Efficient 3-D medical image registration using a distributed blackboard architecture.
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.
Tait
Roger J
RJ
Sch. of Comput. & Informatics, Nottingham Trent Univ., Nottingham, UK. roger.tait@students.ntu.ac.uk
Schaefer
Gerald
G
Hopgood
Adrian A
AA
Zhu
Shao Ying
SY
D000465:Algorithms; D001698:Biomedical Engineering; D003195:Computer Communication Networks; D003199:Computer Systems; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data*; D051188:Knowledge Bases