A fast predictive lossless coder for fMRI data sets

Toshihisa Tanaka, Yumi Murakami, Fabian J. Theis

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

1 Scopus citations

Abstract

We present a novel lossless compression algorithm which compresses sequences of three-dimensional (3D) volumes collected during a functional magnetic resonance imaging (fMRI) experiment. The large data sets involved in this popular biomedical application necessitate fast and efficient compression methods. We propose to use 3D prediction, temporal decorrelation and entropy coding with context modeling for encoding the fMRI scans after preprocessing with the region-of-interest (ROI) masking. The proposed algorithm is conceptually simple and can achieve fast implementation and efficient coding performance. We illustrate computer simulations to show advantages over conventional coding methods.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages2529-2532
Number of pages4
DOIs
StatePublished - 2006
Externally publishedYes
Event2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States
Duration: 8 Oct 200611 Oct 2006

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2006 IEEE International Conference on Image Processing, ICIP 2006
Country/TerritoryUnited States
CityAtlanta, GA
Period8/10/0611/10/06

Keywords

  • Biomedical image coding
  • Image coding
  • Lossless compression
  • Predictive coding
  • fMRI

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