Pattern recognition for cache management in distributed medical imaging environments

  • Carlos Viana-FerreiraEmail author
  • Luís Ribeiro
  • Sérgio Matos
  • Carlos Costa
Original Article



Traditionally, medical imaging repositories have been supported by indoor infrastructures with huge operational costs. This paradigm is changing thanks to cloud outsourcing which not only brings technological advantages but also facilitates inter-institutional workflows. However, communication latency is one main problem in this kind of approaches, since we are dealing with tremendous volumes of data. To minimize the impact of this issue, cache and prefetching are commonly used. The effectiveness of these mechanisms is highly dependent on their capability of accurately selecting the objects that will be needed soon.


This paper describes a pattern recognition system based on artificial neural networks with incremental learning to evaluate, from a set of usage pattern, which one fits the user behavior at a given time. The accuracy of the pattern recognition model in distinct training conditions was also evaluated.


The solution was tested with a real-world dataset and a synthesized dataset, showing that incremental learning is advantageous. Even with very immature initial models, trained with just 1 week of data samples, the overall accuracy was very similar to the value obtained when using 75 % of the long-term data for training the models. Preliminary results demonstrate an effective reduction in communication latency when using the proposed solution to feed a prefetching mechanism.


The proposed approach is very interesting for cache replacement and prefetching policies due to the good results obtained since the first deployment moments.


Pattern recognition PACS Neural network Adaptive model Cache Prefetching 



This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (EMIF Grant No. 115372). Carlos Viana-Ferreira is funded by the FCT Grant SFRH/BD/68280/2010. Sérgio Matos is funded under the FCT Investigator programme.

Compliance with ethical standards

Conflict of interest

Carlos Viana-Ferreira, Luís Ribeiro, Sérgio Matos and Carlos Costa declare that they have no conflict of interest.


