Medical Image Segmentation Using Deformable Models and Local Fitting Binary
This paper presents a customized deformable model
for the segmentation of abdominal and thoracic aortic aneurysms in
CTA datasets. An important challenge in reliably detecting aortic
aneurysm is the need to overcome problems associated with intensity
inhomogeneities and image noise. 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 Gaussian
kernel function in the level set formulation, which extracts the local
intensity information, 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. The results indicate the method is more effective than other
approaches in coping with intensity inhomogeneities.
[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 Medical, Medicine and Health Sciences:64985", author = "B. Bagheri Nakhjavanlo and T. J. Ellis and P. Raoofi and J. Dehmeshki", title = "Medical Image Segmentation Using Deformable Models and Local Fitting Binary", abstract = "This paper presents a customized deformable model
for the segmentation of abdominal and thoracic aortic aneurysms in
CTA datasets. An important challenge in reliably detecting aortic
aneurysm is the need to overcome problems associated with intensity
inhomogeneities and image noise. 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 Gaussian
kernel function in the level set formulation, which extracts the local
intensity information, 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. The results indicate the method is more effective than other
approaches in coping with intensity inhomogeneities.", keywords = "Abdominal and thoracic aortic aneurysms, intensityinhomogeneity, level sets, local fitting binary.", volume = "5", number = "4", pages = "186-4", }