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

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.

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Paper Citation


in Harvard Style

Fernandez-Lozano C., Seoane J., Mesejo P., S. G. Nashed Y., Cagnoni S. and Dorado J. (2013). 2D-PAGE Texture Classification using Support Vector Machines and Genetic Algorithms - An Hybrid Approach for Texture Image Analysis . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013) ISBN 978-989-8565-35-8, pages 5-14. DOI: 10.5220/0004187400050014


in Bibtex Style

@conference{bioinformatics13,
author={Carlos Fernandez-Lozano and Jose A. Seoane and Pablo Mesejo and Youssef S. G. Nashed and Stefano Cagnoni and Julian Dorado},
title={2D-PAGE Texture Classification using Support Vector Machines and Genetic Algorithms - An Hybrid Approach for Texture Image Analysis},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013)},
year={2013},
pages={5-14},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004187400050014},
isbn={978-989-8565-35-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013)
TI - 2D-PAGE Texture Classification using Support Vector Machines and Genetic Algorithms - An Hybrid Approach for Texture Image Analysis
SN - 978-989-8565-35-8
AU - Fernandez-Lozano C.
AU - Seoane J.
AU - Mesejo P.
AU - S. G. Nashed Y.
AU - Cagnoni S.
AU - Dorado J.
PY - 2013
SP - 5
EP - 14
DO - 10.5220/0004187400050014