Geodesic Distance Algorithm for Extracting the Ascending Aorta from 3D CT Images. (20th January 2016)

• Record Type: Journal Article
• Authors: Jang, Yeonggul; Jung, Ho Yub; Hong, Youngtaek; Cho, Iksung; Shim, Hackjoon; Chang, Hyuk-Jae
• Other Names: Tsui Po-Hsiang Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2016(2016)
• Publication Date: 2016-01-20
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2016/4561979
• Languages: English
• ISSNs: 1748-670X

Abstract

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.