Optimized GPU histograms for multi-modal registration

Christoph Vetter, Rudiger Westermann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

GPU-based systems are used more and more for medical image processing because of their parallel processing power and memory bandwidth. Impressive results have been achieved when registering large volume, however, one of themost-used similarity measures for multi-modal registration - mutual information - is not well suited for the streaming architecture because of its memory access pattern. We present two optimization approaches that improve the performance by a factor of four compared to state-of-the-art GPU algorithms in the latest research papers.

Original languageEnglish
Title of host publication2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI'11
Pages1227-1230
Number of pages4
DOIs
StatePublished - 2011
Event2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States
Duration: 30 Mar 20112 Apr 2011

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Country/TerritoryUnited States
CityChicago, IL
Period30/03/112/04/11

Keywords

  • GPU
  • joint histogram
  • mutual information
  • registration

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