OSA's Digital Library

Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 20, Iss. 6 — Mar. 12, 2012
  • pp: 6542–6554

Semi-Huber potential function for image segmentation

Osvaldo Gutiérrez, Ismael de la Rosa, Jesús Villa, Efrén González, and Nivia Escalante  »View Author Affiliations


Optics Express, Vol. 20, Issue 6, pp. 6542-6554 (2012)
http://dx.doi.org/10.1364/OE.20.006542


View Full Text Article

Enhanced HTML    Acrobat PDF (1034 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

In this work, a novel model of Markov Random Field (MRF) is introduced. Such a model is based on a proposed Semi-Huber potential function and it is applied successfully to image segmentation in presence of noise. The main difference with respect to other half-quadratic models that have been taken as a reference is, that the number of parameters to be tuned in the proposed model is smaller and simpler. The idea is then, to choose adequate parameter values heuristically for a good segmentation of the image. In that sense, some experimental results show that the proposed model allows an easier parameter adjustment with reasonable computation times.

© 2012 OSA

OCIS Codes
(100.0100) Image processing : Image processing
(100.2000) Image processing : Digital image processing

ToC Category:
Image Processing

History
Original Manuscript: September 23, 2011
Revised Manuscript: December 24, 2011
Manuscript Accepted: February 27, 2012
Published: March 6, 2012

Citation
Osvaldo Gutiérrez, Ismael de la Rosa, Jesús Villa, Efrén González, and Nivia Escalante, "Semi-Huber potential function for image segmentation," Opt. Express 20, 6542-6554 (2012)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-20-6-6542


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB, (Prentice Hall, 2004).
  2. X. Cufí, X. Muñoz, J. Freixenet, and J. Martí, “A review on image segmentation thechniques integrating region and boundary information,” Adv. Imag. Elect. Phys.120, 1–39 (Elsevier, 2003). [CrossRef]
  3. M. M. Fernández, “Contribuciones al análisis automático y semiautomático de ecografía fetal tridimensional mediante campos aleatorios de Markov y contornos activos. Ayudas al diagnóstico precoz de malformaciones,” PhD Thesis, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, November2001.
  4. K. Sauer and C. Bouman, “Bayesian estimation of transmission tomograms using segmentation based optimization,” IEEE Trans. Nucl. Sci.39(4), 1144–1152 (1992). [CrossRef]
  5. C. Bouman and K. Sauer, “A generalized Gaussian image model for edge-preserving MAP estimation,” IEEE Trans. Image Process.2(3), 296–310 (1993). [CrossRef] [PubMed]
  6. K. Held, E. R. Kops, B. J. Krause, W. M. Wells, R. Kikinis, and H. W. Müller-Gärtner, “Markov random field segmentation of brain MR images,” IEEE Trans. Med. Imaging16(6), 878–886 (1997). [CrossRef]
  7. L. Cordero-Grande, P. Casaseca-de-la-Higuera, M. Martín-Fernández, and C. Alberola-López, “Endocardium and epicardium contour modeling based on Markov random fields and active contours,” in Proc. of IEEE EMBS Annu. Int. Conf., 928–931 (2006).
  8. Y. Zhang, M. Brady, and S. Smith, “Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm,” IEEE Trans. Med. Imaging20(1), 45–57 (2001). [CrossRef] [PubMed]
  9. S. Krishnamachari and R. Chellappa, “Multiresolution Gauss-Markov random field models for texture segmentation,” IEEE Trans. Image Process.6(2), 251–267 (1997). [CrossRef] [PubMed]
  10. D. A. Clausi and B. Yue, “Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery,” IEEE Trans. Geosci. Remote Sens.42(1), 215–228 (2004). [CrossRef]
  11. Y. Li and P. Gong, “An efficient texture image segmentation algorithm based on the GMRF model for classification of remotely sensed imagery,” Int. J. Remote Sens.26(22), 5149–5159 (2005). [CrossRef]
  12. S. Geman and C. Geman, “Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell.6, 721–741 (1984). [CrossRef]
  13. J. E. Besag, “On the statistical analysis of dirty pictures,” J. Roy. Stat. Soc. B48, 259–302 (1986).
  14. S. Z. Li, “MAP image restoration and segmentation by constrained optimization,” IEEE Trans. Image Process.7(12), 1730–1735 (1998). [CrossRef]
  15. R. Pan and S. J. Reeves, “Efficient Huber-Markov edge-preserving image restoration,” IEEE Trans. Image Process.15(12), 3728–3735 (2006). [CrossRef] [PubMed]
  16. M. Rivera and J. L. Marroquin, “Efficent half-quadratic regularization with granularity control,” Image Vision Comput.21, 345–357 (2003). [CrossRef]
  17. M. Rivera, O. Ocegueda, and J. L. Marroquin, “Entropy-controlled quadratic Markov measure field models for efficient image segmentation,” IEEE Trans. Image Process.16(12), 3047–3057 (2007). [CrossRef] [PubMed]
  18. M. Mignotte, “A segmentation-based regularization term for image deconvolution,” IEEE Trans. Image Process.15(7), 1973–1984 (2006). [CrossRef] [PubMed]
  19. H. Deng and D. A. Clausi, “Unsupervised image segmentation using a simple MRF model with a new implementation scheme,” Pattern Recogn.37, 2323–2335 (2004).
  20. O. Lankoande, M. M. Hayat, and B. Santhanam, “Segmentation of SAR images based on Markov random field model,” in Proc. of IEEE Int. Conf. on Systems, Man, and Cybernetics, 2956–2961 (2005).
  21. X. Lei, Y. Li, N. Zhao, and Y. Zhang, “Fast segmentation approach for SAR image based on simple Markov random field,” J. Syst. Eng. Electron.21(1), 31–36 (2010).
  22. J. Marroquin, S. Mitter, and t. Poggio, “Probabilistic solution of ill-posed problems in computational vision,” J. Amer. Statist. Assoc.82(397), 76–89 (1987). [CrossRef]
  23. R. Szeliski, “Bayesian modeling of uncertainty in low-level vision,” Int. J. Comput. Vision5(3), 271–301 (1990). [CrossRef]
  24. J. I. de la Rosa, J. J. Villa, and Ma. A. Araiza, “Markovian random fields and comparison between different convex criteria optimization in image restoration,” in Proc. XVII Int. Conf. on Electronics, Communications and Computers, 9 (CONIELECOMP, 2007).
  25. S. Z. Li, Markov Random Field Modeling in Image Analysis (Springer-Verlag, 2009).
  26. J. E. Besag, “Spatial interaction and the statistical analysis of lattice systems,” J. Roy. Stat. Soc. B36, 192–236 (1974).
  27. J. I. de la Rosa and G. Fleury, “Bootstrap methods for a measurement estimation problem,” IEEE Trans. Instrum. Meas.55(3), 820–827 (2006). [CrossRef]
  28. M. Nikolova and R. Chan, “The equivalence of half-quadratic minimization and the gradient linearization iteration,” IEEE Trans. Image Process.16(6), 1623–1627 (2007). [CrossRef] [PubMed]
  29. T. F. Chan, S. Esedoglu, and M. Nikolova, “Algorithms for finding global minimizers of image segmentation and denoising models,” SIAM J. Appl. Math.66(5), 1632–1648 (2006). [CrossRef]
  30. M. Nikolova, “Functionals for signal and image reconstruction: properties of their minimizers and applications,” Research report to obtain the Habilitation à diriger des recherches, Centre de Mathématiques et de Leurs Applications (CMLA), Ecole Normale Supérieure de Cachan (2006).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited