Active contour driven by scalable local regional information on expandable kernel
Abstract
An active contour that uses the pixel’s intensity on a set of expandable kernels along the propagating contour for image segmentation is presented in this paper. The objective is this study is to employ the scalable kernels to attract the contour to meet the desired boundary. The key characteristics of this scheme is that the kernels gradually expand to find an object’s boundary. So this scheme could penetrate to the concave boundary more effective and efficient than some other schemes. If a Gaussian kernel is applied, it could trace the object with a blurred or smooth boundary. Moreover, the directional selectivity feature enables in capturing two edge’s types with just one initial position. Its performance showed more desirable segmentation outcomes compared to the other existing active contours using regional information when segmenting the noisy image and the non-uniform (or heterogeneous) textures. Meanwhile, the level set implementation enables topological flexibility to our active contour scheme.
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References
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[27] S. Phumeechanya, C. Pluempitiwiriyawej, and S. Thongvigitmanee, Active contour using local regional information on extendable search lines (LRES) for image segmentation, IEICE Trans. Inf. Syst., vol. E93-D, no. 6, pp. 1625-1635, Jun. 2010.
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[32] A. Faisal and C. Pluempitiwiriyawej, Active contour using local region-scalable force with expandable kernel, Proc. 2012 IEEE Int. Conf. Information Science and Technology (ICIST), pp. 18-24, Wuhan, China, Mar. 23-25, 2012.
[33] H. Park, T. Schoepflin, and Y. Kim, Active contour model with gradient direction information: directional snake, IEEE Trans. Image Process., vol. 11, no. 2, pp. 252-256, Feb. 2001.
[34] J. Tang, S. Millington, S. T. Acton, J. Crandall, and S. Hurwitz, Surface extraction and thickness measurement of the articular cartilage from MR images using directional gradient vector flow snakes, IEEE Trans. Biomed. Eng., vol, 53, no. 5, pp. 896-907, May. 2006.
[35] J. Cheng and S. W. Foo, Dynamic directional gradient vector flow for snakes, IEEE Trans. Image Process., vol. 15, no. 6, pp. 1563-1571, Jun. 2006.
[36] S. Phumeechanya, C. Pluempitiwiriyawej, and S. Thongtivigitmanee, Edge’s type selectable active contour using local regional information on extendable search line, Proc. of 2010 IEEE Int. Conf. Image Processing (ICIP), pp. 653-656, Hong Kong, Sep. 26-29, 2010.
[37] A. Faisal and C. Pluempitiwiriyawej, Directional local region-scalable active contour with expandable kernel, Proc. of 2012 Int. Conf. Electrical Engineering/Electronics, Computer, Telecommunication, and Information Technology (ECTI-CON), Hua Hin, Thailand, May 16-18, 2012.
[38] K. Zhang and L. Zhang and K. M. Lam and D. Zhang, A Level Set Approach to Image Segmentation With Intensity Inhomogeneity, IEEE Trans. Cybern., vol. 46, no. 2, pp. 546-557, Feb 2016.
[39] S. Mukherjee and S. T. Acton, Region Based Segmentation in Presence of Intensity Inhomogeneity Using Legendre Polynomials, IEEE Signal Process. Lett., vol. 22, no. 3, pp. 298-302, Mar 2015.
[40] C. Li, R. Huang, Z. Ding, C. Gatenby, D. N. Metaxas and J. C. Gore, A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI, IEEE Trans. Image Process., vol. 20, no. 7, pp. 2007-2016, Jul 2011.
[41] Xiao-Feng Wang, Hai Min, Le Zou and Yi-Gang Zhang, A novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement, Pattern Recogn., vol. 48, no. 1, pp. 189-204, Jan 2015.
[42] K. Zhang AND L. Zhang AND H. Song AND D. Zhang, Reinitialization-free level set evolution via reaction diffusion, IEEE Trans. Image Process., vol. 22, no. 1, pp. 258-271, Jan 2013.
[43] C. Li AND C. Xu AND C. Gui AND M. D. Fox, Distance regularized level set evolution and its application to image segmentation, IEEE Trans. Image Process., vol. 19, no. 12, pp. 3243-3254, Dec 2010.
[44] A. Faisal, S. C. Ng, and K. W. Lai, Multiple active contours using scalable local regional information on expandable kernel, Proc. of 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), Miri, Sarawak, Malaysia, 8 - 10 December 2014.
[45] A. Faisal, S. C. Ng, and K. W. Lai, Multiple LREK active contours for knee meniscus ultrasound image segmentation, IEEE Trans. Med. Imag., vol. 34, no. 10, pp. 2162-2171, Oct 2015.
[2] C. Zimmer and J.-C. Olivo-Marin, Coupled parametric active contours, IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 11, pp. 1838-1842, Nov. 2005.
[3] V. Caselles, R. Kimmel, and G. Sapiro, Geodesic active contours, Int. J. Comput. Vis., vol. 22, no. 1, pp. 61-79, 1997.
