Collection

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

ilastik is a simple, user-friendly tool for interactive image classification, segmentation and analysis. It is built as a modular software framework, which currently has workflows for automated (supervised) pixel- and object-level classification, automated and semi-automated object tracking, semi-automated segmentation and object counting without detection. Most analysis operations are performed lazily, which enables targeted interactive processing of data subvolumes, followed by complete volume analysis in offline batch mode. Using it requires no experience in image processing.

ilastik (the image learning, analysis, and segmentation toolkit) provides non-experts with a menu of pre-built image analysis workflows. ilastik handles data of up to five dimensions (time, 3D space, and spectral dimension). Its workflows provide an interactive experience to give the user immediate feedback on the quality of the results yielded by her chosen parameters and/or labelings.

The most commonly used workflow is pixel classification, which requires very little parameter tuning and instead offers a machine learning technique for segmenting an image based on local image features computed for each pixel.

Other workflows include:

Object classification: Similar to pixel classification, but classifies previously segmented objects by object characteristics in a subsequent step

Autocontext: This workflow improves the pixel classification workflow by running it in multiple stages and showing each pixel the results of the previous stage.

Carving: Semi-automated segmentation of 3D objects (e.g. neurons) based on user-provided seeds

Manual Tracking: Semi-automated cell tracking of 2D+time or 3D+time images based on manual annotations

Automated tracking: Fully-automated cell tracking of 2D+time or 3D+time images with some parameter tuning

Density Counting: Learned cell population counting based on interactively provided user annotation

Strengths: interactive, simple interface (for non-experts), few parameters, larger-than-RAM data, multi-dimensional data (time, 3D space, channel), headless operation, batch mode, parallelized computation, open source

Weaknesses: Pre-built workflows (not reconfigurable), no plugin system, visualization sometimes buggy, must import 3D data to HDF5, tracking requires an external CPLEX installation

Supported Formats: hdf5, tiff, jpeg, png, bmp, pnm, gif, hdr, exr, sif