In this post, we implement gray-level slicing in MATLAB — a point processing technique used in digital image processing to highlight a specific range of gray levels while suppressing everything outside that range. The program reads a grayscale image, asks the user to specify a lower and upper gray-level boundary, and produces two sliced images: one that preserves the background and one that blacks it out.

MATLAB Code

% Gray-Level Slicing in MATLAB
% Highlights pixels within a user-defined gray-level range.
% Two sliced versions are produced:
%   b - background preserved (pixels below r1 unchanged)
%   c - background suppressed (only the range [r1,r2] is shown in white)

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

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

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

% --- User inputs ---
lowerLevel = input('Enter lower gray level : ');   % e.g. 20
upperLevel = input('Enter upper gray level : ');   % e.g. 50

% --- Slicing: preserve background ---
% Pixels above upperLevel are set to 255 (white); others stay the same.
slicedPreserve = grayImage;
for row = 1:imageSize(1)
    for col = 1:imageSize(2)
        if grayImage(row, col) > lowerLevel
            if grayImage(row, col) > upperLevel
                slicedPreserve(row, col) = 255;  % Highlight bright pixels
            end
        end
    end
end

% --- Slicing: suppress background ---
% Pixels inside [lowerLevel, upperLevel] -> white; outside -> black.
slicedSuppress = grayImage;
for row = 1:imageSize(1)
    for col = 1:imageSize(2)
        if grayImage(row, col) > lowerLevel
            if grayImage(row, col) > upperLevel
                slicedSuppress(row, col) = 255;  % In range -> white
            else
                slicedSuppress(row, col) = 0;    % Out of range -> black
            end
        end
    end
end

% --- Display all three images side by side ---
subplot(1, 3, 1);
imshow(grayImage);
title('Original Grayscale');

subplot(1, 3, 2);
imshow(slicedPreserve);
title('Sliced (Background Preserved)');

subplot(1, 3, 3);
imshow(slicedSuppress);
title('Sliced (Background Suppressed)');

How the Code Works

1. Image loading — imread('sample.jpg') reads the image from the working directory. rgb2gray() converts it to a single-channel (8-bit) grayscale matrix where each pixel value is in the range 0–255.
2. User-defined range — The user enters a lower bound (lowerLevel) and an upper bound (upperLevel) that define the gray-level band of interest.
3. Background-preserved slicing — The outer loop walks every pixel. If the gray value is above both thresholds, it is set to 255 (white). Pixels between 0 and lowerLevel are left unchanged. This preserves the scene context.
4. Background-suppressed slicing — The same scan is performed, but now pixels inside the range are set to 255 and pixels outside the range are forced to 0 (black). This isolates only the highlighted band.
5. Display — subplot(1, 3, k) shows all three images side by side so the effect of each slicing strategy is easy to compare.

Sample Input / Output

Enter lower gray level : 20
Enter upper gray level : 50

MATLAB opens a figure window showing three subplots: the original grayscale image, the background-preserved sliced image, and the background-suppressed sliced image.

Output Explanation

1. Original Grayscale — The image after rgb2gray(). Pixel values range from 0 (black) to 255 (white).
2. Background Preserved — Pixels with intensity above 50 are turned white. Pixels with intensity 0–20 are unchanged. The resulting image looks like the original but with bright areas bleached to pure white.
3. Background Suppressed — Only pixels in the range 20–50 remain visible (turned white); everything else becomes black. This produces a high-contrast image isolating the chosen gray-level band.

See Also

• Implementing Digital Negative and Grayscale of Image in MATLAB
• Implementation of Histogram Processing in MATLAB
• Implementing Edge Detection in MATLAB
• Program for Threshold in MATLAB

Conclusion

Gray-level slicing is a simple yet powerful point processing operation. By defining a brightness window, we can highlight features of interest — such as bones in an X-ray or specific tissue in an MRI — and either preserve or discard the surrounding context. This program demonstrates both strategies and provides a foundation for more advanced intensity transformation techniques in digital image processing.