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Proceedings Paper

Automatic quantification of posterior capsule opacification
Author(s): Sarah A. Barman; Bunyarit Uyyanonvara; James Frederick Boyce; Giorgia Sanguinetti; Emma J. Hollick; William R. Meacock; David J. Spalton; Andrew P. Paplinski
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Paper Abstract

After Cataract surgery where a plastic implant lens is implanted into the eye to replace the natural lens, many patients suffer from cell growth across a membrane situated at the back of the lens which degrades their vision. The cell growth is known as Posterior Capsule Opacification (or PCO). It is important to be able to quantify PCO so that the effect of different implant lens types and surgical techniques may be evaluated. Initial results obtained using a neural network to detect PCO from implant lenses are compared to an established but less automated method of detection, which segments the images using texture segmentation in conjunction with co- occurrence matrices. Tests show that the established method performs well in clinical validation and repeatability trials. The requirement to use a neural network to analyze the implant lens images evolved from the analysis of over 1000 images using the established co-occurrence matrix segmentation method. The work shows that a method based on neural networks is a promising tool to automate the procedure of calculating PCO.

Paper Details

Date Published: 6 June 2000
PDF: 10 pages
Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); doi: 10.1117/12.387597
Show Author Affiliations
Sarah A. Barman, King's College London (United Kingdom)
Bunyarit Uyyanonvara, King's College London (United Kingdom)
James Frederick Boyce, King's College London (United Kingdom)
Giorgia Sanguinetti, King's College London (United Kingdom)
Emma J. Hollick, St. Thomas' Hospital (United Kingdom)
William R. Meacock, St. Thomas' Hospital (United Kingdom)
David J. Spalton, St. Thomas' Hospital (United Kingdom)
Andrew P. Paplinski, Monash Univ. (Australia)


Published in SPIE Proceedings Vol. 3979:
Medical Imaging 2000: Image Processing
Kenneth M. Hanson, Editor(s)

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