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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)

Abstract

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.

Keywords

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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References

  1. 1.
    Akgül, C., Rubin, D., Napel, S., Beaulieu, C., Greenspan, H., Acar, B.: Content-based image retrieval in radiology: Current status and future directions. J. Digital Imaging 24, 208–222 (2011)CrossRefGoogle Scholar
  2. 2.
    Alto, H., Rangayyan, R.M., Desautels, J.E.L.: Content-based retrieval and analysis of mammographic masses. Journal of Electronic Imaging 14(2), 1–17 (2005)Google Scholar
  3. 3.
    Balan, A.G.R., Traina, A.J.M., Ribeiro, M.X., Marques, P.M.D.A., Traina Jr., C.: HEAD: the human encephalon automatic delimiter. In: CBMS 2007, Maribor, Slovenia, pp. 171–176. IEEE, Los Alamitos (2007)Google Scholar
  4. 4.
    Barioni, M.C.N., Razente, H.L., Traina, A.J.M., Traina, C.J.: SIREN: A similarity retrieval engine for complex data. In: VLDB 2006, Seoul, South Korea, pp. 1155–1158. ACM, New York (2006)Google Scholar
  5. 5.
    Berchtold, S., Böhm, C., Keim, D.A., Krebs, F., Kriegel, H.P.: On optimizing nearest neighbor queries in high-dimensional data spaces. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 435–449. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Bueno, J.M., Chino, F.J.T., Traina, A.J.M., Traina Jr., C., Marques, P.M.d.A.: How to add content-based image retrieval capability in a PACS. In: CBMS 2002, Maribor, Slovenia, pp. 321–326. IEEE, Los Alamitos (2002)Google Scholar
  7. 7.
    Böhm, C., Berchtold, S., Keim, D.A.: Searching in high-dimensional spaces - index structures for improving the performance of multimedia databases. ACM Computing Surveys 33(3), 322–373 (2001)CrossRefGoogle Scholar
  8. 8.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys 40(2), 1–60 (2008)CrossRefGoogle Scholar
  9. 9.
    Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval: An experimental comparison. Information Retrieval 11(2), 77–107 (2008)CrossRefGoogle Scholar
  10. 10.
    Dimitrovski, I., Guguljanov, P., Loskovska, S.: Implementation of web-based medical image retrieval system in oracle. In: ICAST, pp. 192–197. IEEE, Los Alamitos (2009)Google Scholar
  11. 11.
    Felipe, J.C., Traina Jr., C., Traina, A.J.M.: A new family of distance functions for perceptual similarity retrieval of medical images. J. Digital Imaging 22(2), 183–201 (2009)CrossRefGoogle Scholar
  12. 12.
    Greenspan, H., Pinhas, A.T.: Medical image categorization and retrieval for PACS using the GMM-KL framework. IEEE Trans. on Inf. Technology in Biomedicine 11(2), 190–202 (2005)CrossRefGoogle Scholar
  13. 13.
    Guliato, D., Melo, E.V., Rangayyan, R.M., Soares, R.C.: PostgreSQL-IE: An image-handling extension for PostgreSQL. J. Digital Imaging 22(2), 149–165 (2008)CrossRefGoogle Scholar
  14. 14.
    Hsu, W., Antani, S., Long, L.R., Neve, L., Thoma, G.R.: SPIRS: A web-based image retrieval system for large biomedical databases. International Journal of Medical Informatics 78(1), 13–24 (2009)CrossRefGoogle Scholar
  15. 15.
    IBM Corp.: Image, audio, and video extenders administration and programming guide, DB2 Universal Database Version 8 (2003)Google Scholar
  16. 16.
    Kalpathy-Cramer, J., Hersh, W.: Multimodal medical image retrieval: image categorization to improve search precision. In: MIR 2010, Philadelphia, Pennsylvania, USA, pp. 165–174. ACM, New York (2010)Google Scholar
