Construction of a Mean Surface for the Variability Study of the Cornea

Abstract

In this study, we present an algorithm to build a mean surface, applied to the human cornea, for the study of variability within a population. Due to the smoothness of the corneal surface, there is no anatomical anchor. The main challenge is to match several surfaces from different subjects to build the mean cornea. The key idea is to use a registration step based on a global factor: the volume minimization between two surfaces. We then compute the surface disparity after registration. An iterative algorithm minimizes this disparity to determine the best possible matching. The algorithm re-samples the registered surfaces on a common grid to compute the mean surface. Finally, we compute a disparity map and a mean disparity value after registration to estimate the registration accuracy and to compare our method to the existing one.