In this post, we implement image thresholding in MATLAB. Thresholding is one of the simplest and most widely used segmentation techniques. It converts a grayscale image into a binary image by comparing each pixel’s intensity against a threshold value: pixels below the threshold are set to 0 (black) and pixels at or above the threshold are set to 255 (white). This cleanly separates foreground objects from the background.

MATLAB Code

% Image Thresholding in MATLAB
% Converts a grayscale image to binary using a user-supplied threshold.
% Pixels below the threshold -> 0 (black)
% Pixels >= threshold       -> 255 (white)

clc;        % Clear the command window
clear all;  % Clear all workspace variables

% --- Load image ---
% Place your image in the MATLAB working directory.
originalImage = imread('sample.jpg');       % Read the colour image
grayImage     = rgb2gray(originalImage);    % Convert to grayscale

imageSize = size(grayImage);  % [rows, cols]

% --- Get threshold from user ---
thresholdValue = input('Enter threshold value (0-255) : ');  % e.g. 128

% --- Apply threshold ---
binaryImage = grayImage;  % Copy to preserve border pixels

for i = 1 : imageSize(1)
    for j = 1 : imageSize(2)
        if grayImage(i, j)  black
        else
            binaryImage(i, j) = 255;  % At or above threshold -> white
        end
    end
end

% --- Display results ---
subplot(1, 2, 1);
imshow(grayImage);
title('Original Grayscale Image');

subplot(1, 2, 2);
imshow(binaryImage);
title(['Thresholded at ', num2str(thresholdValue)]);

How the Code Works

1. Image loading — The colour image is read with imread() and converted to an 8-bit grayscale matrix (values 0–255) using rgb2gray().
2. User threshold — input() prompts the user to enter an integer between 0 and 255. A value of 128 splits the intensity range exactly in half; lower values produce more white pixels (brighter threshold), higher values produce more black pixels (darker threshold).
3. Nested loop — Iterates over every pixel in the image. The if condition compares the grayscale value against the threshold and assigns either 0 or 255 to the output pixel, producing a binary (two-level) image.
4. num2str() — Converts the numeric threshold to a string so it can be embedded in the subplot title, making the figure self-documenting.
5. subplot(1, 2, k) — Displays the original and thresholded images side by side for a direct visual comparison.

Sample Input / Output

Enter threshold value (0-255) : 128

MATLAB opens a figure window showing the original grayscale image on the left and the thresholded binary image on the right, with the title “Thresholded at 128”.

Output Explanation

1. Original Grayscale — The input image with the full 0–255 intensity range.
2. Thresholded Image — A pure black-and-white image. Every pixel is either 0 (black) or 255 (white). Bright objects in the original (intensity ≥ 128) appear white; dark regions (intensity < 128) appear black. The result segments the image into two regions based solely on brightness.
3. Changing the threshold value changes which objects are segmented: a lower threshold (e.g. 64) retains more of the image as white, while a higher threshold (e.g. 200) keeps only the brightest objects.

See Also

• Implementing Edge Detection in MATLAB
• Implementation of Histogram Processing in MATLAB
• Implementing Digital Negative and Grayscale of Image in MATLAB
• Implementing HPF in MATLAB

Conclusion

Thresholding is the most fundamental image segmentation technique. While this program implements it using a simple loop for clarity, MATLAB provides the more concise expression binaryImage = grayImage >= thresholdValue which achieves the same result in a single vectorised line. For automatic threshold selection, MATLAB’s graythresh() function uses Otsu’s method to find the optimal threshold without user input, which is useful when processing large batches of images.