A novel image analysis method based on bayesian segmentation for event-related functional MRI

Lejian Huang*, Mary L. Comer, Thomas M. Talavage

*Corresponding author for this work

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


This paper presents the application of the expectation-maximization/ maximization of the posterior marginals (EM/MPM) algorithm to signal detection for functional MRI (fMRI). On basis of assumptions for fMRI 3-D image data, a novel analysis method is proposed and applied to synthetic data and human brain data. Synthetic data analysis is conducted using two statistical noise models (white and autoregressive of order 1) and, for low contrast-to-noise ratio (CNR) data, reveals better sensitivity and specificity for the new method than for the traditional General Linear Model (GLM) approach. When applied to human brain data, functional activation regions are found to be consistent with those obtained using the GLM approach.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Computational Imaging VI
StatePublished - 2008
EventComputational Imaging VI - San Jose, CA, United States
Duration: Jan 28 2008Jan 29 2008

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherComputational Imaging VI
Country/TerritoryUnited States
CitySan Jose, CA


  • AR(1) model
  • EM/MPM algorithm
  • Posterior probability map
  • White noise model
  • fMRI

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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