Automated extraction and quantification of macular drusen from fundal photographs
ABSTRACT The objective quantification of drusen (and other macular lesions) should have applications epidemi-ologically, in the study of the natural history of drusen, and with such instruments as the scanning laser ophthalmoscope. The automated extraction of drusen from photographs is technically difficult because of uneven macular reflectance, and the confusing pattern of darker vessels. We have developed a method using an IBM personal computer, an image digitising board and specially written software. Once the image is digitised, no further input from the operator is necessary. We present the results of manual counting versus automated counting on a small series of patients with drusen. The automated technique is highly reproducible, and will calculate the retinal area occupied by drusen. The area and numbers of drusen can be compared over time, giving an index of progression. Hard drusen are fairly well detected, but the detection of soft drusen with their lower contrast remains a problem. The technique cannot distinguish between drusen and other pale lesions (e.g., atrophic retinal changes).
SourceAvailable from: Jose Manuel Fonseca
Conference Paper: Drusen Deposits on Retina Images: Detection and ModelingMEDSIP-2004; 01/2004
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ABSTRACT: PurposeTo design and validate a software package to quantitate the area subtended by drusen in color fundus photographs for the conduct of efficient, accurate clinical trials in age-related macular degeneration.Ophthalmology 06/1999; 106(6):1119-1125. DOI:10.1016/S0161-6420(99)90257-9 · 6.17 Impact Factor
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ABSTRACT: Age-related macular degeneration (AMD) is the leading cause of vision loss in those over the age of 50 years in the developed countries. The number is expected to increase by ∼1.5 fold over the next ten years due to an increase in ageing population. One of the main measures of AMD severity is the analysis of drusen, pigmentary abnormalities, geographic atrophy (GA) and choroidal neovascularization (CNV) from imaging based on colour fundus photograph, optical coherence tomography (OCT) and other imaging modalities. Each of these imaging modalities has strengths and weaknesses for extracting individual AMD pathology and different imaging techniques are used in combination for capturing and/or quantification of different pathologies. Current dry AMD treatments cannot cure or reverse vision loss. However, the Age-Related Eye Disease Study (AREDS) showed that specific anti-oxidant vitamin supplementation reduces the risk of progression from intermediate stages (defined as the presence of either many medium-sized drusen or one or more large drusen) to late AMD which allows for preventative strategies in properly identified patients. Thus identification of people with early stage AMD is important to design and implement preventative strategies for late AMD, and determine their cost-effectiveness. A mass screening facility with teleophthalmology or telemedicine in combination with computer-aided analysis for large rural-based communities may identify more individuals suitable for early stage AMD prevention. In this review, we discuss different imaging modalities that are currently being considered or used for screening AMD. In addition, we look into various automated and semi-automated computer-aided grading systems and related retinal image analysis techniques for drusen, geographic atrophy and choroidal neovascularization detection and/or quantification for measurement of AMD severity using these imaging modalities. We also review the existing telemedicine studies which include diagnosis and management of AMD, and how automated disease grading could benefit telemedicine. As there is no treatment for dry AMD and only early intervention can prevent the late AMD, we emphasize mass screening through a telemedicine platform to enable early detection of AMD. We also provide a comparative study between the imaging modalities and identify potential study areas for further improvement and future research direction in automated AMD grading and screening.Progress in Retinal and Eye Research 11/2013; DOI:10.1016/j.preteyeres.2013.10.002 · 9.90 Impact Factor