Abstract
α- and β-divergence based nonnegative tensor factorization (NTF) is combined with nonlinear band expansion (NBE) for blind decomposition of the magnetic resonance image (MRI) of the brain. Concentrations and 3D tensor of spatial distributions of brain substances are identified from the Tucker3 model of the 3D MRI tensor. NBE enables to account for the presence of more brain substances than number of bands and, more important, to improve conditioning of the expanded matrix of concentrations of brain substances. Unlike matrix factorization methods NTF preserves local spatial structure in the MRI. Unlike ICA-, NTF-based factorization is insensitive to statistical dependence among spatial distributions of brain substances. Efficiency of the NBE-NTF algorithm is demonstrated over NBE-ICA and NTF-only algorithms on blind decomposition of the realistically simulated MRI of the brain.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chang, C.I. (ed.): Hyperspectral Data Exploitation: Theory and Applications, New York. John Wiley, Chichester (2007)
Du, Q., Kopriva, I., Szu, H.: Independent-component analysis for hyperspectral remote sensing imagery classification. Opt. Eng. 45, 1–13 (2006)
Ouyang, Y.C., Chen, H.M., Chai, J.W., Chen, C.C.C., Poon, S.K., Yang, C.W., Lee, S.K., Chang, C.I.: Band Expansion-Based Over-Complete Independent Component Analysis for Multispectral Processing of Magnetic Resonance Image. IEEE Trans. Biomed. Eng. 55, 1666–1677 (2008)
Nakai, T., Muraki, S., Bagarinao, E., Miki, Y., Takehara, Y., Matsuo, K., Kato, C., Sakahara, H., Isoda, H.: Application of independent component analysis to magnetic resonance imaging for enhancing the contrast of gray and white matter. Neuroimage 21, 251–260 (2004)
Kopriva, I., Cichocki, A.: Blind decomposition of low-dimensional multi-spectral image by sparse component analysis. J. Chemometrics 23, 590–597 (2009)
Kopriva, I., Peršin, A.: Unsupervised decomposition of low-intensity low-dimensional multi-spectral fluorescent images for tumour demarcation. Med. Image Analysis 13, 507–518 (2009)
Kopriva, I., Cichocki, A.: Blind Multi-spectral Image Decomposition by 3D Nonnegative Tensor Factorization. Opt. Lett. 34, 2210–2212 (2009)
Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing. John Wiley, Chichester (2002)
Nascimento, J.M.P., Dias, J.M.B.: Does Independent Component Analysis Play a Role in Unmixing Hyperspectral Data? IEEE Trans. Geosci. Remote Sens. 43, 175–187 (2005)
Nascimento, J.M.P., Dias, J.M.B.: Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data. IEEE Trans. Geosci. Remote Sens. 43, 898–910 (2005)
Li, Y., Cichocki, A., Amari, S.: Analysis of Sparse Representation and Blind Source Separation. Neural Comput. 16, 1193–1234 (2004)
Cichocki, A., Zdunek, R., Phan, A.H., Amari, S.I.: Nonnegative Matrix and Tensor Factorizations - Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. John Wiley, Chichester (2009)
Cichocki, A., Phan, A.H.: Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations. IEICE Trans. Fundamentals E92-A(3), 708–721 (2009)
Tucker, L.R.: Some mathematical notes on three-mode factor analysis. Psychometrika 31, 279–311 (1966)
Kwan, R.K.S., Evans, A.C., Pike, G.B.: MRI Simulation-Based Evaluation of Image-Processing and Classification Methods. IEEE Trans. Med. Imag. 18, 1085–1097 (1999)
Koldovský, Z., Tichavský, P., Oja, E.: Efficient Variant of Algorithm for FastICA for Independent Component Analysis Attaining the Cramér-Rao Lower Bound. IEEE Trans. Neural Net. 17, 1265–1277 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kopriva, I., Cichocki, A. (2010). Nonlinear Band Expansion and 3D Nonnegative Tensor Factorization for Blind Decomposition of Magnetic Resonance Image of the Brain . In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-15995-4_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15994-7
Online ISBN: 978-3-642-15995-4
eBook Packages: Computer ScienceComputer Science (R0)