Abstract
We present a method for automatically decomposing magnetic resonance (MR) spectra of different types of human brain tumours into components which directly reflect their different chemical compositions. The automatic analysis of in vivo MR spectra can be problematic due to their large dimensionality and the low signal to noise ratio. Principal Component Analysis allows an economic representation of the data but the extracted components themselves may bear little relationship to the underlying metabolites represented by the spectra. The Principal Components can be rotated in order to make them more meaningful but this requires expertise to decide on the transformation. In this study, we use Independent Component Analysis and show that this technique can overcome these two drawbacks and provide meaningful and representative components without requiring prior knowledge.
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
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, Inc, second edition (1990)
Howells, S.L.: Maxwell, R.J., and Griffiths, J.R.: Classification of tumour Hnmr spectra by pattern recognition. NMR in Bio-Medicine, (1992) 5:59–64.
Hyvarinen, A. and Oja, E: Independent component analysis: Algorithms and applications. Neural Networks, (2000), 13(4–5):411430.
Hyvarinen, A.: Survey on independent component analysis. Neural Computing Surveys, (1999) 2:94–128.
Lee, Y., Huang, Y.: El-Deredy, W., Lisboa, P., Ards, C., and Harris, P.. Robust methodology for the discrimination of brain tumours from in vivo magnetic resonance spectra. In IEE Proceedings-Science, Measurement and Technology, volume 147, (2000) 309–314.
Hyvarinen, A., Sarela, J., and Vigario, R.: Bumps and spikes: Artefacts generated by independent component analysis with insufficient sample size. In Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA’99),(1999) pages 425–429.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ladroue, C., Tate, A.R., Howe, F.A., Griffiths, J.R. (2002). Unsupervised Feature Extraction of in vivo Magnetic Resonance Spectra of Brain Tumours Using Independent Component Analysis. In: Yin, H., Allinson, N., Freeman, R., Keane, J., Hubbard, S. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2002. IDEAL 2002. Lecture Notes in Computer Science, vol 2412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45675-9_66
Download citation
DOI: https://doi.org/10.1007/3-540-45675-9_66
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44025-3
Online ISBN: 978-3-540-45675-9
eBook Packages: Springer Book Archive