1.3 Tutorial 1: How to display a stack of 2D tif files as a 3D image

This is a tutorial for learning how to use Visilog to import a 3D image.

Scripting:
To play this tutorial, first, download the 3D demo package with this link, and uncompress it on your Visilog Directory.
You have on ./images/3D/foam_files/ directory, a stack of 2D images.
Load the VBA project ./VBA/Samples/demo3d/Demo3d.vba.
You can find the script of this tutorial on the module CreateIm6File
You must have the 3D module to use this script

To import this stack as a 3D image, use the multifile2im6 command

Figure 1: Multifile2im6 command (on Miscellaneous|Image Editing)

To select the stack of images on the input parameter, use left click on the edit area of this parameter to open a Filer. With this filer, select one file of the stack.

Figure 2: Filer to select the stack

For the output image,

Then you can use all Visilog commands from a memory image or a disk image indifferently. The interest of a disk image is to be able to work with image bigger than the memory available.

Figure 3: The 3D loaded image

1.4 Tutorial 2: basics - measuring a catalyst

This is a step-by-step tutorial for learning how to use Visilog, through a specific example illustrating some of the most typical manipulations. In this example we compute the distribution of distances from a catalyst to object edges. This particular image analysis process can be easily transposed to other cases.

To follow this tutorial you should be familiar with the basic concepts of Visilog. In particular you should be able to load files, to interact with the 3D viewer, and to link different commands.

Scripting
To play this tutorial, first, download the 3D demo package with this link, and uncompress it on your Visilog Directory.
Load the VBA project ./VBA/Samples/demo3d/Demo3d.vba.
You can find the script of this tutorial on the module Catalyst
You must have the 3D module to use this script

The 3D image used in this example is acquired by microtomography: an almost spherical support contains catalyst and pores. The catalyst appears with dark levels in the image (low intensity voxels). The pores and background appear with luminous levels (high intensity voxels). Intermediary grey levels corespond to the support.

Figure 4: Microtomography image of catalyst and pores

The goal of this example is to get a distribution of distances between the catalyst voxels and the background. The algorithm may be devided in 3 steps :

  1. object detection (mask of the object)
  2. distance map of the catalyst
  3. distribution of the catalyst distances

Loading data
Start by loading Catalyst.im6 from your disk ( ./images/3D/Catalyst.im6)

Figure 5: Visilog Filer with preview window

This image is stored using Visilog .im6 format. However Visilog can be used with any images supported by amira.

When loading Visilog files, datas stay on disk. In this case, the Visilog algorithms will require only some slices to be loaded in memory: this raises most limitation on the size of the 3D images that can be processed. However, if you have enough memory on your system, loading image fully in memory allows much faster processing. For these tutorial examples you can choose to load the data in memory.
To do that, use the copy command to load image on memory.

Figure 6: Load image on memory

Use the XoY button to display slice image along Z axis:

Figure 7: The initial image

You can now start the first step for detection of the object by thresholding then closing.

Open the Visilog Processing dialog.

The processing dialog contains:

In the command tree, select the Thresholding type folder and choose Binarisation group. The choices area shows the set of commands belonging to Binarisation group. Alternatively to using the command tree, you can enter directly the command name (I_threshold) in the command search text area, which supports automatic completion.

Getting more help
Once a command is selected in Visilog Processing panel, pressing F1 key will display the corresponding reference documentation for this command with your pdf viewer. Both user's guide and reference documentation are located in the doc subdirectory in Visilog installation directory.

Thresholding
The main difficulty of this step comes from the very similar intensities between background and pores. Thresholding the image between 0 and 225 gives a binary image where:

Select the threshold parameter in the pipeline area then change the threshold values to the range 0-225 in the choices area. Press the Go button to start the command. You will see this dialog:

Figure 8: I_threshold command

Interactive commands
Some Visilog commands support interactive input (name starting by I_). Here you can modify interactively the threshold values, with immediate visual feedback. When you are done, or as you already entered the appropriate values in the processing panel, you can press OK.

The I_threshold command has created a binary image. According to Visilog terminology a binary image can be seen as an image label with only interior and exterior material. For binary images Visilog displays the pixels of intensity 1 with a blue color. The command displays automaticaly the resulting image, an appropriate LUT (lookup table) will be selected by default.

Figure 9: Material binary image
Closing object
In order to detect the object you can now apply morphological operations. The mathematical morphology operators are transforms based on shape and size criteria. The morphological closing operator applied on a binary image gives another binary image where : Closing the material binary image fills the pores and thus gives a binary mask of the object.

