Title :
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
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;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
DOI :
10.1109/EMBC.2014.6944680
