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Applications of deformable models for in-dopth analysis and feature extraction from medical images—A review

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Abstract

Medical imaging plays important role for the practice of medicine globally and is rapidly increasing and getting sophisticated day by day. But accurate, fully automatic medical image analysis continues to be an elusive ideal for quantitative exploitation for diagnosis and therapy. The spatial relations between these anatomical structures as well as the biological shape variations are observed over a representative population of individuals. Deformable models (DMs) are being used in digital image analysis to maintain essential characteristics of image shape and intensity while accommodating fluctuations. Among model-based techniques, DMs offer a unique and powerful approach to image analysis that combines geometry, physics, and approximation theory. DMs are highly insightful interactive methods that allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task whenever necessary. The paper reviews the application of deformable models as a capable and robustly applied digital medical image analysis technique of the human body.

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References

  1. Y. McInerneya, G. Hamarneh, M. Shentone, and D. Terzopoulos, “Deformable organisms for automatic medical image analysis,” Med. Image Anal. 6(3), 251–266 (2002).

    Google Scholar 

  2. R. Gonzalez and R. Woods, Digital Image Processing (Prentice Hall, 2002).

    Google Scholar 

  3. Hong Zhang, Lin Yang, D. J. Foran, J. L. Nosher, and P. J. Yim, “3D Segmentation of the liver using free-form deformation based on boosting and deformation gradients,” in Proc. IEEE Int. Symp. on Bio-Medical Imaging: from Nano to Macro (ISBI’09) (Boston, 2009), 494–497, DOI:10.1109/ISBI.2009.5193092.

    Google Scholar 

  4. B. Jahne, Digital Image Processing (Springer-Verlag, 2005), Vol. 16, pp. 449–462.

    Google Scholar 

  5. D. Terzopoulos, On Matching Deformable Models to Images: Direct and Iterative Solutions, Topical Meeting on Machine Vision, Technical Digest Series (Optical Soc. Amer., Washington, 1987), Vol. 12, pp. 160–167.

    Google Scholar 

  6. S. R. Gunn and M. S. Nixon, “A robust snake implementation; a dual active contour,” IEEE Trans. Pattern Anal. Mach. Intellig. 19(1), 63–68 (1997), DOI:10.1109/34.566812.

    Google Scholar 

  7. T. Mcinerney and D. Terzopoulos, “Deformable models in medical image analysis-a survey,” Med. Image Anal. 1(2), 91–108 (1996), DOI:10.1016/s1361-8415(96)80007-7.

    Google Scholar 

  8. M. Kass, A. Witkin, and D. Terzopoulos, “Snakesactive contour models,” Int. J. Comput. Vision 1, 321–331 (1987), DOI:10.1007/BF00133570.

    Google Scholar 

  9. J. V. Miller, D. E. Breen, W. E. Lorensen, R. M. O’Bara, and M. J. Wozny, “Geometrically deformed models: a method for extracting closed geometric models from volume data,” SIGGRAPH Comput. Graph. 25(4), 217–226 (July 1991), DOI:10.1145/127719.122742.

    Google Scholar 

  10. A. Ghanei, H. Soltanian-Zadeh, and J. P. Windham, “A deformable model for hippocampus segmentation: improvements and extension to 3D,” in Proc. IEEE Symp. on Nuclear Science (Anaheim, CA, 1996), Vol. 3, pp. 1797–1801, DOI:10.1109/NSS-MIC.1996.587978.

    Google Scholar 

  11. D. Rueckert, P. Burger, S. M. Forbat, R. D. Mohiaddin, and G. Z. Yang, “Automatic tracking of the aorta in cardio vascular MR images using deformable models,” IEEE Trans. Med. Imag. 16(5), 581–590 (1997), DOI:10.1109/42.640747.

    Google Scholar 

  12. Chenyang Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Trans. Image Processing 7(3), 359–369 (1998), DOI:10.1109/83.661186.

    MATH  MathSciNet  Google Scholar 

  13. Chenyang Xu, D. L. Pharm, and J. L. Prince, Handbook of Medical Imaging, Vol. 2: Medical Image Processing and Analysis, Ed. by J. M. Fitzpatrick and M. Sonka (SPIE Press, 2000), Vol. 2, 129–174.

  14. Xiao Han, Chenyang Xu, and J. L. Prince, “A 2D moving grid geometric deformable model,” in Proc. IEEE Conf. in Computer Vision and Pattern Recognition (CVPR) (Madison, WI, 2003), Vol. 1, pp. I-153–I-160.

    Google Scholar 

  15. Xiao Han, Chenyang Xu, and J. L. Prince, “A topology preserving level set method for geometric deformable models,” IEEE Trans. Pattern Anal. Mach. Intellig. 25(6), 755–767 (2003), DOI:10.1109/TPAMI.2003.1201824.

    Google Scholar 

  16. Myungeun Lee, Soonyoung Park, Wanhyun Cho, Soohyung Kim, and Changbu Jeong, “Segmentation of medical images using a geometric deformable model and its visualization,” Canad. J. Electr. Comput. Eng. 33(1), 15–19 (2008), DOI:10.1109/CJECE.2008.4621790.

    Google Scholar 

  17. J. M. Gauch, H. H. Pien, and J. Shah, “Hybrid deformable models for three dimensional biomedical image segmentation,” in Proc. IEEE Nuclear Sci. Symp. and Medical Imaging Conf. (Norfolk, VA, 1994), Vol. 4, pp. 1935–1939, DOI:10.1109/NSS-MIC.1994.474688.

    Google Scholar 

  18. L. Sarry and J.-Y. Boire, “Three-dimensional tracking of coronary arteries from biplane angiographic sequences using parametrically deformable models,” IEEE Trans. Med. Imaging 20(12), 1341–1351 (2001), DOI:10.1109/42.974929.

    Google Scholar 

  19. V. Caselles, F. Catte, T. Coll, and F. Dibos, “A geometric model for active contours,” Numerische Mathematick 66, 1–31 (1993), DOI:10.1007/BF01385685.

    MATH  MathSciNet  Google Scholar 

  20. R. Malladi, J. A. Sethian, and B. C. Vemuri, “Shape modeling with front propagation: a level set approach,” IEEE Trans. Pattern Anal. Mach. Intellig. 17(2), 158–175 (1995), DOI:10.1109/34.368173.

