Medical Image Segmentation Using Deformable Model and Local Fitting Binary: Thoracic Aorta
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:53136", author = "B. Bagheri Nakhjavanlo and T. S. Ellis and P.Raoofi and Sh.ziari", title = "Medical Image Segmentation Using Deformable Model and Local Fitting Binary: Thoracic Aorta", 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.", volume = "5", number = "1", pages = "23-3", }