Investigation and implementation of image processing algorithms to remove noise from FMRI data for medical applications

Abstract

Functional 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.