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Effectiveness of the finite impulse response model in content-based fMRI image retrieval
Journal article   Open access   Peer reviewed

Effectiveness of the finite impulse response model in content-based fMRI image retrieval

Bing Bai, Paul Kantor and Ali Shokoufandeh
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, v 10(Pt 2), p742
2007
DOI: https://doi.org/10.1007/978-3-540-75759-7_90
PMID: 18044635
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1007/978-3-540-75759-7_90View
Published, Version of Record (VoR) Open

Abstract

Algorithms Brain - physiology Brain Mapping - methods Computer Simulation Evoked Potentials, Visual - physiology Humans Image Interpretation, Computer-Assisted - methods Information Storage and Retrieval - methods Magnetic Resonance Imaging - methods Models, Neurological Nerve Net - physiology Nonlinear Dynamics Signal Processing, Computer-Assisted Visual Perception - physiology
The thresholded t-map produced by the General Linear Model (GLM) gives an effective summary of activation patterns in functional brain images and is widely used for feature selection in fMRI related classification tasks. As part of a project to build content-based retrieval systems for fMRI images, we have investigated ways to make GLM more adaptive and more robust in dealing with fMRI data from widely differing experiments. In this paper we report on exploration of the Finite Impulse Response model, combined with multiple linear regression, to identify the "locally best Hemodynamic Response Function (HRF) for each voxel" and to simultaneously estimate activation levels corresponding to several stimulus conditions. The goal is to develop a procedure for processing datasets of varying natures. Our experiments show that Finite Impulse Response (FIR) models with a smoothing factor produce better retrieval performance than does the canonical double gamma HRF in terms of retrieval accuracy.

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5 citations in Web of Science
5 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Theory & Methods
Imaging Science & Photographic Technology
Neurosciences
Radiology, Nuclear Medicine & Medical Imaging
Robotics
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