Archive (2016–2006)

Progressive Data Transmission for Anatomical Landmark Detection in a Cloud

Journal: Methods of Information in Medicine
Subtitle: A journal stressing, for more than 50 years, the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care
ISSN: 0026-1270
Topic:

Focus Theme: Medical Imaging High Performance Methods
Guest Editors: C. Kulikowski, L. Gong

DOI: https://doi.org/10.3414/ME11-02-0017
Issue: 2012 (Vol. 51): Issue 3 2012
Pages: 268-278

Progressive Data Transmission for Anatomical Landmark Detection in a Cloud

Focus Theme - Medical Imaging High Performance Methods

M. Sofka (1), K. Ralovich (2), J. Zhang (1), S. K. Zhou (1), D. Comaniciu (1)

(1) Siemens Corporate Research, 755 College Road East, Princeton, NJ 08540, USA; (2) Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany

Keywords

Machine Learning, Computer-Assisted Image Processing, image compression, Pattern recognition system, Cloud computing

Summary

Background: In the concept of cloud-computing-based systems, various authorized users have secure access to patient records from a number of care delivery organizations from any location. This creates a growing need for remote visualization, advanced image processing, state-of-the-art image analysis, and computer aided diagnosis.

Objectives: This paper proposes a system of algorithms for automatic detection of anatomical landmarks in 3D volumes in the cloud computing environment. The system addresses the inherent problem of limited bandwidth between a (thin) client, data center, and data analysis server.

Methods: The problem of limited bandwidth is solved by a hierarchical sequential detection algorithm that obtains data by progressively transmitting only image regions required for processing. The client sends a request to detect a set of landmarks for region visualization or further analysis. The algorithm running on the data analysis server obtains a coarse level image from the data center and generates landmark location candidates. The candidates are then used to obtain image neighborhood regions at a finer resolution level for further detection. This way, the landmark locations are hierarchically and sequentially detected and refined.

Results: Only image regions surrounding landmark location candidates need to be trans- mitted during detection. Furthermore, the image regions are lossy compressed with JPEG 2000. Together, these properties amount to at least 30 times bandwidth reduction while achieving similar accuracy when compared to an algorithm using the original data.

Conclusions: The hierarchical sequential algorithm with progressive data transmission considerably reduces bandwidth requirements in cloud-based detection systems.

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