Advertisement

Parametric Contour Model In Medical Image Segmentation

  • Bipul Das
  • Swapna Banerjee
Part of the Topics in Biomedical Engineering. International Book Series book series (ITBE)

The model-based technique offers a unique and efficient approach toward medical image segmentation and analysis due to its power to unify image information within a physical framework. Of the model-based techniques, the deformable model is most effectively used for its ability to unify image statistics — both local and global — in a geometrically constrained framework. The geometric constraint imparts a compact form of shape information.

Keywords

Active Contour Deformable Model Active Contour Model Initial Contour Medical Image Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

7 References

  1. 1.
    Haacke EM, Brown RW, Thompson MR, Venkatesan R. 1999. Magnetic resonance imaging: physical principles and sequence design. New York: Wiley-Liss.Google Scholar
  2. 2.
    Kurut EK, McIlwain EF, Plotnick GD. 2004. Handbook of echo-doppler doppler interpretation, 2d ed. Boston: Blackwell Futura.Google Scholar
  3. 3.
    Calender WA. 2000. Computed tomography: fundamentals, system technology, image quality, applications. Weinheim: Wiley-VCH.Google Scholar
  4. 4.
    Wahl RL. 2002. Principles and practice of positron emission tomography. Philadelphia: Lippincott Williams & Wilkins.Google Scholar
  5. 5.
    Huettel SA, Song AW, McCarthy MC. 2004. Functional magnetic resonance imaging. Sunderland, MA: Sinauer Associates.Google Scholar
  6. 6.
    Wernick MN, Aarsvold JN. 2004. Emission tomography: the fundamentals of PET and SPECT. New York: Academic Press.Google Scholar
  7. 7.
    Fischler M, Elschlager R. 1973. The representation and matching of pictorial structures. IEEE Trans Comput 22(1):67-92.CrossRefGoogle Scholar
  8. 8.
    Widrow B. 1973. The rubber mask technique, part I. Pattern Recognit 5(3):175-211.CrossRefGoogle Scholar
  9. 9.
    Terzopoulos D. 1986. Regularization of inverse visual problems involving discontinuities. IEEE Trans Pattern Anal Machine Intell 8(4):413-424.CrossRefGoogle Scholar
  10. 10.
    Kass M, Witkin A, Terzopoulos D. 1988. Snakes: active contour models. Int J Comput Vision 1:321-331.CrossRefGoogle Scholar
  11. 11.
    Terzopoulos D, Fleischer K. 1988. Deformable models. Visual Comput 4(6):306-331.CrossRefGoogle Scholar
  12. 12.
    McInerney T, Terzopoulos D. 1996. Deformable models in medical image analysis: a survey. Med Image Anal 1(2):91-108.CrossRefGoogle Scholar
  13. 13.
    Cohen LD, Cohen I. 1993. Finite-element methods for active contour models and balloons for 2-d and 3-d images. IEEE Trans Pattern Anal Machine Intell 15(11):1131-1147.CrossRefGoogle Scholar
  14. 14.
    Amini AA, Weymouth TE, Jain RC. 1990. Using Dynamic programming for solving variational problems in vision. IEEE Trans Pattern Anal Machine Intell 12:855-867.CrossRefGoogle Scholar
  15. 15.
    Williams DJ, Shah MA. 1992. Fast algorithm for active contours and curvature estimation. Comput Vision Graphics Image Process: Image Understand 55:14-26.MATHGoogle Scholar
  16. 16.
    Carlbom I, Terzopoulos D, Harris K. 1994. Computer-assisted registration, segmentation, and 3D reconstruction from images of neuronal tissue sections. IEEE Trans Med Imaging 13(2):351-362.CrossRefGoogle Scholar
  17. 17.
    Cohen I, Cohen LD, Ayache N. 1992. Using deformable surfaces to segment 3D images and infer differential structures. Comput Vision Graphics Image Process: Image Understand 56(2):242-263.MATHGoogle Scholar
