Article

Colour Texture Analysis for Classifying the Tear Film Lipid Layer: A Comparative Study

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A. MosqueraAuthors Info & Claims

DICTA '11: Proceedings of the 2011 International Conference on Digital Image Computing: Techniques and Applications

Pages 268 - 273

https://doi.org/10.1109/DICTA.2011.51

Published: 06 December 2011 Publication History

Abstract

This paper presents a comparative study of different texture extraction methods for the automatic classification of the tear film lipid layer based on the categories enumerated by Guillon. From a photography of the eye, a region of interest is detected and its low-level features are extracted, generating a feature vector that describes it, to be finally classified in one of the target categories. This paper discusses several texture analysis methods and colour spaces to generate the feature vectors. The proposed methods have been tested on a dataset composed of 105 images, with a classification rate of over 95\% in some cases.

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Bağbaba AŞen BDelen DUysal B(2018)An Automated Grading and Diagnosis System for Evaluation of Dry Eye SyndromeJournal of Medical Systems10.1007/s10916-018-1086-342:11(1-14)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1007/s10916-018-1086-3
Remeseiro BBarreira NGarcía-Resúa CLira MGiráldez MYebra-Pimentel EPenedo M(2016)iDEASComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2016.02.015130:C(186-197)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.cmpb.2016.02.015
Remeseiro BOliver KTomlinson AMartin EBarreira NMosquera A(2015)Automatic grading system for human tear filmsPattern Analysis & Applications10.1007/s10044-014-0402-x18:3(677-694)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1007/s10044-014-0402-x
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Index Terms

Colour Texture Analysis for Classifying the Tear Film Lipid Layer: A Comparative Study
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Appearance and texture representations
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Random walks and Markov chains

Index terms have been assigned to the content through auto-classification.

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DICTA '11: Proceedings of the 2011 International Conference on Digital Image Computing: Techniques and Applications

December 2011

691 pages

ISBN:9780769545882

Publisher

IEEE Computer Society

United States

Publication History

Published: 06 December 2011

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Bağbaba AŞen BDelen DUysal B(2018)An Automated Grading and Diagnosis System for Evaluation of Dry Eye SyndromeJournal of Medical Systems10.1007/s10916-018-1086-342:11(1-14)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1007/s10916-018-1086-3
Remeseiro BBarreira NGarcía-Resúa CLira MGiráldez MYebra-Pimentel EPenedo M(2016)iDEASComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2016.02.015130:C(186-197)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.cmpb.2016.02.015
Remeseiro BOliver KTomlinson AMartin EBarreira NMosquera A(2015)Automatic grading system for human tear filmsPattern Analysis & Applications10.1007/s10044-014-0402-x18:3(677-694)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1007/s10044-014-0402-x
Remeseiro BPenas MBarreira NMosquera ANovo JGarcía-Resúa C(2013)Automatic classification of the interferential tear film lipid layer using colour texture analysisComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2013.04.007111:1(93-103)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1016/j.cmpb.2013.04.007

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Texture and color analysis for the automatic classification of the eye lipid layer

Automatic classification of the interferential tear film lipid layer using colour texture analysis

Color texture analysis for tear film classification: a preliminary study

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