Literature DB >> 14575780

Automatic 3D vascular tree construction in CT angiography.

Zikuan Chen1, Sabee Molloi.   

Abstract

This study presents an automatic method for 3D reconstruction of vascular trees using computed-tomography angiographic (CTA) images. The program starts with the CTA slices, performs a sequential procedure of 3D image formation, preprocessing, segmentation, thinning, skeleton pruning and tree construction. It ends with vascular trees along with quantitative data about the trees such as values of diameter, length and bifurcation angles. All the involved algorithms are presented with the emphasis given to the skeleton pruning and tree construction algorithms. The skeletons obtained using a 3D thinning algorithm may contain cycles, spurs, isolated sticks, and non-unit-width parts, which hinder tree construction. As a solution to this problem, a skeleton pruning and tree construction algorithm is proposed. At each stage of the automatic procedure, 3D rendering is provided for visual inspection of the computed results. In the final output, the constructed vascular trees are visualized by rendering the 3D trees and the 3D binary image together in a transparent display mode. The program is carried out in a fully automatic fashion, with a few default settings. Occasionally, user intervention is needed at the 3D segmentation stage to impose an appropriate threshold when the automatic 3D segmentation is obviously sub-optimal for vessel delineation. Experimental demonstrations on both coronary artery phantom and a cast of coronary artery tree of a swine animal model are provided.

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Year:  2003        PMID: 14575780     DOI: 10.1016/s0895-6111(03)00039-9

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  13 in total

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6.  Role of 3DCT in laparoscopic total gastrectomy with spleen-preserving splenic lymph node dissection.

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9.  A clique-based method for the edit distance between unordered trees and its application to analysis of glycan structures.

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10.  Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks.

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