A multi-resolution approach for content-based image retrieval on the Grid--application to breast cancer detection.
OBJECTIVES: Hospitals and medical centers are producing more and more data that need to be processed. Those data are confidential, heterogeneous, and limited to the geographic site where they have been produced. Unless properly anonymized, they cannot be distributed on wide area networks. METHODS: Grid technologies allow the globalization of storage and processing resources, and enable large-scale experimentations on distributed data. They constitute a promising tool to treat the different data and analyze the knowledge they contain, while offering secured access and high-performance computing capacities to the different users. Our aim is to evaluate the possibilities of grid technologies for handling medical data. RESULTS AND CONCLUSIONS: In this paper, we focus on a breast cancer diagnosis assistance tool, based on distributed and incremental knowledge construction and a content-based image retrieval system. We analyze the different scenarios of uses of such a tool. We further propose an algorithm that indexes mammographic images for content-based query purposes. This algorithm is tested on images of different resolutions in order to reduce the indexation time and we analyze its performance with experiments on the grid.