Comparing edge-based segmentation and region-based segmentation¶
In this example, we will see how to segment objects from a background. We use the coins image from skimage.data, which shows several coins outlined against a darker background.
import numpy as np
import matplotlib.pyplot as plt
from skimage import data
coins = data.coins()
hist = np.histogram(coins, bins=np.arange(0, 256))
plt.figure(figsize=(8, 3))
plt.subplot(121)
plt.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
plt.axis('off')
plt.subplot(122)
plt.plot(hist[1][:-1], hist[0], lw=2)
plt.title('histogram of grey values')
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Thresholding¶
A simple way to segment the coins is to choose a threshold based on the histogram of grey 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:
plt.figure(figsize=(6, 3))
plt.subplot(121)
plt.imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest')
plt.title('coins > 100')
plt.axis('off')
plt.subplot(122)
plt.imshow(coins > 150, cmap=plt.cm.gray, interpolation='nearest')
plt.title('coins > 150')
plt.axis('off')
margins = dict(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
plt.subplots_adjust(**margins)
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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.filter import canny
edges = canny(coins/255.)
plt.figure(figsize=(4, 3))
plt.imshow(edges, cmap=plt.cm.gray, interpolation='nearest')
plt.axis('off')
plt.title('Canny detector')
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These contours are then filled using mathematical morphology.
from scipy import ndimage
fill_coins = ndimage.binary_fill_holes(edges)
plt.figure(figsize=(4, 3))
plt.imshow(fill_coins, cmap=plt.cm.gray, interpolation='nearest')
plt.axis('off')
plt.title('Filling the holes')
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Small spurious objects are easily removed by setting a minimum size for valid objects.
label_objects, nb_labels = ndimage.label(fill_coins)
sizes = np.bincount(label_objects.ravel())
mask_sizes = sizes > 20
mask_sizes[0] = 0
coins_cleaned = mask_sizes[label_objects]
plt.figure(figsize=(4, 3))
plt.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest')
plt.axis('off')
plt.title('Removing small objects')
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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.filter import sobel
elevation_map = sobel(coins)
plt.figure(figsize=(4, 3))
plt.imshow(elevation_map, cmap=plt.cm.jet, interpolation='nearest')
plt.axis('off')
plt.title('elevation_map')
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Next we find markers of the background and the coins based on the extreme parts of the histogram of grey values.
markers = np.zeros_like(coins)
markers[coins < 30] = 1
markers[coins > 150] = 2
plt.figure(figsize=(4, 3))
plt.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest')
plt.axis('off')
plt.title('markers')
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Finally, we use the watershed transform to fill regions of the elevation map starting from the markers determined above:
from skimage.morphology import watershed
segmentation = watershed(elevation_map, markers)
plt.figure(figsize=(4, 3))
plt.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest')
plt.axis('off')
plt.title('segmentation')
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This last method works even better, and the coins can be segmented and labeled individually.
segmentation = ndimage.binary_fill_holes(segmentation - 1)
labeled_coins, _ = ndimage.label(segmentation)
plt.figure(figsize=(6, 3))
plt.subplot(121)
plt.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
plt.contour(segmentation, [0.5], linewidths=1.2, colors='y')
plt.axis('off')
plt.subplot(122)
plt.imshow(labeled_coins, cmap=plt.cm.spectral, interpolation='nearest')
plt.axis('off')
plt.subplots_adjust(**margins)
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plt.show()
Python source code: download (generated using mpltools 0.6dev)