Extraction of tubular structures over an orientation domain

Péchaud, Mickaël; Keriven, Renaud; Peyré, Gabriel

doi:10.1109/cvpr.2009.5206782

Cited by 54 publications

(33 citation statements)

References 22 publications

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“…Compared to other vessel analysis tasks such as vessel segmentation [11], [14], [19], [50], [55] or vessel centerline extraction [10], [16], [23], [38], [48], there have been only a few automatic or semi-automatic AV classification methods proposed. We now review some of the most important existing approaches for this problem.…”

Section: Prior Workmentioning

confidence: 99%

Retinal Artery-Vein Classification via Topology Estimation

Estrada

Allingham

Mettu

et al. 2015

IEEE Trans. Med. Imaging

148

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We propose a novel, graph-theoretic framework for distinguishing arteries from veins in a fundus image. We make use of the underlying vessel topology to better classify small and midsized vessels. We extend our previously proposed tree topology estimation framework by incorporating expert, domain-specific features to construct a simple, yet powerful global likelihood model. We efficiently maximize this model by iteratively exploring the space of possible solutions consistent with the projected vessels. We tested our method on four retinal datasets and achieved classification accuracies of 91.0%, 93.5%, 91.7%, and 90.9%, outperforming existing methods. Our results show the effectiveness of our approach, which is capable of analyzing the entire vasculature, including peripheral vessels, in wide field-of-view fundus photographs. This topology-based method is a potentially important tool for diagnosing diseases with retinal vascular manifestation.

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Section: Prior Workmentioning

confidence: 99%

Retinal Artery-Vein Classification via Topology Estimation

Estrada

Allingham

Mettu

et al. 2015

IEEE Trans. Med. Imaging

148

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“…For tracking in 3D orientation scores V may be obtained via the crossing preserving vessel enhancements of [19]. In related tracking problems in lifted spaces the lifts are obtained via tubularity measures [11,36,38], or by correlating the image with a set of rotated templates [44].…”

Section: The Cost Cmentioning

confidence: 99%

Nilpotent Approximations of Sub-Riemannian Distances for Fast Perceptual Grouping of Blood Vessels in 2D and 3D

2018

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We propose an efficient approach for the grouping of local orientations (points on vessels) via nilpotent approximations of sub-Riemannian distances in the 2D and 3D roto-translation groups SE(2) and SE(3). In our distance approximations we consider homogeneous norms on nilpotent groups that locally approximate SE(n), and which are obtained via the exponential and logarithmic map on SE(n). In a qualitative validation we show that the norms provide accurate approximations of the true sub-Riemannian distances, and we discuss their relations to the fundamental solution of the sub-Laplacian on SE(n). The quantitative experiments further confirm the accuracy of the approximations. Quantitative results are obtained by evaluating perceptual grouping performance of retinal blood vessels in 2D images and curves in challenging 3D synthetic volumes. The results show that (1) sub-Riemannian geometry is essential in achieving top performance and (2) grouping via the fast analytic approximations performs almost equally, or better, than data-adaptive fast marching approaches on R n and SE(n).

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“…Segmentation methods employ local filtering [42], [37], dynamic programming [4], [10], spanning tree sampling [14], [40], Steiner trees [19], or tubular tracking [30], [43]. Recent work also attempts to distinguish arteries from veins using a combination of color features and vessel tracking [7], [35].…”

Section: Related Workmentioning

confidence: 99%

Tree Topology Estimation

Estrada

Tomasi

Schmidler

et al. 2015

IEEE Trans. Pattern Anal. Mach. Intell.

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imaging techniques allow us to image trees. However, an image of a tree typically includes spurious branch crossings, and the original relationships of ancestry among edges may be lost. We present a methodology for estimating the most likely topology of a rooted, directed, three-dimensional tree given a single two-dimensional image of it. We regularize this inverse problem via a prior parametric tree-growth model that realistically captures the morphology of a wide variety of trees. We show that the problem of estimating the optimal tree has linear complexity if ancestry is known, but is NP-hard if it is lost. For the latter case, we present both a greedy approximation algorithm and a heuristic search algorithm that effectively explore the space of possible trees. Experimental results on retinal vessel, plant root, and synthetic tree datasets show that our methodology is both accurate and efficient.iv

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Extraction of tubular structures over an orientation domain

Cited by 54 publications

References 22 publications

Retinal Artery-Vein Classification via Topology Estimation

Retinal Artery-Vein Classification via Topology Estimation

Nilpotent Approximations of Sub-Riemannian Distances for Fast Perceptual Grouping of Blood Vessels in 2D and 3D

Tree Topology Estimation

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