Oversplitting by watershed

[Tony Yu]

On Mon, Nov 12, 2012 at 7:43 AM, Frank <pennekampster at googlemail.com> wrote:

> Dear group,
>
> I have some issues with the watershed algorithm implemented in scikits
> image. I use a global threshold to segment cells from background, but some
> cells touch and I want them to be split. Watershed seems the appropriate
> way to deal with my problem, however my particles are split in too many
> pieces. Is there a way to adjust the sensitivity of the watershed method?
>
> Many thanks for any suggestion!
>
> The code that I use looks like below. An example image that I want to
> process can be downloaded here: https://dl.dropbox.com/u/10373933/test.jpg
>
> # packages needed to perform image processing and analysis
> import numpy as np
> import scipy as scp
> import matplotlib.pyplot as plt
> import matplotlib.image as mpimg
> import scipy.ndimage as nd
> import skimage
> from skimage import io
> from skimage.morphology import watershed, is_local_maximum
> from skimage.segmentation import find_boundaries, visualize_boundaries
> from skimage.color import gray2rgb
>
> #read files jpeg file
> image = mpimg.imread('c:\\test.jpg')
> image_thresh = image > 140
> labels = nd.label(image_thresh)[0]
> distance = nd.distance_transform_edt(image_thresh)
> local_maxi = is_local_maximum(distance, labels=labels,
> footprint=np.ones((9, 9)))
> markers = nd.label(local_maxi)[0]
> labelled_image = watershed(-distance, markers, mask=image_thresh)
>
> #find outline of objects for plotting
> boundaries = find_boundaries(labelled_image)
> img_rgb = gray2rgb(image)
> overlay = np.flipud(visualize_boundaries(img_rgb,boundaries))
> imshow(overlay)


Hi Frank,

Actually, I don't think the issue is in the watershed segmentation.
Instead, I think the problem is in the marker specification: Using local
maxima creates too many marker points when a blob deviates greatly from a
circle. (BTW, does anyone know if there are any differences between
`is_local_maximum` and `peak_local_max`? Maybe the former should be
deprecated.)

Using the centroids of blobs gives cleaner results. See slightly-modified
example below.

Best,
-Tony

# packages needed to perform image processing and analysis
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as nd

from skimage import io
from skimage import measure
from skimage.morphology import watershed
from skimage.segmentation import find_boundaries, visualize_boundaries
from skimage.color import gray2rgb

#read files jpeg file
image = io.imread('test.jpg')

image_thresh = image > 140
labels = nd.label(image_thresh)[0]
distance = nd.distance_transform_edt(image_thresh)

props = measure.regionprops(labels, ['Centroid'])
coords = np.array([np.round(p['Centroid']) for p in props], dtype=int)
# Create marker image where blob centroids are marked True
markers = np.zeros(image.shape, dtype=bool)
markers[tuple(np.transpose(coords))] = True

labelled_image = watershed(-distance, markers, mask=image_thresh)

#find outline of objects for plotting
boundaries = find_boundaries(labelled_image)
img_rgb = gray2rgb(image)
overlay = visualize_boundaries(img_rgb, boundaries, color=(1, 0, 0))

plt.imshow(overlay)
plt.show()
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