Automatic grading system for human tear films

Beatriz Remeseiro, Katherine M. Oliver, Alan Tomlinson, Eilidh Martin, Noelia Barreira, Antonio Mosquera

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)677-694
Number of pages18
JournalPattern Analysis and Applications
Issue number3
Early online date7 Aug 2014
Publication statusPublished - Aug 2015


  • dry eye syndrome
  • pattern recognition
  • machine learning
  • tear film
  • Doane interferometer
  • texture analysis
  • Tear film
  • Dry eye syndrome
  • Texture analysis
  • Machine learning
  • Pattern recognition

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition


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