# napari-simpleitk-image-processing (n-SimpleITK)
[](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/LICENSE)
[](https://pypi.org/project/napari-simpleitk-image-processing)
[](https://python.org)
[](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/actions)
[](https://codecov.io/gh/haesleinhuepf/napari-simpleitk-image-processing)
[](https://en.wikipedia.org/wiki/Software_release_life_cycle#Alpha)
[](https://napari-hub.org/plugins/napari-simpleitk-image-processing)
[](https://zenodo.org/badge/latestdoi/432729955)
Process images using [SimpleITK](https://simpleitk.org/) in [napari]
## Usage
Filters, segmentation algorithms and measurements provided by this napari plugin can be found in the `Tools` menu.
You can recognize them with their suffix `(n-SimpleITK)` in brackets.
Furthermore, it can be used from the [napari-assistant](https://www.napari-hub.org/plugins/napari-assistant) graphical user interface.
Therefore, just click the menu `Tools > Utilities > Assistant (na)` or run `naparia` from the command line.
All filters implemented in this napari plugin are also demonstrated in [this notebook](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/blob/main/docs/demo.ipynb).
### Gaussian blur
Applies a [Gaussian blur](https://en.wikipedia.org/wiki/Gaussian_blur)
to an image. This might be useful for denoising, e.g. before applying the Threshold-Otsu method.
### Median filter
Applies a [median filter](https://en.wikipedia.org/wiki/Median_filter) to an image.
Compared to the Gaussian blur this method preserves edges in the image better.
It also performs slower.
### Bilateral filter
The [bilateral filter](https://en.wikipedia.org/wiki/Bilateral_filter) allows denoising an image
while preserving edges.
### Threshold Otsu
Binarizes an image using [Otsu's method](https://ieeexplore.ieee.org/document/4310076).
### Connected Component Labeling
Takes a binary image and labels all objects with individual numbers to produce a label image.
### Measurements
This function allows determining intensity and shape statistics from labeled images. I can be found in the `Tools > Measurement tables` menu.
### Signed Maurer distance map
A distance map (more precise: [Signed Maurer Distance Map](https://itk.org/ITKExamples/src/Filtering/DistanceMap/MaurerDistanceMapOfBinary/Documentation.html)) can be useful for visualizing distances within binary images between black/white borders.
Positive values in this image correspond to a white (value=1) pixel's distance to the next black pixel.
Black pixel's (value=0) distance to the next white pixel are represented in this map with negative values.
### Binary fill holes
Fills holes in a binary image.
### Touching objects labeling
Starting from a binary image, touching objects can be splits into multiple regions, similar to the [Watershed segmentation in ImageJ](https://imagej.net/plugins/classic-watershed).
### Morphological Watershed
The [morhological watershed](http://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/32_Watersheds_Segmentation.html)
allows to segment images showing membranes. Before segmentation, a filter such as the Gaussian blur or a median filter
should be used to eliminate noise. It also makes sense to increase the thickness of membranes using a maximum filter.
See [this notebook](https://github.com/clEsperanto/pyclesperanto_prototype/blob/master/demo/segmentation/segmentation_2d_membranes.ipynb) for details.
### Watershed-Otsu-Labeling
This algorithm uses [Otsu's thresholding method](https://ieeexplore.ieee.org/document/4310076) in combination with
[Gaussian blur](https://en.wikipedia.org/wiki/Gaussian_blur) and the
[Watershed-algorithm](https://en.wikipedia.org/wiki/Watershed_(image_processing))
approach to label bright objects such as nuclei in an intensity image. The alogrithm has two sigma parameters and a
level parameter which allow you to fine-tune where objects should be cut (`spot_sigma`) and how smooth outlines
should be (`outline_sigma`). The `watershed_level` parameter determines how deep an intensity valley between two maxima
has to be to differentiate the two maxima.
