Journal article
Assessment of tear film surface quality using dynamic-area high-speed videokeratoscopy
IEEE Transactions on Biomedical Engineering, Vol.56(5), pp.1473-1481
2009
PMID: 19174338
Abstract
A new method for noninvasive assessment of tear film surface quality (TFSQ) is proposed. The method is based on high-speed videokeratoscopy in which the corneal area for the analysis is dynamically estimated in a manner that removes videokeratoscopy interference from the shadows of eyelashes but not that related to the poor quality of the precorneal tear film that is of interest. The separation between the two types of seemingly similar videokeratoscopy interference is achieved by region-based classification in which the overall noise is first separated from the useful signal (unaltered videokeratoscopy pattern), followed by a dedicated interference classification algorithm that distinguishes between the two considered interferences. The proposed technique provides a much wider corneal area for the analysis of TFSQ than the previously reported techniques. A preliminary study with the proposed technique, carried out for a range of anterior eye conditions, showed an effective behavior in terms of noise to signal separation, interference classification, as well as consistent TFSQ results. Subsequently, the method proved to be able to not only discriminate between the bare eye and the lens on eye conditions but also to have the potential to discriminate between the two types of contact lenses.
Details
- Title
- Assessment of tear film surface quality using dynamic-area high-speed videokeratoscopy
- Authors
- David Alonso-Caneiro (Corresponding Author) - Queensland University of TechnologyD. Robert Iskander (Author) - Queensland University of TechnologyMichael J Collins (Author) - Queensland University of Technology
- Publication details
- IEEE Transactions on Biomedical Engineering, Vol.56(5), pp.1473-1481
- Publisher
- Institute of Electrical and Electronics Engineers
- DOI
- 10.1109/TBME.2008.2011993
- ISSN
- 1558-2531
- PMID
- 19174338
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99973597302621
- Output Type
- Journal article
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- Engineering, Biomedical
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