Morphological operations in MATLAB - Mini Project: Build & Apply

Morphological Operations in MATLAB

📖 Scenario: You are working with a simple black and white image representing shapes. You want to clean up small noise and fill small holes using morphological operations.

🎯 Goal: Learn how to apply basic morphological operations like dilation and erosion on a binary image in MATLAB.

📋 What You'll Learn

Create a binary image matrix with specific pattern

Define a structuring element for morphological operations

Apply dilation and erosion using MATLAB functions

Display the original and processed images

💡 Why This Matters

🌍 Real World

Morphological operations help clean up images by removing noise or filling gaps, useful in medical imaging, document scanning, and object detection.

💼 Career

Understanding these operations is important for roles in image processing, computer vision, and machine learning where image data needs preprocessing.

1

Create the binary image matrix

Create a binary matrix called bw with size 7x7 where the center 3x3 block is 1s and the rest are 0s.

MATLAB

% Create the binary image matrix 'bw' with center 3x3 block as 1s
% Your code here

Hint

Use zeros(7,7) to create a matrix of zeros and assign 1s to the center block using indexing.

2

Define the structuring element

Create a structuring element called se using strel('square', 3).

MATLAB

bw = zeros(7,7);
bw(3:5,3:5) = 1;
% Define the structuring element 'se' as a 3x3 square
% Your code here

Hint

Use the strel function with 'square' shape and size 3.

3

Apply dilation and erosion

Create two new matrices: dilated_bw by dilating bw with se, and eroded_bw by eroding bw with se.

MATLAB

bw = zeros(7,7);
bw(3:5,3:5) = 1;
se = strel('square', 3);
% Apply dilation to create 'dilated_bw'
% Apply erosion to create 'eroded_bw'
% Your code here

Hint

Use imdilate and imerode functions with bw and se.

4

Display the images

Use imshow to display bw, dilated_bw, and eroded_bw side by side using subplot. Show bw in the first subplot, dilated_bw in the second, and eroded_bw in the third.

MATLAB

bw = zeros(7,7);
bw(3:5,3:5) = 1;
se = strel('square', 3);
dilated_bw = imdilate(bw, se);
eroded_bw = imerode(bw, se);
% Display the images side by side
% Your code here

Hint

Use subplot(1,3,i) and imshow for each image, then add title for clarity.

Practice

1. Which MATLAB function is used to create a structuring element for morphological operations?

easy

A. imerode

B. strel

C. imopen

D. imdilate

Solution

1. Step 1: Understand structuring element creation

   The structuring element defines the shape used in morphological operations.

2. Step 2: Identify the MATLAB function for structuring elements

   In MATLAB, strel creates structuring elements like disks or lines.

3. Final Answer:

   strel -> Option B

4. Quick Check:

   Structuring element = strel [OK]

Hint: Remember: strel makes shapes for morphology [OK]

Common Mistakes:

• Confusing strel with imopen or imerode
• Using imdilate to create structuring elements
• Thinking imopen creates structuring elements

2. Which of the following is the correct syntax to perform erosion on a binary image BW using a disk structuring element of radius 3?

easy

A. BW2 = imopen(BW, strel('disk', 3));

B. BW2 = imdilate(BW, strel('disk', 3));

C. BW2 = imerode(BW, strel('disk', 3));

D. BW2 = imclose(BW, strel('disk', 3));

Solution

1. Step 1: Identify the erosion function

   In MATLAB, imerode performs erosion on images.

2. Step 2: Check the syntax for erosion with a disk structuring element

   The correct syntax is imerode(image, strel('disk', radius)).

3. Final Answer:

   BW2 = imerode(BW, strel('disk', 3)); -> Option C

4. Quick Check:

   Erosion syntax = imerode(image, strel) [OK]

Hint: imerode + strel('disk', radius) erodes image [OK]

Common Mistakes:

• Using imdilate instead of imerode
• Wrong order of arguments
• Missing quotes around 'disk'

3. What will be the output size of the binary image BW2 after applying BW2 = imdilate(BW, strel('square', 3)); if BW is a 5x5 matrix?

medium

A. Cannot determine without image content

B. 7x7

C. 3x3

D. 5x5

Solution

1. Step 1: Understand dilation size effect

   Dilation expands shapes but does not change image size in MATLAB.

2. Step 2: Check MATLAB behavior on image size

   MATLAB morphological functions keep the output image size same as input.

3. Final Answer:

   5x5 -> Option D

4. Quick Check:

   Output size = input size for imdilate [OK]

Hint: Morphology keeps image size same in MATLAB [OK]

Common Mistakes:

• Assuming dilation increases image size
• Confusing structuring element size with output size
• Thinking output depends on image content

4. Consider the code snippet:
se = strel('line', 5, 45);
BW2 = imerode(BW, se);
What is the likely error if BW is a grayscale image instead of binary?

medium

A. No error; imerode works on grayscale images

B. Error because strel cannot create line elements

C. Error because imerode expects binary images only

D. Error due to missing image conversion to binary

Solution

1. Step 1: Check imerode input types

   MATLAB's imerode supports both binary and grayscale images.

2. Step 2: Verify structuring element creation

   strel('line', 5, 45) correctly creates a line structuring element.

3. Final Answer:

   No error; imerode works on grayscale images -> Option A

4. Quick Check:

   imerode supports grayscale and binary [OK]

Hint: imerode works on grayscale too, no conversion needed [OK]

Common Mistakes:

• Thinking imerode only works on binary
• Assuming strel('line') is invalid
• Forgetting imerode supports grayscale images

5. You want to remove small noise dots from a binary image BW without changing the main shapes. Which morphological operation and structuring element combination is best?

hard

A. imopen with strel('disk', 2)

B. imclose with strel('square', 5)

C. imerode with strel('line', 3, 0)

D. imdilate with strel('diamond', 1)

Solution

1. Step 1: Understand noise removal by morphology

   Opening removes small objects (noise) while preserving shape size.

2. Step 2: Choose suitable structuring element

   A disk with radius 2 is good for removing small dots without harming main shapes.

3. Final Answer:

   imopen with strel('disk', 2) -> Option A

4. Quick Check:

   Opening + disk removes noise best [OK]

Hint: Use imopen with small disk to clean noise [OK]

Common Mistakes:

• Using imclose which fills holes
• Using erosion alone which shrinks shapes
• Using dilation alone which enlarges noise