Retinal blood vessels extraction of challenging images

Toufique Ahmed Soomro, Junbin Gao, Zheng Lihong, Ahmed J. Afifi, Shafiullah Soomro, Manoranjan Paul

Research output: Book chapter/Published conference paperConference paperpeer-review

6 Citations (Scopus)


Retinal fundus examination is necessary for the early diagnosis of eye disease, especially diabetic retinopathy. Population screening often results in poor quality retinal images that complicate the automated diagnosis of retinal features, such as precise segmentation of blood vessels, microaneurysms, cotton stains, and hard exudates. Fluorescein fundus angiogram (FFA) has solved some problems, but it is invasive and has side effects. In this research work, we proposed a method of image enhancement based on contrast-sensitive steps as a valuable aid for the automatic segmentation of pathological (unhealthy) images. Experimental results based on the Digital retinal images for vessel extraction (DRIVE) and STructured analysis of the retina (STARE) databases showed that the proposed image enhancement method improved the performance over other existing methods, from 92% to 95% in accuracy and from 71% to 75% in sensitivity. This significant improvement in the contrast of retinal background images of retinal color has the potential to provide better vessel images for observing ocular diseases.
Original languageEnglish
Title of host publicationData Mining - 16th Australasian Conference, AusDM 2018, Revised Selected Papers
EditorsYanchang Zhao, Graco Warwick, David Stirling, Chang-Tsun Li, Yun Sing Koh, Rafiqul Islam, Zahidul Islam
PublisherSpringer-Verlag London Ltd.
Number of pages13
ISBN (Print)9789811366604
Publication statusPublished - 16 Feb 2019
Event16th Australasian Conference on Data Mining, AusDM 2018 - Charles Sturt University , Bathurst, Australia
Duration: 28 Nov 201830 Nov 2018

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference16th Australasian Conference on Data Mining, AusDM 2018
Internet address


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