Geodesic distance algorithm for extracting the ascending aorta from 3D CT images

Yeonggul Jang, Ho Yub Jung, Youngtaek Hong, Iksung Cho, Hackjoon Shim, Hyuk Jae Chang

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


This paper presents a method for the automatic 3D segmentation of the ascending aorta from coronary computed tomography angiography (CCTA). The segmentation is performed in three steps. First, the initial seed points are selected by minimizing a newly proposed energy function across the Hough circles. Second, the ascending aorta is segmented by geodesic distance transformation. Third, the seed points are effectively transferred through the next axial slice by a novel transfer function. Experiments are performed using a database composed of 10 patients' CCTA images. For the experiment, the ground truths are annotated manually on the axial image slices by a medical expert. A comparative evaluation with state-of-the-art commercial aorta segmentation algorithms shows that our approach is computationally more efficient and accurate under the DSC (Dice Similarity Coefficient) measurements.

Original languageEnglish
Article number4561979
JournalComputational and Mathematical Methods in Medicine
Publication statusPublished - 2016

Bibliographical note

Publisher Copyright:
© 2016 Yeonggul Jang et al.

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Applied Mathematics


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