Abstract
Analysis on magnetic resonance spectra (MRS) data gives a deep insight into pathology of many types of tumors. In this paper, a new method based on independent component analysis (ICA) and correlation analysis is proposed for MRS tumour data structure analysis. First, independent components and their coefficients are derived by ICA. Those components are interpreted in terms of metabolites, which interrelate with each other in tissues. Then correlation analysis is performed to reveal the interrelationship on coefficient of ICs, where residue dependence of components of metabolites remains. The method was performed on MRS data of hepatic encephalopathy. Experimental results reveal the intrinsic data structure and describe the pathological interrelation between parts of the structure successfully.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ladroue, C., et al.: Independent component analysis for automated decomposition of in vivo magnetic resonance spectra. Magnet. Reson. Med. 50, 697–703 (2003)
Anthony, M.L., et al.: Classification of toxin-induced changes in 1H NMR spectra of urine using an artificial neural network. J. Pharmaceut Biomed. 13, 205–211 (1995)
Simonetti, A.W., Melssen, W.J., de Edelenyi, F., van Asten, J.J., Heerschap, A., Buydens, L.M.: Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification. NMR Biomed. 18, 34–43 (2005)
Devos, A., et al.: Classification of brain tumours using short echo time H-1 MR spectra. J. Magn. Reson. 170, 164–175 (2004)
Mountford, C.E., Malycha, R.L.S.P., Gluch, L., Lean, C., Russell, P., Barraclough, B., Gillett, D., Himmelreich, U., Dolenko, B., Nikulin, A.E., Smith, I.C.P.: Diagnosis and prognosis of breast cancer by magnetic resonance spectroscopy of fine-needle aspirates analysed using a statistical classification strategy. Brit. J. Surg. 88, 1234–1240 (2002)
Tate, A.R., et al.: Automated classification of short echo time in vivo 1H brain tumor spectra: A multicenter study. Magnet. Reson. Med. 49, 29–36 (2003)
Howells, S.L., Maxwell, R.J., Griffiths, J.R.: Classification of tumour 1H NMR spectra by pattern recognition. NMR Biomed 5, 59–64 (1992)
James, C.J., Hesse, C.W.: Independent component analysis for biomedical signals. Physiol Meas 26, R15 (2005)
Hyvarinen, A.: Survey on independent component analysis. Neural Comput Surveys 2, 94–128 (1999)
Häussinger, D.: Hepatic encephalopathy: clinical aspects and pathogenesis. Deutsche medizinische Wochenschrift 129(Suppl. 2), 66–67 (2004)
Kreis, R., et al.: Disorders of the brain in chronic hepatic encephalopathy detected with H-1 MR spectroscopy. Radiology 182, 19–27 (1992)
Kreis, R., Farrow, N., Ross, B.D.: Localized 1H NMR spectroscopy in patients with chronic hepatic encephalopathy. Analysis of changes in cerebral glutamine, choline and inositols. NMR Biomed 4, 109–116 (1991)
Gavert, H., Hurri, J., Sarela, J., Hyvrinen, A.: Fast-ICA for matlab 5.x (2001), http://www.cis.hut.fi/projects/ica/fastica/
Kreis, R.: Quantitative localized 1H MR spectroscopy for clinical use. Progress in Nuclear Magnetic Resonance Spectroscopy 31, 155–195 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, J., Sun, Z. (2006). Exploring the Intrinsic Structure of Magnetic Resonance Spectra Tumor Data Based on Independent Component Analysis and Correlation Analysis. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_82
Download citation
DOI: https://doi.org/10.1007/11840930_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
Online ISBN: 978-3-540-38873-9
eBook Packages: Computer ScienceComputer Science (R0)