A robust algorithm for optic disc segmentation and fovea detection in retinal fundus images
Abstract: Accurate optic disc (OD) segmentation and fovea detection in retinal fundus images are crucial for diagnosis in ophthalmology. We propose a robust and broadly applicable algorithm for automated, robust, reliable and consistent fovea detection based on OD segmentation. The OD segmentation is performed with morphological operations and Fuzzy C Means Clustering combined with iterative thresholding on a foreground segmentation. The fovea detection is based on a vessel segmentation via morphological operations and uses the resulting OD segmentation to determine multiple regions of interest. The fovea is determined from the largest, vessel-free candidate region. We have tested the novel method on a total of 190 images from three publicly available databases DRIONS, Drive and HRF. Compared to results of two human experts for DRIONS database, our OD segmentation yielded a dice coefficient of 0.83. Note that missing ground truth and expert variability is an issue. The new scheme achieved an overall success rate of 99.44% for OD detection and an overall success rate of 96.25% for fovea detection, which is superior to state-of-the-art approaches.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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A robust algorithm for optic disc segmentation and fovea detection in retinal fundus images ; volume:3 ; number:2 ; year:2017 ; pages:533-537 ; extent:5
Current directions in biomedical engineering ; 3, Heft 2 (2017), 533-537 (gesamt 5)
- Urheber
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Rust, Caterina
Häger, Stephanie
Traulsen, Nadine
Modersitzki, Jan
- DOI
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10.1515/cdbme-2017-0113
- URN
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urn:nbn:de:101:1-2023030713123917666235
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:55 MESZ
Datenpartner
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Beteiligte
- Rust, Caterina
- Häger, Stephanie
- Traulsen, Nadine
- Modersitzki, Jan
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