Retinal Blood Vessel Segmentation Based on Heuristic Image Analysis

Maja Braović1, Darko Stipaničev1 and Ljiljana Šerić1

  1. Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture
    Ruđera Boškovića 32, 21000 Split, Croatia
    maja.braovic@fesb.hr

Abstract

Automatic analysis of retinal fundus images is becoming increasingly present today, and diseases such as diabetic retinopathy and age-related macular degeneration are getting a higher chance of being discovered in the early stages of their development. In order to focus on discovering those diseases, researchers commonly preprocess retinal fundus images in order to detect the retinal landmarks - blood vessels, fovea and the optic disk. A large number of methods for the auto matic detection of retinal blood vessels from retinal fundus images already exists, but many of them are using unnecessarily complicated approaches. In this paper we demonstrate that a reliable retinal blood vessel segmentation can be achieved with a cascade of very simple image processing methods. The proposed method puts higher emphasis on high specificity (i.e. high probability that the segmented pixels actually belong to retinal blood vessels and are not false positive detections) rather than on high sensitivity. The proposed method is based on heuristically determined parametric edge detection and shape analysis, and is evaluated on the publicly avail able DRIVE and STARE datasets on which it achieved the average accuracy of 96.33% and 96.10%, respectively.

Key words

Retinal blood vessels, fundus images, heuristic analysis, image segmentation

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS180220014B

Publication information

Volume 16, Issue 1 (January 2019)
Year of Publication: 2019
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Braović, M., Stipaničev, D., Šerić, L.: Retinal Blood Vessel Segmentation Based on Heuristic Image Analysis. Computer Science and Information Systems, Vol. 16, No. 1, 227-245. (2019), https://doi.org/10.2298/CSIS180220014B