Statistical Comparison of Classifiers Applied to the Interferential Tear Film Lipid Layer Automatic Classification
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 classifi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2012-01-01
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| Series: | Computational and Mathematical Methods in Medicine |
| Online Access: | http://dx.doi.org/10.1155/2012/207315 |
| Summary: | 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%. |
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| ISSN: | 1748-670X 1748-6718 |