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

  • Carlos Fernandez-LozanoEmail author
  • Jose A. Seoane
  • Pablo Mesejo
  • Youssef S. G. Nashed
  • Stefano Cagnoni
  • Julian Dorado
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 452)


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.


Texture analysis Feature selection Electrophoresis Support Vector Machines Genetic Algorithm Proteomic imaging 



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).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Carlos Fernandez-Lozano
    • 1
    Email author
  • Jose A. Seoane
    • 2
  • Pablo Mesejo
    • 3
  • Youssef S. G. Nashed
    • 3
  • Stefano Cagnoni
    • 3
  • Julian Dorado
    • 1
  1. 1.Information and Communication Technologies Department, Faculty of Computer ScienceUniversity of a CoruñaA CoruñaSpain
  2. 2.MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community MedicineUniversity of BristolBristolUK
  3. 3.Dipartimento Di Ingegneria Dell’InformazioneUniversitá Degli Studi Di ParmaParmaItaly

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