The earliest signs of Retinopathy of Prematurity (ROP) are tortuosity and dilation of retinal vessels. Such vascular changes are considered of primary importance for the diagnosis and the follow-up of the disease. However, a widely accepted computerized system for their quantitative measurement is still missing. Images taken from a preterm baby's eye are often low-contrast, noisy, and blurred. Algorithms that have been successfully applied to analyze adult retinal images do not work well in ROP images. We propose here a novel method for the automatic extraction of vessel centerline in wide-field ROP retinal images, based on a sparse tracking scheme. After a set of seed points is identified all over the image, vessels are traced by connecting those seeds by means of minimum cost paths, whose weights depend on similarity features and alignment evaluated by a custom line operator. The performance of the method was assessed on a dataset of 20 images acquired with the RetCam fundus camera. A sensitivity of 0.78 and a false detection rate of 0.15 were obtained with respect to manual ground truth reference.

Automatic Vessel Segmentation in Wide-field Retina Images of Infants with Retinopathy of Prematurity

POLETTI, ENEA;FIORIN, DIEGO;GRISAN, ENRICO;RUGGERI, ALFREDO
2011

Abstract

The earliest signs of Retinopathy of Prematurity (ROP) are tortuosity and dilation of retinal vessels. Such vascular changes are considered of primary importance for the diagnosis and the follow-up of the disease. However, a widely accepted computerized system for their quantitative measurement is still missing. Images taken from a preterm baby's eye are often low-contrast, noisy, and blurred. Algorithms that have been successfully applied to analyze adult retinal images do not work well in ROP images. We propose here a novel method for the automatic extraction of vessel centerline in wide-field ROP retinal images, based on a sparse tracking scheme. After a set of seed points is identified all over the image, vessels are traced by connecting those seeds by means of minimum cost paths, whose weights depend on similarity features and alignment evaluated by a custom line operator. The performance of the method was assessed on a dataset of 20 images acquired with the RetCam fundus camera. A sensitivity of 0.78 and a false detection rate of 0.15 were obtained with respect to manual ground truth reference.
33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'11)
9781424441211
9781424441228
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/180647
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