diff --git a/doc/examples/plot_lena_bilateral_denoise.py b/doc/examples/plot_lena_bilateral_denoise.py
index 9fd20285..57593867 100644
--- a/doc/examples/plot_lena_bilateral_denoise.py
+++ b/doc/examples/plot_lena_bilateral_denoise.py
@@ -1,3 +1,4 @@
+
 """
 ====================================================
 Denoising the picture of Lena using bilateral filter
diff --git a/doc/examples/plot_marked_watershed.py b/doc/examples/plot_marked_watershed.py
new file mode 100644
index 00000000..8f65e344
--- /dev/null
+++ b/doc/examples/plot_marked_watershed.py
@@ -0,0 +1,53 @@
+"""
+================================
+Markers for watershed transform
+================================
+
+The watershed is a classical algorithm used for **segmentation**, that
+is, for separating different objects in an image.
+
+See Wikipedia_ for more details on the algorithm.
+
+.. _Wikipedia: http://en.wikipedia.org/wiki/Watershed_(image_processing)
+
+"""
+
+import numpy as np
+from scipy import ndimage
+import matplotlib.pyplot as plt
+from skimage.morphology import watershed,disk
+from skimage import rank
+from skimage import data
+from scipy import ndimage
+
+# Generate an initial image with two overlapping circles
+image = data.camera()
+
+# denoise image
+denoised = rank.median(image,disk(2))
+
+# find continuous region (low gradient) --> markers
+markers = rank.gradient(denoised,disk(5))<10
+markers = ndimage.label(markers)[0]
+
+#local gradient
+gradient = rank.gradient(denoised,disk(2))
+
+# process the watershed
+labels = watershed(gradient, markers)
+
+# display results
+fig, axes = plt.subplots(ncols=4, figsize=(8, 2.7))
+ax0, ax1, ax2, ax3 = axes
+
+ax0.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
+ax1.imshow(gradient, cmap=plt.cm.spectral, interpolation='nearest')
+ax2.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest')
+ax3.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
+ax3.imshow(labels, cmap=plt.cm.spectral, interpolation='nearest',alpha=.7)
+
+for ax in axes:
+    ax.axis('off')
+
+plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
+plt.show()