Pregled bibliografske jedinice broj: 71694
3-D deformable model segmentation of abdominal aortic aneurysm
3-D deformable model segmentation of abdominal aortic aneurysm // Medical Imaging 2001; Image processing / Sonka, Milan; Hanson, Kenneth M. (ur.).
San Diego (CA): The Society of Photo-Optical Instrumentation Engineers, 2001. str. 388-394 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 71694 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
3-D deformable model segmentation of abdominal aortic aneurysm
Autori
Subašić, Marko ; Lončarić, Sven ; Sorantin, Erich
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Medical Imaging 2001; Image processing
/ Sonka, Milan; Hanson, Kenneth M. - San Diego (CA) : The Society of Photo-Optical Instrumentation Engineers, 2001, 388-394
Skup
Medical Imaging 2001
Mjesto i datum
San Diego (CA), Sjedinjene Američke Države, 19.02.2001. - 22.02.2001
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
abdominal aortic aneurysm; deformable models; level set; image analysis; image segmentation
Sažetak
Tomography angiography (CTA) images. Output data (3-D model) form the proposed method can be used for measurement
of aortic shape and dimensions. Knowledge of aortic shape and size is very important in planning of minimally invasive
procedure that is for selection of appropriate stent graft device for treatment of AAA. The technique is based on a 3-D
deformable model and utilizes the level-set algorithm for implementation of the method. The method performs 3-D
segmentation of CTA images and extracts a 3-D model of aortic wall. Once the 3-D model of aortic wall is available it is
easy to perform all required measurements for appropriate stent graft selection. The method proposed in this paper uses the
level-set algorithm for deformable models, instead of the classical snake algorithm. The main advantage of the level set
algorithm is that it enables easy segmentation of complex structures, surpassing most of the drawbacks of the classical
approach. We have extended the deformable model to incorporate the a priori knowledge about the shape of the AAA. This
helps direct the evolution of the deformable model to correctly segment the aorta. The algorithm has been implemented in
IDL and C languages. Experiments have been performed using real patient CTA images and have shown good results.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
