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Proceedings Paper

Automatic aortic vessel tree extraction and thrombus detection in multislice CT
Author(s): Krishna Subramanyan; Melinda Steinmiller; Diana Sifri; Daniel Boll
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Paper Abstract

The abdominal aorta is the most common site for an aneurysm, which may lead to hemorrhage and death, to develop. The aim of this study was to develop a semi-automated method to de-lineate the blood flow and thrombus region, subsequently detect the centerline of these vessels to make measurements necessary for stent design from computed tomograms. We developed a robust method of tracking the aortic vessel tree from a user selected seed point using series of image processing such as fast marching method to delineate the blood flow, morphological and distance transforms methods to extract centerlines, and finally by reinitializing the fast marching in a blood filled region subtracted CT volume to obtain the thrombus borders. Fifteen patients were scanned with contrast on Mx8000 CT scanner (Philips Medical Systems), with a 1.3 mm thickness, 1.0 mm slice spacing, and a stack of 512x512x380 volume data sets were reconstructed. The automated image processing took approximately 30 to 90 seconds to compute the centerline and borders of the aortic vessel tree. We compared our results with manual and 3D volume rendering methods and found automatic method is superior in accuracy of spatial localization (0.94-0.97 ANOVA K) and accuracy of diameter determination (0.88-0.98).

Paper Details

Date Published: 15 May 2003
PDF: 10 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.483542
Show Author Affiliations
Krishna Subramanyan, Philips Medical Systems (United States)
Melinda Steinmiller, Philips Medical Systems (United States)
Diana Sifri, Philips Medical Systems Technologies (Israel)
Daniel Boll, Univ. Hospitals of Cleveland (United States)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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