Automatic localization of the optic disc in retinal fundus images using multiple features

Qureshi, Touseef Ahmad; Amin, Hassan; Hussain, Mahfooz; Qureshi, Rashid Jalal; Al-Diri, Bashir

doi:10.1109/bibe.2012.6399726

Cited by 9 publications

(2 citation statements)

References 14 publications

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“…A set of public databases are available publically for researchers, such as the de facto standard datasets DRIVE [31], STARE [33] databases, then CHASE-DB1 [12] for the segmentation of retinal blood vessels benchmarking, MESSIDOR [34], DRIONS-DB [35] for benchmarking and segmentation of optic nerve head [36], DIARETDB0 [37], DIARETDB1 [38], and HEI-MED [39] for benchmarking retinal lesions, like microaneurysms and exudates; several other databases are available such as REVIEW [40] and ONHSD [41]; however, for AV classification, the available datasets are very scarce.…”

Section: Related Workmentioning

confidence: 99%

Multiloss Function Based Deep Convolutional Neural Network for Segmentation of Retinal Vasculature into Arterioles and Venules

Badawi¹,

Fraz

2019

BioMed Research International

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The arterioles and venules (AV) classification of retinal vasculature is considered as the first step in the development of an automated system for analysing the vasculature biomarker association with disease prognosis. Most of the existing AV classification methods depend on the accurate segmentation of retinal blood vessels. Moreover, the unavailability of large-scale annotated data is a major hindrance in the application of deep learning techniques for AV classification. This paper presents an encoder-decoder based fully convolutional neural network for classification of retinal vasculature into arterioles and venules, without requiring the preliminary step of vessel segmentation. An optimized multiloss function is used to learn the pixel-wise and segment-wise retinal vessel labels. The proposed method is trained and evaluated on DRIVE, AVRDB, and a newly created AV classification dataset; and it attains 96%, 98%, and 97% accuracy, respectively. The new AV classification dataset is comprised of 700 annotated retinal images, which will offer the researchers a benchmark to compare their AV classification results.

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Section: Related Workmentioning

confidence: 99%

Multiloss Function Based Deep Convolutional Neural Network for Segmentation of Retinal Vasculature into Arterioles and Venules

Badawi¹,

Fraz

2019

BioMed Research International

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“…In 2-3% of cases -in vessel-congested areas -connections were removed manually using a drawing tool rather than a circle, avoiding excessive loss of detail. The optic nerve head area, which is very congested with vessels, was eliminated from DSJS using [9]. The segments were then identified as the morphologically thinned connected components of DSJS (representing centrelines).…”

Section: Methodology and Databasementioning

confidence: 99%

A Bayesian Framework for the Local Configuration of Retinal Junctions

Qureshi

Hunter

Al-Diri

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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Retinal images contain forests of mutually intersecting and overlapping venous and arterial vascular trees. The geometry of these trees shows adaptation to vascular diseases including diabetes, stroke and hypertension. Segmentation of the retinal vascular network is complicated by inconsistent vessel contrast, fuzzy edges, variable image quality, media opacities, complex intersections and overlaps. This paper presents a Bayesian approach to resolving the configuration of vascular junctions to correctly construct the vascular trees. A probabilistic model of vascular joints (terminals, bridges and bifurcations) and their configuration in junctions is built, and Maximum A Posteriori (MAP) estimation used to select most likely configurations. The model is built using a reference set of 3010 joints extracted from the DRIVE public domain vascular segmentation dataset, and evaluated on 3435 joints from the DRIVE test set, demonstrating an accuracy of 95.2%.

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A manually-labeled, artery/vein classified benchmark for the DRIVE dataset

Qureshi

Habib

Hunter

et al. 2013

Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems

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The classification of retinal vessels into arteries and veins is an important step for the analysis of retinal vascular trees, for which the scientists have proposed several classification methods. An obvious concern regarding the strength of these methodologies is the closeness of the result of a particular method to the gold standard. Unfortunately, the research community lacks benchmarks, resulting in increased subjective error, biased opinion and an uncertain progress. This paper introduces a manually-labeled, artery/vein categorized gold standard image database, as an extension of the most widely used image set DRIVE. The labeling criterion is set after a careful analysis of the physiological facts about the retinal vascular system. In addition, the labeling process also includes several versions of original images to get certainty. A two-step validation phase consists of verification from the trained computer vision observers and a professional ophthalmologist, followed by a comparison with a gold standard set for the junction locations introduced in V4-Like filters. Our gold standard is in highly reliable form; offers research community for the result comparison and progress evaluation. Â© 2013 IEEE

show abstract

Automatic localization of the optic disc in retinal fundus images using multiple features

Cited by 9 publications

References 14 publications

Multiloss Function Based Deep Convolutional Neural Network for Segmentation of Retinal Vasculature into Arterioles and Venules

Multiloss Function Based Deep Convolutional Neural Network for Segmentation of Retinal Vasculature into Arterioles and Venules

A Bayesian Framework for the Local Configuration of Retinal Junctions

A manually-labeled, artery/vein classified benchmark for the DRIVE dataset

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