A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Pattern Recognition and Computer Vision
Handbook of Pattern Recognition and Computer Vision
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture and color analysis for the automatic classification of the eye lipid layer
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Colour Texture Analysis for Classifying the Tear Film Lipid Layer: A Comparative Study
DICTA '11 Proceedings of the 2011 International Conference on Digital Image Computing: Techniques and Applications
Two-dimensional discrete Markovian fields
IEEE Transactions on Information Theory
Texture segmentation using Gaussian-Markov random fields and neural oscillator networks
IEEE Transactions on Neural Networks
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
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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. This papers presents an exhaustive study about the characterisation of the interference phenomena as a texture pattern, using different feature extraction methods in different colour spaces. These methods are first analysed individually and then combined to achieve the best results possible. The principal component analysis (PCA) technique has also been tested to reduce the dimensionality of the feature vectors. The proposed methodologies have been tested on a dataset composed of 105 images from healthy subjects, with a classification rate of over 95% in some cases.