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https://github.com/haesleinhuepf/napari-simpleitk-image-processing

Process images using SimpleITK in napari
https://github.com/haesleinhuepf/napari-simpleitk-image-processing

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Process images using SimpleITK in napari

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README 

        
# napari-simpleitk-image-processing (n-SimpleITK)



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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.



![img.png](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/docs/screenshot_with_assistant.png)



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.



![img.png](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/docs/gaussian_blur.png)



### 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.



![img.png](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/docs/median_filter.png)



### Bilateral filter



The [bilateral filter](https://en.wikipedia.org/wiki/Bilateral_filter) allows denoising an image

while preserving edges.



![img.png](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/docs/bilateral.png)



### Threshold Otsu



Binarizes an image using [Otsu's method](https://ieeexplore.ieee.org/document/4310076).



![img.png](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/docs/threshold_otsu.png)



### Connected Component Labeling



Takes a binary image and labels all objects with individual numbers to produce a label image.



![img.png](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/docs/connected_component_labeling.png)



### Measurements



This function allows determining intensity and shape statistics from labeled images. I can be found in the `Tools > Measurement tables` menu.



![img.png](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/docs/measurements.png)



### 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.



![img.png](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/docs/signed_maured_distance_map.png)



### Binary fill holes



Fills holes in a binary image.



![img.png](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/docs/binary_fill_holes.png)



### 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).



![img.png](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/docs/Touching_object_labeling.png)



### 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.



![img.png](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/docs/morphological_watershed.png)



### 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).



![img.png](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/docs/watershed_otsu_labeling.png)



### 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.



![img.png](https://github.com/haesleinhuepf/napari-simpleitk-image-processing/raw/main/docs/Richardson-Lucy-Deconvolution.png)



## 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



## Features



The user can select categories of features for feature extraction in the user interface. These categories contain the following measurements:

* size:

    * equivalent_ellipsoid_diameter

    * equivalent_spherical_perimeter

    * equivalent_spherical_radius

    * number_of_pixels

    * number_of_pixels_on_border

* intensity:

   * maximum 

   * mean 

   * median

   * minimum 

   * sigma

   * sum

   * variance

* perimeter:

   * perimeter

   * perimeter_on_border

   * perimeter_on_border_ratio

* shape:

   * elongation

   * feret_diameter

   * flatness

   * roundness

* position:

   * centroid 

   * bbox

* moments:

   * principal_axes

   * principal_moments



## 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/