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Platform for distributed image processing and image retrieval

We describe a platform for the implementation of a system for content-based image retrieval in medical applications (IRMA). To cope with the constantly evolving medical knowledge, the platform offers a flexible feature model to store and uniformly access all feature types required within a multi-step retrieval approach. A structured generation history for each feature allows the automatic identification and re-use of already computed features. The platform uses directed acyclic graphs composed of processing steps and control elements to model arbitrary retrieval algorithms. This visually intuitive, data-flow oriented representation vastly improves the interdisciplinary communication between computer scientists and physicians during the development of new retrieval algorithms. The execution of the graphs is fully automated within the platform. Each processing step is modeled as a feature transformation. Due to a high degree of system transparency, both the implementation and the evaluation of retrieval algorithms are accelerated significantly. The platform uses a client-server architecture consisting of a central database, a central job scheduler, instances of a daemon service, and clients which embed user-implemented feature ansformations. Automatically distributed batch processing and distributed feature storage enable the cost-efficient use of an existing workstation cluster.