    Google Scholar 

  21. P. Moore and D. Molloy, “A survey of computer-based deformable models,” in Proc. Int. Machine Vision Image Processing (IMVIP) Conf. (Maynooth, 2007), pp. 55–64, DOI:10.1109/IMVIP.2007.31.

    Google Scholar 

  22. A. Bosnjak, V. Torrealba, M. Acuna, M. Bosnjak, B. Solaiman, G. Montilla, and C. Roux, “Segmentation and VRML visualization of left ventricle in echocardiographic images using 3D deformable models and superquadrics,” in Proc. 22nd Annu. EMBS Int. Conf. (Chicago, July 23–28, 2000), Vol. 3, pp. 1724–1727, DOI:10.1109/IEMBS.2000.900414.

    Google Scholar 

  23. V. Caselles, “Geometric models for active contours,” in Proc. Int. Conf. on Image Processing (Washington, 1995), Vol. 3, pp. 9–12, DOI:10.1109/ICIP.1995.537567.

    Google Scholar 

  24. V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” in Proc. 5th Int. Conf. on Computer Vision (Boston, 1995), pp. 694–699, DOI:10.1109/ICCV.1995.466871.

    Google Scholar 

  25. R. Malladi and J. A. Sethian, “Level set and fast marching methods in image processing and computer vision,” in Proc. Int. Conf. on Image Processing (ICIP) (Lausanne, 1996), Vol. 1, pp. 489–492, DOI:10.1109/ICIP.

    Google Scholar 

  26. J. A. Sethian, Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Material Science (Cambridge Univ. Press, 1999).

    MATH  Google Scholar 

  27. Wang Xin and Tu Yunxia, “A faster b spline snake,” in Proc. IEEE Int. Conf. on Robotics and Biomimetics (Guilin, 2009), pp. 2314–2319, DOI:10.1109/ROBIO.2009.5420738.

    Google Scholar 

  28. A. Gupta, L. Von Kurowski, A. Singh, D. Geiger, C.-C. Liang, M.-Y. Chiu, L. P. Adler, M. Haacke, and D. L. Wilson, “Cardiac MR image segmentation using deformable models,” in Proc. Computers in Cardiology (Los Alamitos, 1993), pp. 747–750, DOI:10.1109/CIC.1993.378377.

    Google Scholar 

  29. Jaesang Park and J. M. Keller, “Snakes on the watershed,” IEEE Trans. Pattern Anal. Mach. Intellig. 23(10), 1201–1205 (2001), DOI:10.1109/34.954609.

    Google Scholar 

  30. A. A. Amini, T. E. Weymouth, and R. C. Jain, “Using dynamic programming for solving variational problems in vision,” IEEE Trans. Pattern Anal. Mach. Intellig. 12(9), 855–867 (1990), DOI:10.1109/34.57681.

    Google Scholar 

  31. A. K. Klein, F. Lee, and A. A. Amini, “Quantitative coronary angiography with deformable spline models,” IEEE Trans. Med. Imag. 16(5), 468–482 (1997), DOI:10.1109/42.640737.

    Google Scholar 

  32. E. L. Valverde, N. Guil, J. Munoz, Q. Li, M. Aoyama, and K. Doi, “A deformable model for image segmentation in noisy medical images,” in Proc. Int. Conf. on Image Processing (Thessaloniki, 2001), Vol. 3, pp. 82–85, DOI:10.1109/ICIP.2001.958056.

    Google Scholar 

  33. T. Mcinerney and D. Terzopoulos, “Topologically adaptable snakes,” in Proc. 5th Int. Conf. on Computer Vision (Boston, June 1995), pp. 840–845, DOI:10.1109/ICCV.1995.466850.

    Google Scholar 

  34. S. H. Rezatofighi, R. A. Zoroofi, R. Sharifian, and H. Soltanian-Zadeh, “Segmentation of nucleus and cytoplasm of white blood cells using gram-schmidt orthogonalization and deformable models,” in Proc. 9th Int. Conf. on Signal Processing (ICSP) (Beijing, 2008), pp. 801–805, DOI:10.1109/ICOSP.2008.4697250.

    Google Scholar 

  35. Sun Zheng, “An intensive restraint topology adaptive snake model and its application in tracking dynamic image sequence,” Inf. Sci. 18(16), 2940–2959 (2010), DOI:10.1016/j.ins.2010.04.030.

    Google Scholar 

  36. J.-O. Lachaud and B. Taton, “Deformable model with adaptive mesh and automated topology changes,” in Proc. 4th Int. Conf. for 3D Imaging and Modeling (3DIM’2003) (Banff, 2003), pp. 12–19, DOI:10.1109/IM.2003.1240227.

    Google Scholar 

  37. Song Chun Zhu and A. Yuille, “Region competition: unifying snakes, region growing and bayes/MDL for image segmentation,” IEEE Trans. Pattern Anal. Mach. Intellig. 18(9), 884–900 (1996), DOI:10.1109/34.537343.

    Google Scholar 

  38. J. Montagnat, H. Delingette, N. Scapel, and N. Ayache, “Surface simplex meshes for 3D medical image segmentation,” in Proc. IEEE Int. Conf. on Robotics and Automation (San Francisco, April 2000), Vol. 1, pp. 864–870, DOI:10.1109/ROBOT.2000.844158.

    Google Scholar 

  39. M. M. Nillesen, R. G. P. Lopata, I. H. Gerrits, H. J. Huisman, J. M. Thijssen, L. Kapusta, and C. L. DeKorte, “Segmentation of 3D cardiac ultra-sound images using correlation of radio frequency data,” in Proc. IEEE Int. Symp. on Biomedical Imaging: from Nano to Macro (ISBI) (Boston, 2009), pp. 522–525, DOI:10.1109/ISBI.2009.5193099.

    Google Scholar 

  40. B. Tsagaan, A. Shimizu, H. Kobatake, K. Miyakawa, and Y. Hanzawa, “Segmentation of Kidney by using a deformable model,” in Proc. Int. Conf. on Image Processing (Thessaloniki, 2001), Vol. 3, pp. 1059–1062, DOI:10.1109/ICIP.2001.958309.

