Abstract
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 language | English |
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Title of host publication | Data Mining - 16th Australasian Conference, AusDM 2018, Revised Selected Papers |
Editors | Yanchang Zhao, Graco Warwick, David Stirling, Chang-Tsun Li, Yun Sing Koh, Rafiqul Islam, Zahidul Islam |
Publisher | Springer-Verlag London Ltd. |
Pages | 347-359 |
Number of pages | 13 |
ISBN (Print) | 9789811366604 |
DOIs | |
Publication status | Published - 16 Feb 2019 |
Event | 16th Australasian Conference on Data Mining, AusDM 2018 - Charles Sturt University , Bathurst, Australia Duration: 28 Nov 2018 → 30 Nov 2018 https://ausdm18.ausdm.org/ |
Publication series
Name | Communications in Computer and Information Science |
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Volume | 996 |
ISSN (Print) | 1865-0929 |
Conference
Conference | 16th Australasian Conference on Data Mining, AusDM 2018 |
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Country/Territory | Australia |
City | Bathurst |
Period | 28/11/18 → 30/11/18 |
Internet address |