This implementation is similar to [Voronoi-Otsu-Labeling in clesperanto](https://github.com/clEsperanto/pyclesperanto_prototype/blob/master/demo/segmentation/voronoi_otsu_labeling.ipynb).
### Richardson-Lucy Deconvolution
[Richardson-Lucy deconvolution](https://en.wikipedia.org/wiki/Richardson%E2%80%93Lucy_deconvolution)
allows to restore image quality if the point-spread-function of the optical system used
for acquisition is known or can be approximated.
## Installation
You can install `napari-simpleitk-image-processing` via using `conda` and `pip`.
If you have never used `conda` before, please go through [this tutorial](https://biapol.github.io/blog/johannes_mueller/anaconda_getting_started/) first.
conda install -c conda-forge napari
pip install napari-simpleitk-image-processing
## See also
There are other napari plugins with similar functionality for processing images and extracting features:
* [morphometrics](https://www.napari-hub.org/plugins/morphometrics)
* [PartSeg](https://www.napari-hub.org/plugins/PartSeg)
* [napari-skimage-regionprops](https://www.napari-hub.org/plugins/napari-skimage-regionprops)
* [napari-cupy-image-processing](https://www.napari-hub.org/plugins/napari-cupy-image-processing)
* [napari-pyclesperanto-assistant](https://www.napari-hub.org/plugins/napari-pyclesperanto-assistant)
* [napari-allencell-segmenter](https://napari-hub.org/plugins/napari-allencell-segmenter)
* [RedLionfish](https://www.napari-hub.org/plugins/RedLionfish)
* [bbii-decon](https://www.napari-hub.org/plugins/bbii-decon)
* [napari-segment-blobs-and-things-with-membranes](https://www.napari-hub.org/plugins/napari-segment-blobs-and-things-with-membranes)
Furthermore, there are plugins for postprocessing extracted measurements
* [napari-feature-classifier](https://www.napari-hub.org/plugins/napari-feature-classifier)
* [napari-clusters-plotter](https://www.napari-hub.org/plugins/napari-clusters-plotter)
## Contributing
Contributions are very welcome. There are many useful algorithms available in
[SimpleITK](https://simpleitk.org/). If you want another one available here in this napari
plugin, don't hesitate to send a [pull-request](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/pulls).
This repository just holds wrappers for SimpleITK-functions, see [this file](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/src/napari_simpleitk_image_processing/_simpleitk_image_processing.py#L51) for how those wrappers
can be written.
## License
Distributed under the terms of the [BSD-3] license,
"napari-simpleitk-image-processing" is free and open source software
## Citation
For implementing this napari plugin, the
[SimpleITK python notebooks](https://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/) were very helpful.
Thus, if you find the plugin useful, consider citing the SimpleITK notebooks:
Z. Yaniv, B. C. Lowekamp, H. J. Johnson, R. Beare,
"SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research", \
J Digit Imaging., 31(3): 290-303, 2018, [https://doi.org/10.1007/s10278-017-0037-8](https://doi.org/10.1007/s10278-017-0037-8).
## Issues
If you encounter any problems, please [file an issue] along with a detailed description.
