Pattern recognition methods for MRS analysis and classification
Pattern recognition methods for MRS analysis and classification
- Author(s): P.J.G. Lisboa ; S.J. Kirby ; A. Vellido ; B. Lee
- DOI: 10.1049/ic:19970473
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- Author(s): P.J.G. Lisboa ; S.J. Kirby ; A. Vellido ; B. Lee Source: IEE Colloquium on Realising the Clinical Potential of Magnetic Resonance Spectroscopy: the Role of Pattern Recognition, 1997 page ()
- Conference: IEE Colloquium on Realising the Clinical Potential of Magnetic Resonance Spectroscopy: the Role of Pattern Recognition
It is clear that statistical classification of MRS has considerable potential as an accurate diagnostic advice tool. However, sample sizes are not yet sufficient to guarantee performance in large-scale clinical trials. In addition, the most commonly used classification strategies, namely Principal Components Analysis of in vitro spectra and Linear Discriminants Analysis of in vivo data, while offering considerable accuracy, are not optimal even within the currently available linear statistical classifiers. The issue of non-linearity and the consequent need of neural network analysis remains rests also on the outcome of larger studies, although the performance of these methods has already been shown to be competitive with that of statistical methods. Further work on principled methods for neural network design and rule-extraction offer the potential, in the near future, for high-performing, transparent non-linear classifiers. Alternative algorithms can also be used for automatic labelling, of clustering, of spectra. Overall, the results of the preliminary studies reported here are encouraging both for the accuracy that has been achieved and the consistency of the variable relevance ranking with clinical expectation. (9 pages)
Inspec keywords: pattern recognition; biomedical NMR; spectral analysis; medical signal processing; NMR spectroscopy
Subjects: Medical magnetic resonance imaging and spectroscopy; Biomagnetism; Digital signal processing; Patient diagnostic methods and instrumentation; Biology and medical computing; Signal processing and detection; Radiation and radioactivity applications in biomedicine
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