ImageJ tutorial
showing the operations
needed to calculate air-filled
porosity for an example soil
column
Reading/Importing data
If you want to load slices in a stack, import your data as
an Image Sequence
Open one image from the folder where your data is stored.
Then this window appears, showing the number of images/files in
your folder.
All the slices are then loaded as a
stack!!!
Adjusting Brightness/Contrast
You can change the brightness and contrast to improve
your ability to see various features in the image. This
operation does not change the pixel information.
After adjustment it is easier to see pores, rocks, and soil
matrix, i.e., low and high density objects
The Filters
There are different filtering techniques, used for:
•Improving the visual quality of the image
•Extracting some attribute of the image (contour…)
These filters change the grey value of a pixel according to
the value of its neighbors (involves varying degree of smoothing)
Plot profile
1.- Draw a line on you image and select
Analyse Plot Profile
2. - You have plot a
profile of CT values
Histogram for one slice
There are different ways to generate histograms in ImageJ
Realigning a stack
We need to reorient the sample to work with it in the horizontal
(z-)dimension (coronal view).
•Save this coronal view by using the ”Output” button
•Because the pixel size varies in the 3 dimensions (it is stretched vertically in
the previous slide), scaling is needed.
•Go to ImageScale… and fill as shown above. A new image is then
opened with the dimension: 300X212pixels (and the image is no longer
stretched)
To measure sample porosity, we need to be sure we are inside the
actual soil, so we need to crop away some slices at the top and
bottom of the column, with the plug-in ”Stacks-Reducing” …
Remove slices : from 0 to 97 and from 419 to 512 for this example.
Select your Region Of Interest
• Area Selection Tools: The first four buttons on the tool bar
allow you to surround an area on the image with a rectangle,
oval, polygon or freehand shape. After selection, these areas
may be altered, analyzed, copied, etc. using the menu
commands. Note that the status bar, below the tool bar, gives
information such as the coordinates (xx, yy) of the selection
on the frame.
• So select the oval tool, and surround the area corresponding
ONLY to the soil without the aluminum core : That is your
Region Of Interest (ROI)
Clear Outside. This technique is useful for clearing
extraneous objects near an area of interest. In our
case, we want to crop away to Aluminium sample
holder.
The ROI of the soil column wihtout the Alu sample holder.
Please keep the position and the dimension of your ROI!
In macro language it is written, ex: makeOval(109, 29, 314,
314);
All the slices in the stack can be processed this way then
Thresholding
Picking a good
threshold value
can be tricky!!
We will elaborate
on this in the
course
•To measure porosity we need to separate the soil pores from the matrix
•Thresholding is a segmentation techniques used to detect objects of a
certain intensity (CT value), - in this case, the pores. To perform this
operation go through the ImageAdjustThreshold menu.
•Transform your Image in 8bits image : ImageType8bits
•Then, invert the image color to show pores in white!
Histogram for all the stack
•
•
•
Run the macro called ”macrobien”
The macro will count the number of
white pixels (pores) in each slide, so
you have to retain ONLY the pores in
white !
If the pores are not white:
– reselect the ROI (you know its
dimension and position from before),
– and use invert color tool INSIDE your
ROI: Ctrl+shift+I or EditInvert
– Then invert the color BUT on all the
picture, only your pores are white
•
•
You have also to deal with the
background color:
EditOptionsColors
In Excel, plot the curve to arrive at a
vertical profile of soil porosity:
slicenumber=f(whitepixelnumber)
Calculation of the porosity
• The number of white (pore) pixels for each slice
• The dimension of your ROI (then you know the volume
of the new core)
• So we sum up the number of white pixels for the slices
relative to the total number of pixels in the volume we’re
working with (pi*r*r*number of slices) for the soil column
used here
• Then you can calculate the total air-filled porosity
3D Visualisation of pores