  1. 1.
    Bellon E, Deprez T, Feron M, Damme WV, Demey J, Galan MD, Standaert S, Bosch BVd (2012) Regional PACS and radiation dose. Int J CARS 7(1):91–95. doi: 10.1007/s11548-012-0697-2 Google Scholar
  2. 2.
    Benjamin M, Aradi Y, Shreiber R (2010) From shared data to sharing workflow: merging PACS and teleradiology. Eur J Radiol 73(1):3–9. doi: 10.1016/j.ejrad.2009.10.014 CrossRefPubMedGoogle Scholar
  3. 3.
    Philbin J, Prior F, Nagy P (2011) Will the next generation of PACS be sitting on a cloud? J Digit Imaging 24(2):179–183. doi: 10.1007/s10278-010-9331-4 PubMedCentralCrossRefPubMedGoogle Scholar
  4. 4.
    Puech P, Boussel L, Belfkih S, Lemaitre L, Douek P, Beuscart R (2007) DicomWorks: software for reviewing DICOM studies and promoting low-cost teleradiology. J Digit Imaging 20(2):122–130. doi: 10.1007/s10278-007-9018-7 PubMedCentralCrossRefPubMedGoogle Scholar
  5. 5.
    Younis MYA, Kifayat K (2013) Secure cloud computing for critical infrastructure: A survey. Liverpool John Moores University, United Kingdom, Tech RepGoogle Scholar
  6. 6.
    Viana-Ferreira C, Matos S, Costa C (2015) Incremental learning versus batch learning for classification of user’s behaviour in medical imaging. Paper presented at the 8th international conference on health informatics, Lisbon, Portugal, January 2015Google Scholar
  7. 7.
    Marques Godinho T, Viana-Ferreira C, Bastiao Silva L, Costa C (2014) A routing mechanism for cloud outsourcing of medical imaging repositories. IEEE J Biomed Health Inf 99:1–1. doi: 10.1109/JBHI.2014.2361633 Google Scholar
  8. 8.
    Zhang J, Lu X, Nie H, Huang Z, van der Aalst WMP (2009) Radiology information system: a workflow-based approach. Int J CARS 4(5):509–516. doi: 10.1007/s11548-009-0362-6 CrossRefGoogle Scholar
  9. 9.
    Silva LB, Costa C, Oliveira J (2013) DICOM relay over the cloud. Int J CARS 8(3):323–333. doi: 10.1007/s11548-012-0785-3 CrossRefGoogle Scholar
  10. 10.
    Costa C, Freitas F, Pereira M, Silva A, Oliveira JL (2009) Indexing and retrieving DICOM data in disperse and unstructured archives. Int J CARS 4(1):71–77. doi: 10.1007/s11548-008-0269-7 CrossRefGoogle Scholar
  11. 11.
    Silva LAB, Pinho R, Ribeiro LS, Costa C, Oliveira JL (2014) A centralized platform for geo-distributed PACS management. J Digit Imaging 27(2):165–173PubMedCentralCrossRefPubMedGoogle Scholar
  12. 12.
    Yakami M, Ishizu K, Kubo T, Okada T, Togashi K (2011) Development and evaluation of a low-cost and high-capacity DICOM image data storage system for research. J Digit Imaging 24(2):190–195. doi: 10.1007/s10278-009-9267-8 PubMedCentralCrossRefPubMedGoogle Scholar
  13. 13.
    Smith AJ (1982) Cache memories. ACM Comput Surv (CSUR) 14(3):473–530CrossRefGoogle Scholar
  14. 14.
    Huang H (2011) PACS and imaging informatics: basic principles and applications. Wiley-Blackwell, HobokenGoogle Scholar
  15. 15.
    Bui AA, McNitt-Gray MF, Goldin JG, Cardenas AF, Aberle DR (2001) Problem-oriented prefetching for an integrated clinical imaging workstation. J Am Med Inf Assoc 8(3):242–253CrossRefGoogle Scholar
  16. 16.
    Yu W, Oral HS, Canon RS, Vetter JS, Sankaran R (2008) Empirical analysis of a large-scale hierarchical storage system. Euro-Par 2008 Parallel Process pp 130–140Google Scholar
  17. 17.
    Yalamanchili C, Vijayasankar K, Zadok E, Sivathanu G (2009) DHIS: discriminating hierarchical storage. In: Proceedings of SYSTOR 2009: the Israeli experimental systems conference. ACM, p 9Google Scholar
  18. 18.
    Doganata YN, Tantawi AN (1994) A cost/performance study of video servers with hierarchical storage. In: Multimedia computing and systems, 1994., Proceedings of the international conference on, 1994. IEEE, pp 393–402Google Scholar
  19. 19.
    Erickson BJ, Bartholmai B (2002) Computer-aided detection and diagnosis at the start of the third millennium. J Digit Imaging 15(2):59–68PubMedCentralCrossRefPubMedGoogle Scholar
  20. 20.
    Pal MB, Jain DC (2014) An approach for web pre-fetching to enhance user interaction of web application using Markov model. In: Communication Systems and Network Technologies (CSNT), 2014 Fourth International Conference on, 7–9 April 2014. pp 373–377. doi: 10.1109/CSNT.2014.80
  21. 21.
    Ali W, Shamsuddin SM, Ismail AS (2012) Intelligent web proxy caching approaches based on machine learning techniques. Decis Support Syst 53(3):565–579. doi: 10.1016/j.dss.2012.04.011 CrossRefGoogle Scholar
  22. 22.
    García R, Verdú E, Regueras LM, de Castro JP, Verdú MJ (2013) A neural network based intelligent system for tile prefetching in web map services. Expert Syst Appl 40(10):4096–4105. doi: 10.1016/j.eswa.2013.01.037 CrossRefGoogle Scholar
  23. 23.
    Liu Sheng OR, Wei C-P, Hu PJ-H, Chang N (2000) Automated learning of patient image retrieval knowledge: neural networks versus inductive decision trees. Decis Support Syst 30(2):105–124. doi: 10.1016/S0167-9236(00)00092-0 CrossRefGoogle Scholar
  24. 24.
    Unertl KM, Johnson KB, Lorenzi NM (2012) Health information exchange technology on the front lines of healthcare: workflow factors and patterns of use. J Am Med Inf Assoc 19(3):392–400. doi: 10.1136/amiajnl-2011-000432 CrossRefGoogle Scholar
  25. 25.
    Jiacun W (2012) Emergency healthcare workflow modeling and timeliness analysis. IEEE Trans Syst Man Cybern Part A Syst Hum 42(6):1323–1331. doi: 10.1109/TSMCA.2012.2210206 CrossRefGoogle Scholar
  26. 26.
    Silva LAB, Costa C, Oliveira JL (2013) An agile framework to support distributed medical imaging scenarios. Paper presented at the IEEE international conference on healthcare informatics 2013 (ICHI 2013), Philadelphia, USAGoogle Scholar
  27. 27.
    Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley, HobokenGoogle Scholar
  28. 28.
    Michalewicz Z, Schmidt M, Michalewicz M, Chiriac C (2006) Adaptive business intelligence. Springer, BerlinGoogle Scholar
  29. 29.
    Campos SC, Costa C, Silva LAB (2012) A network sensor for medical imaging workflows. In: Information Systems and Technologies (CISTI), 2012 7th Iberian Conference on, 20–23 June 2012. pp 1–6Google Scholar
  30. 30.
    Coop R, Mishtal A, Arel I (2013) Ensemble learning in fixed expansion layer networks for mitigating catastrophic forgetting. IEEE Trans Neural Netw Learn Syst 24(10):1623–1634. doi: 10.1109/TNNLS.2013.2264952 CrossRefPubMedGoogle Scholar
  31. 31.
    Meyer-Baese A, Schmid VJ (2014) Pattern recognition and signal analysis in medical imaging. Elsevier, AmsterdamGoogle Scholar
  32. 32.
    Basu JK, Bhattacharyya D, Kim TH (2010) Use of artificial neural network in pattern recognition. Int J Softw Eng Appl 4(2):23–33Google Scholar
  33. 33.
    Viana-Ferreira C, Costa C (2014) DICOM traffic generator based on behavior profiles. Paper presented at the IEEE-EMBS International Conferences on Biomedical and Health Informatics, Valencia, SpainGoogle Scholar

Copyright information

© CARS 2015

Authors and Affiliations

  • Carlos Viana-Ferreira
    • 1
    Email author
  • Luís Ribeiro
    • 1
  • Sérgio Matos
    • 1
  • Carlos Costa
    • 1
  1. 1.Department of Electronics, Telecommunications and Informatics and Institute of Electronics and Telematics Engineering of AveiroUniversity of AveiroAveiroPortugal

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