  18. 18.
    Berger M-O, Mohr R. 1990. Towards autonomy in active contour models. In Proceedings of the 19th international conference on pattern recognition, Vol. 1, pp. 847-857. Washington, DC: IEEE.Google Scholar
  19. 19.
    Leymarie F, Levine MD. 1992. Simulating the grassfire transform using an active contour model. IEEE Trans Pattern Anal Machine Intell 14:56-75.CrossRefGoogle Scholar
  20. 20.
    Lobregt S, Viergever MA. 1995. A discrete dynamic contour model. IEEE Trans Med Imaging 14:12-24.CrossRefGoogle Scholar
  21. 21.
    Falcao AX, Udupa JK, Samarasekera S, Hirsch BE. 1996. User-steered image boundary segmentation. Proc SPIE 2710:278-288.CrossRefGoogle Scholar
  22. 22.
    Falcao A, Udupa JK. 1997. Segmentation of 3D objects using live wire. Proc SPIE 3034:228-235.CrossRefGoogle Scholar
  23. 23.
    Park HW, Schoepflin T, Kim Y. 2001. Active contour model with gradient directional informa- tion: directional snake. IEEE Trans Circ Syst Video Technol 11:252-256.CrossRefGoogle Scholar
  24. 24.
    Das B, Saha PK, Wehrli FW. 2004. Object class uncertainty induced snake with application to medical image segmentation. Proc SPIE 5370:369-380.CrossRefGoogle Scholar
  25. 25.
    Xu C, Prince J. 1998. Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7:359-369.MATHCrossRefMathSciNetGoogle Scholar
  26. 26.
    Das B, Banerjee S. 2004. Inertial snake for contour detection in ultrasonography images. IEEE Proc Image Signal Process 151:235-240.CrossRefGoogle Scholar
  27. 27.
    Paragios N, Deriche R. 2000. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans Pattern Anal Machine Intell 22:266-280.CrossRefGoogle Scholar
  28. 28.
    Ma T, Tagare HD. 1999. Consistency and stability of active contours with euclidean and non- Euclidean arc lengths. IEEE Trans Image Process 8:1549-1560.MATHCrossRefMathSciNetGoogle Scholar
  29. 29.
    Poon CS, Braun M. 1997. Image segmentation by a deformable contour model incorporating region analysis. Phys Med Biol 42:1833-1841.CrossRefGoogle Scholar
  30. 30.
    Amini AA, Duncan JS. 1992. Bending and stretching models for lv wall motion analysis from curves and surfaces. Image Vision Comput 10:418-430.CrossRefGoogle Scholar
  31. 31.
    Ivins J, Porill J. 1994. Statistical snakes: active region models. In Proceedings of the fifth British machine vision conference (BMVC’94), pp. 377-386. Washington, DC: IEEE.Google Scholar
  32. 32.
    Das B, Banerjee S. 2004. Homogeneity induced inertial snake with applications to medical im- age segmentation. In Proceedings of the IEEE Symposium on computer-based medical systems, pp. 304-309. Washington, DC: IEEE Computer Society.CrossRefGoogle Scholar
  33. 33.
    Rougon N, Pr êteux F. 1991. Deformable markers: mathematical morphology for active contour models control. Proc. SPIE 1568:78-89.CrossRefGoogle Scholar
  34. 34.
    Chakraborty A, Staib LH, Duncan JS. 1994. Deformable boundary finding influenced by re- gion homogeneity. In Proceedings of the fourth international conference on computer vision (ICCV’94), pp. 624-627. Washington, DC: IEEE Computer Society.Google Scholar
  35. 35.
    Chakraborty A, Duncan JS. 1995. Integration of boundary finding and region-based segmen- tation using game theory. In Fourteenth international conference on information processing in medical imaging, pp. 189-200. New York: Kluwer.Google Scholar
  36. 36.
    Herlin IL, Ayache N. 1992. Features extraction and analysis methods for sequences of ultrasound images. Image Vision Comput 10:673-682.CrossRefGoogle Scholar
