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An Automated System for 3D Segmentation of CT Angiograms

  • Y. Wang
  • P. LiatsisEmail author
Chapter
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Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 4)

Abstract

This chapter presents a novel automated two-step algorithm for segmentation of the entire arterial tree in 3D contrast-enhanced Computed Tomography Angiography (CTA) datasets. In the first stage of the proposed algorithm, the main branches of the coronary arteries are extracted from the volume datasets based on a generalised active contour model by utilising both local and global intensity features. The use of local regional information allows for accommodating uneven brightness distribution across the image. The global energy term, derived from the histogram distribution of the input images, is used to deform the contour towards to desired boundaries without being trapped in local stationary points. Possible outliers, such as kissing vessel artefacts, are removed in the following stage by the proposed slice-by-slice correction algorithm. Experimental results on real clinical datasets have shown that our method is able to extract the major branches of the coronaries with an average distance of 0.7 voxels to the manually defined reference data. Furthermore, in the presence of kissing vessel artefacts, the outer surface of the coronary tree extracted by the proposed system is smooth and contains less erroneous segmentation as compared to the initial segmentation.

Keywords

Computed tomography angiography 3D segmentation Coronary arteries Active contour models 

List of Abbreviations

CTA

Computed tomography angiography

CAD

Coronary artery disease

CT

Computed tomography

EM

Expectation maximisation

GMM

Gaussian mixture model

LAD

Left anterior descending

LCA

Left coronary artery

LCX

Left circumflex

LM

Left main coronary

RCA

Right coronary artery

WHO

World Health Organization

Notes

Acknowledgments

The authors would like to acknowledge the support of City University, which enabled this research through the award of a University Research Studentship and Dr Gerry Carr-White and Rebecca Preston at St Thomas and Guys Hospitals for their invaluable advice and the provision of the CTA datasets.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.College of AstronauticsNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Department of Electrical and Electronic EngineeringCity University LondonLondonUnited Kingdom

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