Binarization

Prerequisites

Before starting this lesson, you should be familiar with:

• Basic properties of images and pixels

• Data types (unsigned 8-bit)

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 distinguish foreground and background pixels

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

Example figure

Activity

• 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.

ImageJ GUI

• Open a binary image xy_8bit_binary__nuclei.tif and discuss data-type (data-type is unsigned 8-bit values, minimal value is 0, maximal value is 255)
• Open binary file from MATLAB xy_8bit_binary__two_cells_matlabstyle.tif lower and highest value (data-type is unsigned 8-bit values, minimal value is 0, maximal value is 255)
• Verify that binary options are set up correctly.
  • [Process > Binary > Options ..] [x] Black background
• [ File > Open… ] xy_8bit__two_cells.tif
• [ Image > Adjust > Threshold… ] or [Ctrl-Shift-T], vary lower threshold and discuss what happens to preview image
• Choose a value so that both cells are foreground, e.g. 49-255
• Press [Apply] discuss resulting image
• [ File > Open… ] xy_8bit__two_cells.tif
• Apply a higher threshold so that only the high intensity level nuclei remains

ImageJ Macro

open("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__two_cells.tif");
setThreshold(49, 255);
setOption("BlackBackground", true);
run("Convert to Mask");

open("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__two_cells.tif");
setThreshold(88, 255);
setOption("BlackBackground", true);
run("Convert to Mask");

ImageJ Jython

from ij import IJ, ImagePlus
from ij.plugin import Thresholder

inputImage = IJ.openImage("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__two_cells.tif")
IJ.setRawThreshold(inputImage, 44, 255, None)
binaryImage = ImagePlus('Binary image 2 nuclei', Thresholder.createMask(inputImage))
binaryImage.show()
IJ.setRawThreshold(inputImage, 88, 255, None)
binaryImage_1 = ImagePlus('Binary image 1 nuclei', Thresholder.createMask(inputImage))
binaryImage_1.show()

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

import numpy as np
import matplotlib.pyplot as plt
from skimage import io

# load the image from file
image_file = "/image_data/xy_8bit__two_cells.tif"
image = io.imread(image_file)

# binarize the image, so that all values larger than the threshold are foreground
threshold_value = 60
binarized = image > threshold_value

# display the original and the binarized image
fig, ax = plt.subplots(2)
ax[0].imshow(image)
ax[0].set_title("Image")
ax[1].imshow(binarized)
ax[1].set_title("Binarized")

# For associated course material in jupyter, go to https://nbviewer.jupyter.org/github/embl-bio-it/image-analysis-with-python/blob/carpentry/image-analysis-session/image-binarization.ipynb#Image-Binarization
# You can also spin up an interactive binder session: https://gke.mybinder.org/v2/gh/embl-bio-it/image-analysis-with-python/carpentry?filepath=image-analysis-session/image-binarization.ipynb

Exercises

ImageJ GUI

Open image xy_8bit__PCNA.tif and

1. Find a threshold value so that there are 2 foreground nuclei.
2. Find a threshold value so that only the bright dots remain
3. Find threshold interval so that only the boundary of the nuclei remains.

Solution

[File > Open…] xy_8bit__PCNA.tif [Image > Adjust > Threshold…] or [Ctrl-Shift-T] opens the threshold menu

1. Lower threshold, i.e. upper slider, at about 5
2. Lower threshold at about 44
3. Lower threshold at about 4, upper threshold at about 4-5

ImageJ Macro

Open image xy_8bit__PCNA.tif and

1. Find a threshold value so that there are 2 foreground nuclei.
2. Find a threshold value so that only the bright dots remain
3. 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

1. Find a threshold value so that there are 2 foreground nuclei.
2. Find a threshold value so that only the bright dots remain
3. 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

We recommend reading these modules next:

• Automatic threshold for binarization

• Finding objects in a binary image

Learn more:

• Wikipedia: Binary image