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Authors: Daniel G. Villaverde 1 ; Beatriz Remeseiro 1 ; Noelia Barreira 1 ; Manuel G. Penedo 1 and Antonio Mosquera 2

Affiliations: 1 University of A Coruña, Spain ; 2 University of Santiago de Compostela, Spain

ISBN: 978-989-758-015-4

Keyword(s): Tear Film, Dry Eye Syndrome, Color Texture Analysis, Feature Selection, Filter Methods, Machine Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems

Abstract: Dry eye is a common disease which affects a large portion of the population and harms their routine activities. Its diagnosis and monitoring require a battery of tests, each designed for different aspects. One of these clinical tests measures the quality of the tear film and is based on its appearance, which can be observed using the Doane interferometer. The manual process done by experts consists of classifying the interferometry images into one of the five categories considered. The variability existing in these images makes necessary the use of an automatic system for supporting dry eye diagnosis. In this research, a methodology to perform this classification automatically is presented. This methodology includes a color and texture analysis of the images, and also the use of feature selection methods to reduce image processing time. The effectiveness of the proposed methodology was demonstrated since it provides unbiased results with classification errors lower than 9%. Additional ly, it saves time for experts and can work in real-time for clinical purposes. (More)

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Paper citation in several formats:
G. Villaverde, D.; Remeseiro, B.; Barreira, N.; G. Penedo, M. and Mosquera, A. (2014). Feature Selection Applied to Human Tear Film Classification.In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 395-402. DOI: 10.5220/0004809403950402

@conference{icaart14,
author={Daniel G. Villaverde. and Beatriz Remeseiro. and Noelia Barreira. and Manuel G. Penedo. and Antonio Mosquera.},
title={Feature Selection Applied to Human Tear Film Classification},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={395-402},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004809403950402},
isbn={978-989-758-015-4},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Feature Selection Applied to Human Tear Film Classification
SN - 978-989-758-015-4
AU - G. Villaverde, D.
AU - Remeseiro, B.
AU - Barreira, N.
AU - G. Penedo, M.
AU - Mosquera, A.
PY - 2014
SP - 395
EP - 402
DO - 10.5220/0004809403950402

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