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20 April 2000 Multivariate segmentation of fMRI for human brain mapping
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Abstract
fMRI has provided a new option to study cognitive phenomena. Recent developments in medical image processing and analysis allow researchers to study more elaborate cognitive tasks from a wide perspective. These techniques include Statistical Parametric Mapping, Subspace Modeling and Maximum Likelihood Estimation, and Spatio-temporal Analysis using Random Fields. Their common weakness is the assumption of the statistical independence among the image pixels. We have developed a multivariate segmentation method to functional MRI analysis for human brain function study based on the second-order statistics of images. It consists of four steps: (1) detecting the number of the distinctive image regions, (2) generating the scores and determining their rank, (3) forming score plots and clustering in the feature space, (4) projecting clusters from the feature space to the image space to generate object images. We have validated this method on the simulated and fMRI images. The theoretical and experimental results obtained by using this method were in good agreement. The relations between this method and other multivariate image analysis methods are discussed.
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Tianhu Lei and Jayaram K. Udupa "Multivariate segmentation of fMRI for human brain mapping", Proc. SPIE 3978, Medical Imaging 2000: Physiology and Function from Multidimensional Images, (20 April 2000); https://doi.org/10.1117/12.383410
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