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Segmentation of Ascending and Descending Aorta in CTA Images

Authors: H. Özkan

Abstract:

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.

Keywords: Segmentation, Ascending aorta (AscA), Descending aorta (DesA), Computed tomography angiography (CTA), Computer aided detection (CAD)

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328104

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[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. 167ü177.
[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