Spatiotemporal denoising and clustering of fMRI data

Xiaomu Song, Matthew Murphy, Alice M. Wyrwicz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

This paper examines combined spatiotemporal denoising and clustering of functional magnetic resonance imaging (fMRI) time series. Most fMRI denoising methods are implemented either in spatial or temporal domain without taking into account both space and time information. In this work, a spatiotemporal denoising method is developed where spatial denoising is implemented by Bayesian shrinkage that uses temporal prior information obtained by statistical testing on all voxel time courses. After the denoising, a set of spatiotemporal features are extracted and characterized by a Gaussian mixture model, which is applied to detect activated areas. The proposed methods have been tested on both synthetic and experimental data, and the results demonstrate their effectiveness.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages2857-2860
Number of pages4
DOIs
StatePublished - Dec 1 2006
Event2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States
Duration: Oct 8 2006Oct 11 2006

Publication series

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

Other

Other2006 IEEE International Conference on Image Processing, ICIP 2006
CountryUnited States
CityAtlanta, GA
Period10/8/0610/11/06

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Keywords

  • Bayesian shrinkage
  • Functional magnetic resonance imaging
  • Gaussian mixture model
  • Wavelet

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Song, X., Murphy, M., & Wyrwicz, A. M. (2006). Spatiotemporal denoising and clustering of fMRI data. In 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings (pp. 2857-2860). [4107165] (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2006.313025