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.
Similar content being viewed by others
References
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.
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.
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.
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.
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.
Kagadis GC, Kloukinas C, Moore K, Philbin J, Papadimitroulas P, Alexakos C, et al. Cloud computing in medical imaging. Med Phys. 2013;40:070901.
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.
Patel RP. Cloud computing and virtualization technology in radiology. Clin Radiol. 2012;67:1095–100.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Shenshen S, Hong L, Xinran H, et al. Pulmonary nodule segmentation based on EM and Mean-shift. J Image Graph. 2009;14:2016–22.
Mousa MA. Virtualization technology: revolution of virtual desktop infrastructure. J Tech Sci Technol. 2012;1:17–23.
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.
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
Corresponding author
Ethics declarations
Conflict of interest
The authors claimed no competing interest.
Rights and permissions
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12199-016-0582-7