A carotid vasculature segmentation method for computed tomography angiography

Zhao Xuan Gong, Wen Jun Tan, Jin Zhu Yang, Mengjia Xu, Zijian Bian, Da Zhe Zhao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


A new approach for carotid vasculature segmentation of the entire carotid vasculature from computed tomography angiography (CTA) scans for interventional procedure simulation is presented. The method starts with a region growing-based method to segment the aorta, the seed point is automatically selected using a cube search strategy. Secondly, the information of the aorta is used to construct an intensity-based model for the inner part of carotid vasculature. Thirdly, the contour of the vasculature is extracted, whose results can be taken as the initialization of the level set model. Finally, an intensity-based CV model is used to segment the entire carotid arteries. With the optimized parameter settings, the entire carotid vasculature of 11 training datasets are successfully extracted by this method in terms of real-timeness, effectiveness and robustness.

Original languageEnglish (US)
Pages (from-to)912-915
Number of pages4
JournalJournal of Medical Imaging and Health Informatics
Issue number6
StatePublished - Dec 1 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging
  • Health Informatics


  • CTA
  • CV
  • Carotid Vascular
  • Region Growing
  • Regularizing Terms


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