&lt;title&gt;Computer-aided methods for quantitative assessment of longitudinal changes in retinal images presenting with maculopathy&lt;/title&gt;

Solíz, Peter; Wilson, M.; Nemeth, Sheila C; Nguyêñ, Phong Q.

doi:10.1117/12.466917

Cited by 16 publications

(12 citation statements)

References 5 publications

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“…31 For this work, the value was empirically chosen to equal one plus the retinal eccentricity, e.g., 3 for 2°, 5 for 4°, etc. Figures 3(b) and 3(c) show Gaussian-filtered and background-subtracted images, respectively.…”

Section: Cone Density Measurement Algorithmmentioning

confidence: 99%

Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera

et al. 2007

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Section: Cone Density Measurement Algorithmmentioning

confidence: 99%

Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera

et al. 2007

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“…The Levenberg-Marquardt Least-Squares optimization algorithm [10] was used to fit the multiple elementary functions to the image adjusting the functions parameters in order to minimize the mean square error between the model and the image. This approach of modeling the drusen spots improved the drusen segmentation and characterization algorithms already published [11][12][13][14][15][16] by providing a shape consistent segmentation and by being more reproducible.…”

Section: Automatic Quantification Methodsmentioning

confidence: 98%

Variability and Repeatability Tests of ARMD Assessment Using the AD3RI Tool

Mora

Fonseca

Vieira³

2012

Technological Innovation for Value Creation

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Abstract. Age-related macular degeneration (ARMD) is a common disease in the elderly and is currently the main cause of blindness in developed countries. Drusen are one of its risks factors, which are visible in a retinal examination. Its quantitative analysis is important in the follow up of the ARMD. The authors have previously developed two tools for semi-automatic and automatic drusen quantification. In this paper five tests performed on these tools are presented and discussed in order to evaluate their accuracy, variability and repeatability. The statistical results show that the automatic tool is as accurate as the experts in Drusen quantification, with the advantage of being a reproducible method. The semi-automatic quantification method, which was used by the experts for Drusen quantification, proved statistically to produce a high intra-observer agreement.

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“…Different image components are separated and analyzed for signal intensity alterations and a 'texture' map is generated from the distribution data [28]. Thresholding-based drusen segmentation uses binary image data of frequency and pixel intensity against a normative background histogram [29]. While the above automated color photographs drusen algorithms are extremely encouraging, soft drusen detection remains problematic due to the difficulty in accurately delineating the blurred margins of large drusen.…”

Section: Clinical and Experimental Ophthalmologymentioning

confidence: 99%

Drusen Imaging: A Review

Hunter¹

2014

J Clin Exp Ophthalmol

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Drusen represent the hallmark of non-exudative age-related macular degeneration (AMD). Drusen vary in their location within the retina, ranging from sub-retinal pigment epithelium (RPE) drusen and sub-neurosensory retinal drusenoid deposits above the RPE (or pseudo-drusen). In this paper, we review the rapidly advancing imaging techniques currently available to better correlate drusen volume to clinical stage. This includes, but is not limited to, fundus photography, fluorescein angiography, optical coherence tomography, infrared imaging, fundus autofluorescence, confocal adaptive-optics imaging, and hyperspectral retinal imaging. A new understanding of drusen role in the pathogenesis of the disease may be possible with these imaging modalities in future clinical studies.

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<title>Computer-aided methods for quantitative assessment of longitudinal changes in retinal images presenting with maculopathy</title>

Cited by 16 publications

References 5 publications

Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera

Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera

Variability and Repeatability Tests of ARMD Assessment Using the AD3RI Tool

Drusen Imaging: A Review

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