Image Analysis System of fMRI Based on Spatial Independent Component

• Main Authors: Yu-ping Huang, 黃瑜萍
• Other Authors: Chou-ching Lin
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/97769591833939545799

碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 95 === Functional magnetic resounce imaging (fMRI) has been commonly used to measure the hemodynamic response of brain which is proven to be related with the neural activities in the brain. Therefore, it has become a non-invasive clinical tool for the diagnoses of various kinds of brain or neural system diseases. One of the most important tasks in analyzing the fMRI is to identify the neural activation areas from intra- or inter-subject functional images. This is also the major goal of the proposed image analysis system in this thesis. Independent component analysis (ICA) is a technique that attempts to separate sensory image data into spatial independent non-Gaussian components which are then used to determine the component with time course best matched with the time course of stimulation. In this thesis, we have proposed a novel method which is a two-staged process for the selection of spatial independent component. In the first stage, fast Fourier transform is computed and used to rank the frequency response of the spatial independent component. These ranked components are then used to estimate the correlation coefficient with respect to the reference function of hemodynamic response in the second stage. Z statistics is then used to confirm the relationship between brain responses to the structure of human brain. We also applied elastic registration to build a standard brain for inter-subject analysis of neuronal activations. Comparing the results obtained by using the proposed standard brain and by using the Statistical Parametric Mapping (SPM), the proposed method can successfully indicate the regions of neuronal activity that were not correctly identified by SPM.