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.
