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 a 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
Image thresholding
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.
Activities
- Open the binary image xy_8bit_binary__nuclei.tif.
- Discuss the image data type and the pixel values.
- Open the image xy_8bit__two_cells.tif and binarize it by applying a manually defined threshold.
Show activity for:
ImageJ GUI
- Inspect ImageJ binary image
- Open xy_8bit_binary__nuclei.tif
- Discuss data-type and inspect pixel values
- Data-type: unsigned 8-bit, values: 0 and 255
- Inspect MATLAB binary image
- Open xy_8bit_binary__two_cells_matlabstyle.tif
- Discuss data-type and inspect pixel values
- Data-type: unsigned 8-bit, values: 0 and 1
- Find/apply a threshold
- Open xy_8bit__two_cells.tif
- 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]
- [ Image > Adjust > Manual Threshold… ]
Lower threshold level
which is the value that you observed in the aforementioned step that would separate foreground and backgroundUpper threshold level
can be set to the maximum bit depth (in this case 255)- Press OK, this will produce an overlaid image where you can see the regions above the threshold in red.
- Note: The image is not binary yet (check the pixel values)!
- Set binary options: [Process > Binary > Options ..] [X] Black background
- [Process > Binary > Convert to Mask], now the image is binary.
- Open xy_8bit__two_cells.tif
- Repeat above applying a higher threshold so that only the high intensity level nucleus becomes foreground
ImageJ Macro
ImageJ Jython
MATLAB
%These matlab scripts illustrate separating the foreground from the %background using a threshold value provided by the user %Prompt user for a threshold value thres_val = input('Enter a threshold value: '); % Prompt user to choose an image, e.g. xy_8bit__two_cells.tif [file, path] = uigetfile("*.tif"); %Read input image in_image = imread(fullfile(path, file)); %display input image figure; imagesc(in_image); %Binarize input image with the threshold valuein_image bin_image = uint8(in_image>= thres_val); % Display binary image figure; imagesc(bin_image);
KNIME
Python
Exercises
Perform one of the following exercises.
Show exercise/solution for:ImageJ GUI
Open image xy_8bit__PCNA.tif and
- Find a threshold value so that there are 2 foreground nuclei.
- Find a threshold value so that only the bright dots remain
- Find threshold interval so that only the boundary of the nuclei remains.
Solution
[File > Open…] xy_8bit__PCNA.tif
[Analyze > Plot Profile ] or [Ctrl-K]
- Lower threshold, i.e. at about 5
- Lower threshold at about 44
- Lower threshold at about 4, upper threshold at about 4-5
ImageJ Macro
Open image xy_8bit__PCNA.tif and
- Find a threshold value so that there are 2 foreground nuclei.
- Find a threshold value so that only the bright dots remain
- Find threshold interval so that only the boundary of the nuclei remains.
Solution
open("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__PCNA.tif") selectWindow("xy_8bit__PCNA.tif"); run("Duplicate...", "title=[2 nuclei]"); selectWindow("xy_8bit__PCNA.tif"); run("Duplicate...", "title=[boundary]"); selectWindow("xy_8bit__PCNA.tif"); run("Duplicate...", "title=[dots]"); selectWindow("2 nuclei"); setThreshold(5, 255); setOption("BlackBackground", true); run("Convert to Mask"); selectWindow("boundary"); setThreshold(4, 4); setOption("BlackBackground", true); run("Convert to Mask"); selectWindow("dots"); setThreshold(44, 255); setOption("BlackBackground", true); run("Convert to Mask");
ImageJ Jython
Open image xy_8bit__PCNA.tif and
- Find a threshold value so that there are 2 foreground nuclei.
- Find a threshold value so that only the bright dots remain
- Find threshold interval so that only the boundary of the nuclei remains.
Solution
from ij import IJ, ImagePlus from ij.plugin import Thresholder # image is xy_8bit_PCNA.tif inputImage = IJ.openImage("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__PCNA.tif") IJ.setRawThreshold(inputImage, 5, 255, None) binaryImage = ImagePlus('Binary image 2 nuclei', Thresholder.createMask(inputImage)) binaryImage.show() IJ.setRawThreshold(inputImage, 4, 4, None) binaryImage_boundary = ImagePlus('Binary boundary', Thresholder.createMask(inputImage)) binaryImage_boundary.show() IJ.setRawThreshold(inputImage, 44, 255, None) binaryImage_brightdots = ImagePlus('Binary bright dots', Thresholder.createMask(inputImage)) binaryImage_brightdots.show()
Solution with input parameters
#@ Integer (label="Lower threshold") thr1 #@ Integer (label="Upper threshold") thr2 from ij import IJ, ImagePlus from ij.plugin import Thresholder # image is xy_8bit_PCNA.tif should be already open inputImage = IJ.openImage("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__PCNA.tif") IJ.setRawThreshold(inputImage, thr1, thr2, None) binaryImage = ImagePlus('Thresholded image', Thresholder.createMask(inputImage)) binaryImage.show()
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
Follow-up material
Recommended follow-up modules:
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