A Texture-based Classification Method for Proteins in Two-Dimensional Electrophoresis Gel Images - A Feature Selection Method using Support Vector Machines and Genetic Algorithms

Carlos Fernandez-Lozano; Jose A. Seoane; Marcos Gestal; Daniel Rivero; Julian Dorado; Alejandro Pazos

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Paper

A Texture-based Classification Method for Proteins in Two-Dimensional Electrophoresis Gel Images - A Feature Selection Method using Support Vector Machines and Genetic Algorithms

Topics: Color and Texture Analyses; Features Extraction; Machine Learning Technologies for Vision

In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, 401-404, 2013 , Barcelona, Spain

Authors: Carlos Fernandez-Lozano ; Jose A. Seoane ; Marcos Gestal ; Daniel Rivero ; Julian Dorado and Alejandro Pazos

Affiliation: Faculty of Computer Science and University of A Coruña, Spain

Keyword(s): Texture Analysis, Feature Selection, Electrophoresis, Support Vector Machines, Genetic Algorithm.

Related Ontology Subjects/Areas/Topics: Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis

Abstract: In this paper, the influence of textural information is studied in two-dimensional electrophoresis gel images. A Genetic Algorithm-based feature selection technique is used in order to select the most representative textural features and reduced the original set (296 feat.) to a more efficient subset. Such a method makes use of a Support Vector Machines classifier. Different experiments have been performed, the pattern set has been divided into two parts (training and validation) extracting a total of 30%, 20% and 0% of the training data, and a 10-fold cross validation is used for validation. In case of extracting 0% means that training set is used for validation. For each division 10 different trials have been done. Experiments have been carried out in order to measure the behaviour of the system and to achieve the most representative textural features for the classification of proteins in two-dimensional gel electrophoresis images. This information can be useful for a protein segme ntation process. (More)

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Paper citation in several formats:

Fernandez-Lozano, C.; Seoane, J.; Gestal, M.; Rivero, D.; Dorado, J. and Pazos, A. (2013). A Texture-based Classification Method for Proteins in Two-Dimensional Electrophoresis Gel Images - A Feature Selection Method using Support Vector Machines and Genetic Algorithms. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP; ISBN 978-989-8565-47-1; ISSN 2184-4321, SciTePress, pages 401-404. DOI: 10.5220/0004208704010404

@conference{visapp13,
author={Carlos Fernandez{-}Lozano. and Jose A. Seoane. and Marcos Gestal. and Daniel Rivero. and Julian Dorado. and Alejandro Pazos.},
title={A Texture-based Classification Method for Proteins in Two-Dimensional Electrophoresis Gel Images - A Feature Selection Method using Support Vector Machines and Genetic Algorithms},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP},
year={2013},
pages={401-404},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004208704010404},
isbn={978-989-8565-47-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP
TI - A Texture-based Classification Method for Proteins in Two-Dimensional Electrophoresis Gel Images - A Feature Selection Method using Support Vector Machines and Genetic Algorithms
SN - 978-989-8565-47-1
IS - 2184-4321
AU - Fernandez-Lozano, C.
AU - Seoane, J.
AU - Gestal, M.
AU - Rivero, D.
AU - Dorado, J.
AU - Pazos, A.
PY - 2013
SP - 401
EP - 404
DO - 10.5220/0004208704010404
PB - SciTePress