Medical image processing is known as a computationally expensive and data intensive domain. It is thus well-suited for Grid computing. However, Grid computing usually requires the applications to be designed for parallel processing, which is a challenge for medical imaging researchers in hospitals that are most often not used to this. Making parallel programming methods easier to apply can promote Grid technologies in clinical environments. Readily available, functional tools with an intuitive interface are required to really promote healthgrids. Moreover, the tools need to be well integrated with the Grid infrastructure. To facilitate the adoption of Grids in the Geneva University Hospitals we have set up a develop environment based on the Taverna workflow engine. Its usage with a medical imaging application on the hospitals' internal Grid cluster is presented in this paper.