[4] C. Xu and J. L. Prince, Snakes, shapes, and gradient vector flow, IEEE Trans. Image Process., vol. 7, no. 3, pp. 359-369, Mar. 1998.
[5] B. Li and S. T. Acton, Active contour external force using vector field convolution for image segmentation, IEEE Trans. Image Process., vol. 16, no. 8, pp. 2096-2106, Aug. 2007.
[6] T. Wang, I. Cheng, and A. Basu, Fluid vector flow and applications in brain tumor segmentation, IEEE Trans. Biomed. Eng., vol. 56, no. 3, pp. 781-789, Mar. 2009.
[7] S. Osher and J. Sethian, Front propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, J. Comput. Phys., vol. 79. no. 1, pp. 12-49, Nov. 1988.
[8] H.-K. Zhao, T. F. Chan, B. Merriman, and S. Osher. A variational level set approach to multiphase motion, Journal of Computational Physics, vol. 127, pp. 179-195, 1996.
[9] D. Mumford and J. Shah, Optimal approximation by piecewise smooth functions and associated variational problems, Commun. Pure Appl. Math., vol. 42, pp. 577-685, 1989.
[10] A. Tsai, A. Yezzi, and A. S. Willsky, Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification, IEEE Trans. Image Process., vol. 10, no. 8, pp. 1169-1186, Aug. 2001.
[11] L. A. Vese and T. F. Chan, A multiphase level set framework for image segmentation using the Mumford and Shah model, Int. J. Comput. Vis., vol. 50, no. 2, pp. 271-293, Dec. 2002.
[12] T. F. Chan and L. A. Vese, Active contours without edges, IEEE Trans. Image Process., vol. 10, no. 2, pp. 266-277, Feb. 2001.
[13] J. A. Yezzi, A. Tsai, and A. Willsky, A fully global approach to image segmentation via coupled curve evolution equations, J. Vis. Commun. Image Represent., vol. 13, no. 1, pp. 195-216, Mar. 2002.
[14] O. Michailovich, Y. Rathi, and A. Tannenbaum, Image segmentation using active contours driven by the Bhattacharyya gradient flow, IEEE Trans. Image Process., vol. 16, no. 11, pp. 2787-2801, Nov. 2007.
[15] A. Bhattacharyya, On a measure of divergence between two statistical populations defined by their probability distributions, Bull. Calcutta Math. Soc., vol. 35, pp. 99-109, 1943.
[16] C. Li, C. Kao, J. C. Gore, and Z. Ding, Minimization of region-scalable fitting energy for image segmentation, IEEE Trans. Image Process., vol. 17, no. 10, pp. 1940-1949, Oct. 2008.
[17] K. W. Sum and P. Y. S. Cheung. Vessel extraction under non-uniform illumination: A level set approach, IEEE Trans. Biomed. Eng., vol. 55, no. 1, pp. 358-360, Jan. 2008.
[18] M. Jung, G. Peyr and L. D. Cohen, Nonlocal active contour, SIAM J. Imag. Sci., vol. 5, no. 3, pp. 1022-1054, 2012.
[19] T. Brox and D. Cremers, On local region models and a statistical interpretation of the piecewise smooth Mumford-Shah functional, Int. J. Comput. Vis., vol. 84, pp. 184-193, 2008.
[20] J. Piovano, M. Rousson, and T. Papadopoulo, Efficient segmentation of piecewise smooth images, in F. Sgallari, A. Murli, and N. Paragios (Eds.) Scale space and variational methods in computer vision (SSVM), LNCS: Vol. 4485, pp. 709-720. Springer, Heidelberg, 2007.
[21] C. Li, R. Huang, Z. Ding, C. Gatenby, D. N. Metaxas, and J. C. Gore, A level set method for image segmentation in the presence of intensity inhomogeneities with application to mri, IEEE Trans. Image Process., vol. 20, pp. 2007-2016, Jul. 2011.
[22] H. Zhang, X. Ye, and Y. Chen, An efficient algorithm for multiphase image segmentation with intensity bias correction, IEEE Trans. Image Process. vol. 22, no. 10, pp. 3842-3851, May 2013.
[23] J. Piovano and T. Papadopoulo, Local statistics based region segmentation with automatic scale selection, in the Eur. Conf. Computer Vision (ECCV), vol. 5303, pp. 486-499, Marseille, 2008.
[24] S. Lankton and A. Tannenbaum, Localizing region-based active contours, IEEE Trans. Image Process., vol. 17, no. 11, pp. 2029-2039, Nov. 2008.
[25] C. Darolti, A. Mertins, C. Bodensteiner, and U. G. Hofmann, Local region descriptors for active contours evolution, IEEE Trans. Image Process., vol. 17, No. 12, pp. 2275-2288, Dec. 2008.
[26] P. Karaolani, G. D. Sullivan, and K. D. Baker, Active contours using finite element to control local scale, Proceeding of British Machine Vision Conference (BMVC), pp. 481-487, 1992.
