Commenced in January 2007
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Medical Image Segmentation Using Deformable Models and Local Fitting Binary

Authors: B. Bagheri Nakhjavanlo, T. J. Ellis, P. Raoofi, J. Dehmeshki

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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1086219

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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.