Skip to main content
Log in

iMAGE cloud: medical image processing as a service for regional healthcare in a hybrid cloud environment

  • Regular Article
  • Published:
Environmental Health and Preventive Medicine Aims and scope

Abstract

Objectives

To handle the emergence of the regional healthcare ecosystem, physicians and surgeons in various departments and healthcare institutions must process medical images securely, conveniently, and efficiently, and must integrate them with electronic medical records (EMRs). In this manuscript, we propose a software as a service (SaaS) cloud called the iMAGE cloud.

Methods

A three-layer hybrid cloud was created to provide medical image processing services in the smart city of Wuxi, China, in April 2015. In the first step, medical images and EMR data were received and integrated via the hybrid regional healthcare network. Then, traditional and advanced image processing functions were proposed and computed in a unified manner in the high-performance cloud units. Finally, the image processing results were delivered to regional users using the virtual desktop infrastructure (VDI) technology. Security infrastructure was also taken into consideration.

Results

Integrated information query and many advanced medical image processing functions—such as coronary extraction, pulmonary reconstruction, vascular extraction, intelligent detection of pulmonary nodules, image fusion, and 3D printing—were available to local physicians and surgeons in various departments and healthcare institutions.

Conclusions

Implementation results indicate that the iMAGE cloud can provide convenient, efficient, compatible, and secure medical image processing services in regional healthcare networks. The iMAGE cloud has been proven to be valuable in applications in the regional healthcare system, and it could have a promising future in the healthcare system worldwide.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Kowal M, Filipczuk P, Obuchowicz A, Korbicz J, Monczak R. Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Comput Biol Med. 2013;43:1563–72.

    Article  PubMed  Google Scholar 

  2. Fortunati V, Verhaart RF, van der Lijn F, Niessen WJ, Veenland JF, Paulides MM, et al. Tissue segmentation of head and neck CT images for treatment planning: a multiatlas approach combined with intensity modeling. Med Phys. 2013;40:071905.

    Article  PubMed  Google Scholar 

  3. Deserno Né Lehmann TM, Handels H, Maier-Hein Né Fritzsche KH, Mersmann S, Palm C, Tolxdorff T, et al. Viewpoints on medical image processing: from science to application. Curr Med Imaging Rev. 2013;9:79–88.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Lee BY, Wong KF, Bartsch SM, Yilmaz SL, Avery TR, Brown ST, et al. The Regional Healthcare Ecosystem Analyst (RHEA): a simulation modeling tool to assist infectious disease control in a health system. J Am Med Inform Assoc. 2013;20:139–46.

    Article  Google Scholar 

  5. Griebel L, Prokosch HU, Köpcke F, Toddenroth D, Christoph J, Leb I, et al. A scoping review of cloud computing in healthcare. BMC Med Inform Decis Mak. 2015;15:17.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kagadis GC, Kloukinas C, Moore K, Philbin J, Papadimitroulas P, Alexakos C, et al. Cloud computing in medical imaging. Med Phys. 2013;40:070901.

    Article  PubMed  Google Scholar 

  7. Yoo SK, Kim S, Kim T, Baek RM, Suh CS, Chung CY, et al. Economic analysis of cloud-based desktop visualization implementation at a hospital. BMC Med Inform Decis Mak. 2012;12:119.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Patel RP. Cloud computing and virtualization technology in radiology. Clin Radiol. 2012;67:1095–100.

    Article  CAS  PubMed  Google Scholar 

  9. Chai X, Liu L, Xing L. A web-based image processing and plan evaluation platform (WIPPEP) for future cloud-based radiotherapy. Med Phys. 2014;41:113.

    Article  Google Scholar 

  10. Constantinescu L, Kim J, Kumar A, Haraguchi D, Wen L, Feng D. A patient-centric distribution architecture for medical image sharing. Health Inform Sci Syst. 2013;1:3.

    Google Scholar 

  11. Constantinescu L, Kim J, Feng DD. SparkMed: a framework for dynamic integration of multimedia medical data into distributed m-Health systems. IEEE Trans Inf Technol Biomed. 2012;16:40–52.

    Article  PubMed  Google Scholar 

  12. Mishra P, Lewis J, Patankar A, Etmektzoglou A, Svatos M. TU-CD-304-11: veritas 2.0: a cloud-based tool to facilitate research and innovation. Med Phys. 2015;42:3601.

    Article  Google Scholar 

  13. Parsonson L, Grimm S, Bajwa A, Bourn L, Bai L. A cloud computing medical image analysis and collaboration platform. Springer N Y. 2012;12:207–24.

    Google Scholar 

  14. Ojog I, Arias-Estrada M, Gonzalez JA, Flores B. A cloud scalable platform for DICOM image analysis as a tool for remote medical support. The Fifth International Conference on eHealth, Telemedicine, and Social Medicine. France, 2013.

  15. Bednarz T, Wang D, Arzhaeva Y, Lagerstrom R, Vallotton P, Burdett N, et al. Cloud based toolbox for image analysis, processing and reconstruction tasks. Adv Exp Med Biol. 2015;823:191–205.

    Article  PubMed  Google Scholar 

  16. Palanimalai S, Paramasivam I. An enterprise oriented view on the cloud integration approaches—hybrid cloud and big data. Procedia Comput Sci. 2015;50:163–8.

    Article  Google Scholar 

  17. Zhang Y, Yan H, Zou X, Tao F, Zhang L. Image threshold processing based on simulated annealing and OTSU method. In: Proceedings of the 2015 Chinese Intelligent Systems Conference; 2016: pp. 223–231.

  18. Shenshen S, Hong L, Xinran H, et al. Pulmonary nodule segmentation based on EM and Mean-shift. J Image Graph. 2009;14:2016–22.

    Google Scholar 

  19. Mousa MA. Virtualization technology: revolution of virtual desktop infrastructure. J Tech Sci Technol. 2012;1:17–23.

    Google Scholar 

  20. Yoo S, Kim S, Kim T, Kim JS, Baek RM, Suh CS, et al. Implementation issues of virtual desktop infrastructure and its case study for a physician’s round at Seoul National University Bundang Hospital. Healthc Inform Res. 2012;18:259–565.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61300150), Natural Science Foundation of Jiangsu Province (No. BK20151106) and Science Foundation of Wuxi medical management center (No. YGZXZ1524).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gen Yan.

Ethics declarations

Conflict of interest

The authors claimed no competing interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, L., Chen, W., Nie, M. et al. iMAGE cloud: medical image processing as a service for regional healthcare in a hybrid cloud environment. Environ Health Prev Med 21, 563–571 (2016). https://doi.org/10.1007/s12199-016-0582-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12199-016-0582-7

Keywords

Navigation