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Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

M. Sofka, K. Ralovich, J. Zhang, K. Zhou, D. Comaniciu
Progressive Data Transmission for Hierarchical Detection in a Cloud
Proceedings of the 2nd International Workshop on High-Performance Medical Image Computing for Image-Assisted Clinical Intervention and Decision-Making (HP-MICCAI 2010), Bejing, China, 22 Sep 2010. (bib)

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.
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