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Imaris is a software for data visualization, analysis, segmentation and interpretation of 3D and 4D microscopy images. It performs interactive volume rendering that lets users freely navigate even very large datasets (hundreds of GB). It performs both manual and automated detection and tracking of biological “objects” such as cells, nuclei, vesicles, neurons, and many more. ImarisSpots for example is a tool to detect “spherical objects” and track them in time series. Besides the automated detection it gives the user the ability to manually delete and place new spots in 3D space.

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

Reproducing an experiment doesn’t stop at the bench when images are concerned. Icy is an open source bioimaging software package that aims to provide a framework for authors to share, and others to reproduce, research once the sample hits the microscope. Icy was released in April 2011 and is being developed at the Quantitative Image Analysis Unit at the Pasteur Institute in France by Jean-Christophe Olivo-Marin and his team.

ImgLib2 is a generic, multi-dimensional data processing library allowing for processing algorithms to be defined in a data-type, dimension, and container independent manner. Due to its interface-based design, it is easy to write adapters to virtually all existing data containers. It is the basis of KNIME, ImageJ2 and a couple of Fiji plugins.

Fiji is just ImageJ: a distribution of ImageJ (and ImageJ2) together with Java, Java 3D and a lot of plugins organized into a coherent menu structure. The main focus of Fiji is to assist research in life sciences. It is a free, open-source, community-driven project.

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.

PALMsiever is a MATLAB-based application that allows the filtering (sieving) and analysis of localization-microscopy data. It provides the ability to render the data using different visualization algorithms and perform simple measurements on the point-localization data. It is extensible using simple MATLAB scripts and a number of plugins is already provided with the software itself, including a clustering algorithm and 3D rendering.

Strengths: intuitive, easy navigation through the point-localization data

Limitations: no multi-color

This ImageJ plugin creates high resolution PSF images by averaging many bead im- ages as well as exploiting the assumption that the point spread function is rotationally symmetric with respect to the axial axis (z-direction).

Easy-to-use, computationally efficient, two- and three-dimensional, feature point-tracking tool for the automated detection and analysis of particle trajectories as recorded by video imaging in cell biology. 

The tracking process requires no apriori mathematical modelling of the motion, it is self-initialising, it discriminates spurious detections, and it can handle temporary occlusion as well as particle appearance and disappearance from the image region. 

Image segmentation based on the MOSAIC Discrete region competition algorithm. 

‘’’Squassh’’’ is a tool for 2D and 3D segmentation and quantification of subcellular shapes in fluorescence microscopy images. It provides globally optimal detection and segmentation of objects with constant internal intensity distribution, followed by object-based colocalization analysis. The segmentation computed by Region Competition can optionally correct for the PSF of the microscope, hence providing optimally deconvolved segmentations. Part of the mosaic suite