A Clustering-based Algorithm for Extracting the Centerlines of 2D and 3D Objects

Ferchichi, Salah; Wang, Shengrui

doi:10.1109/icpr.2006.44

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Antiga et al [AEIR03] demonstrate this approach with surface models of vessel trees. Other popular approaches for the skeletonization of surface meshes include, similar to the thinning of volumes, a topology‐based thinning of the surface [ATC * 08] and retrieving the centerline from a clustering of the points composing the object [FW06].…”

Section: Data Processing Pipelinementioning

confidence: 99%

Vessel Maps: A Survey of Map‐Like Visualizations of the Cardiovascular System

Eulzer

Meuschke²,

Mistelbauer

et al. 2022

Computer Graphics Forum

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Input Graph LayoutFigure 1: We describe the creation of a vessel map as a three-step process: the layout of the map must be derived, the geometry or spatiality of the data must be mapped, and the data attributes must be mapped. We differentiate between image volume maps, flow volume maps, vessel wall maps, and vessel network maps depending on the primary data structure the map-like visualization is based on.

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Section: Data Processing Pipelinementioning

confidence: 99%

Vessel Maps: A Survey of Map‐Like Visualizations of the Cardiovascular System

Eulzer

Meuschke²,

Mistelbauer

et al. 2022

Computer Graphics Forum

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“…blood vessels and airways, often are described by its centerlines. Ferchichi et al [1] give an overview of different centerline extraction techniques. They are roughly grouped into four classes: topological thinning, distance from boundary transform, "prairie fire" approach and Voronoi diagrams.…”

Section: Introductionmentioning

confidence: 99%

A Fast Vessel Centerline Extraction Algorithm for Catheter Simulation

Egger

Mostarkic

Großkopf

et al. 2007

Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07)

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In this paper, we present a fast and robust algorithm for centerline extraction in blood vessels. The algorithm is suitable for catheter simulation in CT data of blood vessels. It creates an initial centerline based on two user-defined points (start-and endpoint). For curved vessel structures, this initial centerline is computed by Dijkstra's shortest path algorithm. For linear vessel structures, the algorithm directly connects the start-and the endpoint to get the initial centerline. Thereafter, this initial path will be aligned in the blood vessel, resulting in the vessels centerline (i.e. an optimal catheter simulation path). The alignment is done by an active contour model combined with polyhedra placed along it. Results of the proposed centerline algorithm are demonstrated for CTA with variations in anatomy and location of pathology.

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A Clustering-based Algorithm for Extracting the Centerlines of 2D and 3D Objects

Cited by 2 publications

References 10 publications

Vessel Maps: A Survey of Map‐Like Visualizations of the Cardiovascular System

Vessel Maps: A Survey of Map‐Like Visualizations of the Cardiovascular System

A Fast Vessel Centerline Extraction Algorithm for Catheter Simulation

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