3.3.11.11. Watershed and random walker for segmentationΒΆ

This example compares two segmentation methods in order to separate two connected disks: the watershed algorithm, and the random walker algorithm.

Both segmentation methods require seeds, that are pixels belonging unambigusouly to a reagion. Here, local maxima of the distance map to the background are used as seeds.

```import numpy as np
from skimage.segmentation import watershed
from skimage.feature import peak_local_max
from skimage import measure
from skimage.segmentation import random_walker
import matplotlib.pyplot as plt
import scipy as sp

# Generate an initial image with two overlapping circles
x, y = np.indices((80, 80))
x1, y1, x2, y2 = 28, 28, 44, 52
r1, r2 = 16, 20
mask_circle1 = (x - x1) ** 2 + (y - y1) ** 2 < r1**2
mask_circle2 = (x - x2) ** 2 + (y - y2) ** 2 < r2**2
# Now we want to separate the two objects in image
# Generate the markers as local maxima of the distance
# to the background
distance = sp.ndimage.distance_transform_edt(image)
peak_idx = peak_local_max(distance, footprint=np.ones((3, 3)), labels=image)

markers[~image] = -1
labels_rw = random_walker(image, markers)

plt.figure(figsize=(12, 3.5))
plt.subplot(141)
plt.imshow(image, cmap="gray", interpolation="nearest")
plt.axis("off")
plt.title("image")
plt.subplot(142)
plt.imshow(-distance, interpolation="nearest")
plt.axis("off")
plt.title("distance map")
plt.subplot(143)
plt.imshow(labels_ws, cmap="nipy_spectral", interpolation="nearest")
plt.axis("off")
plt.title("watershed segmentation")
plt.subplot(144)
plt.imshow(labels_rw, cmap="nipy_spectral", interpolation="nearest")
plt.axis("off")
plt.title("random walker segmentation")

plt.tight_layout()
plt.show()
```

Total running time of the script: (0 minutes 0.151 seconds)

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