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Efficient 3-D Medical Image Registration Using a Distributed Blackboard Architecture

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4 Author(s)
Roger J. Tait ; School of Computing and Informatics, Nottingham Trent University, Clifton Campus Nottingham, NG11 8NS, U.K., e-mail: ; Gerald Schaefer ; Adrian A. Hopgood ; Shao Ying Zhu

A major drawback of 3-D medical image registration techniques is the performance bottleneck associated with re-sampling and similarity computation. Such bottlenecks limit registration applications in clinical situations where fast execution times are required and become particularly apparent in the case of registering 3-D data sets. In this paper a novel framework for high performance intensity-based volume registration is presented. Geometric alignment of both reference and sensed volume sets is achieved through a combination of scaling, translation, and rotation. Crucially, resampling and similarity computation is performed intelligently by a set of knowledge sources. The knowledge sources work in parallel and communicate with each other by means of a distributed blackboard architecture. Partitioning of the blackboard is used to balance communication and processing workloads. Large-scale registrations with substantial speedups, when compared with a conventional implementation, have been demonstrated

Published in:

Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE

Date of Conference:

Aug. 30 2006-Sept. 3 2006