    Google Scholar 

  41. Jiantao Huang and A. A. Amini, “Anatomical object volumes from deformable B-spline surface models,” in Proc. IEEE Int. Conf. on Image Processing (ICIP’98) (Chicago, Oct. 1998), Vol. 1, pp. 732–736, DOI:10.1109/ICIP.1998.723600.

    Google Scholar 

  42. T. Mcinerney and D. Terzopoulos, “Topology adaptive deformable surfaces for medical image volume segmentation,” IEEE Trans. Med. Imag. 18(10), 840–850 (1999), DOI:10.1109/42.811261.

    Google Scholar 

  43. A. F. Frangi, W. J. Niessen, R. M. Hoogeveen, T. Van Walsum, and M. A. Viergever, “Model-based quantitation of 3D magnetic resonance angiographic images,” IEEE Trans. Med. Imag. 18(10), 946–956 (1999), DOI:10.1109/42.811279.

    Google Scholar 

  44. Dinggang Shen and C. Davatzikos, “An adaptive-focus deformable model using statistical and geometric information,” IEEE Trans. Pattern Anal. Mach. Intellig. 22(8), 906–913 (2000), DOI:10.1109/34.868689.

    Google Scholar 

  45. J. P. Ivins, J. Porrill, and J. P. Frisby, “Deformable model of the human iris for measuring ocular torsion from video images,” in Proc. IEE Conf. on Vision, Image and Signal Processing (IPA 97) (June 1998), Vol. 145, No. 3, pp. 213–220.

    Google Scholar 

  46. F. Mao, J. Gill, D. Downey, and A. Fenster, “Segmentation of carotid artery in ultrasound images,” in Proc. 22nd Annu. Int. Conf. of the IEMBS (Chicago, July 2000), pp. 1734–1737, DOI:10.1109/IEMBS.2000.900417.

    Google Scholar 

  47. S. D. Fenster and J. R. Kender, “Sectored snakes: evaluating learned-energy segmentations,” IEEE Trans. Pattern Anal. Mach. Intellig. 23(9), 1028–1034 (2001), DOI:10.1109/34.955115.

    Google Scholar 

  48. T. Boettger, I. Wolf, T. Kunert, S. Mottl-Link, M. Hastenteuf, R. De Simone, and H. P. Meinzer, “Semi automatic 3D segmentation of live 3D echocardiographic images,” Comp. Cardiol. 31, 73–76 (2004), DOI:10.1109/CIC.2004.1442874.

    Google Scholar 

  49. L. Ibanez, W. Schroeder, L. Ng, and J. Cates, The ITK Software Guide (Insight Software Consortium, 2003). http://www.itk.org/ItkSoftwareGuide.pdf.

    Google Scholar 

  50. J. Tohka, A. Kivimaki, A. Reilhac, J. Mykkanen, and U. Ruotsalainen, “Assessment of Brain surface extraction from PET images using Monte Carlo simulations,” IEEE Trans. Nuclear Sci. 51(5), Part 2, 2641–2648 (2004), DOI:10.1109/TNS.2004.834825. 32

    Google Scholar 

  51. G. Alenya and C. Torras, “Camera motion estimation by tracking contour deformation: precision analysis,” Image Vision Comput. 28, 474–490 (2010), DOI:10.1016/j.imavis.2009.07.011.

    Google Scholar 

  52. M. A. F. Rodrigues, D. F. Gillies, and P. Charters, “Realistic deformable models for simulating the Tongue during laryngoscopy,” in Proc. Int. Workshop on Medical Imaging and Augmented Reality (Hong Kong, 2001), pp. 125–130, DOI:10.1109/MIAR.2001.930274.

    Google Scholar 

  53. J. Mille, R. Bone, P. Makris, and H. Cardot, “Narrow band region approach for active contours and surfaces,” in Proc. 5th Int. Symp. on Image and Signal Processing and Analysis (Istanbul, 2007), pp. 156–161, DOI:10.1109/ISPA.2007.4383682.

    Google Scholar 

  54. B. Al-Diri, A. Hunter, and D. Steel, “An active contour model for segmenting and measuring retinal Vessels,” IEEE Trans. Med. Imag. 28(9), 1488–1497 (2009), DOI:10.1109/TMI.2009.2017941.

    Google Scholar 

  55. M. R. Kaus, V. Pekar, C. Lorenz, R. Truyen, S. Lobregt, and J. Weese, “Automated 3D PDM construction from segmented images using deformable models,” IEEE Trans. Med. Imag. 22(8), 1005–1013 (2003), DOI:10.1109/TMI.2003.815864.

    Google Scholar 

  56. Zheen Zhao and Eam Khwang Teoh, “A new scheme for automated 3D PDM construction using deformable models,” Image Vision Comput. 26(2), 275–288 (2007), DOI:10.1016/j.imavis.2007.06.002.

    Google Scholar 

  57. S. Ghebreab and A. W. M. Smeulders, “Strings: variational deformable models of multivariate continuous boundary features,” IEEE Trans. Pattern Anal. Mach. Intellig. 25(11), 1399–1410 (2003), DOI:10.1109/TPAMI.2003.1240114.

    Google Scholar 

  58. Jianhua Yao, M. Miller, M. Franaszek, and R. M. Summers, “Colonic polyp segmentation in CT colonography-based on fuzzy clustering and deformable models,” IEEE Trans. Med. Imag. 23(11), 1344–1352 (2004), DOI:10.1109/TMI.2004.826941.

    Google Scholar 

  59. Jinsoo Cho and P. J. Benkeser, “Elastically deformable model-based motion-tracking of left ventricle,” in Proc. 26th Annu. Int. Conf. of the IEEE EMBS (San Francisco, 2004), pp. 1925–1928, DOI:10.1109/IEMBS.2004.1403570.

    Google Scholar 

  60. D. C. Fernndez, “Delineating fluid-filled region boundaries in optical coherence tomography images of the retina,” IEEE Trans. Med. Imag. 24(8), 929–945 (2005), DOI:10.1109/TMI.2005.848655.

