napari pystackreg

napari-pystackreg

Robust image registration for napari

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    Robust image registration for napari.

    Summary

    napari-pystackreg offers the image registration capabilities of the python package pystackreg for napari.

    Description

    pyStackReg is used to align (register) one or more images to a common reference image, as is required usually in time-resolved fluorescence or wide-field microscopy. It is directly ported from the source code of the ImageJ plugin TurboReg and provides additionally the functionality of the ImageJ plugin StackReg, both of which were written by Philippe Thevenaz/EPFL (available at http://bigwww.epfl.ch/thevenaz/turboreg/).

    pyStackReg provides the following five types of distortion:

    • Translation
    • Rigid body (translation + rotation)
    • Scaled rotation (translation + rotation + scaling)
    • Affine (translation + rotation + scaling + shearing)
    • Bilinear (non-linear transformation; does not preserve straight lines)

    pyStackReg supports the full functionality of StackReg plus some additional options, e.g., using different reference images and having access to the actual transformation matrices (please see the examples below). Note that pyStackReg uses the high quality (i.e. high accuracy) mode of TurboReg that uses cubic spline interpolation for transformation.

    Please note: The bilinear transformation cannot be propagated, as a combination of bilinear transformations does not generally result in a bilinear transformation. Therefore, stack registration/transform functions won't work with bilinear transformation when using "previous" image as reference image. You can either use another reference ( "first" or "mean" for first or mean image, respectively), or try to register/transform each image of the stack separately to its respective previous image (and use the already transformed previous image as reference for the next image).

    Installation

    You can install napari-pystackreg via pip from PyPI:

    pip install napari-pystackreg

    You can also install napari-pystackreg via conda:

    conda install -c conda-forge napari-pystackreg

    Or install it via napari's plugin installer.

    Plugins > Install/Uninstall Plugins... > Filter for "napari-pystackreg" > Install

    To install latest development version:

    pip install git+https://github.com/glichtner/napari-pystackreg.git

    Usage

    Open Plugin User Interface

    Start up napari, e.g. from the command line:

    napari

    Then, load an image stack (e.g. via File > Open Image...) that you want to register. You can also use the example stack provided by the pluging (File > Open Sample > napari-pystackreg: PC12 moving example). Then, select the napari-pystackreg plugin from the Plugins > napari-pystackreg: pystackreg menu.

    User Interface Options

    A variety of options are available to control the registration process:

    • Image Stack: The image layer that should be registered/transformed.
    • Transformation: The type of transformation that should be applied.
      • Translation: translation
      • Rigid body: translation + rotation
      • Scaled rotation: translation + rotation + scaling
      • Affine: translation + rotation + scaling + shearing
      • Bilinear: non-linear transformation; does not preserve straight lines
    • Reference frame: The reference image for registration.
      • Previous frame: Aligns each frame (image) to its previous frame in the stack
      • Mean (all frames): Aligns each frame (image) to the average of all images in the stack
      • Mean (first n frames): Aligns each frame (image) to the mean of the first n frames in the stack. n is a tuneable parameter.
    • Moving-average stack before register: Apply a moving average to the stack before registration. This can be useful to reduce noise in the stack (if the signal-to-noise ratio is very low). The moving average is applied to the stack only for determining the transformation matrices, but not for the actual transforming of the stack.
    • Transformation matrix file: Transformation matrices can be saved to or loaded from a file for permanent storage.

    Reference frame

    The reference frame is the frame to which the other frames are aligned. The default option is to use the Previous frame, which will register each frame to its respective previous frame in the stack. Alternatively, the reference frame can be set to the mean of all frames in the stack (Mean (all frames)) or the mean of the first n frames in the stack (Mean (first n frames)). The latter option can be useful if the first frames in the stack are more stable than the later frames (e.g. if the first frames are taken before the sample is moved). When selecting the Mean (first n frames) option, the number of frames to use for the mean can be set via the spinbox below the option.

    Moving average before registration

    To increase registration performance with low signal-to-noise ratio stacks, a moving average can be applied to the stack before registration. The moving average is applied to the stack only for determining the transformation matrices, but not for the actual transforming of the stack. That means that the transformed stack will still contain the original frames (however registered), but not the averaged frames.

    When selecting the Moving-average stack before register option, the number of frames to use for the moving average can be set via the spinbox below the option.

    Transformation matrix file

    The transformation matrices can be saved to or loaded from a file for permanent storage. This can be useful if you want to apply the same transformation to another stack (e.g. a different channel of the same sample). The transformation matrices are saved as a numpy array in a binary file (.npy). The file can be loaded via the Load button and saved via the Save button.

    Register/Transform

    To perform the actual registration and transformation steps, click the Register and Transform buttons, respectively.

    The Register button will register the stack to the reference by determining the appropriate transformation matrices, without actually transforming the stack. The transformation matrices can be saved to a file via the Save button in the Transformation matrix file section.

    The Transform button (1) will transform the stack to the reference by applying the transformation matrices that are currently loaded to the stack selected in Image Stack. For the button to become active, either the transformation matrices have to be loaded from a file via the Load button in the Transformation matrix file section, or the Register button has to be clicked first to determine the transformation matrices.

    The Transform button will also add a new image layer to the napari viewer (2) with the transformed stack. The name of the new layer will be the name of the original stack with the prefix Registered.

    Finally, the Register & Transform button will perform both the registration and transformation steps in one go.


    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 Apache Software License 2.0 license, "napari-pystackreg" is free and open source software.

    Issues

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

    Acknowledgments

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

    Version:

    • 0.1.4

    Last updated:

    • 15 January 2023

    First released:

    • 07 July 2022

    License:

    • Apache-2.0

    Supported data:

    • Information not submitted

    Plugin type:

    GitHub activity:

    • Stars: 2
    • Forks: 1
    • Issues + PRs: 0

    Python versions supported:

    Operating system:

    Requirements:

    • magicgui
    • numpy
    • pystackreg (>=0.2.6)
    • qtpy

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