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Level Set Segmentation Based on Local Gaussian Distribution Fitting

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Computer Vision – ACCV 2009 (ACCV 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5994))

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Abstract

In this paper, we present a novel level set method for image segmentation. The proposed method models the local image intensities by Gaussian distributions with different means and variances. Based on the maximum a posteriori probability (MAP) rule, we define a local Gaussian distribution fitting energy with level set functions and local means and variances as variables. The means and variances of local intensities are considered as spatially varying functions. Therefore, our method is able to deal with intensity inhomogeneity. In addition, our model can be applied to some texture images in which the texture patterns of different regions can be distinguished from the local intensity variance. Our method has been validated for images of various modalities, as well as on 3D data, with promising results.

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References

  1. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int’l J. Comp. Vis. 1(4), 321–331 (1987)

    Article  Google Scholar 

  2. Kimmel, R., Amir, A., Bruckstein, A.: Finding shortest paths on surfaces using level set propagation. IEEE Trans. Patt. Anal. Mach. Intell. 17(6), 635–640 (1995)

    Article  Google Scholar 

  3. Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: a level set approach. IEEE Trans. Patt. Anal. Mach. Intell. 17(2), 158–175 (1995)

    Article  Google Scholar 

  4. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int’l J. Comp. Vis. 22(1), 61–79 (1997)

    Article  MATH  Google Scholar 

  5. Xu, C., Prince, J.: Snakes, shapes, and gradient vector flow. IEEE Trans. Imag. Proc. 7(3), 359–369 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  6. Li, C., Xu, C., Gui, C., Fox, M.D.: Level set evolution without re-initialization: A new variational formulation. In: CVPR, vol. 1, pp. 430–436 (2005)

    Google Scholar 

  7. Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Imag. Proc. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  8. Tsai, A., Yezzi, A., Willsky, A.S.: Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. Imag. Proc. 10(8), 1169–1186 (2001)

    Article  MATH  Google Scholar 

  9. Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. Int’l J. Comp. Vis. 46(3), 223–247 (2002)

    Article  MATH  Google Scholar 

  10. Vese, L., Chan, T.: A multiphase level set framework for image segmentation using the Mumford and Shah model. Int’l J. Comp. Vis. 50(3), 271–293 (2002)

    Article  MATH  Google Scholar 

  11. Li, C., Huang, R., Ding, Z., Gatenby, C., Metaxas, D., Gore, J.: A variational level set approach to segmentation and bias correction of medical images with intensity inhomogeneity. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 1083–1091. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  12. Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape. Int’l J. Comp. Vis. 72(2), 195–215 (2007)

    Article  Google Scholar 

  13. Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42(5), 577–685 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  14. Piovano, J., Rousson, M., Papadopoulo, T.: Efficient segmentation of piecewise smooth images. In: Sgallari, F., Murli, A., Paragios, N. (eds.) SSVM 2007. LNCS, vol. 4485, pp. 709–720. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  15. Li, C., Kao, C., Gore, J., Ding, Z.: Implicit active contours driven by local binary fitting energy. In: CVPR, pp. 1–7 (2007)

    Google Scholar 

  16. Vovk, U., Pernus, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imag. 26(3), 405–421 (2007)

    Article  Google Scholar 

  17. An, J., Rousson, M., Xu, C.: Γ-convergence approximation to piecewise smooth medical image segmentation. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 495–502. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  18. Lankton, S., Melonakos, J., Malcolm, J., Dambreville, S., Tannenbaum, A.: Localized statistics for DW-MRI fiber bundle segmentation. In: Computer Vision and Pattern Recognition Workshops, pp. 1–8 (2008)

    Google Scholar 

  19. Brox, T.: From pixels to regions: Partial differential equations in image analysis. PhD thesis, Saarland University, Germany (2005)

    Google Scholar 

  20. Rosenhahn, B., Brox, T., Weickert, J.: Three-dimensional shape knowledge for joint image segmentation and pose tracking. Int’l J. Comp. Vis. 73(3), 242–262 (2007)

    Google Scholar 

  21. Brox, T., Cremers, D.: On the statistical interpretation of the piecewise smooth Mumford-Shah functional. In: Sgallari, F., Murli, A., Paragios, N. (eds.) SSVM 2007. LNCS, vol. 4485, pp. 203–213. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  22. Awate, S.P., Zhang, H., Gee, J.C.: A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: With applications to DTI-tract extraction. IEEE Trans. Med. Imag. 26(11), 1525–1536 (2007)

    Article  Google Scholar 

  23. Zhu, S., Yuille, A.: Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Trans. Patt. Anal. Mach. Intell. 18(9), 884–900 (1996)

    Article  Google Scholar 

  24. Kim, J., Fisher, J., Yezzi, A., Cetin, M., Willsky, A.: Nonparametric methods for image segmentation using information theory and curve evolution. In: ICIP, vol. 3, pp. 797–800 (2002)

    Google Scholar 

  25. Wang, L., He, L., Mishra, A., Li, C.: Active contours driven by local Gaussian distribution fitting energy. Signal Processing 89(12), 2435–2447 (2009)

    Article  Google Scholar 

  26. Li, C.: Active contours with local binary fitting energy. In: IMA Workshop on New Mathematics and Algorithms for 3-D Image Analysis (2006)

    Google Scholar 

  27. Wells, W., Grimson, E., Kikinis, R., Jolesz, F.: Adaptive segmentation of MRI data. IEEE Trans. Med. Imag. 15(4), 429–442 (1996)

    Article  Google Scholar 

  28. Leemput, V., Maes, K., Vandermeulen, D., Suetens, P.: Automated model-based bias field correction of MR images of the brain. IEEE Trans. Med. Imag. 18(10), 885–896 (1999)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science & Technology, Nanjing University of Science and Technology, Nanjing, 210094, China

    Li Wang, Quansen Sun & Deshen Xia

  2. Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, 06269, USA

    Jim Macione

  3. Institute of Imaging Science, Vanderbilt University, Nashville, TN, 37232-2310, USA

    Chunming Li

Authors
  1. Li Wang
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  2. Jim Macione
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  3. Quansen Sun
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  4. Deshen Xia
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  5. Chunming Li
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Editor information

Editors and Affiliations

  1. Department of Machine Intelligence, Peking University, 100871, Beijing, China

    Hongbin Zha

  2. Department of Advanced Information Technology, Kyushu University, 819-0395, Fukuoka, Japan

    Rin-ichiro Taniguchi

  3. Birkbeck College, Department of Computer Science, University of London, WC1E 7HX, London, UK

    Stephen Maybank

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© 2010 Springer-Verlag Berlin Heidelberg

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Wang, L., Macione, J., Sun, Q., Xia, D., Li, C. (2010). Level Set Segmentation Based on Local Gaussian Distribution Fitting. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12307-8_27

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  • DOI: https://doi.org/10.1007/978-3-642-12307-8_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12306-1

  • Online ISBN: 978-3-642-12307-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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