Abstract: Classification of single particle projection images of heterogeneous sets before 2D and 3D analysis is still a major problem in electron microscopy. Images obtained by the microscope not only present a very low signaloise ratio but also a wide range of variability due to the non homogeneous background on which particles lay and tilting differences among other factors. Blind classification procedures are therefore bound to fail or in any case can be hardly reliable, thus making necessary the use of dimensionality reduction tools in order to ease the task of classification and to introduce some kind of control over the process. The purpose of this work is the evaluation of both linear and nonlinear unsupervised feature extraction techniques together with several pattern recognition and automatic classification tools, some of which have not yet been applied and tested in this context. Mapping and classification procedures include statistical and neural network tools.!34
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