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Computationally efficient interference detection in videokeratoscopy images

Alonso-Caneiro, David, Iskander, D. Robert, & Collins, Michael J. (2008) Computationally efficient interference detection in videokeratoscopy images. In 2008 IEEE 10th Workshop on Multimedia Signal Processing, October 8-10, 2008, Cairns, Queensland.

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Abstract

Abstract—An optimal videokeratoscopic image presents a strong well-oriented pattern over the majority of the measured corneal surface. In the presence of interference, arising from reflections from eyelashes or tear film instability, the pattern's flow is disturbed and the local orientation of the area of interference is no longer coherent with the global flow. Detecting and analysing videokeratoscopic pattern interference is important when assessing tear film surface quality, break-up time and location as well as designing tools that provide a more accurate static measurement of corneal topography. In this paper a set of algorithms for detecting interference patterns in videokeratoscopic images is presented. First a frequency approach is used to subtract the background information from the oriented structure and then a gradient-based analysis is used to obtain the pattern's orientation and coherence. The proposed techniques are compared to a previously reported method based on statistical block normalisation and Gabor filtering. The results indicate that the proposed technique leads, in most cases: to a better videokeratoscopic interference detection system, that for a given probability of the useful signal detection (99.7%) has a significantly lower probability of false alarm, and at the same time is computationally much more efficient than the previously reported method.

Impact and interest:

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ID Code: 17376
Item Type: Conference Paper
Keywords: Computationally Efficient Interference Detection, Videokeratoscopy Images
DOI: 10.1109/MMSP.2008.4665127
ISBN: 9781424422944
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
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: 12 Feb 2009 07:34
Last Modified: 29 Feb 2012 23:50

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