ImagePy

Description

This note presents the design of a scalable software package named ImagePy for analysing biological images. Our contribution is concentrated on facilitating extensibility and interoperability of the software through decoupling the data model from the user interface. Especially with assistance from the Python ecosystem, this software framework makes modern computer algorithms easier to be applied in bioimage analysis.

NBLAST

Description

This R package implements the NBLAST neuron similarity algorithm described in a preprint available at http://dx.doi.org/10.1101/006346. In addition to basic pairwise comparison, the package implements search of databases of neurons. There is also suport for all x all comparison for a group of neurons. This can produce a distance matrix suitable for hierarchical clustering, which is also implemented in the package.

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NeuroAnatomy Toolbox

Description

An R package for the (3D) visualisation and analysis of biological image data, especially tracings of single neurons. nat is the core package of a wider suite of neuroanatomy tools introduced at http://jefferislab.github.io.

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wnd-charm

Description

WND-CHARM is a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provides classification accuracy comparable to state-of-the-art task-specific image classifiers. WND-CHARM can extract up to ~3,000 generic image descriptors (features) including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are derived from the raw image, transforms of the image, and compound transforms of the image (transforms of transforms). The features are filtered and weighted depending on their effectiveness in discriminating between a set of predefined image classes (the training set). These features are then used to classify test images based on their similarity to the training classes. This classifier was tested on a wide variety of imaging problems including biological and medical image classification using several imaging modalities, face recognition, and other pattern recognition tasks. WND-CHARM is an acronym that stands for "Weighted Neighbor Distance using Compound Hierarchy of Algorithms Representing Morphology."

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SR-Tesseler

Description

Localization-based super-resolution techniques open the door to unprecedented analysis of molecular organization. This task often involves complex image processing adapted to the specific topology and quality of the image to be analyzed. SR-Tesseler is an open-source segmentation software using Voronoï tessellation constructed from the coordinates of localized molecules. It allows precise, robust and automatic quantification of protein organization at different scales, from the cellular level down to clusters of a few fluorescent markers. SR-Tesseler is insensitive to cell shape, molecular organization, background and noise, allowing comparing efficiently different biological conditions in a non-biased manner, and perform quantifications on various proteins and cell types. SR-Tesseler software comes with a very simple and intuitive graphical user interface, providing direct visual feedback of the results and is freely available under GPLv3 license.

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Density map of a neuron extracted from the Voronoï diagram

Phenoripper

Description

An easy to use, image analysis software package that enables rapid exploration and interpretation of microscopy data.

PhenoBrowser

scikit-image

Description

Image processing library for Python >The scikit-image SciKit (toolkit for SciPy) extends scipy.ndimage to provide a versatile set of image processing routines. It is written in the Python language. This SciKit is developed by the SciPy community. All contributions are most welcome!

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