Package: napari-simpleitk-image-processing
napari-simpleitk-image-processing (n-SimpleITK)Process images usingSimpleITKinnapariUsageFilters, segmentation algorithms and measurements provided by this napari plugin can be found in theToolsmenu.
You can recognize them with their suffix(n-SimpleITK)in brackets.
Furthermore, it can be used from thenapari-assistantgraphical user interface.
Therefore, just click the menuTools > Utilities > Assistant (na)or runnapariafrom the command line.All filters implemented in this napari plugin are also demonstrated inthis notebook.Gaussian blurApplies aGaussian blurto an image. This might be useful for denoising, e.g. before applying the Threshold-Otsu method.Median filterApplies amedian filterto an image.
Compared to the Gaussian blur this method preserves edges in the image better.
It also performs slower.Bilateral filterThebilateral filterallows denoising an image
while preserving edges.Threshold OtsuBinarizes an image usingOtsu's method.Connected Component LabelingTakes a binary image and labels all objects with individual numbers to produce a label image.MeasurementsThis function allows determining intensity and shape statistics from labeled images. I can be found in theTools > Measurement tablesmenu.Signed Maurer distance mapA distance map (more precise:Signed Maurer Distance Map) 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 holesFills holes in a binary image.Touching objects labelingStarting from a binary image, touching objects can be splits into multiple regions, similar to theWatershed segmentation in ImageJ.Morphological WatershedThemorhological watershedallows 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.
Seethis notebookfor details.Watershed-Otsu-LabelingThis algorithm usesOtsu's thresholding methodin combination withGaussian blurand theWatershed-algorithmapproach 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). Thewatershed_levelparameter determines how deep an intensity valley between two maxima
has to be to differentiate the two maxima.
This implementation is similar toVoronoi-Otsu-Labeling in clesperanto.Richardson-Lucy DeconvolutionRichardson-Lucy deconvolutionallows to restore image quality if the point-spread-function of the optical system used
for acquisition is known or can be approximated.InstallationYou can installnapari-simpleitk-image-processingvia usingcondaandpip.
If you have never usedcondabefore, please go throughthis tutorialfirst.conda install -c conda-forge napari
pip install napari-simpleitk-image-processingSee alsoThere are other napari plugins with similar functionality for processing images and extracting features:morphometricsPartSegnapari-skimage-regionpropsnapari-cupy-image-processingnapari-pyclesperanto-assistantnapari-allencell-segmenterRedLionfishbbii-deconnapari-segment-blobs-and-things-with-membranesFurthermore, there are plugins for postprocessing extracted measurementsnapari-feature-classifiernapari-clusters-plotterContributingContributions are very welcome. There are many useful algorithms available inSimpleITK. If you want another one available here in this napari
plugin, don't hesitate to send apull-request.
This repository just holds wrappers for SimpleITK-functions, seethis filefor how those wrappers
can be written.LicenseDistributed under the terms of theBSD-3license,
"napari-simpleitk-image-processing" is free and open source softwareCitationFor implementing this napari plugin, theSimpleITK python notebookswere 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.IssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.