Classification of ophthalmologic images using an ensemble of classifiers

Abstract

The human eye may present refractive errors as myopia, hypermetropia and astigmatism. This article presents the development of an Ensemble of Classifiers as part of a Refractive Errors Measurement System. The system analyses Hartmann-Shack images from human eyes in order to identify refractive errors, wich are associated to myopia, hypermetropia and astigmatism. The ensemble is composed by three different Machine Learning techniques: Artificial Neural Networks, Support Vector Machines and C4.5 algorithm and has been shown to be able to improve the performance achieved). The most relevant data of these images are extracted using Gabor wavelets transform. Machine learning techniques are then employed to carry out the image analysis. © Springer-Verlag Berlin Heidelberg 2005.