Journal of Digital Imaging

, Volume 27, Issue 3, pp 297–308 | Cite as

Optimization of PACS Data Persistency Using Indexed Hierarchical Data

  • Thiago C. Prado
  • Douglas D. J. de Macedo
  • M. A. R. Dantas
  • Aldo von Wangenheim


We present a new approach for the development of a data persistency layer for a Digital Imaging and Communications in Medicine (DICOM)-compliant Picture Archiving and Communications Systems employing a hierarchical database. Our approach makes use of the HDF5 hierarchical data storage standard for scientific data and overcomes limitations of hierarchical databases employing inverted indexing for secondary key management and for efficient and flexible access to data through secondary keys. This inverted indexing is achieved through a general purpose document indexing tool called Lucene. This approach was implemented and tested using real-world data against a traditional solution employing a relational database, in various store, search, and retrieval experiments performed repeatedly with different sizes of DICOM datasets. Results show that our approach outperforms the traditional solution on most of the situations, being more than 600 % faster in some cases.


DICOM Hierarchical data format PACS Data Indexing 


  1. 1.
    Acr: Acr Practice Guideline for the Performance of Screening and Diagnostic Mammography, American College of Radiology, 2008, p 1Google Scholar
  2. 2.
    RCR: Retention and Storage of Images and Radiological Patient Data. The Royal College of Radiologists, London, 2008, p 3Google Scholar
  3. 3.
    BMJ: Verordnung über den Schutz vor Schäden durch Röntgenstrahlen. Bundesministerium der Justiz, 1987Google Scholar
  4. 4.
    CFM: RESOLUÇÃO CFM Nº 1.821/07, CFM, Editor. Conselho Federal de Medicina, 2007Google Scholar
  5. 5.
    HDFGROUP: The HDF Group - Information, Support, and Software. 2011 [cited 2012 Jun 05]; Available from:
  6. 6.
    HDFGROUP: Who uses HDF? 2011 [cited 2012 Jun 05]; Available from:
  7. 7.
    Wallauer J, Macedo DDJ, Andrade R, and Von Wangenheim A. Building a national telemedicine network. IT professional 10(2):12–17, 2008Google Scholar
  8. 8.
    Wangenheim A, Junior CLB, Wagner HM, Cavalcante C: Ways to implement large scale telemedicine: The Santa Catarina Experience. Lat Am J Telehealth 1(3):364–377, 2010Google Scholar
  9. 9.
    Macedo DDJ, Von Wangenheim A, Dantas M, Perantunes HGW: An architecture for DICOM medical images storage and retrieval adopting distributed file systems. Int J High Perf Syst Arch 2(2):99–106, 2009Google Scholar
  10. 10.
    Macedo DDJ, Capretz MAM, Prado TC, von Wangenheim A, Dantas M: An improvement of a different approach for medical image storage. 2011Google Scholar
  11. 11.
    CLucene: CLucene - lightning fast C++ search engine. 2011 [cited 2012 Jun 05]; Available from:
  12. 12.
    HDFGROUP: The HDF Group. Hierarchical data format version 5, 2000-2010. 2010; Available from:
  13. 13.
    Eichelberg M, Riesmeier J, Wilkens T, Hewett AJ, Barth A, Jensch P: Ten years of medical imaging standardization and prototypical implementation: The DICOM standard and the OFFIS DICOM Toolkit (DCMTK), 2004Google Scholar
  14. 14.
    ORACLE: Oracle Database 11g DICOM Medical Image Support. 2011 [cited 2012 Jun 05]; Available from:
  15. 15.
    Rew R, Davis G: NetCDF: an interface for scientific data access. Comput Graph Appl 10(4):76–82, 1990Google Scholar
  16. 16.
    Erik H, Otis G, Michael MC: Lucene in action. Manning Publications Co., 2005Google Scholar
  17. 17.
    Costa C, Freitas F, Pereira M, Silva A, Oliveira J: Indexing and retrieving DICOM data in disperse and unstructured archives. Int J Comput Assist Radiol Surg 4:71–77, 2009PubMedCrossRefGoogle Scholar
  18. 18.
    Gosink L, Shalf J, Stockinger K, Wu K, Bethel W: HDF5-FastQuery: Accelerating Complex Queries on HDF Datasets using Fast Bitmap Indices. In: Scientific and Statistical Database Management, 2006. 18th International Conference on, 2006Google Scholar
