Matlab | Erosion of an Image
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 imageI=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(:)); endendimshow(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,imageimtool(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.