Change on the process panel the input and command in the pipeline area. On input, use the binary image resulting from previous I_threshold command ans select the Closing command (in Morphology type, Open & Close group), then push Go button. Here is the result:

Figure 10: Object binary image

Distance map
The second step is to compute a distance map of the catalyst. Applying the distance algorithm on a binary image gives a grey level image where each voxel intensity represents the minimal distance in voxels from the object boundary. For a given voxel intensity of the object distance map :

You can now apply the distance map command distxxx to the previous result image, leaving the default parameters for the command. Then Visilog displays the object's distance map.

Figure 11: Object distance map

Then thresholding the initial image between 0 and 100 gives a binary image where:

Figure 12: Catalyst binary image
Masking
For computing obtaining the distance map of the catalyst we will used masking. The mask operation : Masking the distance map image by the catalyst image gives a grey image where:

Apply the mask command to the previous result image. Then Visilog displays the catalyst distance map. .

Figure 13: Catalyst distance map
Measurement
Now we can get the distribution of the distances.

For consistent results, we have to consider the voxel calibration: multiplying the distance image by the voxel size converts the image intensities in the metric system (micrometers). Use the Visilog command multiply with second input set to 5 (assuming a voxel size of 5 micrometers). Then retrieve the maximum value, either with Info port dislpayed in Properties panel when selecting the image icon in the pool, or by using the Visilog extrema command.

Then the histogram command histclas can compute and plot for each grey level i, the number of voxels at intensity i. The number of points per each level is graphed as an histogram. Applying histogram on the catalyst distance map plots a graphic that represents the number of catalyst voxels located at a given distance from the object envelope.

Figure 14: Distribution of the distances

1.5 Tutorial 3: separating, measuring and reconstructing individual objects - pores in foam

The 3D image used in this example was generated using data from several slices of oil. This demonstration illustrates the use of automatic segmentation to extract bubbles. The aim is to isolate and quantify these bubbles.

Scripting
To play this tutorial, first, download the 3D demo package with this link, and uncompress it on your Visilog Directory.
Load the VBA project ./VBA/Samples/demo3d/Demo3d.vba.
You can find the script of this tutorial on the module SeparateFoam
You must have the 3D module to use this script

Loading data
Start by loading foam.im6 from your disk ( ./images/3D/foam.im6)

Automated segmentation with separation Here is the result of an automated segmentation based on the watershed algorithm, which can produce better segmentation than the binseparate command:

You can see the sequence of used commands :

In addition user can create an image of the bubble boundaries by using erode command and substract, then convert it to a label.

Measurements
Then, you could, for instance, measure the volume for each individual region unsing the I_analyse command.
You will see analysis ont the result viewer.


Measurement results : individual analysis with volume for each region.

Interactive selection
Generating an individual analysis from the result image allows an interaction between the 3D viewer and the Result Viewer in order to locate each object. Selecting a cell in the spreadsheet displayed by Visilog analysis viewer will move a point dragger in the 3D view to the corresponding object location.

Custom measures
Visilog offers powerful ways to define custom measures results. See Visilog user's guide and reference guide for details.

1.6 Tutorial 4: further image analysis - Average diameters distribution of pores in foam

This example shows how to compute the distribution of diameters of pores in a foam sample.

Scripting
To play this tutorial, first, download the 3D demo package with this link, and uncompress it on your Visilog Directory.
Load the VBA project ./VBA/Samples/demo3d/Demo3d.vba.
You can find the script of this tutorial on the module DiamDistribution
You must have the 3D module to use this script

Loading data
Start by loading FoamPoro.im6 from your disk ( ./images/3D/FoamPoro.im6)

The image used in this example is also acquired by microtomography. It represents foam that is constituted by material and pores Pores appear with dark levels in the image (low intensity voxels). Material appears with luminous levels (high intensity voxels)

Figure 15: Microtomography image of foam pores

The algorithm may be devided in 3 steps :

  1. pores detection
  2. pores post-processing
  3. distribution of the pores diameters

Figure 16: Initial image

First step : Pores detection
Thresholding the image between 0 and 50 gives a binary image where :

Figure 17: Pores image

2nd step : Pores post-processing
The morphological opening operator applied on a binary image gives another binary image where :

Applying morphological opening on the pores image gives a filtered image where noise and artefacts are reduced.

Figure 18: Filtered pores images

The binseparate command detects surfaces that separate agglomerated particules. These surfaces are subtracted from the initial image. On a bidimensional image :

Figure 19: Pores post-processing

binseparate is used with parameter "Depth of valley" = 1

Figure 20: Separated pores image

3rd step : distribution of the pores diameters
For a given particle, the equivalent diameter measure computes the diameter of the spherical particle of same volume. So the equivalent diameter is given by the following formula :

Visilog allows to compute a set of measures for each particle of a 3D image Once the individual analysis performed, an histogram of a given measure may be plot in order to get a representation of the measure distribution.

