Skip to main content
SpringerLink
Log in

INDEPENDENT COMPONENT ANALYSIS OF DYNAMIC CONTRAST-ENHANCED IMAGES: THE NUMBER OF COMPONENTS

  • Conference paper
Computational Methods

  • 1681 Accesses

Abstract

Independent component analysis (ICA) has attracted much interest in dynamic contrast-enhanced (DCE) neuroimaging, as it allows for blind separation of the brain haemodynamic patterns (components). However, the exact number of components is always unknown in practice. In this work, numerical simulation study was carried out to compare the performance of ICA by using the principle component analysis (PCA) to reduce the number of components. Oscillatory indices method and kurtosis method are also discussed for automatically selecting the components of interest.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

REFERENCES

  1. M.J. McKeown et al (1998), Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping, 6, pp. 160–188.

    Article  Google Scholar 

  2. M.J. McKeown and T.J. Sejnowski (1998), Independent component analysis of fMRI data: examining the assumptions. Human Brain Mapping, 6, pp. 368–372.

    Article  Google Scholar 

  3. X.Y. Wu (2004), Functional neuroimaging and analysis. ME Thesis, Nanyang Technological University, Singapore.

    Google Scholar 

  4. Y-H. Kao et al (2003), Haemodynamic segmentation of MR brain perfusion images using independent component analysis, thresholding, and Bayesian estimation. Magnetic Resonance in Medicine, 49, pp. 885–894.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Advanced Computations in Engineering Science, Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117576, Singapore

    X.Y. Wu & G.R. Liu

Authors
  1. X.Y. Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G.R. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. National University of Singapore, Singapore

    G.R. LIU

  2. National University of Singapore, Singapore

    V.B.C. TAN

  3. Hunan University, P.R. China

    X. HAN

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer

About this paper

Cite this paper

Wu, X., Liu, G. (2006). INDEPENDENT COMPONENT ANALYSIS OF DYNAMIC CONTRAST-ENHANCED IMAGES: THE NUMBER OF COMPONENTS. In: LIU, G., TAN, V., HAN, X. (eds) Computational Methods. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-3953-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-1-4020-3953-9_16

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-3952-2

  • Online ISBN: 978-1-4020-3953-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation