NDLI: Significant point characterization in fundus images

Content Provider	IEEE Xplore Digital Library
Author	Morales, S. Naranjo, V. Colomer, A. Alcaniz, M.
Copyright Year	2015
Description	Author affiliation: I3BH/LabHuman, Univ. Politec. de Valencia, Valencia, Spain (Morales, S.; Naranjo, V.; Colomer, A.; Alcaniz, M.)
Abstract	This paper describes a new approach to identify significant points in retinal images. Significant points such as bifurcations and crossovers define and characterize the retinal vascular network. This approach is based on using hit-or-miss transformation to detect terminal, bifurcation and simple crossing points and performs a post-processing stage to identify complex intersections. The post-processing focuses on the idea that the intersection of two vessels creates a sort of close loop formed by the vessels and this effect can be used to differentiate a bifurcation and a crossover. Experimental results show quantitative improvements if the proposed method is compared with other state-of-the-art work by reducing the number of false positives and negatives in the significant point detection. Therefore, the result of this work is an effective significant point detection algorithm and can be useful for cardiovascular disease diagnosis, biometrics and image registration.
Starting Page	331
Ending Page	336
File Size	693824
Page Count	6
File Format	PDF
ISBN	9781479986361
ISSN	2154512X
e-ISBN	9781479986378
DOI	10.1109/IPTA.2015.7367159
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-10
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Frequency modulation Significant points Retinal image Image processing Bifurcation Retina Bifurcations Skeleton Cardiovascular diseases Crossing points Crossovers
Content Type	Text
Resource Type	Article

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Vascular bifurcations and crossovers detection and processing in retinal images

A Comparison Between a Geometrical and an ANN Based Method for Retinal Bifurcation Points Extraction

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Model-based method for improving the accuracy and repeatability of estimating vascular bifurcations and crossovers from retinal fundus images

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