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Matlab | Dilation of an Image

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  • Last Updated : 23 Mar, 2022
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Morphology is known as the broad set of image processing operations that process images based on shapes. It is also known as a tool used for extracting image components that are useful in the representation and description of region shape. 

The basic morphological operations are: 

  • Erosion 
  • Dilation
     

Dilation:

  • Dilation expands the image pixels i.e. it is used for expanding an element A by using structuring element B.
  • Dilation adds pixels to object boundaries.
  • The value of the output pixel is the maximum value of all the pixels in the neighborhood. A pixel is set to 1 if any of the neighboring pixels have the value 1.

Approach: 

  • Read the RGB image.
  • Using function im2bw(), convert the RGB image to a binary image.
  • Create a structuring element or you can use any predefined mask eg. special(‘sobel’).
  • Store the number of rows and columns in an array and loop through it.
  • Create a zero matrix of the size same as the size of our image.
  • Leaving the boundary pixels start moving the structuring element on the image and start comparing the pixel with the pixels present in the neighborhood.
  • If the value of the neighborhood pixel is 1, then change the value of that pixel to 1.

Example:

MATLAB




% MATLAB code for Dilation
% read image
 
I=imread('lenna.png');    
 
% convert to binary
I=im2bw(I); 
 
% create structuring element            
se=ones(5, 5);
 
% store number of rows in P and
% number of columns in Q.          
[P, Q]=size(se);
 
% create a zero matrix of size I.       
In=zeros(size(I, 1), size(I, 2));
 
for i=ceil(P/2):size(I, 1)-floor(P/2)
    for j=ceil(Q/2):size(I, 2)-floor(Q/2)
 
        % take all the neighbourhoods.
        on=I(i-floor(P/2):i+floor(P/2), j-floor(Q/2):j+floor(Q/2)); 
        
        % take logical se
        nh=on(logical(se));   
 
        % compare and take minimum value of the neighbor
        % and set the pixel value to that minimum value.   
        In(i, j)=max(nh(:));     
    end
end
 
imshow(In);

Output:
 

Figure: Input image

Figure: Output image

Let’s take another example for dilation.

Syntax:

  • imread() function is used to read the image.
  • strel() function is used to define the structuring element. We have chosen a disk-shaped SE, of radius 5.
  • imdialate() function is used to perform the dilation operation.
  • imtool() function is used to display the image.

Example:

Matlab




%  MATLAB code for Dilation
% read the image.
k=imread("dilation.png");
 
% define the structuring element.
SE=strel('disk',5);
 
% apply the dilation operation.
d=imdilate(k,SE);
 
%display all the images.
imtool(k,[]);
imtool(d,[]);
 
%see the effective expansion
% in original image
imtool(d-k,[]);

Output:

Figure: Left: Original image, Right: Dilated image

Figure: Expansion in the original image

Code Explanation: 

  • k=imread(“dilation_exmp.png”); this line reads the image.
  • SE=strel(‘disk’,5); this line defines the structuring element.
  • d=imdilate(k,SE); this line applies the dilation operation.
  • imtool(k,[]); this line displays the original image.
  • imtool(e,[]); this line displays the  dilated image.
  • imtool(d-k,[]); this line shows the effective expansion in original image.

The last image shows the extent to which the original image got dilated. We have used the Structuring element of disk-shaped and the image we used is also circular in shape. This gives us the very desired output to understand erosion.


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