Partitioning medical image databases for content-based queries on a Grid.

OBJECTIVES: In this paper we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned into subsets to be processed on different grid nodes. METHODS: A theoretical model of the application complexity and estimates of the grid execution overhead are used to efficiently partition the database. RESULTS: We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time. CONCLUSIONS: Grids are promising for content-based image retrieval in medical databases.