 [](https://badge.fury.io/py/fastmorph)
# fastmorph: multilabel 3D morphological image processing functions.
This is a collection of morphological 3D image operations that are tuned for working with dense 3D labeled images.
We provide the following multithreaded (except where noted) operations:
- Multi-Label Stenciled Dilation, Erosion, Opening, Closing
- Grayscale Stenciled Dilation, Erosion, Opening, Closing
- Multi-Label Spherical Erosion
- Binary Spherical Dilation, Opening, and Closing
- Multi-Label Fill Voids (mostly single threaded)
Highlights compared to other libraries:
- Handles multi-labeled images
- Multithreaded
- High performance single-threaded
- Low memory usage
- Dilate computes mode of surrounding labels
Disadvantages versus other libraries:
- Stencil (structuring element) is fixed size 3x3x3 and all on.
```python
import fastmorph
# may be binary or unsigned integer 2D or 3D image
labels = np.load("my_labels.npy")
# multi-label capable morphological operators
# they use a 3x3x3 all on structuring element
# dilate picks the mode of surrounding labels
# by default only background (0) labels are filled
morphed = fastmorph.dilate(labels, parallel=2)
# processes every voxel
morphed = fastmorph.dilate(labels, background_only=False, parallel=2)
morphed = fastmorph.erode(labels)
morphed = fastmorph.opening(labels, parallel=2)
morphed = fastmorph.closing(labels, parallel=2)
# You can select grayscale dilation, erosion, opening, and
# closing by passing in a different Mode enum.
# The options are Mode.grey and Mode.multilabel
morphed = fastmorph.dilate(labels, mode=fastmorph.Mode.grey)
morphed = fastmorph.erode(labels, mode=fastmorph.Mode.grey)
# Dilate only supports binary images at this time.
# Radius is specified in physical units, but
# by default anisotropy = (1,1,1) so it is the
# same as voxels.
morphed = fastmorph.spherical_dilate(labels, radius=1, parallel=2, anisotropy=(1,1,1))
# open and close require dialate to work and so are binary only for now
morphed = fastmorph.spherical_open(labels, radius=1, parallel=2, anisotropy=(1,1,1))
morphed = fastmorph.spherical_close(labels, radius=1, parallel=2, anisotropy=(1,1,1))
# The rest support multilabel images.
morphed = fastmorph.spherical_erode(labels, radius=1, parallel=2, anisotropy=(1,1,1))
# Note: for boolean images, this function will directly call fill_voids
# and return a scalar for ct
# For integer images, more processing will be done to deal with multiple labels.
# A dict of { label: num_voxels_filled } for integer images will be returned.
# Note that for multilabel images, by default, if a label is totally enclosed by another,
# a FillError will be raised. If remove_enclosed is True, the label will be overwritten.
filled_labels, ct = fastmorph.fill_holes(labels, return_fill_count=True, remove_enclosed=False)
# If the holes in your segmentation are imperfectly sealed, consider
# using the following options.
filled_labels = fastmorph.fill_holes(
labels,
# runs 2d fill on the sides of the cube for each binary image
fix_borders=True,
# does a dilate and then an erode after filling holes
morphological_closing=True,
)
```
## Performance
A test run on an M1 Macbook Pro on `connectomics.npy.ckl`, a 512<sup>3</sup> volume with over 2000 dense labels had the following results for multilabel processing.
```
erode / 1 thread: 1.553 sec
erode / 2 threads: 0.885 sec
erode / 4 threads: 0.651 sec
dilate / background_only=True / 1 thread: 1.100 sec
dilate / background_only=True / 2 threads: 0.632 sec
dilate / background_only=True / 4 threads: 0.441 sec
dilate / background_only=False / 1 thread: 11.783 sec
dilate / background_only=False / 2 threads: 5.944 sec
dilate / background_only=False / 4 threads: 4.291 sec
dilate / background_only=False / 8 threads: 3.298 sec
scipy grey_dilation / 1 thread 14.648 sec
scipy grey_erode / 1 thread: 14.412 sec
skimage expand_labels / 1 thread: 62.248 sec
```
Test run on an M1 Macbook Pro with `ws.npy.ckl` a 512<sup>3</sup> volume with tens of thousands of components for multilabel processing.
