Expand segmentation labels without overlap#

Given several connected components represented by a label image, these connected components can be expanded into background regions using skimage.segmentation.expand_labels(). In contrast to skimage.morphology.dilation() this method will not let connected components expand into neighboring connected components with lower label number.

Original, Sobel+Watershed, Expanded labels
import matplotlib.pyplot as plt
import numpy as np
from skimage import data
from skimage.color import label2rgb
from skimage.filters import sobel
from skimage.measure import label
from skimage.segmentation import expand_labels, watershed

coins = data.coins()

# Make segmentation using edge-detection and watershed.
edges = sobel(coins)

# Identify some background and foreground pixels from the intensity values.
# These pixels are used as seeds for watershed.
markers = np.zeros_like(coins)
foreground, background = 1, 2
markers[coins < 30.0] = background
markers[coins > 150.0] = foreground

ws = watershed(edges, markers)
seg1 = label(ws == foreground)

expanded = expand_labels(seg1, distance=10)

# Show the segmentations.
fig, axes = plt.subplots(
    nrows=1,
    ncols=3,
    figsize=(9, 5),
    sharex=True,
    sharey=True,
)

axes[0].imshow(coins, cmap="Greys_r")
axes[0].set_title("Original")

color1 = label2rgb(seg1, image=coins, bg_label=0)
axes[1].imshow(color1)
axes[1].set_title("Sobel+Watershed")

color2 = label2rgb(expanded, image=coins, bg_label=0)
axes[2].imshow(color2)
axes[2].set_title("Expanded labels")

for a in axes:
    a.axis("off")
fig.tight_layout()
plt.show()

Total running time of the script: (0 minutes 0.618 seconds)

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