Abstract
In this paper we present a cloud computing platform for medical imaging which deals with the issues of scaling a traditionally single-user solution to a software-as-a-service solution. We will first introduce volume rendering for medical imaging, and the issues with volume rendering of medical images on the cloud. We will then describe our method for accelerating CPU based volume rendering on the cloud and for scaling the system to a software-as-a-service solution. Next, we describe the need for universal access to data and the methods and security inherent in the transfer of this data. We describe an analytic engine which is used to augment the solution with additional features. Finally we discuss the design decisions made, the lessons learned, and the results achieved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jaekel, M., Pott, H.: Cloud Computing—Software as a Service in Practice. Siemens (2010)
Jaekel, M., Luhn, A., Cloud Computing—Business Models, Value Creation Dynamics and Advantages for Customers. Siemens (2009)
Dachille, F., Kreeger, K., Baoquan, C., Bitter, I., Kaufman, A., High-Quality Volume Rendering Using Texture Mapping Hardware, ACM SIGGRAPH/EUROGRAPHICS workshop on graphics hardware, Lisbon, Portugal (1998)
Kruger, J., Westermann, R., Acceleration Techniques for GPU-based Volume Rendering, Computer Graphics and Visualisation Group, Technical University Munich. (2003)
Heng, Y., Gu, L., GPU-based Volume Rendering for Medical Image Visualization, Engineering in Medicine and Biology Society, pp. 5145–5148. IEE-EMBS (Collaboration in Medical Imaging:2005)
Shen, R., Boulanger, P., Harware-accelerated volume rendering for real-time medical data visualization, Lecture Notes in Computer Science, Volume 4842, pp. 801–810. Springer-Verlag Berlin Heidelberg (2007)
Meissner, M., Grimm, S., Strasser, W., Packer, J., Latimer, D., Parallel volume rendering of a single chip SIMD architecture, IEEE 2001 symposium on parallel and large-data visualization and graphics, San Diego, California (2001)
Roth, S., Ray Casting for Modelling Solids, Computer Graphics and Image Processing, Volume 18, pp. 109–144. (1982)
Lacroute, P., Levoy, M., Fast Volume Rendering Using a Shear-Warp Factorization of the Viewing Transformation, ACM SIGGRAPH proceedings, (1994)
Grimm, S., Bruckner, S., Kanitsar, A., Gröller, E., A refined data addressing and processing scheme to accelerate volume raycasting, Institute of Computer Graphics and Algorithms, Vienna University of Technology, Computers & Graphics 28, pp. 719–729 (2004)
Stuart, J., Cheng-Kai, C., Kwan-Liu, M., Owens, J.: Multi-GPU Volume Rendering using MapReduce, HPDC ’10 (2010)
Wolff, A., Taylor, S., McCabe, J., Using checklists and reminders in clinical pathways to improve hospital inpatient care, The Medical Journal of Australia, Volume 181 Number 8, pp. 428–431 (2004)
Chen, A., Callender, D., Mansyur, C., Reyna, K., Limitone, E., Goepfert, H., From the University of Texas M. D. Anderson Cancer Center, Department of Head and Neck Surgery (Chen, Callender, and Goepfert), and the Practice Outcomes Program (Mansyur, Reyna, and Limitone), Houston. (2000)
Dowsey, M., Kilgour, M., Santamaris, N., Choong, P., Clinical pathways in hip and knee arthroplasty: a prospective randomized controlled study, The Medical Journal of Australia, 170, pp. 59–62 (1999)
Digital Imaging and Communications in Medicine (DICOM), National Electrical Manufacturers Association, Rosslyn, Virginia (2011)
Positron Emission Tomography, Ollinger, J., Fessler, J., IEEE Signal Processing Magazine, 14(1):43–55, January (1997)
Acknowledgements
Louis Parsonson is sponsored by the EPSRC Industrial CASE award. Special thanks to Biotronics3D for providing the data and hardware infrastructure on which the system was developed. Thanks also to Dr. Adrianna Paluszny of Imperial College London for the fruitful discussions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media New York
About this paper
Cite this paper
Parsonson, L., Grimm, S., Bajwa, A., Bourn, L., Bai, L. (2012). A Cloud Computing Medical Image Analysis and Collaboration Platform. In: Ivanov, I., van Sinderen, M., Shishkov, B. (eds) Cloud Computing and Services Science. CLOSER 2011. Service Science: Research and Innovations in the Service Economy. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-2326-3_11
Download citation
DOI: https://doi.org/10.1007/978-1-4614-2326-3_11
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-2325-6
Online ISBN: 978-1-4614-2326-3
eBook Packages: Computer ScienceComputer Science (R0)