DocumentCode
2475727
Title
Automatic lumen segmentation in CT and PC-MR images of abdominal aortic aneurysm
Author
Almuntashri, A. ; Finol, E. ; Agaian, Sos
Author_Institution
Dept. of Biomed. Eng., Univ. of Texas at San Antonio, San Antonio, TX, USA
fYear
2012
fDate
14-17 Oct. 2012
Firstpage
2891
Lastpage
2896
Abstract
Vascular segmentation through the use of image processing tools provides significant information that allows for the accurate diagnosis, categorization, registration, and visualization of vascular disease. Currently, in the assessment of Abdominal Aortic Aneurysms (AAA), radiologists manually segment different regions on interest on each medical image to create a full volume of the abdominal aorta. Such manual segmentation is a time consuming task, prone to errors and a subjective approach especially when non-contrast enhanced images are present. In this paper, we introduce an automatic system to segment the aortic lumen in non-contrast enhanced CT scans and PC-MR images using digital image processing algorithms where image enhancement, denoising, edge detection, and regional growing algorithms are utilized. The output of this work forms the basis for a future reliable inner and outer wall segmentation of the AAA.
Keywords
computerised tomography; data visualisation; image registration; image segmentation; medical image processing; patient diagnosis; radiology; AAA; CT images; PC-MR images; abdominal aortic aneurysm; automatic lumen segmentation; image processing tools; radiology; vascular disease categorization; vascular disease diagnosis; vascular disease registration; vascular disease visualization; vascular segmentation; Aneurysm; Anisotropic magnetoresistance; Biomedical imaging; Computed tomography; Histograms; Image edge detection; Image segmentation; abdominal aortic aneurysms; anisotropic diffusion; edge detection; lumen; region growing; segmentation;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on
Conference_Location
Seoul
Print_ISBN
978-1-4673-1713-9
Electronic_ISBN
978-1-4673-1712-2
Type
conf
DOI
10.1109/ICSMC.2012.6378232
Filename
6378232
Link To Document