| Name | medialaxis3d JSON |
| Version |
1.0.2
JSON |
| download |
| home_page | None |
| Summary | Module to perform 3D skeletonization using Medial Axis Transform |
| upload_time | 2025-08-01 17:23:25 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
mat
skeletonization
3d
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# medialaxis3d
This package extends the [scikit-image](https://scikit-image.org/) function [medial_axis](https://scikit-image.org/docs/stable/api/skimage.morphology.html#skimage.morphology.medial_axis)
to the 3D case.
## Install
```bash
pip install medialaxis3d
```
### Dependencies
Automatically installed with `pip`:
- `numpy`
- `scipy`
- `cython`
Optional only for visualization
- `napari`
## Documentation
WIP
## Quickstart
Use it without returning the medial distance.
```Python
>>> import numpy as np
>>> import skimage as ski
>>> import medialaxis3d
>>> import napari
>>> rng = np.random.default_rng(1278)
>>> image = ski.data.binary_blobs(length = 128,
>>> blob_size_fraction = 0.2,
>>> n_dim = 3,
>>> volume_fraction = 0.6,
>>> rng = rng)
>>> skeleton = medialaxis3d.medial_axis_3d(image,
>>> return_distance = False,
>>> size = 8,
>>> rng = rng)
>>> viewer = napari.Viewer()
>>> viewer.add_image(image,
>>> rendering = "attenuated_mip",
>>> attenuation = 0.5,
>>> scale = [1, 1, 1])
>>> viewer.add_image(skeleton,
>>> interpolation3d = "nearest",
>>> colormap = "magenta",
>>> scale = [1, 1, 1])
>>> napari.run()
```
<img src="https://raw.githubusercontent.com/jb-sharp/medialaxis3d/main/screenshots/example_nodist1.png" width="32%"/> <img src="https://raw.githubusercontent.com/jb-sharp/medialaxis3d/main/screenshots/example_nodist2.png" width="32%"/> <img src="https://raw.githubusercontent.com/jb-sharp/medialaxis3d/main/screenshots/example_nodist3.png" width="32%"/>
or use it to return the distance as well.
```Python
>>> import numpy as np
>>> import skimage as ski
>>> import medialaxis3d
>>> import napari
>>> rng = np.random.default_rng(1278)
>>> image = ski.data.binary_blobs(length = 128,
blob_size_fraction = 0.2,
n_dim = 3,
volume_fraction = 0.6,
rng = rng)
>>> skeleton, distance = medialaxis3d.medial_axis_3d(image,
>>> return_distance = True,
>>> size = 8,
>>> rng = rng)
>>> viewer = napari.Viewer()
>>> viewer.add_image(image,
>>> rendering = "attenuated_mip",
>>> attenuation = 0.5,
>>> scale = [1, 1, 1])
>>> viewer.add_image(skeleton*distance,
>>> interpolation3d = "nearest",
>>> colormap = "turbo",
>>> scale = [1, 1, 1])
>>> napari.run()
```
<img src="https://raw.githubusercontent.com/jb-sharp/medialaxis3d/main/screenshots/example_nodist1.png" width="32%"/> <img src="https://raw.githubusercontent.com/jb-sharp/medialaxis3d/main/screenshots/example_dist2.png" width="32%"/> <img src="https://raw.githubusercontent.com/jb-sharp/medialaxis3d/main/screenshots/example_dist3.png" width="32%"/>
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"description": "# medialaxis3d\n\nThis package extends the [scikit-image](https://scikit-image.org/) function [medial_axis](https://scikit-image.org/docs/stable/api/skimage.morphology.html#skimage.morphology.medial_axis)\nto the 3D case.\n\n## Install\n\n```bash\npip install medialaxis3d\n```\n\n### Dependencies\nAutomatically installed with `pip`:\n\n- `numpy`\n- `scipy`\n- `cython`\n\nOptional only for visualization\n\n- `napari`\n\n## Documentation \n\nWIP\n\n## Quickstart\n\nUse it without returning the medial distance.\n\n```Python\n>>> import numpy as np\n>>> import skimage as ski\n>>> import medialaxis3d\n>>> import napari\n\n>>> rng = np.random.default_rng(1278)\n\n>>> image = ski.data.binary_blobs(length = 128,\n>>> blob_size_fraction = 0.2,\n>>> n_dim = 3,\n>>> volume_fraction = 0.6,\n>>> rng = rng)\n\n>>> skeleton = medialaxis3d.medial_axis_3d(image, \n>>> return_distance = False, \n>>> size = 8, \n>>> rng = rng)\n\n>>> viewer = napari.Viewer()\n>>> viewer.add_image(image, \n>>> rendering = \"attenuated_mip\", \n>>> attenuation = 0.5, \n>>> scale = [1, 1, 1])\n>>> viewer.add_image(skeleton, \n>>> interpolation3d = \"nearest\", \n>>> colormap = \"magenta\", \n>>> scale = [1, 1, 1])\n>>> napari.run()\n```\n\n<img src=\"https://raw.githubusercontent.com/jb-sharp/medialaxis3d/main/screenshots/example_nodist1.png\" width=\"32%\"/> <img src=\"https://raw.githubusercontent.com/jb-sharp/medialaxis3d/main/screenshots/example_nodist2.png\" width=\"32%\"/> <img src=\"https://raw.githubusercontent.com/jb-sharp/medialaxis3d/main/screenshots/example_nodist3.png\" width=\"32%\"/>\n\nor use it to return the distance as well.\n\n```Python\n>>> import numpy as np\n>>> import skimage as ski\n>>> import medialaxis3d\n>>> import napari\n\n>>> rng = np.random.default_rng(1278)\n>>> image = ski.data.binary_blobs(length = 128,\n blob_size_fraction = 0.2,\n n_dim = 3,\n volume_fraction = 0.6,\n rng = rng)\n\n>>> skeleton, distance = medialaxis3d.medial_axis_3d(image, \n>>> return_distance = True, \n>>> size = 8, \n>>> rng = rng)\n\n>>> viewer = napari.Viewer()\n>>> viewer.add_image(image, \n>>> rendering = \"attenuated_mip\", \n>>> attenuation = 0.5, \n>>> scale = [1, 1, 1])\n>>> viewer.add_image(skeleton*distance, \n>>> interpolation3d = \"nearest\", \n>>> colormap = \"turbo\", \n>>> scale = [1, 1, 1])\n>>> napari.run()\n```\n\n<img src=\"https://raw.githubusercontent.com/jb-sharp/medialaxis3d/main/screenshots/example_nodist1.png\" width=\"32%\"/> <img src=\"https://raw.githubusercontent.com/jb-sharp/medialaxis3d/main/screenshots/example_dist2.png\" width=\"32%\"/> <img src=\"https://raw.githubusercontent.com/jb-sharp/medialaxis3d/main/screenshots/example_dist3.png\" width=\"32%\"/>\n",
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