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Automatic grading system for human tear films

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Abstract

Dry eye syndrome is a prevalent disease which affects a wide range of the population and has a negative impact on their daily activities, such as driving or working with computers. Its diagnosis and monitoring require a battery of tests which measure different physiological characteristics. One of these clinical tests consists in capturing the appearance of the tear film using the Doane interferometer. Once acquired, the interferometry images are classified into one of the five categories considered in this research. The variability in appearance makes the use of a computer-based analysis system highly desirable. For this reason, a general methodology for the automatic analysis and categorization of interferometry images is proposed. The development of this methodology included a deep study based on several techniques for image texture analysis, three color spaces and different machine learning algorithms. The adequacy of this methodology was demonstrated, achieving classification rates over 93 %. Also, it provides unbiased results and allows important time savings for experts.

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Acknowledgments

This research has been partially funded by the Secretaría de Estado de Investigación of the Spanish Government and FEDER funds of the European Union through the research projects TIN2011-25476 and PI12/02075; and by the Consellería de Cultura, Educación e Ordenación Universitaria of the Xunta de Galicia through the agreement for the Singular Research Center CITIC.

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Authors and Affiliations

  1. Departamento de Computación, Universidade da Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain

    Beatriz Remeseiro & Noelia Barreira

  2. Department of Life Sciences, Glasgow Caledonian University, Cowcaddens Road, Glasgow, G4 0BA, UK

    Katherine M. Oliver, Alan Tomlinson & Eilidh Martin

  3. Departamento de Electrónica y Computación, Universidade de Santiago de Compostela, Campus Universitario Sur, 15782, Santiago de Compostela, Spain

    Antonio Mosquera

Authors
  1. Beatriz Remeseiro
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  2. Katherine M. Oliver
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  3. Alan Tomlinson
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  4. Eilidh Martin
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  5. Noelia Barreira
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  6. Antonio Mosquera
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Correspondence to Beatriz Remeseiro.

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Remeseiro, B., Oliver, K.M., Tomlinson, A. et al. Automatic grading system for human tear films. Pattern Anal Applic 18, 677–694 (2015). https://doi.org/10.1007/s10044-014-0402-x

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