  37. 37.
    Gauch JM, Pien HH, Shah J. 1994. Hybrid boundary-based and region-based deformable models for biomedical image segmentation. Proc SPIE 2299: pp. 72-83.CrossRefGoogle Scholar
  38. 38.
    Staib LH, Duncan JS. 1992. Boundary finding with parametrically deformable models. IEEE Trans Pattern Anal Machine Intell 14:1061-1075.CrossRefGoogle Scholar
  39. 39.
    Davatzikos CA, Prince JL. 1995. An active contour model for mapping the cortex. IEEE Trans Med Imaging 14:65-81.CrossRefGoogle Scholar
  40. 40.
    Cootes TF, Taylor CJ, Cooper D, Graham J. 1995. Active shape models: their training and application. Comput Vision Image Understand 61:38-59.CrossRefGoogle Scholar
  41. 41.
    Cootes TF, Edwards GJ, Taylor CJ. 1998. Active appearance models. In Proceedings of the European conference on computer vision, Vol. 2, pp. 484-498. New York: Springer.Google Scholar
  42. 42.
    Storvik G. 1994. A Bayesian approach to dynamic contours through stochastic sampling and simulated annealing. IEEE Trans Pattern Anal Machine Intell 16:976-986.CrossRefGoogle Scholar
  43. 43.
    Lai KF, Chin RT. 1995. Deformable contours: modeling and extraction. IEEE Trans Pattern Anal Machine Intell 17:1084-1090.CrossRefGoogle Scholar
  44. 44.
    Liu L, Sclaroff S. 2001. Medical image segmentation and retrieval via deformable models. In Proceedings of the international conference on image processing (ICIP’2001), Vol. 3, pp. 3-7. Washington, DC: IEEE Computer Society.Google Scholar
  45. 45.
    Olstad B, Torp AH. 1996. Encoding of a priori information in active contour models. IEEE Trans Pattern Anal Machine Intell 18:863-872.CrossRefGoogle Scholar
  46. 46.
    Gastaud M, Barlaud M, Aubert G. 2004. Combining shape prior and statistical features for active contour segmentation. IEEE Trans Circ Syst Video Technol 14:726-734.CrossRefGoogle Scholar
  47. 47.
    Gunn SR, Nixon MS. 1997. A robust snake implementation: a dual active contour. IEEE Trans Pattern Anal Machine IntellI 19:63-68.CrossRefGoogle Scholar
  48. 48.
    Jacob M, Blu T, Unser M. 2004. Efficient energies and algorithms for parametric snakes. IEEE Transactions on Image Processing 13:1231-1244.CrossRefGoogle Scholar
  49. 49.
    Das B, Saha PK, Wolf R, Song HK, Wright AC, Wehrli FW. 2005. Cerebrovascular plaque segmentation by using object class uncertainty snake in mr images. Proc. SPIE 5747:1720-1731.CrossRefGoogle Scholar
  50. 50.
    Yushkevich PA, Piven J, Cody H, Ho S, Gee JC, Gerig G. 2005. User-guided level set seg- mentation of anatomical structures with ITK-SNAP. Insight J 1 (Special Issue on ISC/NA- MIC/MICCAI Workshop on Open-Source Software, November).Google Scholar
  51. 51.
    Caselles V, Kimmel R, Sapiro G. 1995. Geodesic active contours. In Proceedings of the fifth international conference on computer vision (ICCV’95), pp. 694-699. Washington, DC: IEEE Computer Society.CrossRefGoogle Scholar
  52. 52.
    Saha PK, Udupa JK. 2001. Optimum threshold selection using class uncertainty and region homogeneity. IEEE Trans Pattern Anal Machine Intell 23:689-706.CrossRefGoogle Scholar
  53. 53.
    Osher S, Sethian JA. 1988. Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulation. J Computat Phys 79:12-49.MATHCrossRefMathSciNetGoogle Scholar
  54. 54.
    Malladi R, Sethian J, Vemuri BC. 1995. Shape modeling with front propagation: A level set approach. IEEE Trans Pattern Anal Machine Intell 17(2):158-175.CrossRefGoogle Scholar
  55. 55.
    Chan TF, Vese LA. 2001. Active contours without edges. IEEE Trans Image Process 10:266-277.MATHCrossRefGoogle Scholar
