Multivariate image analysis (MIA) is a methodology for analyzing multivariate images, where the image coordinates are position (two- or three-dimensions) and variable number. Multivariate images can have typical sizes 1024 × 1024, 512 × 512, 256 × 256 etc. and have between two and many hundreds of variables. The variables can be wavelength, electron energy, particle mass and many others. Image analysis concentrates mainly on spatial relationships between pixels in a grey level image. MIA concentrates on the correlation of structure between the variables to provide extra information useful for exploring images and classifying regions in them. The many variables can be transformed into a few latent variable images containing condensed information. The sheer size of the data arrays necessitates visualization of raw data, intermediate data and analysis results.
All physical techniques for measuring materials can be made into imaging techniques, describing not only a property, but also its position in a plane or volume. All imaging techniques can be expanded to become multivariate. Multivariate imaging is used in three major fields: remote sensing, medical imaging and microscopy (including macroscopy). In microscopy it can be used to study materials and biological processes by optical, electron and charged particle techniques.
2000. 13540-62 p.