DMCA
Parallel non-rigid registration on a cluster of workstations (2003)
| Venue: | In Sofie Norager, editor, Proc. of HealthGrid’03 |
| Citations: | 9 - 3 self |
BibTeX
@INPROCEEDINGS{Stefanescu03parallelnon-rigid,
author = {Radu Stefanescu and Xavier Pennec and Nicholas Ayache},
title = {Parallel non-rigid registration on a cluster of workstations},
booktitle = {In Sofie Norager, editor, Proc. of HealthGrid’03},
year = {2003}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract: Over recent years, non-rigid registration has become a major issue in medical imaging. It consists in recovering a dense point-to-point correspondence field between two images and usually takes a long time. This is in contrast to the needs of a clinical environment, where usability and speed are major constraints, leading to the necessity of reducing the computation time from slightly less than an hour to just a few minutes. Another constraint is the usual unwillingness of healthcare organizations to invest in expensive high-performance computing solutions. Cluster computing proved to be a convenient solution to our computation needs, offering a large processing power at a low cost. Bi-processor workstation can be used simultaneously for parallel computation and individual day-to-day use and they are already present in many labs and hospitals. Our goal in this article is to provide a more usable tool by taking advantage of the available computation power. Among the fast and efficient non-rigid registration algorithms, we chose the demons algorithm [1,2] for its simplicity and good performances. The parallel implementation decomposes the correspondence field into blocks, each block being assigned to one workstation of the cluster. We take advantage of the inherently regular structure of the algorithm that allows a nearly perfect static load balancing and also its locality, allowing to keep the amount of communication within a reasonable range. We obtained an acceleration of 11 by using 15 2GHz PC’s connected through a 1GB/s Ethernet network and reduced the computation time from 40min to 3min30.
Keyphrases
parallel non-rigid registration computation time clinical environment parallel implementation computation need usable tool ethernet network many lab usual unwillingness convenient solution long time good performance bi-processor workstation dense point-to-point correspondence field reasonable range parallel computation large processing power individual day-to-day use expensive high-performance healthcare organization major constraint regular structure perfect static load balancing medical imaging available computation power low cost correspondence field recent year efficient non-rigid registration algorithm major issue non-rigid registration
