A Semi-automated image segmentation approach for computational fluid dynamics studies of aortic dissection

Author

Anderson, J.R. ; Karmonik, Chistof ; Georg, Yannick ; Bismuth, Jean ; Lumsden, Alan B. ; Schwein, Adeline ; Ohana, Mickael ; Thaveau, Fabien ; Chakfe, Nabil

Author_Institution

MR Core Facilities at the, Houston Methodist Res. Inst., Houston, TX, USA

fYear

2014

fDate

26-30 Aug. 2014

Firstpage

4727

Lastpage

4730

Abstract

Computational studies of aortic hemodynamics require accurate and reproducible segmentation of the aortic tree from whole body, contrast enhanced CT images. Three methods were vetted for segmentation. A semi-automated approach that utilizes denoising, the extended maxima transform, and a minimal amount of manual segmentation was adopted.

Keywords

cardiovascular system; computational fluid dynamics; computerised tomography; haemodynamics; image denoising; image enhancement; image segmentation; medical image processing; aortic dissection; aortic hemodynamics; computational fluid dynamics; extended maxima transform; image denoising; semiautomated image segmentation approach; whole body contrast enhanced CT images; Biomedical imaging; Computational fluid dynamics; Computed tomography; Image edge detection; Image segmentation; Manuals; Vegetation;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE

Conference_Location

Chicago, IL

ISSN

1557-170X

Type

conf

DOI

10.1109/EMBC.2014.6944680

Filename

6944680