Short tutorial on segmentation for the user guide +

example on the same subject used to generate the figures

doc/examples/applications/README.txt

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+Longer examples and demonstrations
+----------------------------------

doc/examples/applications/plot_coins_segmentation.py

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+"""
+===============================================================
+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``. This image shows
+several coins outlined against a darker background. The segmentation of
+the coins cannot be done directly from the histogram of grey values,
+because the background shares enough grey levels with the coins that a
+thresholding segmentation is not sufficient. Simply thresholding the image 
+leads either to missing significant parts of the coins, or to getting parts 
+of the background together with the coins.
+
+We first try an edge-based segmentation. We use the Canny detector to 
+delineate the contours of the coins. These contours are filled using 
+mathematical morphology (``scipy.ndimage.binary_fill_holes``). Small spurious 
+objects are easily removed by applying a threshold on the size of 
+unconnected objects. However, this method is not very robust, since contours 
+that are not perfectly closed are not filled correctly. This happens for one 
+of the coins.
+
+We therefore try a second method, that is region-based. Here we use the 
+watershed transform. An elevation map is provided by the Sobel gradient
+of the image. Markers of the background and the coins are determined from
+the extreme parts of the histogram of grey values.
+
+This second method works even better, and the coins can be segmented and 
+labeled individually.
+
+"""
+
+
+import numpy as np
+from scipy import ndimage
+import matplotlib.pyplot as plt
+import skimage
+from skimage.filter import canny, sobel
+from skimage.morphology import watershed
+
+#------------------ Loading data --------------------------------
+from skimage import data
+coins = data.coins()
+
+#------------ Histogram of grey values ---------------------------
+histo = 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(histo[1][:-1], histo[0], lw=2)
+plt.title('histogram of grey values')
+
+#------------------ Tentative thresholding --------------------------------
+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')
+
+plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
+                    right=1)
+
+
+#------------------ Edge-based segmentation --------------------------------
+edges = canny(coins/255.)
+
+fill_coins = ndimage.binary_fill_holes(edges)
+
+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=(7, 3.))
+plt.subplot(131)
+plt.imshow(edges, cmap=plt.cm.gray, interpolation='nearest')
+plt.axis('off')
+plt.title('Canny detector')
+plt.subplot(132)
+plt.imshow(fill_coins, cmap=plt.cm.gray, interpolation='nearest')
+plt.axis('off')
+plt.title('Filling the holes')
+plt.subplot(133)
+plt.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest')
+plt.axis('off')
+plt.title('Removing small objects')
+plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
+                    right=1)
+
+
+#------------------ Region-based segmentation --------------------------------
+
+markers = np.zeros_like(coins)
+markers[coins < 30] = 1
+markers[coins > 150] = 2
+
+elevation_map = sobel(coins)
+
+
+segmentation = watershed(elevation_map, markers)
+
+plt.figure(figsize=(7, 3))
+plt.subplot(131)
+plt.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest')
+plt.axis('off')
+plt.title('markers')
+plt.subplot(132)
+plt.imshow(elevation_map, cmap=plt.cm.jet, interpolation='nearest')
+plt.axis('off')
+plt.title('elevation_map')
+plt.subplot(133)
+plt.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest')
+plt.axis('off')
+plt.title('segmentation')
+
+plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
+                    right=1)
+
+
+#------------------ Labeling the coins --------------------------------
+labeled_coins, _ = ndimage.label(segmentation - 1)
+
+plt.figure(figsize=(6, 4))
+plt.imshow(labeled_coins, cmap=plt.cm.spectral, interpolation='nearest')
+plt.axis('off')
+
+plt.show()