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

doi:10.1016/j.patcog.2017.03.005

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

Department of Internal Medicine

Research output: Contribution to journal › Article › peer-review

8 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 language	English
Pages (from-to)	222-232
Number of pages	11
Journal	Pattern Recognition
Volume	68
DOIs	https://doi.org/10.1016/j.patcog.2017.03.005
Publication status	Published - 2017 Aug 1

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Publisher Copyright:
© 2017 Elsevier Ltd

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/j.patcog.2017.03.005

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title = "Maximum a posteriori estimation method for aorta localization and coronary seed identification",

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.",

author = "Byunghwan Jeon and Yoonmi Hong and Dongjin Han and Yeonggul Jang and Sunghee Jung and Youngtaek Hong and Seongmin Ha and Hackjoon Shim and Chang, {Hyuk Jae}",

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AU - Jeon, Byunghwan

AU - Hong, Yoonmi

AU - Han, Dongjin

AU - Jang, Yeonggul

AU - Jung, Sunghee

AU - Hong, Youngtaek

AU - Ha, Seongmin

AU - Shim, Hackjoon

AU - Chang, Hyuk Jae

PY - 2017/8/1

Y1 - 2017/8/1

N2 - 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.

AB - 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.

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M3 - Article

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VL - 68

SP - 222

EP - 232

JO - Pattern Recognition

JF - Pattern Recognition

ER -

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

Abstract

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All Science Journal Classification (ASJC) codes

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