[27] S. Phumeechanya, C. Pluempitiwiriyawej, and S. Thongvigitmanee, Active contour using local regional information on extendable search lines (LRES) for image segmentation, IEICE Trans. Inf. Syst., vol. E93-D, no. 6, pp. 1625-1635, Jun. 2010.
[28] T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, Active shape models - their training and application, Comput. Vis. Image Underst., vol. 56, no. 3, pp. 35-59, Jan. 1995.
[29] J. Mille, Narrow band region-based active contours and surfaces for 2D and 3D segmentation, Comput. Vis. Image Underst., vol. 113, no. 9, pp. 946-965, Sep. 2009.
[30] H. Li and A. Yezzi, Local or global minima: flexible dual-front active contours, IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 1, pp. 1-13, Jan. 2007.
[31] R. Ronfard, Region-based strategies for active contour models, Int. J. Comput. Vis., vol. 3, no. 2, pp. 229-251, 1994.
[32] A. Faisal and C. Pluempitiwiriyawej, Active contour using local region-scalable force with expandable kernel, Proc. 2012 IEEE Int. Conf. Information Science and Technology (ICIST), pp. 18-24, Wuhan, China, Mar. 23-25, 2012.
[33] H. Park, T. Schoepflin, and Y. Kim, Active contour model with gradient direction information: directional snake, IEEE Trans. Image Process., vol. 11, no. 2, pp. 252-256, Feb. 2001.
[34] J. Tang, S. Millington, S. T. Acton, J. Crandall, and S. Hurwitz, Surface extraction and thickness measurement of the articular cartilage from MR images using directional gradient vector flow snakes, IEEE Trans. Biomed. Eng., vol, 53, no. 5, pp. 896-907, May. 2006.
[35] J. Cheng and S. W. Foo, Dynamic directional gradient vector flow for snakes, IEEE Trans. Image Process., vol. 15, no. 6, pp. 1563-1571, Jun. 2006.
[36] S. Phumeechanya, C. Pluempitiwiriyawej, and S. Thongtivigitmanee, Edge’s type selectable active contour using local regional information on extendable search line, Proc. of 2010 IEEE Int. Conf. Image Processing (ICIP), pp. 653-656, Hong Kong, Sep. 26-29, 2010.
[37] A. Faisal and C. Pluempitiwiriyawej, Directional local region-scalable active contour with expandable kernel, Proc. of 2012 Int. Conf. Electrical Engineering/Electronics, Computer, Telecommunication, and Information Technology (ECTI-CON), Hua Hin, Thailand, May 16-18, 2012.
[38] K. Zhang and L. Zhang and K. M. Lam and D. Zhang, A Level Set Approach to Image Segmentation With Intensity Inhomogeneity, IEEE Trans. Cybern., vol. 46, no. 2, pp. 546-557, Feb 2016.
[39] S. Mukherjee and S. T. Acton, Region Based Segmentation in Presence of Intensity Inhomogeneity Using Legendre Polynomials, IEEE Signal Process. Lett., vol. 22, no. 3, pp. 298-302, Mar 2015.
[40] C. Li, R. Huang, Z. Ding, C. Gatenby, D. N. Metaxas and J. C. Gore, A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI, IEEE Trans. Image Process., vol. 20, no. 7, pp. 2007-2016, Jul 2011.
[41] Xiao-Feng Wang, Hai Min, Le Zou and Yi-Gang Zhang, A novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement, Pattern Recogn., vol. 48, no. 1, pp. 189-204, Jan 2015.
[42] K. Zhang AND L. Zhang AND H. Song AND D. Zhang, Reinitialization-free level set evolution via reaction diffusion, IEEE Trans. Image Process., vol. 22, no. 1, pp. 258-271, Jan 2013.
[43] C. Li AND C. Xu AND C. Gui AND M. D. Fox, Distance regularized level set evolution and its application to image segmentation, IEEE Trans. Image Process., vol. 19, no. 12, pp. 3243-3254, Dec 2010.
[44] A. Faisal, S. C. Ng, and K. W. Lai, Multiple active contours using scalable local regional information on expandable kernel, Proc. of 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), Miri, Sarawak, Malaysia, 8 - 10 December 2014.
[45] A. Faisal, S. C. Ng, and K. W. Lai, Multiple LREK active contours for knee meniscus ultrasound image segmentation, IEEE Trans. Med. Imag., vol. 34, no. 10, pp. 2162-2171, Oct 2015.
Published
2020-06-15
How to Cite
FAISAL, Amir; PLUEMPITIWIRIYAWEJ, Charnchai.
Active contour driven by scalable local regional information on expandable kernel.
Journal of Science and Applicative Technology, [S.l.], v. 4, n. 1, p. 1-14, june 2020.
ISSN 2581-0545.
Available at: <https://journal.itera.ac.id/index.php/jsat/article/view/262>. Date accessed: 24 apr. 2024.
doi: https://doi.org/10.35472/jsat.v4i1.262.
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