A Component is an implementation of certain image processing / analysis algorithms.

Each component alone does not solve a Bioimage Analysis problem.

These problems can be addressed by combining such components into workflows.

QuickFit 3

Description

QuickFit 3 is a data evaluation software for FCS Fluorescence Correlation Spectroscopy and imagingFCS (imFCS) measurements, developed in the group B040 (Prof. Jörg Langowski) at the German Cancer Research Center (DKFZ). Actually QuickFit 3 itself is a project manager and all functionality is added as plugins. A set of tested plugins for FCS, imagingFCS and some microscopy-related image processing tasks is supplied together with the software.

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Replace Red with Magenta

Description

This plugin converts all occurrences of red in a red/green image with magenta, effectively replacing it with a magenta/green merge.

Note: the plugin completely ignores the blue channel, and replaces it with a copy of the red channel.

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Napari image viewer

Description

napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing large multi-dimensional images. It’s built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (e.g. numpyscipy). It includes critical viewer features out-of-the-box, such as support for large multi-dimensional data, and layering and annotation. By integrating closely with the Python ecosystem, napari can be easily coupled to leading machine learning and image analysis tools (e.g. scikit-imagescikit-learnTensorFlowPyTorch), enabling more user-friendly automated analysis.

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Icy Spot Tracking

Description

Up to version 2 it was known as the ‘Probabilistic particle tracker’ plugin.

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Icy Label Extractor

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Image registration in Matlab with Image Processing Toolbox

Description

Align two images using intensity correlation, feature matching, or control point mapping

Together, Image Processing Toolbox™ and Computer Vision Toolbox™ offer four image registration solutions: interactive registration with a Registration Estimator app, intensity-based automatic image registration, control point registration, and automated feature matching. 

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LBADSA

Description

LBADSA is based on the fitting of the Young-Laplace equation to the image data to measure drops.

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DropSnake

Description

DropSnake is based on B-spline snakes (active contours) to shape and measure a drop.

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FastSME

Description

FastSME: Faster and Smoother Manifold Extraction From 3D Stack.

3D image stacks are routinely acquired to capture data that lie on undulating 3D manifolds yet processed in 2D by biologists. Algorithms to reconstruct the specimen morphology into a 2D representation from the 3D image volume are employed in such scenarios. In this paper, we present FastSME, which offers several improvements on the baseline SME algorithm which enables accurate 2D representation of data on a manifold from 3D volumes, however is computationally expensive. The improvements are achieved in terms of processing speed (3X-10X speed-up depending on image size), minimizing sensitivity to initialization, and also increases local smoothness of the recovered manifold resulting in better reconstructed 2D composite image. We compare the proposed FastSME against the baseline SME as well as other accessible state-of-the-art tools on synthetic and real microscopy data. Our evaluation on multiple metrics demonstrates the efficiency of the presented method in maintaining fidelity of manifold shape and hence specimen morphology.

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SME

Description

Smooth 2D Manifold Extraction (SME).

Three-dimensional fluorescence microscopy followed by image processing is routinely used to study biological objects at various scales such as cells and tissue. However, maximum intensity projection, the most broadly used rendering tool, extracts a discontinuous layer of voxels, obliviously creating important artifacts and possibly misleading interpretation. Here we propose smooth manifold extraction, an algorithm that produces a continuous focused 2D extraction from a 3D volume, hence preserving local spatial relationships. We demonstrate the usefulness of our approach by applying it to various biological applications using confocal and wide-field microscopy 3D image stacks. We provide a parameter-free ImageJ/Fiji plugin that allows 2D visualization and interpretation of 3D image stacks with maximum accuracy.

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SME