Centerline Extraction for Image Segmentation Using Gradient and Direction Vector Flow Active Contours

Shuqun Zhang, Jianyang Zhou

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DOI: 10.4236/jsip.2013.44052                

Abstract

In this paper, we propose a fast centerline extraction method to be used for gradient and direction vector flow of active contours. The gradient and direction vector flow is a recently reported active contour model capable of significantly improving the image segmentation performance especially for complex object shape, by seamlessly integrating gradient vector flow and prior directional information. Since the prior directional information is provided by manual line drawing, it can be inconvenient for inexperienced users who might have difficulty in finding the best place to draw the directional lines to achieve the best segmentation performance. This paper describes a method to overcome this problem by automatically extracting centerlines to guide the users for providing the right directional information. Experimental results on synthetic and real images demonstrate the feasibility of the proposed method.

Keywords

Image Segmentation; Active Contours; Gradient Vector Flow; Direction Vector Flow

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	M. Kass, A. Witkin and D. Terzopoulos, “Snakes: Active Contour Models,” International Journal of Computer Vision, Vol. 1, No. 4, 1988, pp. 321-331. http://dx.doi.org/10.1007/BF00133570
[2]	L. D. Cohen and I. Cohen, “Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 11, 1993, pp. 1131-1147. http://dx.doi.org/10.1109/34.244675
[3]	H. K. Park and M. J. Chung, “External Force of Snake: Virtual Electric Field,” Electronics Letters, Vol. 38, No, 24, 2002, pp. 1500-1502. http://dx.doi.org/10.1049/el:20021037
[4]	X. Xie and M. Mirmehdi, “Magnetostatic Field for the Active Contour Model: A Study in Convergence,” Proceedings of the 17th British Machine Vision Conference, Edinburgh, 4-7 September 2006, pp. 127-136.
[5]	K. W. Sum and Paul Y. S. Cheung, “Boundary Vector Field for Parametric Active Contours,” Pattern Recognition, Vol. 40, No. 6, 2007, pp. 1635-1645. http://dx.doi.org/10.1016/j.patcog.2006.11.006
[6]	B. Li and S. T. Acton, “Active Contour External Force Using Vector Field Convolution for Image Segmentation,” IEEE Transactions on Image Processing, Vol. 16, No. 8, 2007, pp. 2096-2106. http://dx.doi.org/10.1109/TIP.2007.899601
[7]	C. Xu and J. L. Prince, “Generalized Gradient Vector Flow External Forces for Active Contours,” Signal Processing, Vol. 71, No. 2, 1998, pp. 131-139. http://dx.doi.org/10.1016/S0165-1684(98)00140-6
[8]	C. Xu and J. L. Prince, “Snakes, Shapes, and Gradient Vector Flow,” IEEE Transactions on Image Processing, Vol. 7, No. 3, 1998, pp. 359-369. http://dx.doi.org/10.1109/83.661186
[9]	X. Xie and M. Mirmehdi, “RAGS: Region-Aided Geometric Snake,” IEEE Transactions on Image Processing, Vol. 13, No. 5, 2004, pp. 640-652. http://dx.doi.org/10.1109/TIP.2004.826124
[10]	N. Ray and S. T. Acton, “Motion Gradient Vector Flow: An External Force for Tracking Rolling Leukocytes with Shape and Size Constrained Active Contours,” IEEE Transactions on Medical Imaging, Vol. 23, No. 12, 2004, pp. 1466-1478. http://dx.doi.org/10.1109/TMI.2004.835603
[11]	H. Park, T. Schoepflin and Y. Kim, “Active Contour Model with Gradient Directional Information: Directional Snake,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11, No. 2, 2001, pp. 252-256. http://dx.doi.org/10.1109/76.905991
[12]	G. Zhu, S. Zhang, Q. Zeng and C. Wang, “Gradient Vector Flow Active Contours with Prior Directional Information,” Pattern Recognition Letters, Vol. 31, No. 9, 2010, pp. 845-856. http://dx.doi.org/10.1016/j.patrec.2010.01.011
[13]	G. Zhu, S. Zhang, X. Chen and C. Wang, “Novel Gradient Vector Flow-Based Balloon Force for Active Contours,” Journal of Electronic Imaging, Vol. 18, No. 2, 2009, pp. 023007-1-023007-8. http://dx.doi.org/10.1117/1.3132005
[14]	M. S. Hassouna and A. A. Farag, “Variational Curve Skeletons Using Gradient Vector Flow,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 12, 2009, pp. 2257-2274. http://dx.doi.org/10.1109/TPAMI.2008.271