Amira 3D Pro, Avizo 3D Pro
Context
When working on 3D data with various particles, signals, cells or other small distinct elements, we might be interested in visualizing them as a point cloud with each point being located at the original element's barycenter. It is then possible to represent the density associated to each element visually by using either the size of the points or their color.
Prerequisites
An Amira-Avizo 3D Pro license, or any license which provides access to the features of 3D Pro, is required to perform the following steps.
Process
1. Load and label your data
1.1. Load your data as you would with any other workflow using the Open Data... button.
1.2. If your data is not segmented yet, perform segmentation on the data using an appropriate method for your sample.
Various methods are presented in the Image Segmentation Tutorials within the in-app User's Guide.
2. Analyze your data
2.1. Right-click on your label data and create a Measure And Analyze -> Individual Measures -> Label Analysis module.
This module will be automatically attached to your data and configured with the necessary parameters.
2.2. Run this new module using the Apply button.
A table will be displayed with various attributes computed on your segmented data. This table is also visible as a new data type in the Project View.
If your input data was a binary segmentation, a multi-label segmentation will also be generated as output.
3. Generate a point cloud
3.1. Right-click on the table data created at step 2.2., and create a Convert -> Spreadsheet to Point Cloud module.
This module will be automatically attached to your data and configured with the necessary parameters.
Should you have any doubt about the parameters, the output should be set to Point Cloud and the coordinates to the XYZ barycenters.
3.2. Run this new module using the Apply button.
This will generate a new data corresponding to a point cloud representation.
3.3. If you want to display your point cloud at this stage, you can right-click on the data and create a Display -> Point Cloud View module.
This will display a 3D visualization of your point cloud. If the points do not seem visible, you can raise the Point Size or Sphere Scale parameter to a higher value.
4. Density representation
4.1. Right-click on the point cloud created at step 3.2., and create a Measure and Analyze -> Point Cloud Density module.
This module will be automatically attached to your data and configured with the necessary parameters.
4.2. Run this new module using the Apply button.
This will generate a new data which is an equivalent point cloud with the additional density property computed and associated to each point.
4.3. To display your point cloud, you can right-click on the data and create a Display -> Point Cloud View module.
This will display a 3D visualization of your point cloud. If the points do not seem visible, you can raise the Point Size or Sphere Scale parameter to a higher value.
4.4. To scale the points according to their density, modify the following properties of the Point Cloud View.
4.4.1. In the Options port, enable the Spheres display.
4.4.2. Enable the Scale Spheres port.
4.4.3. In the Scale Data port, set the scaling attribute to Density [d].
4.4.4. Adjust the Sphere Scale slider as needed until the spheres are at the desired size.
The value to use will depend on your data, its element density, and the units used.
If you are still unable to observe the spheres, that means they are significantly smaller or larger than the used scaling factor. In this case, adjust the minimum and maximum scale factor using the "..." button.
4.5. To colorize the points according to their density, modify the following properties of the Point Cloud View.
4.5.1. In the Color port, set the coloring attribute to Density [d].
4.5.2. In the Colormap port, click on Edit and select a color mapping with a gradient of colors such as Physics.icol.
4.5.3. In the Colormap port, click on Edit and then on Adjust Range to adapt the coloring to the range of density values present in the point cloud.
It is possible to combine the size representation (4.4) and the color representation (4.5) in a single Point Cloud View.