Image analysis

SimpleITK provides a simplified interface to ITK in a variety of languages. A user can either download pre-built binaries, if they are available for the desired platform and language, or SimpleITK can be built from the source code. Currently, Python binaries are available on Microsoft Windows, GNU Linux and Mac OS X. C# and Java binaries are available for Windows. We are also working towards supporting R packaging.

Bisque (Bio-Image Semantic Query User Environment) : Store, visualize, organize and analyze images in the cloud. It also allow to run workflows using a set of deployed tools, such as CellProfiler, RootTipMultin Nuclear Tracker, Microtubule tracker etc...

Bisque was developed for the exchange and exploration of biological images.

The Bisque system supports several areas useful for imaging researchers from image capture to image analsysis and querying. The bisque system is centered around a database of images and metadata. Search and comparison of datasets by image data and content is supported. Novel semantic analyses are integrated into the system allowing high level semantic queries and comparison of image content.

• Bisque is free and open-source
• Flexible textual and graphical annotations
• Cloud scalability: PBs of images, millions of annotations
• Distributed storage: local, iRODS, S3
• Integrated image analysis, high-throughput with Condor
• Analysis in MATLAB, Python, Java+ImageJ
• 100+ biological image formats
• Very large 5D images (100+ GB)

CaPTk is a software platform for analysis of radiographic cancer images, currently focusing on brain, breast, and lung cancer. CaPTk integrates advanced, validated tools performing various aspects of medical image analysis, that have been developed in the context of active clinical research studies and collaborations toward addressing real clinical needs. With emphasis given in its use as a very lightweight and efficient viewer, and with no prerequisites for substantial computational background, CaPTk aims to facilitate the swift translation of advanced computational algorithms into routine clinical quantification, analysis, decision making, and reporting workflow. Its long-term goal is providing widely used technology that leverages the value of advanced imaging analytics in cancer prediction, diagnosis and prognosis, as well as in better understanding the biological mechanisms of cancer development.

Image analysis tools to be used within Galaxy