Mapping and fuzzy classification of macromolecular images using self-organizing neural networks.
In this work the effectiveness of the fuzzy kohonen clustering network (FKCN) in the unsupervised classification of electron microscopic images of biological macromolecules is studied. The algorithm combines Kohonen's self-organizing feature maps (SOFM) and Fuzzy c-means (FCM) in order to obtain a powerful clustering technique with the best properties inherited from both. Exploratory data analysis using SOFM is also presented as a step previous to final clustering. Two different data sets obtained from the G40P helicase from B. Subtilis bacteriophage SPP1 have been used for testing the proposed method, one composed of 2458 rotational power spectra of individual images and the other composed by 338 images from the same macromolecule. Results of FKCN are compared with self-organizing feature maps (SOFM) and manual classification. Experimental results prove that this new technique is suitable for working with large, high-dimensional and noisy data sets and, thus, it is proposed to be used as a classification tool in electron microscopy.
Pascual
A
A
Centro Nacional de Biotecnología-CSIC, Universidad Autónoma, Madrid, Spain. pascual@cnb.uam.es
Bárcena
M
M
Merelo
J J
JJ
Carazo
J M
JM
D000465:Algorithms; D017103:Bacillus Phages / Q000201:enzymology; D004265:DNA Helicases / Q000648:ultrastructure*; D017143:Fuzzy Logic; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D014764:Viral Proteins
D014764:Viral Proteins; D004265:DNA Helicases; C115901:G40P helicase protein, Bacteriophage SPP1