napari-mahotas-image-processing 0.1.2

Image processing based using the Mahotas library in napari

napari-mahotas-image-processing (n-mahotas)

Image processing based using the Mahotas library in napari

Usage

Gaussian blur

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

Otsu's threshold

Binarizes an image using scikit-image's threshold Otsu algorithm, also known as Otsu's method.

Split connected objects

In case objects stick together after thresholding, this tool might help. It aims to deliver similar results as ImageJ's watershed implementation.

Connected component labeling

Takes a binary image and produces a label image with all separated objects labeled differently. Under the hood, it uses mahotas' label function.

Seeded watershed

Starting from an image showing high-intensity membranes and a seed-image where objects have been labeled, objects are labeled that are constrained by the membranes. Hint: you may want to blur the membrane channel a bit in advance.

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

Installation

Before installing this napari plugin, please install mahotas, e.g. using conda:

conda config --add channels conda-forge
conda install mahotas

Afterwards, you can install napari-mahotas-image-processing via pip:

pip install napari-mahotas-image-processing

To install latest development version :

pip install git+https://github.com/haesleinhuepf/napari-mahotas-image-processing.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-mahotas-image-processing" is free and open source software

Issues

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