@InProceedings{sofka:HPmiccai10,
  author =	 {Michal Sofka and Kristof Ralovich and Jingdan Zhang and S.Kevin Zhou,
              and Dorin Comaniciu},
  title =	 {Progressive Data Transmission for Hierarchical Detection in a Cloud},
  booktitle =    {Proceedings of the 2nd International Workshop on High-Performance
                  Medical Image Computing for Image-Assisted Clinical Intervention
				  and Decision-Making (HP-MICCAI 2010)},
  year =         2010,
  month =        {24~} # sep,
  address =      {Bejing, China},                  
  pages =	 {},
  abstract =	 {In response to the growing need for image analysis
                  services in the cloud computing environment, this
                  paper proposes an automatic system for detecting
                  landmarks in 3D volumes. The inherent problem of
                  limited bandwidth between a (thin) client, Data
                  Center (DC), and Data Analysis (DA) server is
                  addressed by a hierarchical detection algorithm that
                  obtains data by progressively transmitting only
                  image regions re- quired for processing. The client
                  sends a request for a visualization of a specific
                  landmark. The algorithm obtains a coarse level image
                  from DC and outputs landmark location
                  candidates. The coarse landmark location candidates
                  are then used to obtain image neighborhood regions
                  at a finer resolution level. The final location is
                  computed as the robust mean of the strongest
                  candidates after refinement at the subsequent
                  resolution levels.  The feedback about candidates
                  detected at a coarser resolution makes it possible
                  to only transmit image regions surrounding these
                  candidates at a finer resolution rather then the
                  entire images. 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.},
  url =		 {http://www.sofka.com/publications.html}
}