ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation.

Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data.

For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both.

ITK is implemented in C++. ITK is cross-platform, using the CMake build environment to manage the compilation process. An automated wrapping process generates interfaces between C++ and interpreted programming languages such as Python, Tcl and Java.

This enables developers to create software using a variety of programming languages. ITK's C++ implementation style is referred to as generic programming (i.e., using templated code).

Such C++ templating means that the code is highly efficient, and that many software problems are discovered at compile-time, rather than at run-time during program execution.

Here are some key features of "ITK":

· Support the Visible Human Project.
· Establish a foundation for future research.
· Create a repository of fundamental algorithms.
· Develop a platform for advanced product development.
· Support commercial application of the technology.
· Create conventions for future work.
· Grow a self-sustaining community of software users and developers.

Requirements:

· CMake

What's New in This Release: [ read full changelog ]

This release offers a fix to a long standing issue in ITK regarding the computation of physical coordinates associated with pixels. This fix is enabled by default, but if you need to revert it to the previous behavior for backward compatibility reasons, you can disable it by turning off the CMake flag:
· ITK_USE_CENTERED_PIXEL_COORDINATES_CONSISTENTLY. Thanks to Tom Vercauteren and Michel Audette for their hard work on getting this difficult issue fixed.

· Many improvements and fixes in the I/O infrastructure were contributed by Brad Lowekamp, making it now possible to manage large image files through the streaming infrastructure of the data pipeline. In particular Brad contributed examples illustrating how to process the entire Visible Human dataset via an ITK pipeline.