[napari]: https://github.com/napari/napari
[Cookiecutter]: https://github.com/audreyr/cookiecutter
[@napari]: https://github.com/napari
[MIT]: http://opensource.org/licenses/MIT
[BSD-3]: http://opensource.org/licenses/BSD-3-Clause
[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt
[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt
[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0
[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt
[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin
[file an issue]: https://github.com/haesleinhuepf/napari-simpleitk-image-processing/issues
[napari]: https://github.com/napari/napari
[tox]: https://tox.readthedocs.io/en/latest/
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/
Raw data
{
"_id": null,
"home_page": "https://github.com/haesleinhuepf/napari-simpleitk-image-processing",
"name": "napari-simpleitk-image-processing",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "",
"keywords": "",
"author": "Robert Haase",
"author_email": "robert.haase@tu-dresden.de",
"download_url": "https://files.pythonhosted.org/packages/e2/b0/e0af502c5d0d59f9f8b3b1ad4ca914b75075cab3c22a3500b3c79655fad3/napari-simpleitk-image-processing-0.4.5.tar.gz",
"platform": null,
"description": "# napari-simpleitk-image-processing (n-SimpleITK)\r\n\r\n[](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/LICENSE)\r\n[](https://pypi.org/project/napari-simpleitk-image-processing)\r\n[](https://python.org)\r\n[](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/actions)\r\n[](https://codecov.io/gh/haesleinhuepf/napari-simpleitk-image-processing)\r\n[](https://en.wikipedia.org/wiki/Software_release_life_cycle#Alpha)\r\n[](https://napari-hub.org/plugins/napari-simpleitk-image-processing)\r\n[](https://zenodo.org/badge/latestdoi/432729955)\r\n\r\nProcess images using [SimpleITK](https://simpleitk.org/) in [napari]\r\n\r\n## Usage\r\n\r\nFilters, segmentation algorithms and measurements provided by this napari plugin can be found in the `Tools` menu. \r\nYou can recognize them with their suffix `(n-SimpleITK)` in brackets.\r\nFurthermore, it can be used from the [napari-assistant](https://www.napari-hub.org/plugins/napari-assistant) graphical user interface. \r\nTherefore, just click the menu `Tools > Utilities > Assistant (na)` or run `naparia` from the command line.\r\n\r\n\r\n\r\nAll filters implemented in this napari plugin are also demonstrated in [this notebook](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/blob/main/docs/demo.ipynb).\r\n\r\n### Gaussian blur\r\n\r\nApplies a [Gaussian blur](https://en.wikipedia.org/wiki/Gaussian_blur)\r\nto an image. This might be useful for denoising, e.g. before applying the Threshold-Otsu method.\r\n\r\n\r\n\r\n### Median filter\r\n\r\nApplies a [median filter](https://en.wikipedia.org/wiki/Median_filter) to an image. \r\nCompared to the Gaussian blur this method preserves edges in the image better. \r\nIt also performs slower.\r\n\r\n\r\n\r\n### Bilateral filter\r\n\r\nThe [bilateral filter](https://en.wikipedia.org/wiki/Bilateral_filter) allows denoising an image\r\nwhile preserving edges.\r\n\r\n\r\n\r\n### Threshold Otsu\r\n\r\nBinarizes an image using [Otsu's method](https://ieeexplore.ieee.org/document/4310076).\r\n\r\n\r\n\r\n### Connected Component Labeling\r\n\r\nTakes a binary image and labels all objects with individual numbers to produce a label image.\r\n\r\n\r\n\r\n### Measurements\r\n\r\nThis function allows determining intensity and shape statistics from labeled images. I can be found in the `Tools > Measurement tables` menu.\r\n\r\n\r\n\r\n### Signed Maurer distance map\r\n\r\nA distance map (more precise: [Signed Maurer Distance Map](https://itk.org/ITKExamples/src/Filtering/DistanceMap/MaurerDistanceMapOfBinary/Documentation.html)) can be useful for visualizing distances within binary images between black/white borders. \r\nPositive values in this image correspond to a white (value=1) pixel's distance to the next black pixel.