PATTERN-RECOGNITION AND CLASSIFICATION OF IMAGES OF BIOLOGICAL MACROMOLECULES USING ARTIFICIAL NEURAL NETWORKS

The goal of this work was to analyze an image data set and to detect t he structural variability within this set. Two algorithms for pattern recognition based on neural networks are presented, one that performs an unsupervised classification (the self-organizing map) and the other a supervised classification (the learning vector quantization). The a pproach has a direct impact in current strategies for structural deter mination from electron microscopic images of biological macromolecules . In this work we performed a classification of both aligned but heter ogeneous image data sets as well as basically homogeneous but otherwis e rotationally misaligned image populations, in the latter case comple tely avoiding the typical reference dependency of correlation-based al ignment methods. A number of examples on chaperonins are presented. Th e approach is computationally fast and robust with respect to noise. P rograms are available through ftp.