IOVS Stem Cells
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 QUICK SEARCH:   [advanced]


     


(Investigative Ophthalmology and Visual Science. 2006;47:4415-4421.)
© 2006 by The Association for Research in Vision and Ophthalmology, Inc.
DOI:  10.1167/iovs.06-0191

This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via Web of Science (1)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Patterson, A. J.
Right arrow Articles by Crabb, D. P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Patterson, A. J.
Right arrow Articles by Crabb, D. P.

Improving the Repeatability of Topographic Height Measurements in Confocal Scanning Laser Imaging Using Maximum-Likelihood Deconvolution

Andrew J. Patterson,1 David F. Garway-Heath,2 and David P. Crabb1

1From the Department of Optometry and Visual Science, City University, London, United Kingdom; and 2Glaucoma Research Unit, Moorfields Eye Hospital, London, United Kingdom.

PURPOSE. To evaluate maximum likelihood (ML) blind deconvolution as a technique for improving the repeatability of topographic height measurements obtained from scanning laser tomography (Heidelberg Retinal Tomograph [HRT]; Heidelberg Engineering, Heidelberg, Germany).

METHODS. ML blind deconvolution is an image-processing technique that estimates the original scene from a degraded image. This technique has been used in confocal scanning laser microscopy to remove "out-of-focus" haze in three-dimensional confocal image stacks. ML blind deconvolution requires no prior estimation of the point-spread function (PSF), as opposed to classic linear deconvolution methods. Instead, the algorithm estimates an initial PSF based on the optical setup of the confocal scanning device and optics of the eye and iteratively proceeds to a solution. The improvement in repeatability of height measurements from mean topography images within scan (intrascan) and between scans (interscan) afforded by ML deconvolution was evaluated in a test–retest series of HRT images from 40 ocular hypertensive and glaucomatous patients with varying degrees of media opacity.

RESULTS. There was an improvement in intrascan repeatability in 38 out of the 40 mean topography images (median improvement 2.5 µm, inter-quartile range 2.19, P < 0.001), and an improvement in interscan repeatability in 33 of the 40 mean topographies (median improvement, 1.0 µm, interquartile range 3.49, P < 0.001). There was a positive association between the magnitude of the improvement in repeatability and the level of mean pixel height standard deviation (MPHSD), intrascan (P = 0.004) and interscan (P = 0.002).

CONCLUSIONS. ML blind deconvolution algorithm improves the repeatability of topographic height measurements from the HRT. This improvement was greater in patients with poorer quality images.








HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Copyright © 2006 by the Association for Research in Vision and Ophthalmology