87
Views
1
CrossRef citations to date
0
Altmetric
Original Articles

Structural feature analysis of the vascular network in retinal images

ORCID Icon, , , ORCID Icon &
Pages 37-48 | Received 10 May 2017, Accepted 04 Nov 2017, Published online: 07 Dec 2017
 

Abstract

The extraction of structural information from the vascular network captured in a retinal image is an essential component of computer-aided diagnosis for many ophthalmological, cardiovascular, and systemic disorders. Although several methods have been reported in the past to tackle this problem, ensuring clarity and high accuracy while segmenting various objects from a retinal image still poses a challenge. In this work, we have proposed a novel technique to extract the underlying structure of blood vessels from a retinal image that includes segmentation of optic disc region and identification of veins, arteries, and bifurcation points in the vascular network. Unlike previous approaches, the proposed method relies mostly on the processing of digital-geometric features and integrates them with conventional image analysis for effective segmentation of various objects in the vascular network. The method outperforms several prior work in terms of segmentation accuracy, and experiments on several retinal images reveal encouraging results. The performance of the proposed technique is evaluated by comparing clinically-assessed ground-truth with automated findings.

Notes

No potential conflict of interest was reported by the authors.

A preliminary version of this paper appeared in the Proceedings, IWCIA, LNCS, vol. 9448, pp. 261–275, 2015.

Additional information

Funding

The first author would like to acknowledge Department of Science & Technology, Government of India, for financial support vide [ref. number SR/WOS-A/ET-1022/2014]; Woman Scientist Scheme to carry out this work. We also acknowledge the use of DRIVE database (http://www.isi.uu.nl/Research/Databases/DRIVE/download.php) and STARE database (http://www.cecas.clemson.edu/~ahoover/stare/images/all-images.zip) images for implementation and testing of the proposed approach.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access
  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart
* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.