Segmentation of Ascending and Descending Aorta in CTA Images

In this study, a new and fast algorithm for Ascending Aorta (AscA) and Descending Aorta (DesA) segmentation is presented using Computed Tomography Angiography images. This process is quite important especially at the detection of aortic plaques, aneurysms, calcification or stenosis. The applied method has been carried out at four steps. At first step, lung segmentation is achieved. At the second one, Mediastinum Region (MR) is detected to use in the segmentation. At the third one, images have been applied optimal threshold and components which are outside of the MR were removed. Lastly, identifying and segmentation of AscA and DesA have been carried out. The performance of the applied method is found quite well for radiologists and it gives enough results to the surgeries medically.

Authors:



References:
[1] Verdonck, B., Bloch, I., Maˆıtre, H., et al: Accurate segmentation of
blood vessels from 3D medical images. In: IEEE Int Conf on Image
Process. (1996) 311-314
[2] Wink, O., Niessen, W.J., Viergever, M.A.: Fast delineation and
visualization of vessels in 3-D angiographic images. IEEE Trans Med
Imaging 19 (2000) 337-346
[3] Osher, S.J. and Sethian, J.A. Fronts propagating with curvature
dependent speed. J. Comput. Pysc, vol.79, pp. (1988)12-49,.
[4] Kass, M., Witkin, A., Terzopoulos, D., Snakes: active contour models,
International Journal of Computer Vision 1 (1988) 321-331.
[5] Li, C., Kao, C., Gore, J., Ding, Z., Minimization of region-scalable
fitting energy for image segmentation, IEEE Transactions on Image
Processing 17 (2008) 1940-1949.
[6] Katz, W.T., Merickel, M.B.: Aorta detection in magnetic resonance
images using multiple artificial neural networks. In: Annual Int Conf of
the IEEE Eng Med Biol Mag. (1990) 1302-1303
[7] Tek, H., Akova, F., Ayvaci, A.: Region competition via local watershed
operators. In: IEEE Comput Soc Conf on Comput Vis and Pattern
Recog. (2005) 361-368
[8] Pohle, R., Toennies, K.D.: Segmentation of medical images using
adaptive region growing. In: SPIE Med Imaging Conf. (2001), volume
4322.
[9] Boskamp T, Rinck D, Link F, et al. New Vessel Analysis Tool for
Morphometric Quantication and Visualization of Vessels in CT and MR
Imaging Data Sets, Radiographics, (2004);24(1):287-297.
[10] Li, C.M., Xu, C.Y., Gui, C.F., Fox, M.D., Level set evolution without
reinitialization: a new variational formulation, in: IEEE Conference on
Computer Vision and Pattern Recognition, San Diego, 2005, pp. 430-
436.
[11] Lie, J., Lysaker, M. ,Tai, X.C., A binary level set model and some
application to Mumford-Shah image segmentation, IEEE Transaction on
Image Processing 15(2006)1171-1181.
[12] Loncari'c, S., Subasi'c, M., Soratin, E.: 3-D deformable model for
abdominal aortic aneurysm segmentation from CT images. First Int
Workshop on Image and Signal Process and Anal (2000)
[13] Lorenz C, Renisch S, SchlathÄolter T, et al. Simultaneous segmentation
and tree reconstruction of the coronary arteries in MSCT images. vol.
5031. SPIE; (2003) p. 167├╝177.
[14] WÄorz S, Rohr K. Segmentation and Quantication of Human Vessels
Using a 3-D Cylindrical Intensity Model. IEEE Trans Image Process.
(2007);16(8):1994-2004.
[15] Kovacs T, Cattin P, Alkadhi H, et al. Automatic Segmentation of the
Vessel Lumen from 3D CTA Images of Aortic Dissection. Procs BVM.
(2006), p. 161-165.
[16] ├ûzkan H., Osman O., ┼×ahin S., Atasoy M. M., Barutca H., Boz A.F.,
Olsun A., "Lung Segmentation Algorithm for CAD System in CTA
Images" World Academy of science Engineering end Technology
(ICBCBBE 2011), July 24- 26, 2011, Paris