Maximum a posteriori estimation method for aorta localization and coronary seed identification

Byunghwan Jeon, Yoonmi Hong, Dongjin Han, Yeonggul Jang, Sunghee Jung, Youngtaek Hong, Seongmin Ha, Hackjoon Shim, Hyuk Jae Chang

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

We propose a robust method for the automatic identification of seed points for the segmentation of coronary arteries from coronary computed tomography angiography (CCTA). The detection of the aorta and the two ostia for use as seed points is required for the automatic segmentation of coronary arteries. Our method is based on a Bayesian framework combining anatomical and geometrical features. We demonstrate the robustness and accuracy of our method by comparison with two conventional methods on 130 CT cases.

Original languageEnglish
Pages (from-to)222-232
Number of pages11
JournalPattern Recognition
Volume68
DOIs
Publication statusPublished - 2017 Aug 1

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Angiography
Tomography

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this

Jeon, Byunghwan ; Hong, Yoonmi ; Han, Dongjin ; Jang, Yeonggul ; Jung, Sunghee ; Hong, Youngtaek ; Ha, Seongmin ; Shim, Hackjoon ; Chang, Hyuk Jae. / Maximum a posteriori estimation method for aorta localization and coronary seed identification. In: Pattern Recognition. 2017 ; Vol. 68. pp. 222-232.
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Maximum a posteriori estimation method for aorta localization and coronary seed identification. / Jeon, Byunghwan; Hong, Yoonmi; Han, Dongjin; Jang, Yeonggul; Jung, Sunghee; Hong, Youngtaek; Ha, Seongmin; Shim, Hackjoon; Chang, Hyuk Jae.

In: Pattern Recognition, Vol. 68, 01.08.2017, p. 222-232.

Research output: Contribution to journalArticle

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AU - Jeon, Byunghwan

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AU - Han, Dongjin

AU - Jang, Yeonggul

AU - Jung, Sunghee

AU - Hong, Youngtaek

AU - Ha, Seongmin

AU - Shim, Hackjoon

AU - Chang, Hyuk Jae

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