    Google Scholar 

  61. Yi Fang, “Separation of white matter lesion from volumetric MR images using deformable models,” in Proc. Int. Conf. on Intelligent Sensing and Information Processing (ICISIP) (Chennai, 2005), pp. 491–496, DOI:10.1109/ICISIP.2005.1529504.

    Google Scholar 

  62. V. Zagrodsky, V. Walimbe, C. R. Castro-Pareja, Jian Xin Qin, Jong-Min Song, and R. Shekhar, “Registration-assisted segmentation of real-time 3D echocardiographic data using deformable models,” IEEE Trans. Med. Imag. 24(9), 1089–1099 (2005), DOI:10.1109/TMI.2005.852057.

    Google Scholar 

  63. Yiqiang Zhan and Dinggang Shen, “Deformable segmentation of 3D ultrasound prostate images using statistical texture matching method,” IEEE Trans. Med. Imag. 25(3), 256–272 (2006), DOI:10.1109/TMI.2005.862744.

    MathSciNet  Google Scholar 

  64. A. A. Young, D. L. Kraitchman, L. Dougherty, and L. Axel, “Tracking and finite element analysis of stripe deformation in magnetic resonance tagging,” IEEE Trans. Med. Imag. 14(3), 413–421 (1995), DOI:10.1109/42.414605.

    Google Scholar 

  65. A. A. Young, “Model tags: direct three-dimensional tracking of heart wall motion from tagged magnetic resonance images,” Med. Image Anal. 3(4), 361–372 (1999), DOI:10.1016/S1361-8415(99)80029-2.

    Google Scholar 

  66. Meihe Xu, P. M. Thompson, and A. W. Toga, “Adaptive reproducing kernel particle method for extraction of the cortical surface,” IEEE Trans. Med. Imag. 25(6), 755–767 (2006), DOI:10.1109/TMI.2006.873614.

    Google Scholar 

  67. Hieu Tat Nguyen, M. Worring, and R. Van Den Boomgaard, “Watersnakes: energy-driven watershed segmentation,” IEEE Trans. Pattern Anal. Mach. Intellig. 25(3), 330–342 (2003), DOI:10.1109/TPAMI.2003.1182096.

    Google Scholar 

  68. H. Tek, F. Akova, and A. Ayvaci, “Region competition via local watershed operators,” in Proc. IEEE Computer Soc. Conf. on Computer Vision and Pattern Recognition (CVPR) (San Diego, June 2005), Vol. 2, pp. 361–368, DOI:10.1109/CVPR.2005.300.

    Google Scholar 

  69. Jierong Cheng, Say Wei Foo, and S. M. Krishnan, “Watershed-presegmented snake for boundary detection and tracking of left ventricle in echocardiographic images,” IEEE Trans. Inf. Techn. Biomed. 10(2), 414–416 (2006), DOI:10.1109/TITB.2005.859887.

    Google Scholar 

  70. Xiaoxu Wang, Weijun He, D. Metaxas, R. Mathew, and E. White, “Cell segmentation and tracking using texture-adaptive snakes,” in Proc. 4th IEEE Int. Symp. on Biomedical Imaging from Nano to Macro (ISBI) (Washington, 2007), pp. 101–104, DOI:10.1109/ISBI.2007.356798.

    Google Scholar 

  71. E. Bresch and S. Narayanan, “Region segmentation in the frequency domain applied to upper airway realtime magnetic resonance images,” IEEE Trans. Med. Imag. 28(3), 323–338 (2009), DOI:10.1109/TMI.2008.928920.

    Google Scholar 

  72. A. A. Young, D. L. Kraitchman, and L. Axel, “Deformable models for tagged MR images: reconstruction of two and three-dimensional heart wall motion,” in Proc. IEEE Workshop on Biomedical Image Analysis (Seattle, 1994), pp. 317–323, DOI:10.1109/BIA.1994.315840.

    Google Scholar 

  73. Shaoting Zhang, Jinghao Zhou, Xiaoxu Wang, Sukmoon Chang, D. N. Metaxas, G. Pappas, F. Delis, N. D. Volkow, G.-J. Wang, P. K. Thanos, and C. Kambhamettu, “3D Segmentation of rodent brains using deformable models and variational methods,” in Proc. IEEE Comput. Soc. Conf. on Computer Vision and Pattern Recognition (Miami Beach, 2009), pp. 94–100, DOI:10.1109/CVPRW.2009.5204051.

    Google Scholar 

  74. Ting Chen, Xiaoxu Wang, Sohae Chung, D. Metaxas, and L. Axel, “Automated 3D motion tracking using gabor filter bank, robust point matching, and deformable models,” IEEE Trans. Med. Imag. 29(1), 1–11 (2010), DOI:10.1019/TMI.2009.2021041.

    Google Scholar 

  75. M. Groher, D. Zikic, and N. Navab, “Deformable 2D–3D registration of vascular structures in a one view scenario,” IEEE Trans. Med. Imag. 28(6), 847–860 (2009), DOI:10.1109/TMI.2008.2011519.

    Google Scholar 

  76. K. Siddiqi, Y. B. Lauziere, A. Tannenbaum, and W. Zucker, “Area and length minimizing flows for shape segmentation,” IEEE Trans. Image Processing 7(3), 433–443 (1998), DOI:10.1109/83.661193.

    Google Scholar 

  77. Weijia Shen, A. A. Kassim, and Wang Shih-Chang, “A fast boundary tracing scheme using image patch classification,” in Proc. Int. Conf. on Biomedical Engineering and Informatics (Sanya, Hainan, 2008), Vol. 1, pp. 787–791, DOI:10.1109/BMEI.2008.228.

    Google Scholar 

  78. S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and Y. Yezzi, “Gradient flows and geometric active contour models,” in Proc. 5th Int. Conf. on Computer Vision (Boston, 1995), 810–815, DOI:10.1109/ICCV.1995.466855.

    Google Scholar 

  79. V. Caselles, R. Kimmel, G. Sapiro, and C. Sbert, “Minimal surfaces based object segmentation,” IEEE Trans. Pattern Anal. Mach. Intellig. 19(4), 394–398 (1997).

