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Activity Number: 398
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #311727 View Presentation
Title: A Bayesian Approach for Signal Detection in Noisy Images
Author(s): Khalil Shafie*+ and Mohammad Reza Farid Rouhani
Companies: University of Northern Colorado and Shahid Beheshti University
Keywords: Bayes factor ; Gaussian random fields ; signal detection ; Hilbert valued Normal variables
Abstract:

Rohani et. al (2006) considered the problem of searching for activation in brain images obtained from functional magnetic resonance imaging (fMRI) and the corresponding functional signal detection problem. They developed a Bayesian procedure to detect signals existing within noisy images when the image is modeled as a scale space random field. The purpose of this paper is to extend the scale space result to a more general setting. In a general abstract setting, using Radon-Nikodym derivative, an extended definition of Bayes factor for testing the point null hypothesis is presented. Using this extended definition, a Bayesian testing procedure for signal detection in noisy images when both signal and noise considered as an element of an infinite dimensional Hilbert space is introduced. The method is applied to the problem of searching for activation in brain images obtained by functional magnetic resonance imaging (fMRI).


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