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IEEE Transactions on Biomedical Engineering
Article . 2004 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
DBLP
Article . 2020
Data sources: DBLP
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Automatic Pupillometry From Digital Images

Authors: D. Robert Iskander; Michael J. Collins 0001; Siegfried Mioschek; Martin Trunk;

Automatic Pupillometry From Digital Images

Abstract

Determination of two-dimensional characteristics of the anterior surface of the eye is becoming increasingly important in modern optometry and ophthalmology practice. In particular, accurate estimation of the pupil size and centration is crucial in customized refractive surgery, corneal transplantation, and advanced contact lens fitting. The pupil parameters change under different lighting conditions so they often need to be related to some fixed reference such as the limbus outline. However, current commercial pupillometers do not estimate limbus position. We present a novel algorithm for automatic extraction of pupil parameters from digital images that takes the relative limbus information into account. The algorithm utilizes several customized image processing techniques that form a robust procedure which performs well for a wide range of clinical images. We apply the developed algorithm to images obtained by a standard digital camera, and specialized ophthalmic instruments such as a wavefront sensor and a high-speed imaging system.

Country
Australia
Related Organizations
Keywords

Biometry, Video Recording, Iris, 006, Pupil, Signal Processing, Computer-Assisted, Limbus Corneae, Image Enhancement, Pattern Recognition, Automated, Ophthalmoscopy, Subtraction Technique, Image Interpretation, Computer-Assisted, Photography, Humans, Algorithms

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    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    26
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
26
Average
Top 10%
Top 10%
Green
bronze