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.
Keywords
Cloud computing - machine learning - pattern recognition system - computer-assisted
image processing - image compression
