Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/45731
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dc.contributor.authorVrinda Madan
dc.date.accessioned2011-06-16T08:03:58Z
dc.date.available2011-06-16T08:03:58Z
dc.date.copyright2011en_US
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/10356/45731
dc.description.abstractFunctional MRI or functional Magnetic Resonance Imaging (fMRI) is a technique for measuring brain activity. It works by detecting the changes in blood oxygenation and flow that occur in response to neural activity – when a brain area is more active it consumes more oxygen and to meet this increased demand blood flow increases to the active area. FMRI can be used to produce activation maps showing which parts of the brain are involved in a particular mental process. FMRI data is severely contaminated by noise, in large part due to physiological noise caused by respiratory and cardiac variations over time. This Final Year Project aims to analyze different methods for reduction of this noise including Gaussian Filtering, Bilateral Filtering and Anisotropic Averaging. The project employs both the Model-Driven Analysis Techniques and Hypothesis-Based Analysis Techniques to compare the aforementioned filters.en_US
dc.format.extent67 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronicsen_US
dc.titleInvestigation and implementation of image processing algorithms to remove noise from FMRI data for medical applicationsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorMohammed Yakoob Siyalen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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