\r\nBlack pixel's (value=0) distance to the next white pixel are represented in this map with negative values.\r\n\r\n\r\n\r\n### Binary fill holes\r\n\r\nFills holes in a binary image.\r\n\r\n\r\n\r\n### Touching objects labeling\r\n\r\nStarting from a binary image, touching objects can be splits into multiple regions, similar to the [Watershed segmentation in ImageJ](https://imagej.net/plugins/classic-watershed).\r\n\r\n\r\n\r\n### Morphological Watershed\r\n\r\nThe [morhological watershed](http://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/32_Watersheds_Segmentation.html)\r\nallows to segment images showing membranes. Before segmentation, a filter such as the Gaussian blur or a median filter\r\nshould be used to eliminate noise. It also makes sense to increase the thickness of membranes using a maximum filter. \r\nSee [this notebook](https://github.com/clEsperanto/pyclesperanto_prototype/blob/master/demo/segmentation/segmentation_2d_membranes.ipynb) for details.\r\n\r\n\r\n\r\n### Watershed-Otsu-Labeling\r\n\r\nThis algorithm uses [Otsu's thresholding method](https://ieeexplore.ieee.org/document/4310076) in combination with \r\n[Gaussian blur](https://en.wikipedia.org/wiki/Gaussian_blur) and the \r\n[Watershed-algorithm](https://en.wikipedia.org/wiki/Watershed_(image_processing)) \r\napproach to label bright objects such as nuclei in an intensity image. The alogrithm has two sigma parameters and a \r\nlevel parameter which allow you to fine-tune where objects should be cut (`spot_sigma`) and how smooth outlines \r\nshould be (`outline_sigma`). The `watershed_level` parameter determines how deep an intensity valley between two maxima \r\nhas to be to differentiate the two maxima. \r\nThis implementation is similar to [Voronoi-Otsu-Labeling in clesperanto](https://github.com/clEsperanto/pyclesperanto_prototype/blob/master/demo/segmentation/voronoi_otsu_labeling.ipynb).\r\n\r\n\r\n\r\n\r\n### Richardson-Lucy Deconvolution\r\n\r\n[Richardson-Lucy deconvolution](https://en.wikipedia.org/wiki/Richardson%E2%80%93Lucy_deconvolution)\r\nallows to restore image quality if the point-spread-function of the optical system used \r\nfor acquisition is known or can be approximated.\r\n\r\n\r\n\r\n\r\n## Installation\r\n\r\nYou can install `napari-simpleitk-image-processing` via using `conda` and `pip`.\r\nIf you have never used `conda` before, please go through [this tutorial](https://biapol.github.io/blog/johannes_mueller/anaconda_getting_started/) first.\r\n\r\n conda install -c conda-forge napari\r\n pip install napari-simpleitk-image-processing\r\n\r\n## See also\r\n\r\nThere are other napari plugins with similar functionality for processing images and extracting features:\r\n* [morphometrics](https://www.napari-hub.org/plugins/morphometrics)\r\n* [PartSeg](https://www.napari-hub.org/plugins/PartSeg)\r\n* [napari-skimage-regionprops](https://www.napari-hub.org/plugins/napari-skimage-regionprops)\r\n* [napari-cupy-image-processing](https://www.napari-hub.org/plugins/napari-cupy-image-processing)\r\n* [napari-pyclesperanto-assistant](https://www.napari-hub.org/plugins/napari-pyclesperanto-assistant)\r\n* [napari-allencell-segmenter](https://napari-hub.org/plugins/napari-allencell-segmenter)\r\n* [RedLionfish](https://www.napari-hub.org/plugins/RedLionfish)\r\n* [bbii-decon](https://www.napari-hub.org/plugins/bbii-decon) \r\n* [napari-segment-blobs-and-things-with-membranes](https://www.napari-hub.org/plugins/napari-segment-blobs-and-things-with-membranes)\r\n\r\nFurthermore, there are plugins for postprocessing extracted measurements\r\n* [napari-feature-classifier](https://www.napari-hub.org/plugins/napari-feature-classifier)\r\n* [napari-clusters-plotter](https://www.napari-hub.org/plugins/napari-clusters-plotter)\r\n\r\n## Contributing\r\n\r\nContributions are very welcome. There are many useful algorithms available in \r\n[SimpleITK](https://simpleitk.org/). If you want another one available here in this napari\r\nplugin, don't hesitate to send a [pull-request](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/pulls).