Robust regression and outlier detection
Robust regression and outlier detection
Real-time biomechanical simulation of volumetric brain deformation for image guided neurosurgery
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
A data distributed parallel algorithm for nonrigid image registration
Parallel Computing
| Hi-index | 0.00 |
High performance computing has become a key step to introduce computer tools, like real-time registration, in the medical field. To achieve real-time processing, one usually simplifies and adapts algorithms so that they become application and data specific. This involves designing and programming work for each application, and reduces the generality and robustness of the method. Our goal in this paper is to show that a general registration algorithm can be parallelized on an inexpensive and standard parallel architecture with a mall amount of additional programming work, thus keeping intact the algorithm performance.For medical applications, we show that a cheap cluster of dual-processor PCs connected by an Ethernet network is a good trade-off between the power and the cost of the parallel platform. Portability, scalability and safety requirements led us to choose OpenMP to program multiprocessor machines and MPI to coordinate the different nodes of the cluster. The resulting computation times are very good on small and medium resolution images, and they are still acceptable on high resolution MR images (resp. 19, 45 and 95 seconds on 5 dual-processors Pentium III 933 MHz).