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Proceedings Paper

Comparative study of methods for automatic classification of macromolecular image sets: preliminary investigation with realistic simulations
Author(s): Ana Guerrero; Noel Bonnet; Sergio Marco; Jose L. Carrascosa
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Paper 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 signal/noise 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.

Paper Details

Date Published: 14 April 2000
PDF: 12 pages
Proc. SPIE 3962, Applications of Artificial Neural Networks in Image Processing V, (14 April 2000); doi: 10.1117/12.382902
Show Author Affiliations
Ana Guerrero, CERN (Switzerland)
Noel Bonnet, Univ. de Reims Champagne-Ardennes (France)
Sergio Marco, Univ. de Tours and Univ. Autonoma de Madrid (France)
Jose L. Carrascosa, Univ. Autonoma de Madrid (Spain)


Published in SPIE Proceedings Vol. 3962:
Applications of Artificial Neural Networks in Image Processing V
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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