NDLI: A supervised method for retinal blood vessel segmentation using line strength, multiscale Gabor and morphological features

Content Provider	IEEE Xplore Digital Library
Author	Fraz, M.M. Remagnino, P. Hoppe, A. Velastin, S. Uyyanonvara, B. Barman, S.A.
Copyright Year	2011
Description	Author affiliation: Faculty of Science Engineering and Computing, Kingston University, London, United Kingdom (Fraz, M.M.; Remagnino, P.; Hoppe, A.; Velastin, S.; Barman, S.A.) \|\| Department of Information Technology, Thammasat University, Thailand (Uyyanonvara, B.)
Abstract	The change in morphology, diameter, branching pattern and/or tortuosity of retinal blood vessels is an important indicator of various clinical disorders of the eye and the body. This paper reports a supervised methodology for segmentation of the retinal vasculature from ocular fundus images. A 7-D feature vector is constructed by computing the outputs of morphological linear operators, line strengths and oriented Gabor filters at multiple scales. The feature vector encodes the spatial intensity measures along with vessel geometry at multiple scales. A Bayesian Classifier; the Gaussian Mixture Model is used for classification of the retinal image into vessels and non-vessel pixels. The methodology is evaluated using the images of two publicly available databases, the DRIVE database and the STARE database. Method performance on both sets of test images is better than the $2^{nd}$ human observer and other existing methodologies available in the literature.
Starting Page	410
Ending Page	415
File Size	1670290
Page Count	6
File Format	PDF
ISBN	9781457702433
e-ISBN	9781457702426
DOI	10.1109/ICSIPA.2011.6144129
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-16
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Databases Observers Retina Feature extraction Classification algorithms Gabor filters
Content Type	Text
Resource Type	Article

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