Note
Click here to download the full example code
maskSLIC Demonstration¶
This example is about comparing the segmentations obtained using the plain SLIC method 1 and its masked version maskSLIC 2.
The maskSLIC method is an extension of the SLIC method for the generation of superpixels in a region of interest. maskSLIC is able to overcome border problems that affects SLIC method, particularely in case of irregular mask.
- 1
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, SLIC Superpixels Compared to State-of-the-art Superpixel Methods, TPAMI, May 2012. DOI:10.1109/TPAMI.2012.120
- 2
Irving, Benjamin. “maskSLIC: regional superpixel generation with application to local pathology characterisation in medical images.”, 2016, , arXiv:1606.09518

import matplotlib.pyplot as plt
from skimage import data
from skimage import color
from skimage import morphology
from skimage import segmentation
# Input data
img = data.immunohistochemistry()
# Compute a mask
lum = color.rgb2gray(img)
mask = morphology.remove_small_holes(
morphology.remove_small_objects(
lum < 0.7, 500),
500)
mask = morphology.opening(mask, morphology.disk(3))
# SLIC result
slic = segmentation.slic(img, n_segments=200, start_label=1)
# maskSLIC result
m_slic = segmentation.slic(img, n_segments=100, mask=mask, start_label=1)
# Display result
fig, ax_arr = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(10, 10))
ax1, ax2, ax3, ax4 = ax_arr.ravel()
ax1.imshow(img)
ax1.set_title("Origin image")
ax2.imshow(mask, cmap="gray")
ax2.set_title("Mask")
ax3.imshow(segmentation.mark_boundaries(img, slic))
ax3.contour(mask, colors='red', linewidths=1)
ax3.set_title("SLIC")
ax4.imshow(segmentation.mark_boundaries(img, m_slic))
ax4.contour(mask, colors='red', linewidths=1)
ax4.set_title("maskSLIC")
for ax in ax_arr.ravel():
ax.set_axis_off()
plt.tight_layout()
plt.show()
Total running time of the script: ( 0 minutes 1.552 seconds)