C++

An exponential curve fitting library used for Fluorescence Lifetime Imaging (FLIM) and Spectral Lifetime Imaging (SLIM), available as:

• Source code as C/C++ and Java library

• ImageJ plugin

• Standalone Open-Source Windows application

Publications:

• Barber, P.R., Proc. SPIE, 5700, 2005.pdf
• Barber, P.R., J. R. Soc. Interface, 6, 2009.pdf

ITK is an open-source, cross-platform system that provides developers with an extensive suite of software tools for image analysis.

Developed through extreme programming methodologies, ITK employs leading-edge algorithms for registering and segmenting multidimensional data. It is widely used and contributed in the medical imaging field.

Strengths

Highly optimized C++, well commented Consistently updated (new) algorithms many tools and softwares are built upon it connected with VTK Insight Journal (open code and sample data) Extensive list of examples & tutorials

Limitations

yet detached from the bioimage analysis world hard to use for end users without development skills

This library gives the numpy-based infrastructure functions for image processing with a focus on bioimage informatics. It provides image filtering and morphological processing as well as feature computation (both image-level features such as Haralick texture features and SURF local features). These can be used with other Python-based libraries for machine learning to build a complete analysis pipeline.

Mahotas is appropriate for users comfortable with programming or builders of end-user tools.

==== Strengths

The major strengths are in speed and quality of documentation. Almost all of the functionality is implemented in for multiple dimensions. It can be used with other Python packages which provide additional functionality.

Mahotas and all packages on which it relies are open-source.

A General purpose image processing toolkit written in C++ based on ITK, VTK, Qt, and Boost. Main features: algorithms for cell segmentation, cell tracing, cell tracking, and vessel tracing. Registration and mosaicing algorithms for large scale datasets. Visualization tools actively linked to inspect and edit results. Strengths: - Open-source, free, multi platform, code is highly parallelized, uses git for version control - Large scale processing, also efficient visualization of such datasets. - Active learning module for classification - Most of the algorithms have been extended to handle 16-bit images, and 3D Images. - Possibility to create complex pipelines thanks to it’s modular architecture - Editing tools are designed to save the editing operation which can later be used to validate the algorithms performance - Advance preprocessing algorithms like curvelets, tensor voting, and wrappers around ITK-algorithms - Multiple viewers included to inspect results such as: Histograms, scatter plots, tables, kymograph, all of them linked together. - Strong emphasis to work on multichannel images (up to 40 channels) - Rich number of cell features included Weakness: - GUI is suboptimal compared to commercial packages. - Tracking module requires an external library CPLEX. - No support for brightfield images - No native interoperability with other software packages - More documentation needed / tutorial needed

OMERO is a free, open source image management software. It is client-server based system which supports 5D images, including big images and high-content screening data. Data are stored on a server using relational database. They are accessed using 3 main clients, a desktop client, a web client and a command line tool. There are bindings from OMERO to other image analysis packages, like FLIMfit, OMERO.searcher. The data in OMERO are organized in groups. A user can be a member of one or more groups. This groups can be collaborative or private, there are 4 levels of permissions to access/edit/annotate/delete the data of other users.

The package is supported not only by community forums, but also by a dedicated team which helps users to solve their problems and deals with the bugs submitted via error submission system.

###Strengths

Open source, scalable software, Supports diverse sets of imaging applications and domains (EM,LM, HCS, DigPath) Cross-platform, Java-based application, API support for Java, Python, C++, Django, On-line Forums, Automatic QA and upload of software errors Multi-dimensional images, Web access, Free Demo-server accounts

Limitations

Enterprise-scale software, so complex install, requires expertise, Actively developing API, Python scripts and functions still developing