    MathSciNet  Google Scholar 

  80. A. Tsai, A. Yezzi, Jr., W. Wells, C. Tempany, D. Tucker, A. Fan, W. E. Grimson, and A. Willsky, “A shape-based approach to the segmentation of medical imagery using level sets,” IEEE Trans. Med. Imag. 22(2), 137–154 (2003), DOI:10.1109/TMI.2002.808355.

    Google Scholar 

  81. G. H. P. Ho and Pengcheng Shi, “Domain partitioning level set surface for topology constrained multi-object segmentation,” in Proc. IEEE Int. Symp. on Biomedical Imaging: Nano to Macro, (Arlington, 2004), Vol. 2, pp. 1299–1302, DOI:10.1109/ISBI.2004.1398784.

    Google Scholar 

  82. Yan Jie, He Shijuan, and Yang Yamei, Brain contour finding by deformable model method,” in Proc. 6th Int. Conf. on Signal Processing (ICSP) (Beijing, 2002), pp. 684–686, DOI:10.1109/ICOSP.2002.1181148.

    Google Scholar 

  83. Haiyan Wang and B. K. Ghosh, “Geometric deformable model and segmentation,” in Proc. Int. Conf. on Image Processing (ICIP) (Chicago, 1998), pp. 328–332, DOI:10.1109/ICIP.1998.727209.

    Google Scholar 

  84. K. S. Shreedhara and M. A. Kumar, “A new stopping force to level set method for medical image segmentation,” in Proc. 1st Indian Annu. Conf. (INDICON) (Kharagpur, Dec. 2004), pp. 191–194, DOI:10.1109/INDICO.2004.1497736.

    Google Scholar 

  85. C. Gout and S. Vieira-Teste, “Using deformable models to segment complex structures under geometric constraints,” in Proc. 4th Southwest Symp. on Image Analysis and Interpretation (Austin, TX, 2000), pp. 101–105, DOI:10.1109/IAI.2000.839580.

    Google Scholar 

  86. Haiyan Wang and B. K. Ghosh, “A dynamic systems approach to shape estimation via geometric deformable models,” in Proc. 38th Conf. on Decision and Control Phoenix (Arizona, 1999), pp. 4149–4154, DOI:10.1109/CDC.1999.828012.

    Google Scholar 

  87. Haiyan Wang and B. K. Ghosh, “Geometric active deformable models in shape modeling,” IEEE Trans. Image Processing 9(2), 302–308 (2000), DOI:10.1109/83.821748.

    Google Scholar 

  88. S. Loncaric, M. Subasic, and E. Sorantin, “3D deformable model for abdominal aortic aneurysm segmentation from CT images,” in Proc. 1st Int. Workshop on Image Signal Processing and Analysis (Pula, June 2000), pp. 139–144, DOI:10.1109/ISPA.2000.914904.

    Google Scholar 

  89. J. S. Suri, Kecheng Liu, S. Singh, S. N. Laxminarayan, Xiaolan Zeng, and L. Reden, “Shape recovery algorithms using level sets in 2D/3D medical imagery: a state-of-the-art review,” IEEE Trans. Inf. Technol. Biomed. 6(1), 8–28 (2002), DOI:10.1109/4233.992158.

    Google Scholar 

  90. A. C. Jalba, M. H. F. Wilkinson, and J. B. T. M. Roerdink, “CPM: A deformable model for shape recovery and segmentation based on charged particles,” IEEE Trans. Pattern Anal. Mach. Intellig., 26(10), 1320–1335 (2004), DOI:10.1109/TPAMI.2004.84.

    Google Scholar 

  91. Min Xiao, Shunren Xia, and Shiwei Wang, “Geometric active contour model with color and intensity priors for medical image segmentation,” in Proc. 27th Int. Conf. on Engineering in Medicine and Biology Society (EMBS) (Shanghai, 2005), pp. 6496–6499, DOI:10.1109/IEMBS.2005.1615987.

    Google Scholar 

  92. Guanglei Xiong, Xiaobo Zhou, and Liang Ji, “Automated segmentation of drosophila RNAi fluorescence cellular images using deformable models,” IEEE Trans. Circuits Syst. I: Reg. Pap. 53(11), 2415–2424 (2006), DOI:10.1109/TCSI.2006.884461

    Google Scholar 

  93. Ning Li, Miaomiao Liu, and Youfu Li, “Image segmentation algorithm using watershed transform and level set method,” in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP) (Honolulu, 2007), Vol. 1, pp. 613–616, DOI:10.1109/ICASSP.2007.365982.

    Google Scholar 

  94. J. Landre, S. Lebonvallet, Su. Ruan, Li Xiaobing, Qiu Tianshuang, and F. Brunotte, “A Deformable model-based system for 3D analysis and visualization of tumor in PET/CT images,” in Proc. 30th Annu. Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBS) (Vancouver, Aug. 2008), pp. 3130–3133, DOI:10.1109/IEMBS.2008.4649867.

    Google Scholar 

  95. J. Anquez, E. D. Angelini, and I. Bloch, “Segmentation of fetal 3D ultrasound based on statistical prior and deformable model,” in Proc. 5th Int. Symp. on Biomedical Imaging: from Nano to Macro (ISBI) (Paris, 2008), pp. 17–20, DOI:10.1109/ISBI.2008.4540921.

    Google Scholar 

  96. D. Vukadinovic, T. Van Walsum, S. Rozie, T. De Weert, R. Manniesing, A. Van Der Lugt, and W. Niessen, “Carotid artery segmentation and plaque quantification in CTA,” in Proc. IEEE Int. Symp. on Biomedical Imaging: from Nano to Macro (ISBI’09) (Boston, 2009), pp. 835–838, DOI:10.1109/ISBI.2009.5193182.

    Google Scholar 

  97. B. Liu, H. D. Cheng, Jianhua Huang Jiawei Tian, Xianglong Tang and Jiafengliu Liu, “Probability density difference-based active contour for ultrasound image segmentation,” Pattern Recogn. 43, 2028–2042 (2010), DOI:10.1016/j.patcog.2010.01.002.

    MATH  Google Scholar 

  98. Xiao Han, Chenyang Xu, Duygu Tosun, and J. L. Prince, “Cortical surface reconstruction using a topology preserving geometric deformable model,” in Proc. IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) (Kauai, Dec. 2001), pp. 213–220, DOI:10.1109/MMBIA.2001.991736.

