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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Multiscale Integration for Pattern Recognition in Neuroimaging
verfasst von : Margarita Zaleshina, Alexander Zaleshin
Erschienen in: Machine Learning, Optimization, and Big Data
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Abstract
Multiscale, multilevel integration is a valuable method for the recognition and analysis of combined spatial-temporal characteristics of specific brain regions. Using this method, primary experimental data are decomposed both into sets of spatially independent images and into sets of time series. The results of this decomposition are then integrated into a single space using a coordinate system that contains metadata regarding the data sources. The following modules can be used as tools to optimize data processing: (a) the selection of reference points; (b) the identification of regions of interest; and (c) classification and generalization. Multiscale integration methods are applicable for achieving pattern recognition in computed tomography and magnetic resonance imaging, thereby allowing for comparative analyses of data processing results.