A Sequential 3D Thinning Algorithm and Its Medical Applications

Palágyi, Kálmán; Sorantin, Erich; Balogh, Emese; Kuba, Attila; Halmai, C.; Erdohelyi, B.; Hausegger, K. A.

doi:10.1007/3-540-45729-1_42

Cited by 109 publications

(72 citation statements)

References 5 publications

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“…Depending on the kernel used for the thinning process also the medial axis is derivable. Palagyi et al [16] introduced a time linear algorithm using 6 sub-iterations. Its application on tree point clouds was proposed by Gorte and Pfeifer [13] and later extended [12].…”

Section: Descriptorsmentioning

confidence: 99%

SkelTre

2010

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Terrestrial laser scanners capture 3D geometry of real world objects as a point cloud. This paper reports on a new algorithm developed for the skeletonization of a laser scanner point cloud. The skeletonization algorithm proposed in this paper consists of three steps: (i) extraction of a graph from an octree organization, (ii) reduction of the graph to a skeleton, and (iii) embedding of the skeleton into the point cloud. For these three steps, only one input parameter is required. The results are validated on laser scanner point clouds representing 2 classes of objects; first on botanic trees as a special application and secondly on popular arbitrary objects. The presented skeleton found its first application in obtaining botanic tree parameters like length and diameter of branches and is presented here in a new, generalized version. Its definition as Reeb Graph, proofs the usefulness of the skeleton for applications like shape analysis. In this paper we show that the resulting skeleton contains the Reeb Graph and investigate the practically relevant parameters: centeredness and topological correctness. The robustness of this skeletonization method against undersampling, varying point density and systematic errors of the point cloud is demonstrated on real data examples.

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Section: Descriptorsmentioning

confidence: 99%

SkelTre

2010

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“…The fast marching algorithm stops the surface in the vicinity of object boundaries, thus yielding the approximate object surface. In order to achieve an accurate segmentation, a skeletonization algorithm [6] is applied to the result of presegmentation to extract the aortic centerline for each phase.…”

Section: Aortic Surface Presegmentationmentioning

confidence: 99%

Automated 4D Segmentation of Aortic Magnetic Resonance Images

Zhao¹,

Zhang²,

Wahle³

et al. 2006

Procedings of the British Machine Vision Conference 2006

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Automated and accurate segmentation of the aorta in 4D (3D+time) cardiovascular magnetic resonance (MR) image data is important for early detection of congenital aortic disease leading to aortic aneurysms and dissections. An automated 4D segmentation method is reported in this study. Our automated segmentation method combines level-set and optimal surface segmentation algorithms so that the final aortic surfaces in n cardiac phases are determined in a single optimization process. 4D MR image data sets acquired from 21 normal and connective tissue disorder subjects were used to evaluate the performance of our method. The automated 4D segmentation results produced accurate aortic surfaces in 16 cardiac phases, covering the aorta from the left-ventricular outflow tract to the diaphragm, yielding subvoxel accuracy with signed surface positioning errors of -0.07±1.16 voxel (-0.10±2.05 mm).

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“…Following the extraction process, centreline segments were automatically generated for the blood vessels of interest using the 3D thinning algorithm of Palágyi et al [20].…”

Section: Sectioning the Lung Volume Geometrymentioning

confidence: 99%

Non-uniform central airways ventilation model based on vascular segmentation

Ibrahim

Rona

Hainsworth

2015

Computers in Biology and Medicine

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Improvements in the understanding of the physiology of the central airways require an appropriate representation of the non-uniform ventilation at its terminal branches. This paper proposes a new technique for estimating the non-uniform ventilation at the terminal branches by modelling the volume change of their distal peripheral airways, based on vascular segmentation. The vascular tree is used for sectioning the dynamic CT-based 3D volume of the lung at 11 time points over the breathing cycle of a research animal. Based on the mechanical coupling between the vascular tree and the remaining lung tissues, the volume change of each individual lung segment over the breathing cycle was used to estimate the non-uniform ventilation of its associated terminal branch. The 3D lung sectioning technique was validated on an airway cast model of the same animal pruned to represent the truncated dynamic CT based airway geometry. The results showed that the 3D lung sectioning technique was able to estimate the volume of the missing peripheral airways within a tolerance of 2%. In addition, the time-varying non-uniform ventilation distribution predicted by the proposed sectioning technique was validated against CT measurements of lobar ventilation and showed good agreement. This significant modelling advance can be used to estimate subject-specific non-uniform boundary conditions to obtain subjectspecific numerical models of the central airway flow.

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A Sequential 3D Thinning Algorithm and Its Medical Applications

Cited by 109 publications

References 5 publications

SkelTre

SkelTre

Automated 4D Segmentation of Aortic Magnetic Resonance Images

Non-uniform central airways ventilation model based on vascular segmentation

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