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Magnetic resonance spectroscopy has become an important clinical tool, serving as a quantitative indicator of the chemical composition of tissue, whether sampled in vitro or in vivo. In particular, 1H-MRS provides detailed biochemical information of tissues of interest and is increasingly used in tumors diagnosis and grading, where a number of automated decision support have shown promising results. Statistical pattern recognition (PR) techniques have an important role in the allocation of spectra to diagnostic classes because they utilise the multivariate nature of spectraHowever, the very large number of spectral components combined with significant noise effects, especially when acquired in vivo, pose substantial difficulties for their statistical analysis, and severely limit to generalise to future data the conclusion from studies using limited number of samples. Linear statistical models commonly be applied after the dimensionality of the spectrum has been reduced to a fraction of the number of samples available, and the reproducibility of the results obtained is strongly dependent on how this parsimony is achieved. Previous studies have passed on clinically identified variables of interest, typically area ratios or attempted to identify the most predictive frequency components. We propose a systematic variable selection methodology based on a well established statistical technique know as the bootstrap to select subset of variable for linear discriminant analysis (LDA) to classify different tissue types to class. This is complemented by the further use of the bootstrap to provide robust estimates of the accuracy of the discriminant models when they are applied to future data.The multivariate structure of MRS also makes it very difficult to visualise the separation between different tissues types. A 2-dimensional map of the class membership of the spectra also has considerable potential as a graphical aid for interactive decision support for differential diagnosis by clinicians. Therefore, we propose the application of a planar representation of the spectra, which preserves their relationship to the multivariate class means and variances. In this way, the discriminatory power for different selections of frequency components can be visually demonstrated. Finally, a reject option is introduced into the graphical display with the aim of minimising the risks of misclassification by the explicit visualisation of the overlap between spectra from tumor.