Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
Medical Image Segmentation Using Deformable Model and Local Fitting Binary: Thoracic Aorta

Authors: B. Bagheri Nakhjavanlo, T. S. Ellis, P.Raoofi, Sh.ziari

Abstract:

This paper presents an application of level sets for the segmentation of abdominal and thoracic aortic aneurysms in CTA datasets. An important challenge in reliably detecting aortic is the need to overcome problems associated with intensity inhomogeneities. Level sets are part of an important class of methods that utilize partial differential equations (PDEs) and have been extensively applied in image segmentation. A kernel function in the level set formulation aids the suppression of noise in the extracted regions of interest and then guides the motion of the evolving contour for the detection of weak boundaries. The speed of curve evolution has been significantly improved with a resulting decrease in segmentation time compared with previous implementations of level sets, and are shown to be more effective than other approaches in coping with intensity inhomogeneities. We have applied the Courant Friedrichs Levy (CFL) condition as stability criterion for our algorithm.

Keywords: Image segmentation, Level-sets, Local fitting binary, Thoracic aorta.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331395

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1075

References:


[1] J.A.Sethian. Level Set Methods and Fast Marching methods. Cambridge University Press, 1999.
[2] T.F. Chan and L. A. Vese. Active Contours without Edges. IEEE Trans Image Proc, 10(2):266-277, 2001.
[3] A. Tsai, A Yezzi, and A.S. Willsky. Curve evolution implementation of Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. Image. Proc. 10:1169- 1186, 2001.
[4] S.J. Osher and J.A. Sethian. Fronts propagating with curvature dependent speed. J. Comput. Pysc, vol.79, pp. 12-49, 1988.
[5] M. Kass, A. Witkin, D. Terzopoulos, Snakes: active contour models, International Journal of Computer Vision 1 (1988) 321-331.
[6] C. Li, C. Kao, J. Gore, Z. Ding, Minimization of region-scalable fitting energy for image segmentation, IEEE Transactions on Image Processing 17 (2008) 1940-1949.
[7] V. Caselles, R. Kimmel, G. Sapiro, Geodesic active contours, International Journal of Computer Vision 22 (1) (1997) 61-79.
[8] G.P. Zhu, Sh.Q. Zhang, Q.SH. Zeng, Ch.H. Wang, Boundary-based image segmentation using binary level set method, SPIE OE Letters 46(5) (2007).
[9] C.M. Li, C.Y. Xu, C.F. Gui, M.D. Fox, Level set evolution without reinitialization: a new variational formulation, in: IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005, pp. 430-436.
[10] J.Lie, M.Lysaker, X.C.Tai, A binary level set model and some application to Mumford-Shah image segmentation, IEEE Transaction on Image Processing 15(2006)1171-1181.