title: Progressive data transmission for anatomical landmark detection in a cloud.
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.
  
  
  OBJECTIVE
  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.
authors.affiliation: Siemens Corporate Research, 755 College Road East, Princeton, NJ 08540, USA. michal.sofka@siemens.com
authors.forename: M
authors.initials: M
authors.lastname: Sofka
authors.forename: K
authors.initials: K
authors.lastname: Ralovich
authors.forename: J
authors.initials: J
authors.lastname: Zhang
authors.forename: S K
authors.initials: SK
authors.lastname: Zhou
authors.forename: D
authors.initials: D
authors.lastname: Comaniciu
mesh_terms: D022126:Access to Information; D000465:Algorithms; D000715:Anatomy / Q000295:instrumentation*; D001185:Artificial Intelligence; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000295:instrumentation; D008490:Medical Informatics / Q000295:instrumentation; D010336:Pathology / Q000295:instrumentation*; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods