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Level set based on new Signed Pressure Force Function for Echocardiographic image segmentation

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Abstract (2. Language): 
In the present paper a novel region based active contour method is developed by formulating a new signed pressure force (SPF) function. The method has been applied to the echocardiographic images for getting the desired boundary. The method is useful for finding the automatic boundary detection of other images (Microbiological, MRI, CT, Natural and welding joint etc.) as well. Level set method in combination with original SPF has not been able to give satisfactory results during the segmentation of echocardiographic images. There are lots of noises present in the echocardiographic images those create difficulties in the segmentation process. The proposed method resolves all these difficulties in such a manner that the output image is having the proper boundary detection without any disturbances and noises. The very important advantage of this method is that it gives a very fast response in terms of time taken by CPU and the number of iterations. Fast response is very important in the clinical area especially in diagnosis purpose. The presented model is an advancement of Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method. Proposed model is more robust against images with weak edge and intensity inhomogeneity when compared with the performance of earlier methods.
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References: 

[1] M. Kass, A. Witkin, D. Terzopoulos, “Snakes: active contour models,” International Journal of Computer Vision, vol. 1, pp.
321–331, 1988.
[2] V. Caselles, R. Kimmel, G. Sapiro, “Geodesic active contours,” in: Proceeding of IEEE International Conference on
Computer Vision’95, Boston MA, pp 694–699, 1995.
[3] S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, “Conformal curvature flows: From phase transitions to active
vision,” Arch. Ration. Mech. Anal., vol 134, no. 3, pp. 275-301, 1996.
[4] V. Caselles, R. Kimmel, G. Sapiro, “Geodesic active contours,” International Journal of Computer Vision, vol. 22, no.1, pp.
61–79, 1997.
[5] Chan T, Vese L, “Active contours without edges,” IEEE Transaction on Image Processing, vol. 10, no. 2, pp. 266–277,
2001.
[6] G. P. Zhu, S. Q. Zhang, Q. S. Zeng, C. H. Wang, “Boundary-based image segmentation using binary level set method,”
Optical Engineering vol. 46:050501, 2007.
Kalpana Saini, M.L. Dewal, and Manojkumar Rohit
ISSN : 2028-9324 Vol. 3 No. 2, June 2013 569
[7] G. Zhu, S. Zhang, Q. Zeng, and C. Wang C, “Boundary-based image segmentation using binary level set method,” SPIE
Letters, vol.46, no. 5, 2007.
[8] R. Ronfard, “Region-based strategies for active contour models”, Int. 1.Comp. Vis., vol.13, no. 2, pp. 229-251, 1994.
[9] C. Samson, L. Blanc-Feraud, G. Aubert and J. Zerubia, “A variational model for image classification and restoration,” IEEE
Trans. Patt. Anal. Mach. Intell., vol. 22, no. 5, pp. 460- 472, 2000.
[10] C. M. Li, C. Y. Xu, C. F. Gui, M. D. Fox, “Level set evolution without re-initialization: a new variational formulation,” in:
IEEE Conference on Computer Vision and Pattern Recognition, San Diego, pp. 430–436, 2005.
[11] N.Paragios, R. Deriche, “Geodesic active contours and level sets for detection and tracking of moving objects,” IEEE
Transaction on Pattern Analysis and Machine Intelligence, pp. 221–15, 2000.
[12] C. Xu, J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Transaction on Image Processing, vol. 7, pp. 359-369,
1998.
[13] J. Lie, M. Lysaker, X. C. Tai, “A binary level set model and some application to Munford–Shah image segmentation,” IEEE
Transaction on Image Processing, vol. 15:1171-1181.
[14] Mumford D, Shah J, “Optimal approximation by piecewise smooth function and associated variational problems,”
Communication on Pure and Applied Mathematics 42, pp. 577-685, 1989.
[15] L. Chen, Y. Zhou, Y. G. Wang and Yang, “GACV: geodesic-aided C-V method,” Pattern Recognition, vol. 39, pp. 1391-
1395, 2006.
[16] L. Pi L, J. Fan and C.M. Shen, “Color image segmentation for objects of interest with modified geodesic active contour
method”, Math. Image and Vis, vol. 27, pp. 51-57, 2007.
[17] S. Y. Yu, Y. Zhang, Y. Wang, and J. Yang, “Color-texture image segmentation by combing region and photometric
invariant edge information,” in: MCAM 2007, 4577, pp. 286-294, 2007.
[18] Z. Ying, L. Guangyao, S. Xiehua, and X. Xinmin, “Geometric active contours without re-initialization for image
segmentation,” Pattern Recogn., vol.42, no. 9, pp. 1970-1976, 2009.
[19] K. Zhang, L. Zhang, H. Song, W. Zhou, “Active contours with selective local or global segmentation: A new formulation
and level set method,” Image and Vision Computing vol. 28, pp. 668-676, 2010.

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