@inproceedings{eb27ebda649e497395d223211f91d6d1,
title = "A novel image analysis method based on bayesian segmentation for event-related functional MRI",
abstract = "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.",
keywords = "AR(1) model, EM/MPM algorithm, Posterior probability map, White noise model, fMRI",
author = "Lejian Huang and Comer, {Mary L.} and Talavage, {Thomas M.}",
year = "2008",
doi = "10.1117/12.774977",
language = "English (US)",
isbn = "9780819469861",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "Proceedings of SPIE-IS and T Electronic Imaging - Computational Imaging VI",
note = "Computational Imaging VI ; Conference date: 28-01-2008 Through 29-01-2008",
}