Independent component analysis in PET
Independent component analysis (ICA) is a statistical and computational technique that represents a multidimensional random vector as a linear combination of non-gaussian random variables (independent components) that are as independent as possible (Hyvärinen & Oja, 2000). ICA is a non-gaussian version of factor analysis, somewhat similar to principal component analysis (PCA), allowing blind separation of signal components. It has become a standard analysis technique in machine learning and signal processing. In analysis of PET data, ICA has been used for automatic image segmentation (Margadán-Méndez et al., 2004, 2010; Juslin et al., 2005, 2007), especially in order to extract input function from dynamic image (Lee et al., 2001, 2002; Naganawa et al., 2005a, 2005b, 2007, 2008; Chen et al., 2007; Mabrouk et al., 2013, 2014).
The FastICA algorithm is a computationally efficient method for performing the estimation of ICA (Hyvärinen, 1999).
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Hyvärinen A, Karhunen J, Oja E: Independent Component Analysis. Wiley, 2001. ISBN 0-471-22131-7.
Stone JV: Independent Component Analysis: A Tutorial Introduction. MIT Press, 2004. ISBN 0-262-69315-1.
Updated at: 2019-11-12
Created at: 2019-11-12
Written by: Vesa Oikonen