```
erode / 1 thread: 2.380 sec
erode / 2 threads: 1.479 sec
erode / 4 threads: 1.164 sec
dilate / background_only=True / 1 thread: 1.598 sec
dilate / background_only=True / 2 threads: 1.011 sec
dilate / background_only=True / 4 threads: 0.805 sec
dilate / background_only=False / 1 thread: 25.182 sec
dilate / background_only=False / 2 threads: 13.513 sec
dilate / background_only=False / 4 threads: 8.749 sec
dilate / background_only=False / 8 threads: 6.640 sec
scipy grey_dilation / 1 thread 21.109 sec
scipy grey_erode / 1 thread: 20.305 sec
skimage expand_labels / 1 thread: 63.247 sec
```
Here is the performance on a completely zeroed 512<sup>3</sup> volume for multilabel processing.
```
erode / 1 thread: 0.462 sec
erode / 2 threads: 0.289 sec
erode / 4 threads: 0.229 sec
dilate / background_only=True / 1 thread: 2.337 sec
dilate / background_only=True / 2 threads: 1.344 sec
dilate / background_only=True / 4 threads: 1.021 sec
dilate / background_only=False / 1 thread: 2.267 sec
dilate / background_only=False / 2 threads: 1.251 sec
dilate / background_only=False / 4 threads: 0.944 sec
dilate / background_only=False / 8 threads: 0.718 sec
scipy grey_dilation / 1 thread 13.516 sec
scipy grey_erode / 1 thread: 13.326 sec
skimage expand_labels / 1 thread: 35.243 sec
```
### Memory Profiles
<center>
<img src="https://github.com/seung-lab/fastmorph/blob/15c4c27ad3255c8ef959ceb67facd65e18eff2e4/memory-profile-dilate-bg-only-false.jpg" />
</center>
<center>
<img src="https://github.com/seung-lab/fastmorph/blob/15c4c27ad3255c8ef959ceb67facd65e18eff2e4/memory-profile-dilate-bg-only-true.jpg" />
</center>
<center>
<img src="https://github.com/seung-lab/fastmorph/blob/15c4c27ad3255c8ef959ceb67facd65e18eff2e4/memory-profile-skimage-expand_labels.jpg" />
</center>
Raw data
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"description": " [](https://badge.fury.io/py/fastmorph)\n\n# fastmorph: multilabel 3D morphological image processing functions.\n\nThis is a collection of morphological 3D image operations that are tuned for working with dense 3D labeled images. \n\nWe provide the following multithreaded (except where noted) operations:\n\n- Multi-Label Stenciled Dilation, Erosion, Opening, Closing\n- Grayscale Stenciled Dilation, Erosion, Opening, Closing\n- Multi-Label Spherical Erosion\n- Binary Spherical Dilation, Opening, and Closing\n- Multi-Label Fill Voids (mostly single threaded)\n\nHighlights compared to other libraries:\n\n- Handles multi-labeled images\n- Multithreaded\n- High performance single-threaded\n- Low memory usage\n- Dilate computes mode of surrounding labels\n\nDisadvantages versus other libraries:\n\n- Stencil (structuring element) is fixed size 3x3x3 and all on.\n\n\n```python\nimport fastmorph\n\n# may be binary or unsigned integer 2D or 3D image\nlabels = np.load(\"my_labels.npy\")\n\n\n# multi-label capable morphological operators\n# they use a 3x3x3 all on structuring element\n# dilate picks the mode of surrounding labels\n\n# by default only background (0) labels are filled\nmorphed = fastmorph.dilate(labels, parallel=2)\n# processes every voxel\nmorphed = fastmorph.dilate(labels, background_only=False, parallel=2)\n\nmorphed = fastmorph.erode(labels)\nmorphed = fastmorph.opening(labels, parallel=2)\nmorphed = fastmorph.closing(labels, parallel=2)\n\n# You can select grayscale dilation, erosion, opening, and \n# closing by passing in a different Mode enum.\n# The options are Mode.grey and Mode.multilabel\nmorphed = fastmorph.dilate(labels, mode=fastmorph.Mode.grey)\nmorphed = fastmorph.erode(labels, mode=fastmorph.Mode.grey)\n\n# Dilate only supports binary images at this time.\n# Radius is specified in physical units, but\n# by default anisotropy = (1,1,1) so it is the \n# same as voxels.\nmorphed = fastmorph.spherical_dilate(labels, radius=1, parallel=2, anisotropy=(1,1,1))\n\n# open and close require dialate to work and so are binary only for now\nmorphed = fastmorph.spherical_open(labels, radius=1, parallel=2, anisotropy=(1,1,1))\nmorphed = fastmorph.spherical_close(labels, radius=1, parallel=2, anisotropy=(1,1,1))\n\n# The rest support multilabel images.