Skip to main content
SpringerLink
Log in

Efficient segmentation with the convex local-global fuzzy Gaussian distribution active contour for medical applications

  • Published:
Annals of Mathematics and Artificial Intelligence Aims and scope Submit manuscript

Abstract

A new active contour (LGFGD) was developed in our earlier conference paper. This contour uses local and global information along with Gaussian distribution. The present paper derives the main LGFGD equation and investigates its parameters σ, λ and m. Specific values are determined (for σ, λ, m) to ensure high accuracy of segmentation of medical images containing nonhomogeneous and noisy regions with week boundaries. To validate the model, a new set of experiments was performed with new images including 24 skin lesion images with ground truth. Thus, a statistic of the LGFGD performance was calculated regarding the model’s interval of confidence. Comparison with contemporary methods from the field is provided as well.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)

    Book  MATH  Google Scholar 

  2. Boscolo, R., Brown, M., McNitt-Gray, M.: Medical image segmentation with knowledge-guided robust active contours. Radiographics 22(2), 437–448 (2002)

    Article  Google Scholar 

  3. Caselles, V., Catte, F., Coll, T., Dibos, F.: A geometric model for active contours in image processing. Numerische Mathematik 66, 1–33 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  4. Chan, T., Vese, L.: Active contour without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  5. Chen, Z., Qui, T., Ruan, S.: Fuzzy adaptive level set algorithm for brain tissue segmentation. In: Proceedings of International Conference Signal Processing, pp. 1047–1050. Beijing (2008)

  6. Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3, 32–57 (1973)

    Article  MATH  MathSciNet  Google Scholar 

  7. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1, 321–332 (1988)

    Article  Google Scholar 

  8. Khoo, V.S., Dearnaley, D.P., Finnigan, D.J., Padhani, A., Tanner, S.F., Leach, M.O.: Magnetic resonance imaging (MRI): Considerations and applications in radiotheraphy treatment planning. Radiother. Oncol. 42, 1–15 (1997)

    Article  Google Scholar 

  9. Krinidis, S., Chatzis, V.: Fuzzy energy-based active contour. IEEE Trans. Image Process. 18(12), 2747–2755 (2009)

    Article  MathSciNet  Google Scholar 

  10. Li, C., Kao, C., Gore, J., Ding, Z.: Implicit active contour driven by local binary fitting energy. In: Proceeding IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007)

  11. Shattuck, D.W., Sandor-Leahy, S.R., Schaper, K.A., Rottenberg, D.A., Leahy, R.M.: Magnetic resonance image tissue classification using a partial volume model. Neuroimage 13, 856–76 (2001)

    Article  Google Scholar 

  12. Sirakov, N.M., Ushkala, K.: An integral active contour model for Convex hull and boundary extraction. In: Proceeding of Advances in Visual Computing, 5th International Symposium, ISVC (2), pp. 1031–1040 (2009)

  13. Song, B., Chan, T.: A fast algorithm for level set based optimization. UCLA CAM Report 02-68 (2002)

  14. Thieu, Q. T., Luong, M., Rocchisani, J. M., Sirakov, N. M., Viennet, E.: Segmentation by a local and global fuzzy Gaussian distribution energy minimization of an active contour model. IWCIA 2012, 298–312 (2012)

    MathSciNet  Google Scholar 

  15. Tylski, P., Stute, S., Grotus, N., Doyeux, K., Hapdey, S., Gardin, I., Vanderlinden, B., Buvat, I.: Comparative assessment of methods for estimating tumor volume and standardized uptake value in 18F-FDG PET. J. Nuclear Med. 51(2), 268–76 (2010). Epub 2010 Jan 15

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  17. Wang, L., Li, C., Sun, Q., Xia, D., Kao, C.Y.: Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Comput. Med. Imaging Graph. 33, 520–531 (2009)

    Article  Google Scholar 

  18. Wang, X F, Huang, D S, Xu, H.: An efficient local Chan-Vese model for image segmentation. Pattern Recog. 43, 603–618 (2010)

    Article  MATH  Google Scholar 

  19. Xu, C., Pham, D.L., Prince, J.L.: Medical image segmentation using deformable models. In: SPIE Handbook on Medical Imaging: Medical Image Processing and Analysis, pp. 29–174 (2000)

  20. Zhu, S.C., Lee, T.S., Yuille, A.L.: Region competition: Unifying snakes, region growing, Energy/Bayes/MDL for multi-band image segmentation. In: Proceedings of ICCV’1995, pp. 416–416 (1995)

  21. Zijdenbos, A.P., Dawant, B.M.: Brain segmentation and white matter lesion detection in MR images. Crit. Rev. Biomed. Eng. 22, 401–465 (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. L2TI laboratory, Paris 13 University, 99 avenue Jean Baptiste Clement, 93430, Villetaneuse, France

    Quang Tung Thieu, Marie Luong & Emmanuel Viennet

  2. University Hospitals Paris-Seine-Saint Denis, 93000, Bobigny, France

    Jean-Marie Rocchisani

  3. LAGA Laboratory, Paris 13 University, 99 avenue Jean Baptiste Clement, 93430, Villetaneuse, France

    Jean-Marie Rocchisani

  4. Department of Computer Science, Department of Mathematics, Texas A&M University-Commerce, Commerce, Texas, 75429, USA

    Nikolay Metodiev Sirakov

Authors
  1. Quang Tung Thieu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marie Luong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean-Marie Rocchisani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nikolay Metodiev Sirakov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Emmanuel Viennet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Quang Tung Thieu.

Additional information

Thieu, Q. T., Luong, M., Rocchisani, J. M., Sirakov, N. M., Viennet, E.: Segmentation by a Local and Global Fuzzy Gaussian Distribution Energy Minimization of an Active Contour Model, IWCIA 2012: 298-312 (2012).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thieu, Q.T., Luong, M., Rocchisani, JM. et al. Efficient segmentation with the convex local-global fuzzy Gaussian distribution active contour for medical applications. Ann Math Artif Intell 75, 249–266 (2015). https://doi.org/10.1007/s10472-014-9413-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10472-014-9413-y

Keywords

Mathematics Subject Classifications (2010)

Search

Navigation