Thresholding
Prerequisites
Before starting this lesson, you should be familiar with:
Learning Objectives
After completing this lesson, learners should be able to:
Describe the relationship between an intensity image and a derived binary image
Apply threshold to segment an image into foreground and background regions
Motivation
One strategy to detect objects or specific regions in images is to first distinguish so-called background pixels, which do not contain objects or interesting regions from foreground pixels, which mark the areas of interest. This process is called two class semantic segmentation and is often referred to as image binarization. The foreground regions can then be further processed, e.g. to detect objects or perform intensity measurements.
Concept map
Figure
Activities
Threshold bright and dim cells
- Open xy_8bit__two_cells.tif
- Visualise the image and inspect its value range using a histogram
- Threshold the image to segment both cells
- Threshold the image to only segment the brighter cell
Show activity for:
ImageJ GUI
- Find and apply a threshold
- Open xy_8bit__two_cells.tif
- Set binary options: [Process > Binary > Options ..]
- Black background
- Inspect pixel values to find a threshold separating fore- and background
- Hover over the image and observe the pixel values in ImageJ status bar
- Draw line profile and [Analyze > Plot Profile ] or [Ctrl-K]
- Inspect histogram using [Analyze > Histogram] or [Ctrl-H]
- If you do this for the whole image the background peak dominates the histogram
- Draw a ROI on the image such that there is 50/50 foreground and background
- Interactively find and apply a threshold
- [Image > Adjust > Threshold…] or [Ctrl-Shift-T]
- Dark Background
- Upper slider is lower threshold level, which is the value that you observed in the aforementioned step that would separate foreground and background
- Lower slide is the upper threshold level. This can be set to the maximum value of the bit depth (in this case 255)
- Press
Apply
to create a binary image- Appreciate that the binary image only has two values: 0 and 255
- Repeat the operation so that only the brightest cell remains visible
ImageJ Macro
ImageJ Jython
skimage napari
Galaxy
- Navigate to Galaxy
- Upload an image
- In the Tools panel on the left side, click
Upload Data
.- Click the
Paste/Fetch data
button.- Paste the URLs of the images : xy_8bit__two_cells.tif
- Click the
Start
button and wait for the upload to complete.- Once the upload is finished, click the
Close
button. The image will now be available in your Galaxy history.- Apply a threshhold
- In the Tools panel on the left side, search
Threshold image
.- Choose the tool named
Threshold image with scikit-image
, and click on it.- Select the image
xy_8bit__two_cells.tif
from theSelect image
dropdown list.- Select
Manual
from theThresholding method
dropdown list.- Set
Threshold value
to49
.- Toggle
Invert output labels
toYes
- Click the
Run Tool
button and wait for the job to finish (The job will turn green).- Click on the job in your Galaxy history to download the resulting image.
Apply various thresholds to one image
- Open xy_8bit__PCNA.tif
- Apply a threshold value such that there are two foreground nuclei
- Apply a higher threshold such that is only one foreground nucleus
- Apply an even higher threshold such that only the intra-nuclear speckles are foreground
- Optionally, also find a threshold interval so that only the boundaries of the nuclei remain
Show activity for:
ImageJ GUI
- [File > Open…] xy_8bit__PCNA.tif
- Use [Analyze > Plot Profile ]/[Ctrl-K] to identify possible threshold values
- Use [Image > Adjust > Threshold…] to create binary masks. Suggestion: Duplicate the image first , do not forget to press
Apply
Example solutions:
Lower threshold level
~ 5Lower threshold level
~ 44Lower threshold level
~ 4 andUpper threshold level
~ 4-5
ImageJ Macro
ImageJ Jython
ImageJ Jython + input parameters
skimage napari
Galaxy
- Navigate to Galaxy
- Upload an image
- In the Tools panel on the left side, click
Upload Data
.- Click the
Paste/Fetch data
button.- Paste the URLs of the images : xy_8bit__PCNA.tif
- Click the
Start
button and wait for the upload to complete.- Once the upload is finished, click the
Close
button. The image will now be available in your Galaxy history.- Apply a threshhold
- In the Tools panel on the left side, search
Threshold image
.- Choose the tool named
Threshold image with scikit-image
, and click on it.- Select the image
xy_8bit__PCNA.tif
from theSelect image
dropdown list.- Select
Manual
from theThresholding method
dropdown list.- Experience with different
Threshold value
, e.g.5
,44
,4.5
- Toggle
Invert output labels
toYes
- Click the
Run Tool
button and wait for the job to finish (The job will turn green).- Click on the job in your Galaxy history to download the resulting image.
Threshold based on noise measurement
Here we set the threshold value t
such that it is higher than the background intensity plus some noise level.
- Open the image xy_8bit__two_cells.tif
- Measure the mean
m
and standard deviations
of the intensity in a backgroun region. - Set the threshold as
t = m + N*s
choosing someN
larger than 1;N
effectively determines the statistical significance with which the values abovet
are foreground pixels.
Show activity for:
ImageJ GUI
- Open xy_8bit__two_cells.tif
- Draw a squared ROI
- [Analyze > Measure] or [Ctrl-M}
- Eventually add
[x] Mean gray value
and[x] Standard deviation
to your result table, [Results > Set Measurements]- Note the value of
Mean
andStdDev
and compute their sum- Choose a multiple of this sum for the threshold
- Test your choice [Image > Adjust > Threshold…]
- Press
Apply
to create a binary image
Assessment
Fill in the blanks
- Pixels in a binary image can have maximally ___ different values.
- If the threshold is larger than the maximal pixel value in the intensity image, all pixels in the binary image have a value of ___.
Solution
- Pixels in a binary image can have maximally 2 different values.
- If the threshold is larger than the maximal pixel value in the intensity image, all pixels in the binary image have a value of 0.
True or False
- There is only one correct threshold value in order to convert an intensity image into a binary image.
- Binary images are always unsigned 8-bit where the foreground is 255.
Solution
- There is only one correct threshold value in order to convert an intensity image into a binary image. False
- Binary images are always unsigned 8-bit where the foreground is 255. False
Explanations
A common algorithm for binarization is thresholding. A threshold value t
is chosen, either manually or automatically,
and all pixels with intensities below t
are set to 0, whereas pixels with intensities >= t
are set to the value for the foreground.
Depending on the software the foreground value can be different (e.g. 1 in MATLAB or 255 in ImageJ). At any pixel (x,y)
:
p_im(x,y) < t
->
p_bin(x,y) = 0
p_im(x,y) >= t
->
p_bin(x,y) = 1
where, p_im and p_bin are the intensity and binary images respectively.
It is also possible to define an interval of threshold values, i.e. a lower and upper threshold value. Pixels with intensity values within this interval belong to the foreground and vice versa.
Follow-up material
Recommended follow-up modules:
Learn more: