Proceedings Article | 8 March 2007
KEYWORDS: Image processing, Diffusion tensor imaging, Medical imaging, Functional magnetic resonance imaging, Image analysis, Brain, Magnetic resonance imaging, 3D image processing, Operating systems, Image segmentation
While there are many publicly available software packages for medical image processing, making them available to end
users in clinical and research labs remains non-trivial. An even more challenging task is to mix these packages to form
pipelines that meet specific needs seamlessly, because each piece of software usually has its own input/output formats,
parameter sets, and so on. To address these issues, we are building WHIPPET (Washington Heterogeneous Image
Processing Pipeline EnvironmenT), a collaborative platform for integrating image analysis tools from different sources.
The central idea is to develop a set of Python scripts which glue the different packages together and make it possible to
connect them in processing pipelines. To achieve this, an analysis is carried out for each candidate package for
WHIPPET, describing input/output formats, parameters, ROI description methods, scripting and extensibility and
classifying its compatibility with other WHIPPET components as image file level, scripting level, function extension
level, or source code level. We then identify components that can be connected in a pipeline directly via image format
conversion. We set up a TWiki server for web-based collaboration so that component analysis and task request can be
performed online, as well as project tracking, knowledge base management, and technical support. Currently WHIPPET
includes the FSL, MIPAV, FreeSurfer, BrainSuite, Measure, DTIQuery, and 3D Slicer software packages, and is
expanding. Users have identified several needed task modules and we report on their implementation.
