Collection

u-track is a multiple-particle tracking Matlab software that is designed to (1) track dense particle fields, (2) close gaps in particle trajectories resulting from detection failure, and (3) capture particle merging and splitting events resulting from occlusion or genuine aggregation and dissociation events. Its core is based on formulating correspondence problems as linear assignment problems and searching for a globally optimal solution.

Data can be read using bio-format and interfaced with OMero data base.

It comes as a standalone software, but can be used as a library, which is according to the authors the most widely used version of it.

• Version 2.2 adds parallel processing functionality for multi-movie datasets when using the GUI.
• Version 2.1 enables the analysis of movies stored on an OMERO server
• Version 2.0 includes two new tracking applications: microtubule plus-end tracking (previously distributed as plusTipTracker) and nuclei tracking
• A third optional processing step has been added to the analysis workflow, track analysis, with two methods: motion analysis and microtubule plus-end classification

For more information, please see Jaqaman et al., Nature Methods 5, pp. 695-702 (2008). Besides basic particle tracking, the software supports the features described in Applegate et al. J. Struct. Biol. 176(2):168-84. 2011 for tracking microtubule plus end markers; and in Ng et al. J. Cell Biol. 199(3):545-63. 2012 for tracking fluorescently-labeled cell nuclei.

 

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

Schnitzcells is a MATLAB based software that allows for quantitative analysis of fluorescent time-lapse movies of living cells. The software package is developed most specifically for bacteria and has been instrumental in analyzing E.coli and B. subtilis movies. The software contains functions that segment cells (based on either fluorescence or phase images),tracks cells in a frame-to-frame manner,build lineage trees and quantitatively extracts fluorescence.

Strength: tools for manually editing segmentation and lineage, well documented, free matlab source code, sample data

Limitations: no support, changes need to be done directly in the matlab code

The Matlab Computer Vision System Toolbox extends the Matlab core functionality with general purpose image processing functions for feature detection & extraction, object detection & tracking and motion estimation. Strengths: - Most functions extend to nD - optimized functions (muti-threaded for some) - Matlab community (Matlab central) - relatively low entry-threshold for functionality - Tutorials & Webinars Limitations: - no embedded visualization of nD Microscopy data

The Matlab image processing toolbox extends the Matlab core functionality with general purpose image processing capabilities. This ranges from image access (read / write), common filters (convolution, morphology, order based, Wiener, feature extraction, image enhancement, ...), image transformation (rotation, affine transformation, ...) to segmentation algorithms (thresholding, watershed, region growing). There is also an extensive list of functions to deal with binary or label mask and perform for instance connected particle analysis or morphological operations. Strengths: - Most functions extend to nD - optimized functions (muti-threaded for some) - Matlab community (Matlab central) - relatively low entry-threshold for functionality - Tutorials & Webinars Limitations: - no embedded visualization of nD Microscopy data