Help wanted: implementation of 3D medial axis skeletonization
silvertrumpet999 at gmail.com
Fri Nov 20 00:23:49 EST 2015
It looks like the lobster and bonsai can be downloaded directly as raw
volumes (8 bit only, but will serve these purposes perfectly well) here:
This simple wrapper for np.fromfile will load them
import numpy as np
def loadraw(rawfile, shape=None, dtype=np.uint8):
Load RAW volume to a NumPy array.
rawfile : string
Path to *.raw volume.
shape : tuple
Shape of the volume. If not provided, output will be a rank-1 stream
which can be reshaped as desired.
dtype : NumPy dtype
Dtype of the raw image volume.
vol = np.fromfile(rawfile, dtype=dtype)
if shape is not None:
vol = vol.reshape(shape)
For the lobster, use shape=(56, 324, 301) and recall the voxel spacing has
a ratio of 1.4:1:1
For the bonsai, use shape=(256, 256, 256) and the volume is isotropic
On Monday, November 9, 2015 at 8:42:26 PM UTC-5, stefanv wrote:
> On 2015-11-07 09:46:17, 'Kevin Keraudren' via scikit-image <
> scikit-image at googlegroups.com> wrote:
> > I don’t want to volunteer for this project, but I just wanted to
> > mention that the 3D skeletonization from ITK is easily accessible to
> > Python through SimpleITK, see example below for the lobster
> > dataset. SimpleITK could be used for comparison or validation of the
> > proposed scikit-image algorithm.
> Thanks for the pointer. In this case, one of the purposes of the
> exercise is to stay away from a heavy dependency such as ITK.
> > PS: is there another way to load those *.pvm datasets in Python
> > without converting them to raw and hardcoding the image dimension and
> > pixel type? An skimage.io.imread() plugin?
> I have no idea about .pvm files, but perhaps we should start a set of
> plugin gists on the wiki somewhere?
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