library

The PYthon Microscopy Environment is an open-source package providing image acquisition and data analysis functionality for a number of microscopy applications, but with a particular emphasis on single molecule localisation microscopy (PALM/STORM/PAINT etc ...). The package is multi platform, running on Windows, Linux, and OSX.

It comes with 3 main modules:

• PYMEAcquire - Instrument control and simulation
• dh5view - Image Data Analysis and Viewing
• VisGUI - Visualising Localization Data Sets

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. 

A Python based workflow management software that allows to create workflows that seamlessly scale from a single workstation to a high performance computing cluster or cloud environments. 

An implementation of Belief Propagation for factor graphs, also known as the sum-product algorithm

This is a (Cython-based) Python wrapper for Philipp Krähenbühl's Fully-Connected CRFs (version 2).

PyTorch is an open-source machine learning library for Python, based on Torch, used for applications such as natural language processing.

The Binary Pattern Dictionary Learning (BPDL) package is suitable for image analysis on a set/sequence of images to determine an atlas of a compact region. In particular, the application can be maping gene activation accross many samples, brain activations in a time domain, etc.

The project introduces a cross-platform framework for comparison of image registration methods with landmark validation (registration precision is measured by user landmarks). The project contains a set of sample images with related landmark annotations and experimental evaluation of state-of-the-art image registration methods.

Some key features of the framework:

• automatic execution of image registration of a sequence of image pairs
• integrated evaluation of registration performances using Target Registration Error (TRE)
• integrated visualization of performed registration
• running several image registration experiment in parallel
• resuming unfinished sequence of registration benchmark
• handling around dataset and creating own experiments
• rerun evaluation and visualisation for finished experiments

Collection of several basic standard image segmentation methods focusing on medical imaging. In particular, the key block/applications are (un)supervised image segmentation using superpixels, object centre detection and region growing with a shape prior. Besides the open-source code, there is also a few sample images.

A set of ImageJ Built-in Macro Functions used to perform operations on the ImageJ platform.