Amira / Avizo Xtra library

Description

Collection of add-ons (recipes, scripts, demos,…) that will help you improve your day-to-day use of Amira-Avizo and PerGeos Software and make you gain both time and efficiency.
Use the Search field to look for specific keywords related to your domain of interest. The different filters also help you target specific resources.

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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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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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Mathematica

Description

Wolfram Mathematica (usually termed Mathematica) is a modern technical computing system spanning most areas of technical computing — including neural networksmachine learningimage processinggeometrydata sciencevisualizations, and others. The system is used in many technical, scientific, engineering, mathematical, and computing fields.

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

DeepCell

Description

 

DeepCell is neural network library for single cell analysis, written in Python and built using TensorFlow and Keras.

DeepCell aids in biological analysis by automatically segmenting and classifying cells in optical microscopy images. This framework consumes raw images and provides uniquely annotated files as an output.

The jupyter session in the read docs are broken, but the one from the GitHub are functional (see usage example )

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EvaluateSegmentation Tool

Description

A command line tool that allows to quantitatively compare two volumes of binary segmentations. Implements 22 different metrics for comparing segmentations such as Dice Coefficient, Hausdorff Distance and average Distance.