@Article{sofka:mim12,
  author =	 {Michal Sofka and Kristof Ralovich and Jingdan Zhang and S.Kevin Zhou
              and Dorin Comaniciu},
  title =	 {Progressive Data Transmission for Anatomical Landmark Detection in a Cloud},
  journal =	 {Methods of Information in Medicine},
  year =	 2012,
  note =	 {Invited Paper. In Press},
  abstract =	 {
    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 transmitted during detection. Furthermore, the image regions are \emph{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},
  url =		 {http://www.sofka.com/publications.html}
}