Separation of the retinal vascular graph in arteries and veins based upon structural knowledge

Rothaus, Kai; Jiang, Xiaoyi; Rhiem, Paul

doi:10.1016/j.imavis.2008.02.013

Cited by 98 publications

(60 citation statements)

References 18 publications

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“…However, works related to the separation of vascular blood vessels networks in its venous and arterial branches has received a limited attention so far. Computerized analysis of retinal fundus images potentially reduce the workload of ophthalmologists and can improve in many ways the diagnostic efficiency, mainly when it is related to the quality of the patient's retinal vascular network [1][2][3][4][5][6][7][8]13]. Its clinical status can say too much to physicians about the general status of the vascular network located in other zones in the human body.…”

Section: Antecedentsmentioning

confidence: 99%

A Semi-supervised Puzzle-Based Method for Separating the Venous and Arterial Vascular Networks in Retinal Images

Felipe-Riveron

Villalobos-Castaldi

Suaste-Gómez

et al. 2014

Lecture Notes in Computer Science

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Abstract. The focus of this work is to create a methodology to separate the entire vascular network into its independent veins and arteries networks in optical human fundus images. It has been developed following the logical procedure used by humans when they assemble a puzzle. In the development of the methodology we take into consideration physiological properties, topological properties of the tree structure and morphological properties of both networks, that is, they have only bifurcations, crosses and ending points, and also that crosses are produced always between venous and arterial branches. For arterial blood vessels we get a classification capability, based on the pixel counting, of 84.88% while for venous was 82.87%. This indicates that the methodology classified correctly as average 83.80% of the total blood vessels in the images.

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Section: Antecedentsmentioning

confidence: 99%

A Semi-supervised Puzzle-Based Method for Separating the Venous and Arterial Vascular Networks in Retinal Images

Felipe-Riveron

Villalobos-Castaldi

Suaste-Gómez

et al. 2014

Lecture Notes in Computer Science

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“…Rothaus et al 23 proposed to solve the problem in a more global way. Based on a presegmentation image, they obtained the skeletonized vessel segments and identified bifurcations and crossing points.…”

Section: Introductionmentioning

confidence: 99%

Automated construction of arterial and venous trees in retinal images

Abràmoff

Garvin

2015

J. Med. Imag

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Abstract. While many approaches exist to segment retinal vessels in fundus photographs, only a limited number focus on the construction and disambiguation of arterial and venous trees. Previous approaches are local and/or greedy in nature, making them susceptible to errors or limiting their applicability to large vessels. We propose a more global framework to generate arteriovenous trees in retinal images, given a vessel segmentation. In particular, our approach consists of three stages. The first stage is to generate an overconnected vessel network, named the vessel potential connectivity map (VPCM), consisting of vessel segments and the potential connectivity between them. The second stage is to disambiguate the VPCM into multiple anatomical trees, using a graph-based metaheuristic algorithm. The third stage is to classify these trees into arterial or venous (A/V) trees. We evaluated our approach with a ground truth built based on a public database, showing a pixelwise classification accuracy of 88.15% using a manual vessel segmentation as input, and 86.11% using an automatic vessel segmentation as input.

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“…Recent methods have also focused on contextual information present in bifurcations and crossings of vessels. These methods make use of the fact that vessels that join in a bifurcation must be of the same type, and two crossing vessels must be of opposite type [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to previous graph-based methods such as [7][8][9], here the classification is defined as an optimization problem, based on a non-submodular energy function that is minimized exactly and efficiently using the Quadratic Pseudo-Boolean Optimization (QPBO) graph cut algorithm. Optimization of this energy generates a classification of all detected vessels based on local features, while the contextual features limit the number of possible configurations of vessel labels.…”

Section: Introductionmentioning

confidence: 99%

Retinal Artery/Vein Classification via Graph Cut Optimization

Eppenhof

Bekkers

Berendschot³

et al. 2015

Proceedings of the Ophthalmic Medical Image Analysis Second International Workshop

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Abstract. In many diseases with a cardiovascular component, the geometry of microvascular blood vessels changes. These changes are specific to arteries and veins, and can be studied in the microvasculature of the retina using retinal photography. To facilitate large-scale studies of artery/vein-specific changes in the retinal vasculature, automated classification of the vessels is required. Here we present a novel method for artery/vein classification based on local and contextual feature analysis of retinal vessels. For each vessel, local information in the form of a transverse intensity profile is extracted. Crossings and bifurcations of vessels provide contextual information. The local and contextual features are integrated into a non-submodular energy function, which is optimized exactly using graph cuts. The method was validated on a ground truth data set of 150 retinal fundus images, achieving an accuracy of 88.0% for all vessels and 94.0% for the six arteries and six veins with highest caliber in the image.

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Separation of the retinal vascular graph in arteries and veins based upon structural knowledge

Cited by 98 publications

References 18 publications

A Semi-supervised Puzzle-Based Method for Separating the Venous and Arterial Vascular Networks in Retinal Images

A Semi-supervised Puzzle-Based Method for Separating the Venous and Arterial Vascular Networks in Retinal Images

Automated construction of arterial and venous trees in retinal images

Retinal Artery/Vein Classification via Graph Cut Optimization

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