    Google Scholar 

  99. X. Hang, N. L. Greenberg, and J. D. Thomas, “A geometric deformable model for echocardiographic image segmentation,” Comput. Cardiol. 4, 77–80 (2002).

    Google Scholar 

  100. X. Hang, N. L. Greenberg, and J. D. Thomas, “Left ventricle quantification in 3D echocardiography using a geometric deformable model,” Comput. Cardiol. 31, 649–652 (2004), DOI:10.1109/CIC.2004.1443022.

    Google Scholar 

  101. Jian Chen and A. A. Amini, “Quantifying 3D vascular structures in MRA images using hybrid PDE and geometric deformable models,” IEEE Trans. Med. Imag. 23(10), 1251–1262 (2004), DOI:10.1109/TMI.2004.834612.

    Google Scholar 

  102. Jie Li, W. C. Regli, and Wei Sun, “Mathematical representation of the vascular structure and applications,” in Proc. ACM Symp. on Solid and Physics Modeling (SPM) (Beijing, June 2007), pp. 373–378, DOI:10.1145/1236246.1236300.

    Google Scholar 

  103. H. F. Liu, H. P. Ho, and R. C. Shi, “Local weak form geometric active contours for medical image segmentation,” in Proc. IEEE Int. Symp. on Biomedical Imaging: Nano to Macro (Arlington, 2004), Vol. 1, pp. 189–192, DOI:10.1109/ISBI.2004.1398506.

    Google Scholar 

  104. M. Mora, C. Tauber, and H. Batatia, “Robust level set for heart cavities detection in ultrasound images,” Comput. Cardiol. 32, 235–238 (2005), DOI:10.1109/CIC.2005.1588080.

    Google Scholar 

  105. S. F. Hamidpourl, A. Ahmadian, R. A. Zoroofi, and J. H. Bidgoli, “Hybrid segmentation of colon boundaries in CT images based on geometric deformable model,” in Proc. IEEE Int. Conf. on Signal Processing and Communications (ICSPC) (Dubai, Nov. 2007), pp. 967–970, DOI:10.1109/ICSPC.2007.4728482.

    Google Scholar 

  106. N. N. Kachouie, P. Fieguth, and S. Rahnamayan, “An elliptical level set method for automatic TRUS prostate image segmentation,” in Proc. IEEE Int. Symp. on Signal Processing and Information Technology (Vancouver, 2006), pp. 191–196, DOI:10.1109/ISSPIT.2006.270795.

    Google Scholar 

  107. Xin Liu, D. L. Langer, M. A. Haider, T. H. Van Der Kwast, A. J. Evans, M. N. Wernick, and I. S. Yetik, “Unsupervised segmentation of the prostate using MR images based on level set with a shape prior,” in Proc. IEEE Annu. Int. Conf. of the Engineering in Medicine and Biology Society (EMBC) (Minneapolis, 2009), pp. 3613–3616, DOI:10.1109/IEMBS.2009.5333519.

    Google Scholar 

  108. J. Stough, P. M. Pizer, E. L. Chaney, and M. Rao, “Clustering on image boundary regions for deformable model segmentation,” in Proc. IEEE Int. Symp. on Biomedical Imaging: Nano to Macro (Arlington, Apr. 2004), Vol. 1, pp. 436–439, DOI:10.1109/ISBI.2004.1398568.

    Google Scholar 

  109. P. T. Fletcher, S. M. Pizer, A. G. Gash, and S. Joshi, “Deformable M-rep segmentation of object complexes,” in Proc. IEEE Int. Symp. on Bio-Medical Imaging (Washington, July 2002), pp. 26–29, DOI:10.1109/ISBI.2002.1029184.

    Google Scholar 

  110. J. V. Stough, R. E. Broadhurst, S. M. Pizer, and E. L. Chaney, “Clustering on local appearance for deformable model segmentation,” in Proc. 4th IEEE Int. Symp. on Biomedical Imaging from Nano to Macro (ISBI) (Washington, 2007), pp. 960–963, DOI:10.1109/ISBI.2007.357013.

    Google Scholar 

  111. M. Garbey and G. Zouridakis, “Modeling tumor growth: from differential deformable models to growth prediction of tumors detected in PET images,” Eng. Med. Biol. Soc. 3, 2687–2690 (2003), DOI:10.1109/IEMBS.2003.1280470.

    Google Scholar 

  112. J. Feng, H. H. S. Ip, and S. H. Cheng, “A 3D geometric deformable model for tubular structure segmentation,” in Proc. 10th Int. Multimedia Modeling Conf. (MMM04) (2004), pp. 174–180, DOI:10.1109/MULMM.2004.1264983.

    Google Scholar 

  113. T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Trans. Image Processing 10(2), 266–277 (2001), DOI:10.1109/83.902291.

    MATH  Google Scholar 

  114. A. Tsai, A. Yezzi, Jr., and A. S. Willsky, “Curve evolution implementation of the mumfordshah functional for image segmentation, denoising, interpolation, and magnification,” IEEE Trans. Image Processing 10(8), 1169–1186 (2001), DOI:10.1109/83.935033.

    MATH  Google Scholar 

  115. T. A. El Doker, “Unsupervised iterative segmentation and recognition of anatomic structures in medical imagery using second-order B-Spline descriptors and geometric quasi-invariants,” in Proc. 3rd IEEE Symp. on Bioinformatics and Bioengineering (BIBE) (Bethesda, MD, 2003).

    Google Scholar 

  116. Yong Seok Yoo, Kyoung Mu Lee, Il Dong Yun, and Sang Uk Lee, “Asymmetric multi-phase deformable model for colon segmentation,” in Proc. IEEE Int. Conf. on Image Processing (ICIP) (Genoa, 2005), Vol. 2, pp. 1242–1245, DOI:10.1109/ICIP.2005.1530287.

    Google Scholar 

  117. Hongda Mao, Huafeng Liu, and Pengcheng Shi, “Neighbor-constrained active contours without edges,” in Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops, CVPR Workshops (Anchorage, June 2008), pp. 1–7, DOI:10.1109/CVPRW.2008.4562995.

