NDLI: Automatic vessel segmentation in wide-field retina images of infants with Retinopathy of Prematurity

Content Provider	IEEE Xplore Digital Library
Author	Poletti, E. Fiorin, D. Grisan, E. Ruggeri, A.
Copyright Year	2011
Description	Author affiliation: Department of Information Engineering, University of Padua, Padua, Italy (Poletti, E.; Fiorin, D.; Grisan, E.; Ruggeri, A.)
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.
Starting Page	3954
Ending Page	3957
File Size	1366246
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6090982
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Retina Biomedical imaging Retinopathy Feature extraction Sensitivity Cameras Joining processes
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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