# 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, which shows several coins outlined against a darker background.

```import numpy as np
import matplotlib.pyplot as plt

from skimage import data

coins = data.coins()
hist = np.histogram(coins, bins=np.arange(0, 256))

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
ax1.axis('off')
ax2.plot(hist[1][:-1], hist[0], lw=2)
ax2.set_title('histogram of grey values')
```

## Thresholding¶

A simple way to segment the coins is to choose a threshold based on the histogram of grey values. Unfortunately, thresholding this image gives a binary image that either misses significant parts of the coins or merges parts of the background with the coins:

```fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
ax1.imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest')
ax1.set_title('coins > 100')
ax1.axis('off')
ax2.imshow(coins > 150, cmap=plt.cm.gray, interpolation='nearest')
ax2.set_title('coins > 150')
ax2.axis('off')
margins = dict(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
```

## Edge-based segmentation¶

Next, we try to delineate the contours of the coins using edge-based segmentation. To do this, we first get the edges of features using the Canny edge-detector.

```from skimage.filter import canny
edges = canny(coins/255.)

fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(edges, cmap=plt.cm.gray, interpolation='nearest')
ax.axis('off')
ax.set_title('Canny detector')
```

These contours are then filled using mathematical morphology.

```from scipy import ndimage

fill_coins = ndimage.binary_fill_holes(edges)

fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(fill_coins, cmap=plt.cm.gray, interpolation='nearest')
ax.axis('off')
ax.set_title('Filling the holes')
```

Small spurious objects are easily removed by setting a minimum size for valid objects.

```from skimage import morphology
coins_cleaned = morphology.remove_small_objects(fill_coins, 21)

fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest')
ax.axis('off')
ax.set_title('Removing small objects')
```

However, this method is not very robust, since contours that are not perfectly closed are not filled correctly, as is the case for one unfilled coin above.

## Region-based segmentation¶

We therefore try a region-based method using the watershed transform. First, we find an elevation map using the Sobel gradient of the image.

```from skimage.filter import sobel

elevation_map = sobel(coins)

fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(elevation_map, cmap=plt.cm.jet, interpolation='nearest')
ax.axis('off')
ax.set_title('elevation_map')
```

Next we find markers of the background and the coins based on the extreme parts of the histogram of grey values.

```markers = np.zeros_like(coins)
markers[coins < 30] = 1
markers[coins > 150] = 2

fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest')
ax.axis('off')
ax.set_title('markers')
```

Finally, we use the watershed transform to fill regions of the elevation map starting from the markers determined above:

```segmentation = morphology.watershed(elevation_map, markers)

fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest')
ax.axis('off')
ax.set_title('segmentation')
```

This last method works even better, and the coins can be segmented and labeled individually.

```from skimage.color import label2rgb

segmentation = ndimage.binary_fill_holes(segmentation - 1)
labeled_coins, _ = ndimage.label(segmentation)
image_label_overlay = label2rgb(labeled_coins, image=coins)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
ax1.contour(segmentation, [0.5], linewidths=1.2, colors='y')
ax1.axis('off')
ax2.imshow(image_label_overlay, interpolation='nearest')
ax2.axis('off')

```plt.show()