    Google Scholar 

  118. Xian Fan, P.-L. Bazin, J. Bogovic, Ying Bai, and J. L. Prince, “A multiple geometric deformable model framework for homeomorphic 3D Medical image segmentation,” in Proc. IEEE Computer Soc. Conf. on Computer Vision and Pattern Recognition (CVPR) Workshops (Anchorage, June 2008), DOI:10.1109/CVPRW.2008.4563013.

    Google Scholar 

  119. H.-H. Chang, D. J. Valentino, G. R. Duckwiler, and A. W. Toga, “Segmentation of brain MR images using a charged fluid model,” IEEE Trans. Biomed. Eng. 54(10), 1798–1813 (2007), DOI:10.1109/TBME.2007.895104.

    Google Scholar 

  120. I. Dindoyal, T. Lambrou, Jing Deng, and A. Todd-Pokropek, “Level set snake algorithms on the fetal heart,” in Proc. 4th IEEE Int. Symp. on Biomedical Imaging from Nano to Macro (ISBI) (Washington, 2007), pp. 864–867, DOI:10.1109/ISBI.2007.356989.

    Google Scholar 

  121. Xiao Han, Chenyang Xu, and J. L. Prince, “A topology preserving level set method for geometric deformable models,” IEEE Trans. Pattern Anal. Mach. Intellig. 25(6), 755–767 (2003), DOI:10.1109/TPAMI.2003.1201824.

    Google Scholar 

  122. Ying Bai, Xiao Han, and J. L. Prince, “Topology-preserving geometric deformable model on adaptive quadtree grid,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (Minneapolis, 2007), pp. 1–8, DOI:10.1109/CVPR.2007.383335.

    Google Scholar 

  123. Jian Ling, K. Bartels, and D. Nicolella, “A deformable statistical shape model applied to three-dimensional lumbar vertebra images,” in Proc. IEEE Southwest Symp. on Image Analysis and Interpretation (SSIAI) (Santa Fe, 2008), pp. 133–136, DOI:10.1109/SSIAI.2008.4512303.

    Google Scholar 

  124. M. Farzinfar, Eam Khwang Teoh, and Zhong Xue, “A coupled implicit shape-based deformable model for segmentation of MR images,” in Proc. 10th Int. Conf. on Control, Automation, Robotics and Vision (ICARCV) (Hanoi, Dec. 2008), pp. 651–656, DOI:10.1109/ICARCV.2008.4795594.

    Google Scholar 

  125. D. Jayadevappa, S. Srinivas Kumar, and D. S. Murty, “A hybrid segmentation model based on watershed and gradient vector flow for the detection of brain tumor,” Int. J. Signal Processing, Image Processing Pattern Recogn. 2(3), (2009).

    Google Scholar 

  126. S. Ourselin and R. Li, “Extension of deformable models: hybrid approaches for analysis of medical images,” in Proc. 27th Annu. Conf. of the Engineering in Medicine and Biology Soc. (Shanghai, Sep. 2005), pp. 7182–7185, DOI:10.1109/IEMBS.2005.1616165.

    Google Scholar 

  127. K. Zhang, L. Zhang, H. Song, and W. Zhou, “Active contours with selective local or global segmentation: a new formulation and level set method,” Image Vision Comput. 28, 668–676 (2010), DOI:10.1016/j.ima-vis.2009.10.009.

    Google Scholar 

  128. Tian Shen, Hongsheng Li, Zhen Qian, and Xiaolei Huang, “Active volume models for 3D medical image segmentation,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (Miami, 2009), pp. 707–714, DOI:10.1109/CVPR.2009.5206563.

    Google Scholar 

  129. Yaoyao Zhu, Tian Shen, D. Lopresti, and Xiaolei Huang, “Interactive polygons in region-based deformable contours for medical images,” in Proc. IEEE Int. Symp. on Biomedical Imaging: from Nano to Macro (ISBI) (Boston, 2009), pp. 37–40, DOI:10.1109/ISBI.2009.5192977.

    Google Scholar 

  130. Duan, Qi, E. D. Angelini, and A. F. Laine, “Real-time segmentation by active geometric functions,” Comput. Meth. Programs Biomed. 98, 223–230 (2010), DOI:10.1109/ISBI.2007.356989.

    Google Scholar 

  131. Chaijie Duan, Zhengrong Liang, Shangliang Bao, Hongbin Zhu, Su Wang, Guangxiang Zhang, J. J. Chen, and Hongbing Lu, “A coupled level set framework for bladder wall segmentation with application to MR cystography,” IEEE Trans. Med. Imag. 29(3), 903–915 (2010), DOI:10.1109/TMI.2009.2039756.

    Google Scholar 

  132. Lei He, Songfeng Zheng, and Li Wang, “Integrating local distribution information with level set for boundary extraction,” J. Visual Commun. Image Rep. 21 343–354 (2010), DOI:10.1016/j.jvcir.2010. 02.009.

    Google Scholar 

  133. D. N. Metaxas, Zhen Qian, Xiaolei Huang, Rui Huang, Ting Chen, and L. Axel, “Hybrid deformable models for medical segmentation and registration,” in Proc. 9th Int. Conf. on Control, Automation, Robotics and Vision (ICARCV) (Dec. 2006), pp. 1–6, DOI:10.1109/ICARCV.2006.345077.

    Google Scholar 

  134. Xiaolei Huang and D. N. Metaxas, “Metamorphs: deformable shape and appearance models,” IEEE Trans. Pattern Anal. Mach. Intellig. 30(8), 1444–1459 (2007), DOI:10.1109/TPAMI.2007.70795.

    Google Scholar 

  135. Shaoting Zhang, Xiaoxu Wang, D. Metaxas, Ting Chen, and L. Axel, “LV surface reconstruction from sparse tMRI using laplacian surface deformation and optimization,” in Proc. IEEE Int. Symp. on Biomedical Imaging (ISBI) from Nano to Macro (Boston, 2009), pp. 698–701, DOI:10.1109/ISBI.2009. 5193143.

