A high performance computing approach to the registration of medical imaging data
Parallel Computing - Special double issue on biomedical applications
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Introduction to the cell multiprocessor
IBM Journal of Research and Development - POWER5 and packaging
Toward real-time image guided neurosurgery using distributed and grid computing
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
CellSort: high performance sorting on the cell processor
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Cell broadband engine architecture and its first implementation: a performance view
IBM Journal of Research and Development
Cell/B.E. blades: building blocks for scalable, real-time, interactive, and digital media servers
IBM Journal of Research and Development
Data mining on the cell broadband engine
Proceedings of the 22nd annual international conference on Supercomputing
Data Mining Algorithms on the Cell Broadband Engine
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Radioastronomy Image Synthesis on the Cell/B.E.
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Non-parametric diffeomorphic image registration with the demons algorithm
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Point matching is crucial for many computer vision applications. Establishing the correspondence between a large number of data points is a computationally intensive process. Some point matching related applications, such as medical image registration, require real time or near real time performance if applied to critical clinical applications like image assisted surgery. In this paper, we report a new multicore platform based parallel algorithm for fast point matching in the context of landmark based medical image registration. We introduced a non-regular data partition algorithm which utilizes the K -means clustering algorithm to group the landmarks based on the number of available processing cores, which optimize the memory usage and data transfer. We have tested our method using the IBM Cell Broadband Engine (Cell/B.E.) platform. The results demonstrated a significant speed up over its sequential implementation. The proposed data partition and parallelization algorithm, though tested only on one multicore platform, is generic by its design. Therefore the parallel algorithm can be extended to other computing platforms, as well as other point matching related applications.