Biologically-Inspired Supervised Vasculature Segmentation in SLO Retinal Fundus Images

Abbasi-Sureshjani, Samaneh; Smit-Ockeloen, Ingrid M Iris; Zhang, Jiong; Romeny, BM Bart ter Haar

doi:10.1007/978-3-319-20801-5_35

Cited by 46 publications

(36 citation statements)

References 10 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%

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%

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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“…This multi-orientation framework [14,15,21,31] also allows us to generically deal with crossings, as we will show with the application to vessel tracking [6,8,9,52] and segmentation [1,33,65]. Moreover, due to the neat organization of image data on the Lie-group SE(2), we are able to design effective detection algorithms [6,7], geometric feature analysis techniques such as bifurcation detection/analysis [55] and [40].…”

Section: Multi-orientation Analysismentioning

confidence: 99%

“…and where σ s and σ o are used to define the spatial scale 1 2 σ 2 s and orientation scale 1 2 σ 2 o of the Gaussian kernel. Note that the spatial Gaussian distribution G σ s : R 2 → R + must be isotropic to preserve commutator relations of the SE(2) group for scales σ s > 0, i.e., to preserve left-invariance.…”

Section: Left-invariant Gaussian Derivatives In Se(2)mentioning

confidence: 99%

“…Very few studies are dedicated to SLO image analysis (e.g., [63]). The proposed method [1], termed BIMSO (Brain-Inspired Multi-Scale and multi-Orientation), has four main steps: preprocessing, feature extraction, classification and postprocessing. The method is developed for the green channel (RGB and SLO).…”

Section: Multi-scale and Multi-orientation Segmentation (Bimso) For Smentioning

confidence: 99%

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Brain-inspired algorithms for retinal image analysis

Romeny

Bekkers

Zhang

et al. 2016

Machine Vision and Applications

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Retinal image analysis is a challenging problem due to the precise quantification required and the huge numbers of images produced in screening programs. This paper describes a series of innovative brain-inspired algorithms for automated retinal image analysis, recently developed for the B Bart M. ter Haar Romeny B.M.terHaarRomeny@tue.nl 123 B. M. ter Haar Romeny et al.optic nerve head detection, crossing-preserving enhancement and segmentation of retinal vasculature, arterio-venous ratio, fractal dimension, and vessel tortuosity and bifurcations. Many of these algorithms outperform state-of-the-art techniques. The methods are currently validated in collaborating hospitals, with a rich accompanying base of metadata, to phenotype and validate the quantitative algorithms for optimal classification power.

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“…1(h)). For images taken with the scanning laser ophthalmoscopy (SLO) technology, we use similar multi-scale and multi-orientation features in a supervised manner for enhancing and segmenting the blood vessels [2].…”

Section: Vessel Enhancement and Segmentationmentioning

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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Biologically-Inspired Supervised Vasculature Segmentation in SLO Retinal Fundus Images

Cited by 46 publications

References 10 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

Brain-inspired algorithms for retinal image analysis

Infrastructure for Retinal Image Analysis

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