MedFMI-SiR: A Powerful DBMS Solution for Large-Scale Medical Image Retrieval

  • Daniel S. Kaster
  • Pedro H. Bugatti
  • Marcelo Ponciano-Silva
  • Agma J. M. Traina
  • Paulo M. A. Marques
  • Antonio C. Santos
  • Caetano TrainaJr.
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6865)


Medical systems increasingly demand methods to deal with the large amount of images that are daily generated. Therefore, the development of fast and scalable applications to store and retrieve images in large repositories becomes an important concern. Moreover, it is necessary to handle textual and content-based queries over such data coupled with DICOM image metadata and their visual patterns. While DBMSs have been extensively used to manage applications’ textual information, content-based processing tasks usually rely on specific solutions. Most of these solutions are targeted to relatively small and controlled datasets, being unfeasible to be employed in real medical environments that deal with voluminous databases. Moreover, since in existing systems the content-based retrieval is detached from the DBMS, queries integrating content- and metadata-based predicates are executed isolated, having their results joined in additional steps. It is easy to realize that this approach prevent from many optimizations that would be employed in an integrated retrieval engine. In this paper we describe the MedFMI-SiR system, which handles medical data joining textual information, such as DICOM tags, and intrinsic image features integrated in the retrieval process. The goal of our approach is to provide a subsystem that can be shared by many complex data applications, such as data analysis and mining tools, providing fast and reliable content-based access over large sets of images. We present experiments that show that MedFMI-SiR is a fast and scalable solution, being able to quickly answer integrated content- and metadata-based queries over a terabyte-sized database with more than 10 million medical images from a large clinical hospital.


Image Retrieval Query Processing DICOM Image Query Plan Image Retrieval System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Daniel S. Kaster
    • 1
    • 2
  • Pedro H. Bugatti
    • 2
  • Marcelo Ponciano-Silva
    • 2
  • Agma J. M. Traina
    • 2
  • Paulo M. A. Marques
    • 3
  • Antonio C. Santos
    • 3
  • Caetano TrainaJr.
    • 2
  1. 1.Department of Computer ScienceUniversity of LondrinaLondrinaBrazil
  2. 2.Department of Computer ScienceUniversity of São PauloSão CarlosBrazil
  3. 3.Department of Internal MedicineRPMS/University of São Paulo (USP)Brazil

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