Statistical classification strategy

It is essential for the clinical implementation of the MRS technology that high sensitivity and specificity are available reproducibly. This requirement was the driving force in the development of the statistical classification strategy (SCS)-based multivariate analysis methods that form the focus of this review. This review aims to summarize the clinical MRS studies reported to date that have included clinical outcomes and/or histopathological assessment of the entire biopsy specimen examined by MRS and where the data have been analysed in recognition of the criteria essential for robust data classification. 

Fig. 1. Statistical classification strategy (SCS) a schematic road map of how the SCS method is developed for individual databases. GA ORS, genetic algorithm based optimal region selection LDA, linear discriminant analysis LOO, leave-one-out (method of cross-validation) coeff, coefficients.

The data cube combines spectral and spatial information and therefore includes the requisite statistics for spectral classifications. However, new chemometric strategies have to be applied to interpret chemical imaging results. 

Statistical Techniques Experimental Design Optimization Strategies Multivariate Classification Techniques Multivariate Calibration Techniques Expert Systems Multicriterla Decision Making Signal Processing... 

Further details such as stopping criteria for growing decision trees, strategies for pruning decision trees and for constructing classification trees are described in [36]. An example of a classification tree to detect the presence of bromine in a mass spectrum is shown on pages 205-215. We will consider similar problems in Chapter 8, using an implementation by B. D. Ripley ([251], Chapter 7) via an interface to the statistics... 

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