\nmorphed = fastmorph.spherical_erode(labels, radius=1, parallel=2, anisotropy=(1,1,1))\n\n# Note: for boolean images, this function will directly call fill_voids\n# and return a scalar for ct \n# For integer images, more processing will be done to deal with multiple labels.\n# A dict of { label: num_voxels_filled } for integer images will be returned.\n# Note that for multilabel images, by default, if a label is totally enclosed by another,\n# a FillError will be raised. If remove_enclosed is True, the label will be overwritten.\nfilled_labels, ct = fastmorph.fill_holes(labels, return_fill_count=True, remove_enclosed=False)\n\n# If the holes in your segmentation are imperfectly sealed, consider\n# using the following options.\nfilled_labels = fastmorph.fill_holes(\n\tlabels, \n\t# runs 2d fill on the sides of the cube for each binary image\n\tfix_borders=True, \n\t# does a dilate and then an erode after filling holes\n\tmorphological_closing=True,\n)\n\n```\n\n## Performance\n\nA test run on an M1 Macbook Pro on `connectomics.npy.ckl`, a 512<sup>3</sup> volume with over 2000 dense labels had the following results for multilabel processing.\n\n```\nerode / 1 thread: 1.553 sec\nerode / 2 threads: 0.885 sec\nerode / 4 threads: 0.651 sec\ndilate / background_only=True / 1 thread: 1.100 sec\ndilate / background_only=True / 2 threads: 0.632 sec\ndilate / background_only=True / 4 threads: 0.441 sec\ndilate / background_only=False / 1 thread: 11.783 sec\ndilate / background_only=False / 2 threads: 5.944 sec\ndilate / background_only=False / 4 threads: 4.291 sec\ndilate / background_only=False / 8 threads: 3.298 sec\nscipy grey_dilation / 1 thread 14.648 sec\nscipy grey_erode / 1 thread: 14.412 sec\nskimage expand_labels / 1 thread: 62.248 sec\n```\n\nTest run on an M1 Macbook Pro with `ws.npy.ckl` a 512<sup>3</sup> volume with tens of thousands of components for multilabel processing.\n\n```\nerode / 1 thread: 2.380 sec\nerode / 2 threads: 1.479 sec\nerode / 4 threads: 1.164 sec\ndilate / background_only=True / 1 thread: 1.598 sec\ndilate / background_only=True / 2 threads: 1.011 sec\ndilate / background_only=True / 4 threads: 0.805 sec\ndilate / background_only=False / 1 thread: 25.182 sec\ndilate / background_only=False / 2 threads: 13.513 sec\ndilate / background_only=False / 4 threads: 8.749 sec\ndilate / background_only=False / 8 threads: 6.640 sec\nscipy grey_dilation / 1 thread 21.109 sec\nscipy grey_erode / 1 thread: 20.305 sec\nskimage expand_labels / 1 thread: 63.247 sec\n```\n\nHere is the performance on a completely zeroed 512<sup>3</sup> volume for multilabel processing.\n\n```\nerode / 1 thread: 0.462 sec\nerode / 2 threads: 0.289 sec\nerode / 4 threads: 0.229 sec\ndilate / background_only=True / 1 thread: 2.337 sec\ndilate / background_only=True / 2 threads: 1.344 sec\ndilate / background_only=True / 4 threads: 1.021 sec\ndilate / background_only=False / 1 thread: 2.267 sec\ndilate / background_only=False / 2 threads: 1.251 sec\ndilate / background_only=False / 4 threads: 0.944 sec\ndilate / background_only=False / 8 threads: 0.718 sec\nscipy grey_dilation / 1 thread 13.516 sec\nscipy grey_erode / 1 thread: 13.326 sec\nskimage expand_labels / 1 thread: 35.243 sec\n```\n\n### Memory Profiles\n\n<center>\n<img src=\"https://github.com/seung-lab/fastmorph/blob/15c4c27ad3255c8ef959ceb67facd65e18eff2e4/memory-profile-dilate-bg-only-false.jpg\" />\n</center>\n\n<center>\n<img src=\"https://github.com/seung-lab/fastmorph/blob/15c4c27ad3255c8ef959ceb67facd65e18eff2e4/memory-profile-dilate-bg-only-true.jpg\" />\n</center>\n\n<center>\n<img src=\"https://github.com/seung-lab/fastmorph/blob/15c4c27ad3255c8ef959ceb67facd65e18eff2e4/memory-profile-skimage-expand_labels.jpg\" />\n</center>\n",
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