Morphology

Image Processing in Python

Rebeca Gonzalez

Data Engineer

Binary images

Morphological filtering

Better for binary images
Can extend for grayscale

Morphological operations

Dilation
Erosion

Structuring element

Shapes in scikit-image

from skimage import morphology


square = morphology.square(4)

[[1 1 1 1]
 [1 1 1 1]
 [1 1 1 1]
 [1 1 1 1]]

rectangle = morphology.rectangle(4, 2)

[[1 1]
 [1 1]
 [1 1]
 [1 1]]

Erosion in scikit-image

from skimage import morphology


# Set structuring element to the rectangular-shaped
selem = rectangle(12,6)


# Obtain the erosed image with binary erosion
eroded_image = morphology.binary_erosion(image_horse, selem=selem)

Erosion in scikit-image

# Show result
plot_comparison(image_horse, eroded_image, 'Erosion')

Binary erosion with default selem

# Binary erosion with default selem
eroded_image = morphology.binary_erosion(image_horse)

Dilation in scikit-image

from skimage import morphology

# Obtain dilated image, using binary dilation
dilated_image = morphology.binary_dilation(image_horse)


# See results
plot_comparison(image_horse, dilated_image, 'Erosion')

Dilation in scikit-image

Let's practice!

Image Processing in Python