  56. 56.
    Paragios N, Deriche R. 2000. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans Pattern Anal Machine Intell 22:266-280.CrossRefGoogle Scholar
  57. 57.
    Vald és-Cristerna R, Medina-Ba ñuelos V, Y á ñez-Su árez O. 2004. Coupling of radial-basis net- work and active contour model for multispectral brain mri segmentation. IEEE Trans Biomed Eng 51:459-470.CrossRefGoogle Scholar
  58. 58.
    Samadani R. 1992. Changes in connectivity in active contour models. In Proceedings of the Workshop on Visualization, pp. 337-343. Washington, DC: IEEE Computer Society.Google Scholar
  59. 59.
    McInerney T, Terzopoulos D. 1995. Topologically Adaptable Snakes. In Proceedings of the fifth international conference on computer vision (ICCV’95), pp. 840-845. Washington, DC: IEEE Computer Society.CrossRefGoogle Scholar
  60. 60.
    McInerney T, Terzopoulos D. 1999. Topology adaptive deformable surfaces for medical image volume segmentation. IEEE Trans Med Imaging 18:840-851.CrossRefGoogle Scholar
  61. 61.
    Ji L, Yan H. 2001. Robust topology-adaptive snakes for image segmentation. In Proceedings of the international conference on image processing (ICIP’2001), Vol. 2, pp. 797-800.Google Scholar
  62. 62.
    Lorenson WE, Cline HE. 1987. Marching cubes, a high resolution 3d surface construction algorithm. Comput Graphics 21:163-169.CrossRefGoogle Scholar
  63. 63.
    Giraldi GA, Strauss E, Oliveira AA. 2000. A boundary extraction method based on dual-t-snakes and dynamic programming. In IEEE proceedings of computer vision and pattern recognition (CVPR’2000), Vol. 1, pp. 44-49. Washington, DC: IEEE Computer Society.Google Scholar
  64. 64.
    Miki ć I, Krucinski S, Thomas JD. 1998. Segmentation and tracking in echocardiographic se- quences: active contours guided by optical flow estimates. IEEE Trans Med Imaging 17:274-285.CrossRefGoogle Scholar
  65. 65.
    Leymarie F, Levine MD. 1993. Tracking deformable objects in the plane using an active contour model. IEEE Trans Pattern Anal Machine Intell 15:617-634.CrossRefGoogle Scholar
  66. 66.
    Curwen RW, Amini AA, Duncan JS, Lee F. 1994. Tracking vascular motion in x-ray image sequences with Kalman snakes. Comput Cardiol, 1:109-112.Google Scholar
  67. 67.
    Freedman D, Zhang T. 2004. Active contours for tracking distributions. IEEE Trans Image Process 13:518-526.CrossRefGoogle Scholar
  68. 68.
    Ray N, Acton ST, Altes T, Lange EE, Brookeman JR. 2003. Merging parametric active contours within homogeneous image regions for mri-based lung segmentation. IEEE Trans Med Imaging 22:189-200.CrossRefGoogle Scholar
  69. 69.
    Ray N, Acton ST. 2004. Motion gradient vector flow: an external force for tracking rolling leukocytes with shape and size constrained active contours. IEEE Trans Med Imaging 23:1466-1478.CrossRefGoogle Scholar
  70. 70.
    Gwydir SH, Buettner HM, Dunn SM. 1994. Non-rigid motion analysis and feature labelling of the growth cone. In Proceedings of the IEEE Workshop on biomedical image analysis, pp. 80-87. Washington, DC: IEEE Computer Society.CrossRefGoogle Scholar
  71. 71.
    Lengyel J, Greenberg DP, Popp R. 1995. Time-dependent three-dimensional intravascular ultrasound. J Comput Graphics 29:457-464.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Bipul Das
    • 1
  • Swapna Banerjee
    • 2
  1. 1.Imaging Technology DivisionGE India Technology CentreBangaloreIndia
  2. 2.Department of Electronics and ECEIndian Institute of TechnologyKharagpurIndia

Personalised recommendations