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Estimating corneal surface topography in videokeratoscopy in the presence of strong signal interference

Alonso-Caneiro, David, Iskander, D. Robert, & Collins, Michael J. (2008) Estimating corneal surface topography in videokeratoscopy in the presence of strong signal interference. IEEE Transactions on Biomedical Engineering, 55(10), pp. 2381-2387.

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Abstract

Videokeratoscopy techniques rely on a number of factors in order to achieve accurate estimates of corneal surface topography. Good tear film quality, minimal reflections from eyelashes, and minimal eye movements are essential for corneal topography estimates to be reliable. However, in practice, these ideal conditions may not always be fulfilled, especially in cases of subjects diagnosed with dry eye syndrome, having narrow palpebral apertures, long eyelashes, or nystagmus (uncontrolled eye movements). Such nonoptimal conditions of image acquisition result in poorer estimates of corneal topography. The aim of this paper was to devise a technique that would provide more accurate estimation of corneal topography in such situations and particularly when the source of signal interference is strong. This was achieved by developing a set of algorithms that extract the interference from the acquired raw videokeratoscopic image and filter the topography according to the interference location. The experiments carried out with test surfaces and real corneas showed that this new technique leads to a significant improvement in the topography estimator. Additionally, it is an interference indication procedure that, in the future, could be used for the purpose of tear film quality estimation.

Impact and interest:

10 citations in Scopus
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9 citations in Web of Science®

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ID Code: 18464
Item Type: Journal Article
Additional Information: Manuscript received December 11, 2007; revised March 12, 2008. First published June 10, 2008; current version published September 26, 2008.
Additional URLs:
Keywords: Cornea, Keratometry, Statistical image processing, Tear film
DOI: 10.1109/TBME.2008.923766
ISSN: 0018-9294
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > OPTOMETRY AND OPHTHALMOLOGY (111300) > Vision Science (111303)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > OPTOMETRY AND OPHTHALMOLOGY (111300) > Optometry and Ophthalmology not elsewhere classified (111399)
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Optometry & Vision Science
Copyright Owner: Copyright © 2008 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 28 Apr 2009 08:33
Last Modified: 01 Mar 2012 13:42

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