\r\nThis repository just holds wrappers for SimpleITK-functions, see [this file](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/src/napari_simpleitk_image_processing/_simpleitk_image_processing.py#L51) for how those wrappers\r\ncan be written.\r\n\r\n## License\r\n\r\nDistributed under the terms of the [BSD-3] license,\r\n\"napari-simpleitk-image-processing\" is free and open source software\r\n\r\n## Citation\r\n\r\nFor implementing this napari plugin, the \r\n[SimpleITK python notebooks](https://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/) were very helpful. \r\nThus, if you find the plugin useful, consider citing the SimpleITK notebooks:\r\n\r\nZ. Yaniv, B. C. Lowekamp, H. J. Johnson, R. Beare, \r\n\"SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research\", \\\r\nJ Digit Imaging., 31(3): 290-303, 2018, [https://doi.org/10.1007/s10278-017-0037-8](https://doi.org/10.1007/s10278-017-0037-8).\r\n\r\n## Issues\r\n\r\nIf you encounter any problems, please [file an issue] along with a detailed description.\r\n\r\n[napari]: https://github.com/napari/napari\r\n[Cookiecutter]: https://github.com/audreyr/cookiecutter\r\n[@napari]: https://github.com/napari\r\n[MIT]: http://opensource.org/licenses/MIT\r\n[BSD-3]: http://opensource.org/licenses/BSD-3-Clause\r\n[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt\r\n[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt\r\n[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0\r\n[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt\r\n[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin\r\n\r\n[file an issue]: https://github.com/haesleinhuepf/napari-simpleitk-image-processing/issues\r\n\r\n[napari]: https://github.com/napari/napari\r\n[tox]: https://tox.readthedocs.io/en/latest/\r\n[pip]: https://pypi.org/project/pip/\r\n[PyPI]: https://pypi.org/\r\n",
"bugtrack_url": null,
"license": "BSD-3-Clause",
"summary": "Process and analyze images using SimpleITK in napari",
"version": "0.4.5",
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "79a35ef9bd91ec2f0803c19cd91095e94e2a59e02dc270f5f1e37b74b10e117c",
"md5": "406cad5452e5154954b6d3544d214da8",
"sha256": "c294c266fc6af007393b88297615e092ef7670b6c22f257f24625d45c5068577"
},
"downloads": -1,
"filename": "napari_simpleitk_image_processing-0.4.5-py3-none-any.whl",
"has_sig": false,
"md5_digest": "406cad5452e5154954b6d3544d214da8",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 17297,
"upload_time": "2023-01-15T16:52:24",
"upload_time_iso_8601": "2023-01-15T16:52:24.503482Z",
"url": "https://files.pythonhosted.org/packages/79/a3/5ef9bd91ec2f0803c19cd91095e94e2a59e02dc270f5f1e37b74b10e117c/napari_simpleitk_image_processing-0.4.5-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "e2b0e0af502c5d0d59f9f8b3b1ad4ca914b75075cab3c22a3500b3c79655fad3",
"md5": "67e692c88cd69f98b07cf143d9804827",
"sha256": "b690d38aa67cdce3ae9e9b38f5ea98aa2bb3fcfb7b41943c56c68ed5df70ba3c"
},
"downloads": -1,
"filename": "napari-simpleitk-image-processing-0.4.5.tar.gz",
"has_sig": false,
"md5_digest": "67e692c88cd69f98b07cf143d9804827",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 18861,
"upload_time": "2023-01-15T16:52:26",
"upload_time_iso_8601": "2023-01-15T16:52:26.233067Z",
"url": "https://files.pythonhosted.org/packages/e2/b0/e0af502c5d0d59f9f8b3b1ad4ca914b75075cab3c22a3500b3c79655fad3/napari-simpleitk-image-processing-0.4.5.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-01-15 16:52:26",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "haesleinhuepf",
"github_project": "napari-simpleitk-image-processing",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"tox": true,
"lcname": "napari-simpleitk-image-processing"
}
JSON
