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Matlab | Erosion of an Image

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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

In this article, we will be discussing Erosion.

Erosion:

  • Erosion shrink-ens the image pixels i.e. it is used for shrinking of element A by using element B.
  • Erosion removes pixels on object boundaries.:
  • The value of the output pixel is the minimum value of all the pixels in the neighborhood. A pixel is set to 0 if any of the neighboring pixels have the value 0.

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 0, then change the value of that pixel to 0.

Example: 

MATLAB




% Matlab code for Erosion
% 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)=min(nh(:));     
    end
end
 
imshow(In);


Output:
 

figure: Input image

figure: Output Image

Let’s take another image to perform Erosion and here we use different MATLAB functions.

Syntax:

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

Example:

Matlab




% MATLAB code for Erison
% read the image.
k=imread("erosion.png");
 
%define the structuring element.
SE=strel('disk',5);
 
%apply the erosion operation.
e=imerode(k,SE);
 
%display all the images.
imtool(k,[]);
imtool(e,[]);
 
%see the effective reduction in org,image
imtool(k-e,[]);


Output:

Figure: Left: Original image, Right: Eroded image

Figure: Output image

Code explanation:

  • k=imread(“erosion_exmp.png”); this line reads the image.
  • SE=strel(‘disk’,5); this line defines the structuring element.
  • e=imerode(k,SE); this line applies the erosion operation.
  • imtool(k,[]); this line displays the original image.
  • imtool(e,[]); this line displays the eroded image.
  • imtool(k-e,[]); this line shows the effective reduction in original image.

The last image shows the extent to which the original image got eroded. 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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Last Updated : 30 Nov, 2021
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