Multivariate Image Analysis and Segmentation in Microanalysis

Instruments for microanalysis are now able to provide several images of the same specimen area. In this paper, two groups of methods are described for handling these multivariate maps. One group concerns dimensionality reduction, i.e., the projection of N-dimensional data sets onto a M-dimensional parameter space (M<N). It is shown that, in addition to linear mapping which can be performed by Multivariate Statistical Analysis, nonlinear mapping can also be performed (Multi-dimensional Scaling, Sammon’s mapping, Self-Organizing mapping). The other group concerns Automatic Correlation Partitioning (ACP). With these methods, pixels are grouped into several classes according to the different signals recorded. This can be done by classical clustering methods (K-means, fuzzy Cmeans) or by new methods which do not make hypotheses concerning the shape of clusters in the parameter space.