Adaptive Cornea Modeling from Keratometric Data

Purpose: To introduce an iterative, multiscale procedure that allows for better reconstruction of the shape of the anterior surface of the cornea from altimetric data collected by a corneal topographer. Methods: The report describes, first, an adaptive, multiscale mathematical algorithm for the p...

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Main Authors: Martínez-Finkelshtein, Andrei, Ramos López, Darío, Castro, Gracia M., Alio y Sanz, Jorge L.
Format: info:eu-repo/semantics/article
Language:English
Published: The Association for Research in Vision and Ophthalmology 2017
Online Access:http://hdl.handle.net/10835/4879
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author Martínez-Finkelshtein, Andrei
Ramos López, Darío
Castro, Gracia M.
Alio y Sanz, Jorge L.
author_facet Martínez-Finkelshtein, Andrei
Ramos López, Darío
Castro, Gracia M.
Alio y Sanz, Jorge L.
author_sort Martínez-Finkelshtein, Andrei
collection DSpace
description Purpose: To introduce an iterative, multiscale procedure that allows for better reconstruction of the shape of the anterior surface of the cornea from altimetric data collected by a corneal topographer. Methods: The report describes, first, an adaptive, multiscale mathematical algorithm for the parsimonious fit of the corneal surface data that adapts the number of functions used in the reconstruction to the conditions of each cornea. The method also implements a dynamic selection of the parameters and the management of noise. Then, several numerical experiments are performed, comparing it with the results obtained by the standard Zernike-based procedure. Results: The numerical experiments showed that the algorithm exhibits steady exponential error decay, independent of the level of aberration of the cornea. The complexity of each anisotropic Gaussian-basis function in the functional representation is the same, but the 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 the centers, as well as the size of the shape parameters, provides additional spatial information about the regions of higher irregularity. Conclusions: The methodology 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 ring–based topographers, that will be decisive in early detection of corneal diseases such as keratoconus.
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spelling oai:repositorio.ual.es:10835-48792023-04-12T19:38:50Z Adaptive Cornea Modeling from Keratometric Data Martínez-Finkelshtein, Andrei Ramos López, Darío Castro, Gracia M. Alio y Sanz, Jorge L. Purpose: To introduce an iterative, multiscale procedure that allows for better reconstruction of the shape of the anterior surface of the cornea from altimetric data collected by a corneal topographer. Methods: The report describes, first, an adaptive, multiscale mathematical algorithm for the parsimonious fit of the corneal surface data that adapts the number of functions used in the reconstruction to the conditions of each cornea. The method also implements a dynamic selection of the parameters and the management of noise. Then, several numerical experiments are performed, comparing it with the results obtained by the standard Zernike-based procedure. Results: The numerical experiments showed that the algorithm exhibits steady exponential error decay, independent of the level of aberration of the cornea. The complexity of each anisotropic Gaussian-basis function in the functional representation is the same, but the 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 the centers, as well as the size of the shape parameters, provides additional spatial information about the regions of higher irregularity. Conclusions: The methodology 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 ring–based topographers, that will be decisive in early detection of corneal diseases such as keratoconus. 2017-06-21T10:03:13Z 2017-06-21T10:03:13Z 2011 info:eu-repo/semantics/article http://hdl.handle.net/10835/4879 en Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess The Association for Research in Vision and Ophthalmology
spellingShingle Martínez-Finkelshtein, Andrei
Ramos López, Darío
Castro, Gracia M.
Alio y Sanz, Jorge L.
Adaptive Cornea Modeling from Keratometric Data
title Adaptive Cornea Modeling from Keratometric Data
title_full Adaptive Cornea Modeling from Keratometric Data
title_fullStr Adaptive Cornea Modeling from Keratometric Data
title_full_unstemmed Adaptive Cornea Modeling from Keratometric Data
title_short Adaptive Cornea Modeling from Keratometric Data
title_sort adaptive cornea modeling from keratometric data
url http://hdl.handle.net/10835/4879
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AT ramoslopezdario adaptivecorneamodelingfromkeratometricdata
AT castrograciam adaptivecorneamodelingfromkeratometricdata
AT alioysanzjorgel adaptivecorneamodelingfromkeratometricdata