Comparing edge-based and region-based segmentation#

In this example, we will see how to segment objects from a background. We use the coins image from, which shows several coins outlined against a darker background.

import numpy as np
import matplotlib.pyplot as plt

from skimage import data
from skimage.exposure import histogram

coins = data.coins()
hist, hist_centers = histogram(coins)

fig, axes = plt.subplots(1, 2, figsize=(8, 3))
axes[1].plot(hist_centers, hist, lw=2)
axes[1].set_title('histogram of gray values')
histogram of gray values
Text(0.5, 1.0, 'histogram of gray values')


A simple way to segment the coins is to choose a threshold based on the histogram of gray values. Unfortunately, thresholding this image gives a binary image that either misses significant parts of the coins or merges parts of the background with the coins:

fig, axes = plt.subplots(1, 2, figsize=(8, 3), sharey=True)

axes[0].imshow(coins > 100,
axes[0].set_title('coins > 100')

axes[1].imshow(coins > 150,
axes[1].set_title('coins > 150')

for a in axes:

coins > 100, coins > 150

Edge-based segmentation#

Next, we try to delineate the contours of the coins using edge-based segmentation. To do this, we first get the edges of features using the Canny edge-detector.

from skimage.feature import canny

edges = canny(coins)

fig, ax = plt.subplots(figsize=(4, 3))
ax.set_title('Canny detector')
Canny detector

These contours are then filled using mathematical morphology.

from scipy import ndimage as ndi

fill_coins = ndi.binary_fill_holes(edges)

fig, ax = plt.subplots(figsize=(4, 3))
ax.set_title('filling the holes')
filling the holes

Small spurious objects are easily removed by setting a minimum size for valid objects.

from skimage import morphology

coins_cleaned = morphology.remove_small_objects(fill_coins, 21)

fig, ax = plt.subplots(figsize=(4, 3))
ax.set_title('removing small objects')
removing small objects

However, this method is not very robust, since contours that are not perfectly closed are not filled correctly, as is the case for one unfilled coin above.

Region-based segmentation#

We therefore try a region-based method using the watershed transform. First, we find an elevation map using the Sobel gradient of the image.

from skimage.filters import sobel

elevation_map = sobel(coins)

fig, ax = plt.subplots(figsize=(4, 3))
ax.set_title('elevation map')
elevation map

Next we find markers of the background and the coins based on the extreme parts of the histogram of gray values.


Finally, we use the watershed transform to fill regions of the elevation map starting from the markers determined above:


This last method works even better, and the coins can be segmented and labeled individually.

from skimage.color import label2rgb

segmentation_coins = ndi.binary_fill_holes(segmentation_coins - 1)
labeled_coins, _ = ndi.label(segmentation_coins)
image_label_overlay = label2rgb(labeled_coins, image=coins, bg_label=0)

fig, axes = plt.subplots(1, 2, figsize=(8, 3), sharey=True)
axes[0].contour(segmentation_coins, [0.5], linewidths=1.2, colors='y')

for a in axes:

plot coins segmentation

Total running time of the script: (0 minutes 1.535 seconds)

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