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Application of the Fuzzy Kohonen Clustering Network to biological macromolecules images classification

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Engineering Applications of Bio-Inspired Artificial Neural Networks (IWANN 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1607))

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Abstract

In this work we study the effectiveness of the Fuzzy Kohonen Clustering Network (FKCN) in the unsupervised classification of electron microscopic images of biological macromolecules. The algorithm combines Kohonen's Self-Organizing Feature Maps (SOM) and Fuzzy c-means clustering technique (FCM) in order to obtain a powerful clustering technique that inherits their best properties. 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 Maps (SOM) and manual classification. Experimental results have proved that this new technique is suitable for working with large, high dimensional and noisy data sets. This method is proposed to be used as a classification tool in Electron Microscopy.

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Authors and Affiliations

  1. Centro Nacional de Biotecnología-CSIC, Universidad Autónoma, 28049, Madrid, Spain

    Alberto Pascual, Montserrat Barcéna & José-María Carazo

  2. Dpto Arquitectura y Tecnología de las Computadoras, Facultad de Ciencias, GeNeura Team, Campus Fuentenueva, s/n, 18071, Granada, Spain

    J. J. Merelo

Authors
  1. Alberto Pascual
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  2. Montserrat Barcéna
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  3. J. J. Merelo
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  4. José-María Carazo
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José Mira Juan V. Sánchez-Andrés

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© 1999 Springer-Verlag Berlin Heidelberg

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Pascual, A., Barcéna, M., Merelo, J.J., Carazo, JM. (1999). Application of the Fuzzy Kohonen Clustering Network to biological macromolecules images classification. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100500

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  • DOI: https://doi.org/10.1007/BFb0100500

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66068-2

  • Online ISBN: 978-3-540-48772-2

  • eBook Packages: Springer Book Archive

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