Statistical Comparison of Classifiers Applied to the Interferential Tear Film Lipid Layer Automatic Classification. (5th April 2012)

• Record Type: Journal Article
• Authors: Remeseiro, B.; Penas, M.; Mosquera, A.; Novo, J.; Penedo, M. G.; Yebra-Pimentel, E.
• Other Names: Crum Bill Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2012(2012)
• Publication Date: 2012-04-05
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2012/207315
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : The tear film lipid layer is heterogeneous among the population. Its classification depends on its thickness and can be done using the interference pattern categories proposed by Guillon. The interference phenomena can be characterised as a colour texture pattern, which can be automatically classified into one of these categories. 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 presents an exhaustive study about the problem at hand using different texture analysis methods in three colour spaces and different machine learning algorithms. All these methods and classifiers have been tested on a dataset composed of 105 images from healthy subjects and the results have been statistically analysed. As a result, the manual process done by experts can be automated with the benefits of being faster and unaffected by subjective factors, with maximum accuracy over 95%.