Segmentation of the thoracic aorta in noncontrast cardiac CT images

Olga C. Avila-Montes, Uday Kurkure, Ryo Nakazato, Daniel S. Berman, Damini Dey, Ioannis A. Kakadiaris

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Studies have shown that aortic calcification is associated with cardiovascular disease. In this study, a method for localization, centerline extraction, and segmentation of the thoracic aorta in noncontrast cardiac-computed tomography (CT) images, toward the detection of aortic calcification, is presented. The localization of the right coronary artery ostium slice is formulated as a regression problem whose input variables are obtained from simple intensity features computed from a pyramid representation of the slice. The localization, centerline extraction, and segmentation of the aorta are formulated as optimal path detection problems. Dynamic programming is applied in the Hough space for localizing key center points in the aorta which guide the centerline tracing using a fast marching-based minimal path extraction framework. The input volume is then resampled into a stack of 2-D cross-sectional planes orthogonal to the obtained centerline. Dynamic programming is again applied for the segmentation of the aorta in each slice of the resampled volume. The obtained segmentation is finally mapped back to its original volume space. The performance of the proposed method was assessed on cardiac noncontrast CT scans and promising results were obtained.

Original languageEnglish (US)
Article number6542633
Pages (from-to)936-949
Number of pages14
JournalIEEE Journal of Biomedical and Health Informatics
Issue number5
StatePublished - 2013


  • Aorta
  • image segmentation
  • noncontrast computed tomography
  • regression
  • slice localization

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management


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