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International Joint Conference on Biomedical Engineering Systems and Technologies

BIOSTEC 2013: Biomedical Engineering Systems and Technologies pp 117–130Cite as

Texture Classification of Proteins Using Support Vector Machines and Bio-inspired Metaheuristics

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 452))

Abstract

In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process.

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Acknowledgements

This work is by “Development of new image analysis techniques in 2D Gel for biomedical research” (Ref. 10SIN105004PR), CN2102/217, CN2011/034 and CN2012/130 by Xunta de Galicia. Jose A. Seoane acknowledges Medical Research Council Project Grant G1000427. Pablo Mesejo and Youssef S.G. Nashed are funded by the European Commission (MIBISOC Marie Curie Initial Training Network, FP7 PEOPLE-ITN-2008, GA n. 238819).

Author information

Authors and Affiliations

  1. Information and Communication Technologies Department, Faculty of Computer Science, University of a Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain

    Carlos Fernandez-Lozano & Julian Dorado

  2. MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS82BN, UK

    Jose A. Seoane

  3. Dipartimento Di Ingegneria Dell’Informazione, Universitá Degli Studi Di Parma, Viale G. Usberti 181/a, 43100, Parma, Italy

    Pablo Mesejo, Youssef S. G. Nashed & Stefano Cagnoni

Authors
  1. Carlos Fernandez-Lozano
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  2. Jose A. Seoane
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  3. Pablo Mesejo
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  4. Youssef S. G. Nashed
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  5. Stefano Cagnoni
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  6. Julian Dorado
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Corresponding author

Correspondence to Carlos Fernandez-Lozano .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Mireya Fernández-Chimeno

  2. Instituto Gulbenkian de Ciência, Oeiras, Portugal

    Pedro L. Fernandes

  3. Boston College, Chestnut Hill, Massachusetts, USA

    Sergio Alvarez

  4. University of Guelph, Guelph, Canada

    Deborah Stacey

  5. University of Vic, Vic, Spain

    Jordi Solé-Casals

  6. Technical University of Lisbon, Lisbon, Portugal

    Ana Fred

  7. New University of Lisbon, Lisboa, Portugal

    Hugo Gamboa

Appendix

Appendix

Table 3a. Results with different SVM Gaussian kernels.
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Table 3b. Results with different SVM polynomial kernels.
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Table 4. Study of texture parameters between best SVM kernels in accuracy.
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Fernandez-Lozano, C., Seoane, J.A., Mesejo, P., Nashed, Y.S.G., Cagnoni, S., Dorado, J. (2014). Texture Classification of Proteins Using Support Vector Machines and Bio-inspired Metaheuristics. In: Fernández-Chimeno, M., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2013. Communications in Computer and Information Science, vol 452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44485-6_9

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  • DOI: https://doi.org/10.1007/978-3-662-44485-6_9

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