Figure 21: Distribution of the equivalent diameters

1.7 Tutorial 5: further image analysis - Average thickness of material in foam

This example shows how to compute the thickness of material in a foam sample.

Scripting
To play this tutorial, first, download the 3D demo package with this link, and uncompress it on your Visilog Directory.
You have on ./images/3D/foam_files/ directory, a stack of 2D images.
Load the VBA project ./VBA/Samples/demo3d/Demo3d.vba.
You can find the script of this tutorial on the module AverageThickness
You must have the 3D module to use this script

Loading data
Start by loading FoamPoro.im6 from your disk ( ./images/3D/FoamPoro.im6).

The image used in this example is acquired by microtomography. It represents foam that is constituted by material and pores. Pores appear with dark levels in the image (low intensity voxels). Material appears with luminous levels (high intensity voxels).

Figure 22: Microtomography image of foam pores

The algorithm may be devided in 4 steps :

  1. porosity detection
  2. detection of the separation surfaces
  3. distance map of the material
  4. calculation of the material average thickness

First step : porosity detection
In a same way as in previous example, let's get the pores binary image, using thresholding and morphological opening operators.

Figure 23: Binary image of porosity

2nd step : detection of the separation surfaces
The goal of this step is to detect surfaces that cross the material at an equidistance from two pores. SKIZ (SKeleton by Influence Zones) operation :

Figure 24: SKIZ image

The logical_not operation inverts the levels of a binary image. Applying logical_not on a SKIZ image gives a binary image where :

So SKIZ and logical_not combination provides a binary image of surfaces that separate pores through the material.

Figure 25: Separation surfaces image

3rd step : distance map of the material
Applying logical_not on the binary image of porosity gives a binary image where :

Figure 26: Material binary image

The distance algorithm applied on a binary image gives a grey level image where each voxel intensity represents the minimal distance in voxels from the object boundary. For a given voxel intensity :

Figure 27: Distance map of material
The mask operation: Masking the distance map image by the separation surfaces image gives a grey image where:

Figure 28: Distance map of the separation surfaces

4th step : calculation of the material average thickness

Then the average thickness of the material is given by the following formula :

Where :

The volume3D command gives the sum of the voxels intensities in a 3D image.

So :

Histogram of thickness

To compute the histogram of thickness you must first multiply the image of distance map of the separation surfaces by 2 * calibration.
To do that, use the multiply command.
Then use histclass command to compute and display the histogram of thickness

Figure 29: Histogram of thickness

1.8 Tutorial 6: further image analysis - Wrapping

This example shows how to set background and pores to different grey level values in order to be able to analyse pores later on.

Scripting
To play this tutorial, first, download the 3D demo package with this link, and uncompress it on your Visilog Directory.
You have on ./images/3D/foam_files/ directory, a stack of 2D images.
Load the VBA project ./VBA/Samples/demo3d/Demo3d.vba.
You can find the script of this tutorial on the module Wrapping
You must have the 3D module to use this script

Loading data
Start by loading Corail.im6 from your disk ( ./images/3D/Corail.im6).

The image used in this example is acquired by medical scanner. It represents coral that is constituted by material and pores. Pores appear with dark levels in the image (low intensity voxels). Material appears with luminous levels (high intensity voxels). In order to analyse inside pores structure, we have to set the background level to another value.

Figure 30: Medical scanner image of coral

The algorithm may be devided in 4 steps :

  1. coral detection
  2. numerical filtering to remove small elements
  3. "spherical" closing and holes filling to get a full coral mask
  4. use of basic operations to change the background level

First step : coral detection
In a same way as in previous example, let's get the material binary image, using threshold.

Figure 31: Binary image of coral

2nd step : numerical filtering to remove small elements
The goal of this step is to remove binary elements whose volume is lower than the objects' mean volume.

Figure 32: Filtered coral image

3rd step : "spherical" closing and holes filling
The purpose of this step is to get round mask of the coral. If the closing is made using the "classical" closing command then the resulting voxels aspect will be cubic. But if you use the closing operation considering different neighbourhood, the result will have a spherical aspect. This way to proceed works with all morpholigical operations as shown on the following figure considering dialtion.

Figure 33: Voxel aspect resulting from either a "classical" or a "spherical" dilation

For this image, a closing using a 6 size structuring element is big enough to connect bordering pores.
Thus this closing is made using the following steps :

Finally, to get a full mask of the coral, we use the hole_fill command considering 26 neighbours.

Figure 34: Hole filled coral image

4th step : use of basic operations to change the background level

Figure 35: Before and after wrapping coral image

Convex hull can be extracted thanks to the following steps :
We can now extract coral's pores following these steps :

Figure 36: Convex hull and pores image