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Geodesic Distance Algorithm for Extracting the Ascending Aorta from 3D CT Images


Affiliations
1 Brain Korea 21 Project for Medical Science, Yonsei University, Seoul 120-752, Korea, Republic of
2 Division of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies, Yongin 449-791, Korea, Republic of
3 Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul 120-752, Korea, Republic of
4 Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul 120-752, Korea, Republic of
 

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.
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  • Geodesic Distance Algorithm for Extracting the Ascending Aorta from 3D CT Images

Abstract Views: 96  |  PDF Views: 1

Authors

Yeonggul Jang
Brain Korea 21 Project for Medical Science, Yonsei University, Seoul 120-752, Korea, Republic of
Ho Yub Jung
Division of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies, Yongin 449-791, Korea, Republic of
Youngtaek Hong
Brain Korea 21 Project for Medical Science, Yonsei University, Seoul 120-752, Korea, Republic of
Iksung Cho
Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul 120-752, Korea, Republic of
Hackjoon Shim
Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul 120-752, Korea, Republic of
Hyuk-Jae Chang
Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul 120-752, Korea, Republic of

Abstract


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.