follow-up material

Recommended follow-up modules:

Learn more: