Morphological operations (erosion, dilation) in SciPy

Introduction

Morphological operations help us change shapes in images or data. They make objects smaller or bigger to find patterns or clean noise.

To remove small dots or noise from a black and white image.

To fill small holes inside objects in an image.

To separate objects that are close together in a photo.

To highlight or grow certain features in a shape or image.

Syntax

SciPy

from scipy.ndimage import binary_erosion, binary_dilation

# Erosion
result = binary_erosion(input_array, structure=structuring_element)

# Dilation
result = binary_dilation(input_array, structure=structuring_element)

input_array is the image or data as a 2D array of True/False or 1/0.

structuring_element defines the shape used to erode or dilate, like a small square or circle.

Examples

This erodes a 3x3 block of True values, shrinking the shape by removing edge pixels.

SciPy

from scipy.ndimage import binary_erosion
import numpy as np

image = np.array([[1,1,1],[1,1,1],[1,1,1]], dtype=bool)
eroded = binary_erosion(image)

This dilates a single True pixel, growing it to neighbors.

SciPy

from scipy.ndimage import binary_dilation
import numpy as np

image = np.array([[0,0,0],[0,1,0],[0,0,0]], dtype=bool)
dilated = binary_dilation(image)

Sample Program

This code shows how erosion shrinks the square by removing edge pixels, and dilation grows it by adding pixels around the edges.

SciPy

from scipy.ndimage import binary_erosion, binary_dilation
import numpy as np

# Create a simple 5x5 image with a square in the center
image = np.array([
    [0,0,0,0,0],
    [0,1,1,1,0],
    [0,1,1,1,0],
    [0,1,1,1,0],
    [0,0,0,0,0]
], dtype=bool)

# Define a 3x3 square structuring element
structure = np.ones((3,3), dtype=bool)

# Apply erosion
eroded_image = binary_erosion(image, structure=structure)

# Apply dilation
dilated_image = binary_dilation(image, structure=structure)

print("Original image:\n", image.astype(int))
print("\nEroded image:\n", eroded_image.astype(int))
print("\nDilated image:\n", dilated_image.astype(int))

Important Notes

Erosion removes pixels on object edges, making shapes smaller.

Dilation adds pixels to object edges, making shapes bigger.

Choosing the right structuring element shape and size affects results a lot.

Summary

Morphological operations change shapes in images by shrinking or growing them.

Erosion removes edge pixels; dilation adds edge pixels.

These operations help clean images and find patterns.