Simultaneous decomposition of multivariate images using three-way data analysis: Application to the comparison of cereal grains by confocal laser scanning microscopy

We present a three-way data analysis method adapted to compare multispectral images such as those acquired by fluorescence microscopy. Spectral images are multivariate and can be considered as sets of pixels for which different spectral intensities have been measured. The algorithm proposed is based on the simultaneous decomposition of the covariance matrices calculated, in the present work, for fluorescence variables. This method allows the extraction of components common to each image and reveals their specificities by means of specific weights. The technique is illustrated by the analysis of microscopic image sequences acquired for five varieties of wheat and barley grains in 19 fluorescence conditions. The method made it possible to compare the fluorescence behaviors observed in the images and the strong similarities of the external tissues for the five cereals were highlighted.