    Google Scholar 

  136. S. Demirci, G. Lejeune, and N. Navab, “Hybrid deformable model for aneurysm segmentation,” in Proc. IEEE Int. Symp. on Biomedical Imaging: from Nano to Macro (ISBI’09) (Boston, 2009), pp. 33–36, DOI:10.1109/ISBI.2009.5192976.

    Google Scholar 

  137. M. Subasic, S. Loncaric, and E. Sorantin, “Region-based deformable model for aortic wall segmentation,” in Proc. 3rd Int. Symp. on Image and Signal Processing and Analysis (Roma, 2003), Vol. 2, pp. 731–735, DOI:10.1109/ISPA.2003.1296372.

    Google Scholar 

  138. Ning Lin, and J. S. Duncan, “Generalized robust point matching using an extended free-form deformable model: application to cardiac images,” in Proc. IEEE Int. Symp. on Biomedical Imaging: Nano to Macro (Arlington, 2004), Vol. 1, pp. 320–323, DOI:10.1109/ISBI.2004.1398539.

    Google Scholar 

  139. A. Huang, R. Abugharbieh, and R. Tam, “A hybrid geometric statistical deformable model for automated 3D segmentation in brain MRI,” IEEE Trans. Biomed. Eng. 56(7), 1838–1848 (2009), DOI:10.1109/TBME.2009.2017509.

    Google Scholar 

  140. Pingkun Yun and A. A. Kassim, “Medical image segmentation using minimal path deformable models with implicit shape priors,” IEEE Trans. Inf. Techn. Biomed. 10(4), 677–684 (2006), DOI:10.1109/TITB.2006.874199.

    Google Scholar 

  141. R. Chandrashekara, R. H. Mohiaddin, and D. Rueckert, “Analysis of 3D myocardial motion in tagged MR images using nonrigid image registration,” IEEE Trans. Med. Imag. 23(10), 1245–1250 (2004), DOI:10.1109/TMI.2004.834607.

    Google Scholar 

  142. A. Gholipour, N. Kehtarnavaz, K. Gopinath, R. Briggs, M. Devous and R. Haley, “Distortion correction via non-rigid registration of functional to anatomical magnetic resonance brain images,” in Proc. IEEE Int. Conf. on Image Processing (ICIP) (Atlanta, 2006), pp. 1181–1184, DOI:10.1109/ICIP.2006.312768.

    Google Scholar 

  143. A. Gholipour, N. Kehtarnavaz, R. W. Briggs, K. S. Gopinath, W. Ringe, A. Whittemore, S. Cheshkov, and K. Bakhadirov, “Validation of non-rigid registration between functional and anatomical magnetic resonance brain images,” IEEE Trans. Biomed. Eng. 55(2), 563–571 (Feb. 2008), DOI:10.1109/TBME.2007.912641.

    Google Scholar 

  144. J. Park, M. Metaxas, A. Young, and L. Axel, “Model-based analysis of cardiac motion from tagged MRI data,” in Proc. 7th IEEE Symp. on Computer Based Medical Systems (Winston-Salem, NC, 1994), pp. 40–45, DOI:10.1109/CBMS.1994.315983.

    Google Scholar 

  145. J. Park, D. Metaxas, and L. Axel, “Deformable models with parameter functions for left ventricle 3D wall motion analysis and visualization,” Comput. Cardiol. 22, 241–244 (1995), DOI:10.1109/CIC.1995.482617.

    Google Scholar 

  146. J. Park, D. Metaxas, A. A. Young, L. Axel, and A. Alistair, “Deformable models with parameter functions for cardiac motion analysis from tagged MRI data,” IEEE Trans. Med. Imag. 15(3), 278–289 (1996), DOI:10.1109/42.500137.

    Google Scholar 

  147. Jiang Qian, T. Mitsal, and E. A. Hoffman, “A physically based model for the registration of a 2D image sequence,” in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (Atlanta, 1996), Vol. 4, pp. 2195–2198, DOI:10.1109/ICASSP.1996.545856.

    Google Scholar 

  148. C. Nikou, F. Heitz, and J.-P. Armspach, “Brain segmentation from 3D MRI using statistically learned physics-based deformable models,” in Proc. IEEE Symp. on Nuclear Science (Toronto, 1998), Vol. 3, pp. 2045–2049, DOI:10.1109/NSSMIC.1998.773935.

    Google Scholar 

  149. A. D. Gilliam, F. H. Epstein, and S. T. Acton, “Cardiac motion recovery via active trajectory field models,” IEEE Trans. Inf. Techn. Biomed. 13(2), 226–235 (2009), DOI:10.1109/TITB.2008.2009221.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering M.N.M. Jain College of Engineering, Chennai, Tamilnadu, India

    A. Srinivasan

  2. Department of Computer Science and Engineering Shree Motilal Kanhaiyalal Fomra Institute of Technology, Chennai, Tamilnadu, India

    S. Sundaram

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A. Srinivasan completed his ME, PhD in computer Science and Engineering at Madras Institute of Technology, Anna University, Chennai. He has finished his Post Doctorate at Nan yang Technological University, Singapore. He has 21 years of Teaching and Research Experience in Computer Science and Engineering field and one year of Industrial Experience. He has published 52 Research publications in National and International journals and conferences. He is on the editorial board in Journal of Computer Science and Information Technology [JCSIT] and Review Board Member to ten reputed International Journals in Computer Science and Engineering field. Currently he is working as Principal, Senior Professor and Head in Information Technology Department, Misrimal Navajee Munoth Jain Engineering College, Anna University, Chennai, India. He is a Senior Member IEEE, ACM and Life Member CSI, ISTE. His fields of interests are Digital Image processing, Face Recognition and Distributed Systems.

R. S. Shanmuga sundaram received the BE degree in Electronics and Communication Engineering from the University of Madras of India in 1996 and the ME degree from the Bharathidasan University of India in 2001. He is currently working toward the PhD degree in the Department of Information and Communication Engineering at the Anna University. His research interests are in medical image processing, deformable models and segmentation. He is a life member of ACS and ISTE.

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Srinivasan, A., Sundaram, S. Applications of deformable models for in-dopth analysis and feature extraction from medical images—A review. Pattern Recognit. Image Anal. 23, 296–318 (2013). https://doi.org/10.1134/S1054661813020132

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