Machine learning techniques for ocular errors analysis

The conventional techniques for refractive error measurements (myopia, hypermetropia, and astigmatism) have been considered inadequate for several optometry researches. In this context, they have investigated alternative methodologies for refractive error measurement. A new strategy is the determination of refractive errors from images of the globe of the eye. A process named Hartmann-Shack can obtain these images. The HS images should be analysed in order to extract relevant information for identification of refractive errors. The present paper investigates a technique based on radial basis functions (RBFs), an artificial neural network (ANN), and on support vector machines (SVMs), which automatically performs analysis of images from the globe of the eye and identifies refractive errors. The most relevant data of these images are extracted using Gabor wavelets transform, and then these machine learning techniques carry out the image analysis