DocumentCode :
2117822
Title :
3-D Vascular Tree Segmentation Using Level-Set Deformable Model
Author :
Dekanic, Kresimir ; Loncaric, Sven
Author_Institution :
Univ. of Zagreb, Zagreb
fYear :
2007
fDate :
27-29 Sept. 2007
Firstpage :
407
Lastpage :
412
Abstract :
This paper describes a novel 3-D level-set deformable model-based approach for segmentation of medical computed tomography (CT) images of human brain vascular tree. The method employs a 3-D edge detection method to establish the initial contours. Afterwards a velocity field is created using the gradient vector flow algorithm. The deformable model is then initialized and solved using a level-set method. Experimental validation of the method has been conducted on CT images of real patients. Comments on performance and possible improvements are discussed.
Keywords :
brain; computerised tomography; deformation; edge detection; image segmentation; medical image processing; 3D vascular tree segmentation; CT images; edge detection; gradient vector flow algorithm; human brain vascular tree; level-set deformable model; medical computed tomography; Active contours; Biomedical imaging; Computed tomography; Deformable models; Image analysis; Image edge detection; Image segmentation; Magnetic analysis; Object segmentation; Topology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing and Analysis, 2007. ISPA 2007. 5th International Symposium on
Conference_Location :
Istanbul
ISSN :
1845-5921
Print_ISBN :
978-953-184-116-0
Type :
conf
DOI :
10.1109/ISPA.2007.4383728
Filename :
4383728
Link To Document :
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