  17. 17.
    Kaster, D.S., Bugatti, P.H., Traina, A.J.M., Traina Jr., C.: FMI-SiR: A flexible and efficient module for similarity searching on Oracle database. JIDM 1(2), 229–244 (2010)Google Scholar
  18. 18.
    Lehmann, T.M., Güld, M., Thies, C., Fischer, B., Spitzer, K., Keysers, D., Ney, H., Kohnen, M., Schubert, H., Wein, B.B.: Content-based image retrieval in medical applications. Methods of Informatics in Medicine 43, 354–361 (2004)Google Scholar
  19. 19.
    Long, L.R., Antani, S., Deserno, T.M., Thoma, G.R.: Content-based image retrieval in medicine: Retrospective assessment, state of the art, and future directions. IJHISI 4(1), 1–16 (2009)Google Scholar
  20. 20.
    Müller, H., Deselaers, T., Deserno, T.M., Kalpathy–Cramer, J., Kim, E., Hersh, W.: Overview of the imageCLEFmed 2007 medical retrieval and medical annotation tasks. In: Peters, C., Jijkoun, V., Mandl, T., Müller, H., Oard, D.W., Peñas, A., Petras, V., Santos, D. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 472–491. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of content-based image retrieval systems in medical applications-clinical benefits and future directions. Int. Journal of Medical Informatics 73(1), 1–23 (2004)CrossRefGoogle Scholar
  22. 22.
    Névéol, A., Deserno, T.M., Darmoni, S.J., Güld, M.O., Aronson, A.R.: Natural language processing versus content-based image analysis for medical document retrieval. J. of the American Society for Information Science and Technology 60(1), 123–134 (2009)CrossRefGoogle Scholar
  23. 23.
    Oracle Corp.: Oracle interMedia User’s Guide, 10g Release 2 (10.2) (2005)Google Scholar
  24. 24.
    Oracle Corp.: Oracle Multimedia DICOM Developer’s Guide, 11g Release 2 (2009)Google Scholar
  25. 25.
    Pereira Jr., R.R., de Azevedo-Marques, P.M., Honda, M.O., Kinoshita, S.K., Engelmann, R., Muramatsu, C., Doi, K.: Usefulness of texture analysis for computerized classification of breast lesions on mammograms. Journal of Digital Imaging 20(3), 248–255 (2007)CrossRefGoogle Scholar
  26. 26.
    Rahman, M.M., Antani, S.K., Thoma, G.R.: A classification-driven similarity matching framework for retrieval of biomedical images. In: MIR 2010, Philadelphia, Pennsylvania, USA, pp. 147–154. ACM, New York (2010)Google Scholar
  27. 27.
    Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann, San Francisco (2006)zbMATHGoogle Scholar
  28. 28.
    Silva, Y.N., Aref, W.G., Ali, M.H.: The similarity join database operator. In: ICDE 2010, Long Beach, California, USA, pp. 892–903. IEEE, Los Alamitos (2010)Google Scholar
  29. 29.
    Tan, Y., Zhang, J., Hua, Y., Zhang, G., Huang, H.: Content-based image retrieval in picture archiving and communication systems. In: Medical Imaging 2006: PACS and Imaging Informatics, San Diego, CA, USA, vol. 6145, pp. 282–289. SPIE, San Jose (2006)Google Scholar
  30. 30.
    Traina Jr., C., Traina, A.J.M., Faloutsos, C., Seeger, B.: Fast indexing and visualization of metric datasets using Slim-trees. IEEE Trans. on Knowl. and Data Eng. 14(2), 244–260 (2002)CrossRefGoogle Scholar
  31. 31.
    Traina Jr., C., Traina, A.J.M., Vieira, M.R., Arantes, A.S., Faloutsos, C.: Efficient processing of complex similarity queries in RDBMS through query rewriting. In: CIKM 2006, Arlington, VA, USA, pp. 4–13. ACM, New York (2006)Google Scholar

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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