An adaptive algorithm for the cornea modeling from keratometric data

TL;DR

This paper introduces an adaptive, multi-scale algorithm for fitting corneal surface data that dynamically adjusts to each cornea's conditions, enabling real-time, noise-resilient reconstruction useful for early disease detection.

Contribution

The proposed method offers a novel adaptive approach that automatically determines the number of functions and parameters needed for accurate corneal modeling, outperforming standard Zernike polynomial methods.

Findings

Steady exponential error decay regardless of corneal aberration level

Effective real-time reconstruction of keratometric data

Enhanced spatial information on corneal irregularities

Abstract

In this paper we describe an adaptive and multi-scale algorithm for the parsimonious fit of the corneal surface data that allows to adapt the number of functions used in the reconstruction to the conditions of each cornea. The method implements also a dynamical selection of the parameters and the management of noise. It can be used for the real-time reconstruction of both altimetric data and corneal power maps from the data collected by keratoscopes, such as the Placido rings based topographers, decisive for an early detection of corneal diseases such as keratoconus. Numerical experiments show that the algorithm exhibits a steady exponential error decay, independently of the level of aberration of the cornea. The complexity of each anisotropic gaussian basis functions in the functional representation is the same, but their parameters vary to fit the current scale. This scale is determined only by the residual errors and not by the number of the iteration. Finally, the position and clustering of their centers, as well as the size of the shape parameters, provides an additional spatial information about the regions of higher irregularity. These results are compared with the standard approximation procedures based on the Zernike polynomials expansions.