Abstract: The invention relates to a process for cyclic, interactive image analysis with the steps of selection by the user of image regions of interest in the image, performance of various preprogrammed transformations on the image at all image points, performance of a normalization of the transformation results, division of the whole normalized transformation space into classes with the aid of separation rules which are derived from the transformation result values in the image regions selected by the user, allocation of the classification obtained in the transformation space to the individual image points, and representation of the thus classified image in the image space.

Abstract: In a method for a self-organizing neural network for representing multidimensional, nonlinear imaging functions onto simpler imaging functions use divider-membranes are employed for achieving an error free representation of the imaging function via the learning sample, allowing for a high level of generalization. Kohonen cell borders coincide with a required imaging function. The neural network can independently determine a number of neurons necessary for an error-free solution of a problem. A readout of the neural network can occur through the calculation of the minimum of the squares of the distances.