2D-page texture classification using support vector machines and genetic algorithms an hybrid approach for texture image analysis

Carlos Fernandez-Lozano, Jose A. Seoane, Pablo Mesejo, Youssef S.G. Nashed, Stefano Cagnoni, Julian Dorado

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper, a novel texture classification method from two-dimensional electrophoresis gel images is presented. Such a method makes use of textural features that are reduced to a more compact and efficient subset of characteristics by means of a Genetic Algorithm-based feature selection technique. Then, the selected features are used as inputs for a classifier, in this case a Support Vector Machine. The accuracy of the proposed method is around 94%, and has shown to yield statistically better performances than the classification based on the entire feature set. We found that the most decisive and representative features for the textural classification of proteins are those related to the second order co-occurrence matrix. This classification step can be very useful in order to discard over-segmented areas after a protein segmentation or identification process.

Original languageEnglish (US)
Title of host publicationBIOINFORMATICS 2013 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms
Pages5-14
Number of pages10
StatePublished - May 27 2013
EventInternational Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2013 - Barcelona, Spain
Duration: Feb 11 2013Feb 14 2013

Other

OtherInternational Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2013
CountrySpain
CityBarcelona
Period2/11/132/14/13

Keywords

  • Electrophoresis
  • Feature selection
  • Genetic algorithm
  • Support vector machines
  • Texture analysis

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Modeling and Simulation

Fingerprint Dive into the research topics of '2D-page texture classification using support vector machines and genetic algorithms an hybrid approach for texture image analysis'. Together they form a unique fingerprint.

Cite this