napari-simpleitk-image-processing

Process and analyze images using SimpleITK in napari

    Workflow step:
    Image SegmentationCell segmentation
    Image SegmentationConnected-component analysis
    Image correctionIllumination correction
    Image correction
    Image reconstructionImage denoising
    Image enhancementImage denoising
    Image SegmentationImage thresholding
    Image Segmentation
    Object feature extraction
    Image enhancementSmoothing
    Morphological operations
    Image feature detection
    Image SegmentationSemi-automatic segmentation
    Image feature detectionEdge detection
    Morphological operationsTop-hat transform
    Morphological operationsClosing
    Morphological operationsDilation
    Morphological operationsOpening
    Morphological operationsErosion
    Image enhancement

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License PyPI Python Version tests codecov Development Status napari hub DOI

Process images using SimpleITK 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 graphical user interface. Therefore, just click the menu Tools > Utilities > Assistant (na) or run naparia from the command line.

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All filters implemented in this napari plugin are also demonstrated in this notebook.

Gaussian blur

Applies a Gaussian blur to an image. This might be useful for denoising, e.g. before applying the Threshold-Otsu method.

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Median filter

Applies a median filter to an image. Compared to the Gaussian blur this method preserves edges in the image better. It also performs slower.

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Bilateral filter

The bilateral filter allows denoising an image while preserving edges.

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Threshold Otsu

Binarizes an image using Otsu's method.

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Connected Component Labeling

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

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Measurements

This function allows determining intensity and shape statistics from labeled images.

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Signed Maurer distance map

A 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.

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Binary fill holes

Fills holes in a binary image.

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Touching objects labeling

Starting from a binary image, touching objects can be splits into multiple regions, similar to the Watershed segmentation in ImageJ.

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Morphological Watershed

The morhological watershed 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 for details.

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Watershed-Otsu-Labeling

This algorithm uses Otsu's thresholding method in combination with Gaussian blur and the Watershed-algorithm 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.

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Richardson-Lucy Deconvolution

Richardson-Lucy deconvolution allows to restore image quality if the point-spread-function of the optical system used for acquisition is known or can be approximated.

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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 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:

Furthermore, there are plugins for postprocessing extracted measurements

Contributing

Contributions are very welcome. There are many useful algorithms available in SimpleITK. If you want another one available here in this napari plugin, don't hesitate to send a pull-request. This repository just holds wrappers for SimpleITK-functions, see this file 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 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.

Issues

If you encounter any problems, please file an issue along with a detailed description.

Version:

  • 0.4.4

Release date:

  • 24 September 2022

First released:

  • 28 November 2021

License:

  • BSD-3-Clause

Supported data:

GitHub activity:

  • Stars: 9
  • Forks: 1
  • Issues + PRs: 4

Python versions supported:

Operating system:

Requirements:

  • napari-plugin-engine (>=0.1.4)
  • numpy
  • simpleitk
  • napari-tools-menu (>=0.1.17)
  • napari-time-slicer
  • napari-skimage-regionprops (>=0.5.1)
  • napari-assistant (>=0.3.10)
  • pandas
  • stackview (>=0.3.2)

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