  19. 19.
    Sahoo S, Agrawal G: Supporting XML Based High-Level Abstractions on HDF5 Datasets: A Case Study in Automatic Data Virtualization. In: Eigenmann RL, Midkiff S Eds. Languages and Compilers for High Performance Computing. Springer, Berlin, 2005, pp 922–922Google Scholar
  20. 20.
    Folino G, Shah AA, Kransnogor N: On the storage, management and analysis of (multi) similarity for large scale protein structure datasets in the grid. in Computer-Based Medical Systems, 2009. CBMS 2009. 22nd International Symposium on, 2009Google Scholar
  21. 21.
    Cohen S, Hurley P, Schulz KW, Barth WL, Benton B: Scientific formats for object-relational database systems: a study of suitability and performance. SIGMOD Rec 35:10–15, 2006CrossRefGoogle Scholar
  22. 22.
    Magnus M, Prado TC, Wangenheim Av, Macedo DDJ, Dantas MAR: A Study of NetCDF as an Approach for High Performance Medical Image Storage. Journal of Physics: Conference Series, 2012Google Scholar
  23. 23.
    Abduljwad F, Ning W, De X: SMX/R: Efficient way of storing and managing XML documents using RDBMSs based on paths. 2010Google Scholar
  24. 24.
    NEMA: Digital Imaging and Communications in Medicine (DICOM) Part 1: Introduction and Overview, 2011Google Scholar
  25. 25.
    NEMA: Digital Imaging and Communications in Medicine (DICOM) Part 3: Information Object Definition, 2011Google Scholar
  26. 26.
    NEMA: Digital Imaging and Communications in Medicine (DICOM) Part 5: Data Structures and Encoding, 2011Google Scholar
  27. 27.
    NEMA: Digital Imaging and Communications in Medicine (DICOM) Part 4: Service Class Specifications, 2011Google Scholar
  28. 28.
    NEMA: Digital Imaging and Communications in Medicine (DICOM) Part 8: Network Communication Support for Message Exchange, 2011Google Scholar
  29. 29.
    NEMA: Digital Imaging and Communications in Medicine (DICOM) Part 10: Media Storage and File Format for Media Interchange, 2011Google Scholar
  30. 30.
    Douglas K: Postgre SQL. Sams, 2005Google Scholar
  31. 31.
    ISO/IEC: Information technology -- Database languages, in Part 1: Framework (SQL/Framework). 2008, ISO/IECGoogle Scholar
  32. 32.
    Pourma E, Folk M: Balancing Performance and Preservation Lessons learned with HDF5, 2010Google Scholar
  33. 33.
    HDFGROUP: How is HDF5 different than HDF4? 2011 [cited 2012 Jun 05]; Available from:
  34. 34.
    PyTables: PyTables - Getting the most *out* of your data. 2011 [cited 2012 Jun 05]; Available from:
  35. 35.
    Nam B, Sussman A: Improving access to multi-dimensional self-describing scientific datasets, 2003Google Scholar
  36. 36.
    Salton G, Wong A, Yang CS: A vector space model for automatic indexing. Commun 18(11):613–620, 1975Google Scholar
  37. 37.
    Maia RS, von Wangenheim A, Nobre LF: A statewide telemedicine network for public health in Brazil. 2006Google Scholar
  38. 38.
    PostgreSQL: SQL Conformance. 2009 [cited 2012 Jun 05]; Available from:
  39. 39.
    Amaral E, Comunello E, Dantas MAR, Macedo DDJ: Replicação Distribuída de Imagens Médicas sob o Formato de Dados HDF5, in 8th International Information and Telecommunication Technologies Symposium 2009, I2TS: Florianópolis, SC - BrazilGoogle Scholar
  40. 40.
    Lucene A: Lucene FAQ. 2011 12-2011 [cited 2012 Jun 05]; Available from:

Copyright information

© Society for Imaging Informatics in Medicine 2014

Authors and Affiliations

  • Thiago C. Prado
    • 1
  • Douglas D. J. de Macedo
    • 2
    • 3
  • M. A. R. Dantas
    • 1
  • Aldo von Wangenheim
    • 1
  1. 1.Department of Informatics and StatisticsINEFlorianópolisBrazil
  2. 2.Post-Graduate Program in Knowledge Engineering and Management—PPGEGCFederal University of Santa Catarina—UFSCFlorianópolisBrazil
  3. 3.Departamento de Informática e Estatística—Sala 320Universidade Federal de Santa Catarina—UFSCFlorianópolisBrazil

Personalised recommendations