The article introduces a new image analysis approach for measuring refractive errors in the human eye (myopia, hypermetropia and astigmatism) using Machine Learning techniques. These refractive errors are identified through the analysis of images of the eye obtained with a specific technique known as Hartmann-Shack (or Shack-Hartmann), which are preprocessed with histogram analysis considering spatial and geometrical information on the application domain. Afterwards, feature vectors are extracted using two techniques: Principal Component Analysis and Gabor Wavelets Transform. Finally, the dataset with the extracted feature vectors is analyzed using Support Vector Machines. In spite of the limitations of the image dataset, encouraging results were obtained, suggesting the potential of the proposed approach in Optometry/Ophthalmology.
Machine Learning; Refractive Errors; Intelligent Systems; Hartmann-Shack Images; Support Vector Machines
