Abstract
Grahn, H., Szeverenyi, N.M., Roggenbuck, M. and Geladi, P., 1989. Tissue discrimination in multivariate magnetic resonance imaging: a predictive multivariate approach. Chemometrics and Intelligent Laboratory Systems, 7: 87-93. It is possible to form a regresssion equation between two stacks of multivariate images. Discriminant partial least squares was used as a model for the regression. The independent variables were greyvalues of magnetic resonance images. As the dependent variables, binary images corresponding to different tissue types were chosen. The models can be used to calculate predictions. These turn out to be excellent for visual interpretation and pattern recognition. Examples are given for an animal study where different tissue types of two animals were used.
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