# Matlab | Dilation 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

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:

• 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:

• 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:

• 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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