Yeah, this works well!
BTW, the unwanted thicker bounds could be removed by a simple binary dialation.
Thanks!
On Thu, Mar 28, 2013 at 9:14 PM, Juan Nunez-Iglesias jni.soma@gmail.comwrote:
I think the following should work:
# ws is your watershed label mapfrom scipy import ndimage as nd bound = nd.grey_erosion(ws) != nd.grey_dilation(ws)
The only bad thing about this is that you'll get slightly thick boundaries. I'm not sure if that's a problem for you.
On Thu, Mar 28, 2013 at 10:37 AM, Zetian Yang zetian.yang@gmail.comwrote:
Thanks Walt,
I need a binary image in which the edges of every labeled region are set to 1. I've found the `find_boundaries` function in the skimage.segmentation module, but its outcome didn't fit my requirement.
Currently, I'm using the following algorithm to solve my question.
bound = np.zeros(data.shape) labels = data.max() for label in labels: label_data = data==label bound += label_data - scipy.ndimage.binary_erosion(label_data)It seemed work, but I'm not sure its correctness and is there a more efficient method?
On Thu, Mar 28, 2013 at 12:00 AM, Stéfan van der Walt stefan@sun.ac.zawrote:
Hi Zetian
On Wed, Mar 27, 2013 at 5:07 AM, Zetian Yang zetian.yang@gmail.com wrote:
I have been trying the watershed algorithm in the skimage package and
it is
really fantastic. Recently I have a problem where the edge of one
segmented
image is need. Is there a convenient way to extract edges of the
watersheded
result?
I'm glad you find the package useful! Do you need the edges as coordinates, or do you need a bitmap of the edges? We have marching squares for contour finding, edge detection, etc.
Stéfan
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Zetian Yang