Scikit Image - Dilation

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Dilation is a fundamental operation in mathematical morphology that increases the size of bright regions and shrinks dark regions in an image. This is done by setting the value of a pixel to the maximum value over all the pixels in its neighborhood defined by a structuring element. And it is widely used in image processing tasks, including expanding object boundaries, filling small gaps, noise reduction, object enlargement, and feature enhancement.

The scikit-image library provides the functions like morphology.dilation() and morphology.binary_dilation() to perform dilation and binary dilation operations on images.

Using the skimage.morphology.dilation() function

The morphology.dilation() function is used for grayscale morphological dilation of an image. It works by setting the pixel values in the output image to the maximum overall pixel values within a local neighborhood centered around it.

Syntax

Following is the syntax of this function −

skimage.morphology.dilation(image, footprint=None, out=None, shift_x=False, shift_y=False)

Parameters

  • image (ndarray): This is the input image on which dilation will be performed.
  • footprint (ndarray or tuple, optional): The neighborhood to be used for dilation, expressed as a 2-D array of 1s and 0s. If None is provided, a cross-shaped footprint (connectivity=1) is used. You can also specify a custom footprint as a sequence of smaller footprints. For example, footprint=[(np.ones((9, 1)), 1), (np.ones((1, 9)), 1)] would apply a 9x1 footprint followed by a 1x9 footprint, resulting in a net effect that is the same as footprint=np.ones((9, 9)). This can help reduce the computational cost for certain footprints.
  • out (ndarray, optional): The output array where the result of the morphology will be stored. If None is passed, a new array will be allocated for the result.
  • shift_x and shift_y (bool, optional): These parameters control whether the footprint should be shifted about the center point. This only affects eccentric footprints (i.e., footprints with even-numbered sides).

Return Value

It returns a dilated array of the same shape and type as the input image.

Example

The following example demonstrates how to use the dilation() function on an image.

import numpy as np
import matplotlib.pyplot as plt
from skimage.morphology import square, dilation

# Create an example input image 
bright_pixel = np.array([[0, 0, 0, 0, 0],
                         [0, 0, 0, 0, 0],
                         [0, 0, 1, 0, 0],
                         [0, 0, 0, 0, 0],
                         [0, 0, 0, 0, 0]], dtype=np.uint8)

# Perform dilation using a square-shaped footprint of size 3x3
result = dilation(bright_pixel, square(3))

# Create subplots to display the input and dilated images
fig, axes = plt.subplots(1, 2, figsize=(10, 5))
ax = axes.ravel()

# Display the input image
ax[0].imshow(bright_pixel, cmap=plt.cm.gray)
ax[0].set_title('Input Image')
ax[0].set_axis_off()

# Display the eroded image
ax[1].imshow(result, cmap=plt.cm.gray)
ax[1].set_title('Dilated Image')
ax[1].set_axis_off()

plt.tight_layout()
plt.show()

Output

On executing the above program, you will get the following output −

Using the skimage.morphology.binary_dilation() function

The morphology.binary_dilation() function is used for fast binary morphological dilation of a binary image. Binary dilation is similar to grayscale dilation, but it works faster for binary images. It enlarges the bright regions and shrinks the dark regions.

Syntax

Following is the syntax of this function −

skimage.morphology.binary_dilation(image, footprint=None, out=None)

Parameters

  • image (ndarray): This is the binary input image on which dilation will be performed.
  • footprint (ndarray or tuple, optional): The neighborhood to be used for dilation, expressed as a 2-D array of 1s and 0s. If None is provided, a cross-shaped footprint (connectivity=1) is used. You can also specify a custom footprint as a sequence of smaller footprints, which can help reduce computational cost for certain footprints.
  • out (ndarray, optional): The output array where the result of the morphology will be stored. If None is passed, a new array will be allocated for the result.

Return Value

It returns a dilated binary image with values in [False, True].

Example

Here is an example that demonstrates how to use the binary_dilation() function on an image.

import numpy as np
import matplotlib.pyplot as plt
from skimage.morphology import square, binary_dilation

# Create an example binary input image
binary_pixel = np.array([[0, 0, 0, 0, 0],
                         [0, 0, 0, 0, 0],
                         [0, 0, 1, 0, 0],
                         [0, 0, 0, 0, 0],
                         [0, 0, 0, 0, 0]], dtype=np.uint8)

# Perform binary dilation using a square-shaped footprint of size 3x3
result = binary_dilation(binary_pixel, square(3))

# Create subplots to display the input and dilated binary images
fig, axes = plt.subplots(1, 2, figsize=(10, 5))
ax = axes.ravel()

# Display the input image
ax[0].imshow(binary_pixel, cmap=plt.cm.gray)
ax[0].set_title('Input Image')
ax[0].set_axis_off()

# Display the eroded image
ax[1].imshow(result, cmap=plt.cm.gray)
ax[1].set_title('Dilated Binary Image')
ax[1].set_axis_off()

plt.tight_layout()
plt.show()

Output

On executing the above program, you will get the following output −

Example

The following example performs both the grayscale and binary dilation on an image using the morphology.dilation() and morphology.binary_dilation() functions respectively.

import numpy as np
import matplotlib.pyplot as plt
from skimage.morphology import disk, dilation, binary_dilation
from skimage import io, color
from skimage.util import img_as_ubyte

# Load a grayscale input image
image = io.imread('Images/Circle2.jpg', as_gray=True)

# Create a binary image
threshold_value = 0.5 
binary_image = image > threshold_value

# Perform grayscale dilation using a disk-shaped structuring element of radius 5
grayscale_dilated = dilation(image, disk(2))

# Perform binary dilation using a disk-shaped structuring element of radius 2
binary_dilated = binary_dilation(binary_image, disk(2))

# Create subplots to display the input, grayscale dilated, and binary dilated images
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
ax = axes.ravel()

# Display the input grayscale image
ax[0].imshow(image, cmap=plt.cm.gray)
ax[0].set_title('Input Grayscale Image')
ax[0].set_axis_off()

# Display the grayscale dilated image
ax[1].imshow(grayscale_dilated, cmap=plt.cm.gray)
ax[1].set_title('Grayscale Dilated Image')
ax[1].set_axis_off()

# Display the binary dilated image
ax[2].imshow(binary_dilated, cmap=plt.cm.gray)
ax[2].set_title('Binary Dilated Image')
ax[2].set_axis_off()

plt.tight_layout()
plt.show()

Output

On executing the above program, you will get the following output −

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