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Segmentation of vessels: The corkscrew algorithm

 
: Wesarg, S.; Firle, E.

:

Fitzpatrick, J.M. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Medical imaging 2004: Image processing. Vol.3 : 16 - 19 February 2004, San Diego, California, USA. Papers presented at the Image Processing Conference of the 2004 SPIE Medical Imaging Symposium
Bellingham/Wash.: SPIE, 2004 (SPIE Proceedings Series 5370)
ISBN: 0-8194-5283-1
S.1609-1620
Image Processing Conference <2004, San Diego/Calif.>
Medical Imaging Symposium <2004, San Diego/Calif.>
Englisch
Konferenzbeitrag
Fraunhofer IGD ()
segmentation; medical imaging; computed tomography; cardiology

Abstract
Medical imaging is nowadays much more than only providing data for diagnosis. It also links 'classical' diagnosis to modern forms of treatment such as image guided surgery. Those systems require the identification of organs, anatomical regions of the human body etc., i. e. the segmentation of structures from medical data sets. The algorithms used for these segmentation tasks strongly depend on the object to be segmented. One structure which plays an important role in surgery planning are vessels that are found everywhere in the human body. Several approaches for their extraction already exist. However, there is no general one which is suitable for all types of data or all sorts of vascular structures. This work presents a new algorithm for the segmentation of vessels. It can be classified as a skeleton-based approach working on 3D data sets, and has been designed for a reliable segmentation of coronary arteries. The algorithm is a semi-automatic extraction technique requirering the definition of the start and end the point of the (centerline) path to be found. A first estimation of the vessel's centerline is calculated and then corrected iteratively by detecting the vessel's border perpendicular to the centerline. We used contrast enhanced CT data sets of the thorax for testing our approach. Coronary arteries have been extracted from the data sets using the 'corkscrew algorithm' presented in this work. The segmentation turned out to be robust even if moderate breathing artifacts were present in the data sets.

: http://publica.fraunhofer.de/dokumente/N-21508.html