A Computational Model for Predicting Visual Acuity from Wavefront Aberration Measurements

Abstract

The main purpose of this thesis is to create and validate a visual acuity model with experimentally obtained aberrations of human eyes. The other motivation is to come up with a methodology to objectively predict the potential benefits of photorefractive procedures such as customized corrections and presbyopic LASIK treatments. A computational model of visual performance was implemented in MATLAB based on a template matching technique. Normalized correlation was used as a pattern matching algorithm. This simulation describes an ideal observer limited by optics, neural filtering, and neural noise. Experimental data in this analysis were the eye’s visual acuity (VA) and 15 modes of Zernike aberration coefficients obtained from 3 to 6 year old children (N=20; mean age= 4.2; best corrected VA= 0 (in log MAR units)) using the Welch Allyn Suresight instrument. The model inputs were Sloan Letters and the output was VA. The images of Sloan letters were created at LogMAR values from -0.6 to 0.7 in steps of 0.05. Ten different alphabet images, each in ten sizes, were examined in this model. For each simulated observer the results at six noise levels (white Gaussian noise) and three levels of threshold (probability of the correct answer for the visual acuity) were analyzed to estimate the minimum RMS error between the visual acuity of model results and experimental result.