The Application Of Independent Component Analysis Algorithm Based On Region Growing For Processing The FMRI Data

Since the 1990s, brain functional imaging techniques are widely applied and extensively developed. The best virtue of these techniques is that it is non-invasive, i. e. they enable us to have direct looks into the brain repeatedly without physical damage.The performance of logical operation on data plays an important role in functional magnetic resonance imaging (fMRI) studies. With the development of the technology of human brain mapping such as PET and fMRI etc., scientists have obtained much useful information about localization of functional brain regions, functions of some brain regions and the relationship among various functional regions. Only through analyzing these data sets, picking up interesting information from many noises, and abstracting underlying rules can we reach the aim of studying brain function.fMRI data analysis has become an important field. There are two types of methods for analyzing fMRI data: data-drive methods and model-drive methods. In this thesis, the Independent Component Analysis(ICA) based on region growing has been used to processing the functional magnetic resonance imaging data, the ICA algorithm is a methods based on data-drive .