napari-skimage-regionprops (nsr)

A napari plugin for measuring properties of labeled objects based on scikit-image

Usage

From the menu Tools > Measurement > Regionprops (nsr) you can open a dialog where you can choose an intensity image, a corresponding label image and the features you want to measure:

If you want to interface with the labels and see which table row corresponds to which labeled object, use the label picker and activate the show selected checkbox.

If you closed a table and want to reopen it, you can use the menu Tools > Measurements > Show table (nsr) to reopen it. You just need to select the labels layer the properties are associated with.

For visualizing measurements with different grey values, as parametric images, you can double-click table headers.

Usage, programmatically

You can also control the tables programmatically. See this example notebook for details.

Features

The user can select categories of features for feature extraction in the user interface. These categories contain measurements from the scikit-image regionprops list of measurements library:

• size:
  • area
  • bbox_area
  • convex_area
  • equivalent_diameter
• intensity:
  • max_intensity
  • mean_intensity
  • min_intensity
  • standard_deviation_intensity (extra_properties implementation using numpy)
• perimeter:
  • perimeter
  • perimeter_crofton
• shape
  • major_axis_length
  • minor_axis_length
  • orientation
  • solidity
  • eccentricity
  • extent
  • feret_diameter_max
  • local_centroid
  • roundness as defined for 2D labels by ImageJ
  • circularity as defined for 2D labels by ImageJ
  • aspect_ratio as defined for 2D labels by ImageJ
• position:
  • centroid
  • bbox
  • weighted_centroid
• moments:
  • moments
  • moments_central
  • moments_hu
  • moments_normalized

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

See also

There are other napari plugins with similar functionality for extracting features:

• morphometrics
• PartSeg
• napari-simpleitk-image-processing
• napari-cupy-image-processing
• napari-pyclesperanto-assistant

Furthermore, there are plugins for postprocessing extracted measurements

• napari-feature-classifier
• napari-clusters-plotter
• napari-accelerated-pixel-and-object-classification

Installation

You can install napari-skimage-regionprops via pip:

pip install napari-skimage-regionprops

Or if you plan to develop it:

git clone https://github.com/haesleinhuepf/napari-skimage-regionprops
cd napari-skimage-regionprops
pip install -e .

If there is an error message suggesting that git is not installed, run conda install git.

Contributing

Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.

License

Distributed under the terms of the BSD-3 license, "napari-skimage-regionprops" is free and open source software

Issues

If you encounter any problems, please create a thread on image.sc along with a detailed description and tag @haesleinhuepf.