Automatic detection of vascular bifurcations and crossings in retinal images using orientation scores

Abbasi-Sureshjani, Samaneh; Smit-Ockeloen, Iris; Bekkers, Erik J.; Dashtbozorg, Behdad; Romeny, Bart ter Haar

doi:10.1109/isbi.2016.7493241

Cited by 35 publications

(42 citation statements)

References 16 publications

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“…Moreover, we use the IOSTAR dataset [6,20] for testing the robustness of the method. Our networks are trained on the DRIVE Dataset, but all of them are tested on the unseen IOSTAR dataset for further validation.…”

Section: Datasetsmentioning

confidence: 99%

“…The previous work on vessel bifurcations and junctions has involved using orientation scores to detect bifurcations and junctions in retinal images [6]. In contrast to the method we proposed, which is a fully automated system that uses only the image to determine the diagnosis, the work in [6] required 24 orientation processes for each image before training.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to the method we proposed, which is a fully automated system that uses only the image to determine the diagnosis, the work in [6] required 24 orientation processes for each image before training. However, the results in the paper show that the features within the image are extractable.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography

Pratt

Williams

et al. 2017

J. Imaging

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Abstract:The analysis of retinal blood vessels present in fundus images, and the addressing of problems such as blood clot location, is important to undertake accurate and appropriate treatment of the vessels. Such tasks are hampered by the challenge of accurately tracing back problems along vessels to their source. This is due to the unresolved issue of distinguishing automatically between vessel bifurcations and vessel crossings in colour fundus photographs. In this paper, we present a new technique for addressing this problem using a convolutional neural network approach to firstly locate vessel bifurcations and crossings and then to classifying them as either bifurcations or crossings. Our method achieves high accuracies for junction detection and classification on the DRIVE dataset and we show further validation on an unseen dataset from which no data has been used for training. Combined with work in automated segmentation, this method has the potential to facilitate: reconstruction of vessel topography, classification of veins and arteries and automated localisation of blood clots and other disease symptoms leading to improved management of eye disease.

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Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography

Pratt

Williams

et al. 2017

J. Imaging

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“…Not only the vessel properties at these points provides relevant clinical information [11], but also they are considered as key points in vessel tracking methods, which represent the graphical model of the vasculature. For the vessel junction detection, we have used the BICROS method described in [1]. In this method, after applying appropriate preprocessing, the image is lifted with anisotropic (single-sided) cake wavelets with 24 orientations as described in Sec.…”

Section: Bifurcations and Crossing Detectionmentioning

confidence: 99%

“…1(i)). The method has been validated quantitatively and qualitatively using two public datasets [1].…”

Section: Bifurcations and Crossing Detectionmentioning

confidence: 99%

Infrastructure for Retinal Image Analysis

Dashtbozorg

Abbasi-Sureshjani

Zhang

et al. 2016

Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop

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Abstract. This paper introduces a retinal image analysis infrastructure for the automatic assessment of biomarkers related to early signs of diabetes, hypertension and other systemic diseases. The developed application provides several tools, namely normalization, vessel enhancement and segmentation, optic disc and fovea detection, junction detection, bifurcation/crossing discrimination, artery/vein classification and red lesion detection. The pipeline of these methods allows the assessment of important biomarkers characterizing dynamic properties of retinal vessels, such as tortuosity, width, fractal dimension and bifurcation geometry features.

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A Multi-task Network to Detect Junctions in Retinal Vasculature

Uslu

Bharath

2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

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Junctions in the retinal vasculature are key points to be able to extract its topology, but they vary in appearance, depending on vessel density, width and branching/crossing angles. The complexity of junction patterns is usually accompanied by a scarcity of labels, which discourages the usage of very deep networks for their detection. We propose a multitask network, generating labels for vessel interior, centerline, edges and junction patterns, to provide additional information to facilitate junction detection. After the initial detection of potential junctions in junctionselective probability maps, candidate locations are re-examined in centerline probability maps to verify if they connect at least 3 branches. The experiments on the DRIVE and IOSTAR showed that our method outperformed a recent study in which a popular deep network was trained as a classifier to find junctions. Moreover, the proposed approach is applicable to unseen datasets with the same degree of success, after training it only once.

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Automatic detection of vascular bifurcations and crossings in retinal images using orientation scores

Cited by 35 publications

References 16 publications

Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography

Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography

Infrastructure for Retinal Image Analysis

A Multi-task Network to Detect Junctions in Retinal Vasculature

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