Segmentation of skin lesions in 2-D and 3-D ultrasound images using a spatially coherent generalized rayleigh mixture model

IEEE Transactions on Medical Imaging

Volumn 31, Issue 8, 2012, Pages 1509-1520

  Pereyra, Marcelo ^a   Dobigeon, Nicolas ^a   Batatia, Hadj ^a   Tourneret, Jean Yves ^a  

^a UNIVERSITÉ DE TOULOUSE   (France)

Author keywords

Bayesian estimation; Gibbs sampler; heavy tailed Rayleigh distribution; mixture model; Potts Markov field

Indexed keywords

BAYESIAN ESTIMATIONS; GIBBS SAMPLERS; MIXTURE MODEL; POTTS-MARKOV FIELD; RAYLEIGH DISTRIBUTIONS;

BAYESIAN NETWORKS; ESTIMATION; IMAGE SEGMENTATION; MIXTURES; PARAMETER ESTIMATION; TISSUE; ULTRASONICS;

THREE DIMENSIONAL;

ALGORITHM; ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; COMPUTER SIMULATION; ECHOGRAPHY; FOREARM; HISTOLOGY; HUMAN; IMAGE QUALITY; MELANOMA; METHODOLOGY; PATHOLOGY; REPRODUCIBILITY; SKIN; SKIN TUMOR; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING;

ALGORITHMS; BAYES THEOREM; COMPUTER SIMULATION; FOREARM; HUMANS; IMAGING, THREE-DIMENSIONAL; MELANOMA; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PHANTOMS, IMAGING; REPRODUCIBILITY OF RESULTS; SKIN; SKIN NEOPLASMS;

EID: 84864595844     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2012.2190617     Document Type: Article

References (64)

1. M. A. Pereyra, N. Dobigeon,H.Batatia, and J.-Y. Tourneret, "Labeling skin tissues in ultrasound images using a generalized Rayleigh mixture model," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Proc. (ICASSP), Prague, Czech Republic, May 2011, pp. 729-732.
2. M. A. Pereyra, N. Dobigeon, H. Batatia, and J.-Y. Tourneret, "Segmentation of ultrasound images using a spatially coherent generalized Rayleigh mixture model," in Proc. Eur. Signal Process. Conf. (EUSIPCO), Barcelona, Spain, Sep. 2011, pp. 664-668.
3. J. Noble and D. Boukerroui, "Ultrasound image segmentation: A survey," IEEE Trans. Med. Imag., vol. 25, no. 8, pp. 987-1010, Aug. 2006. (Pubitemid 44192172)
4. A. Belaid, D. Boukerroui, Y. Maingourd, and J.-F. Lerallut, "Phasebased level set segmentation of ultrasound images," IEEE Trans. Inf. Technol. Biomed., vol. 15, no. 1, pp. 138-147, Jan. 2011.
5. R. Schneider, D. Perrin,N.Vasilyev, G.Marx,P. deNido, andR.Howe, "Mitral annulus segmentation from 3-D ultrasound using graph cuts," IEEE Trans. Med. Imag., vol. 29, no. 9, pp. 1676-1687, Sep. 2010.
6. K. Somkantha, N. Theera-Umpon, and S. Auephanwiriyakul, "Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features," IEEE Trans. Biomed. Eng., vol. 58, no. 3, pp. 567-573, Mar. 2011.
7. P. Yan, S. Xu,B. Turkbey, and J. Kruecker, "Adaptively learning local shape statistics for prostate segmentation in ultrasound," IEEE Trans. Biomed. Eng., vol. 58, no. 3, pp. 633-641, Mar. 2011.
8. G. Unal, S. Bucher, S. Carlier, G. Slabaugh, T. Fang, and K. Tanaka, "Shape-driven segmentation of the arterial wall in intravascular ultrasound images," IEEE Trans. Inf. Technol. Biomed., vol. 12, no. 3, pp. 335-347, May 2008. (Pubitemid 351761566)
9. G. Carneiro, B. Georgescu, S. Good, and D. Comaniciu, "Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree," IEEE Trans. Med. Imag., vol. 27, no. 9, pp. 1342-1355, Sep. 2008.
10. P. Paramanathan and R. Uthayakumar, "Tumor growth in the fractal space-time with temporal density," in Control, Computation and Information Systems, ser. Communications in Computer and Information Science. Berlin, Germany: Springer, 2011, vol. 140, pp. 166-173.
11. K. Horsch, M. L. Giger, L. A. Venta, and C. J. Vyborny, "Automatic segmentation of breast lesions on ultrasound," Med. Phys., vol. 28, no. 8, pp. 1652-1659, 2001. (Pubitemid 32783208)
12. I.-S. Jung, D. Thapa, and G.-N. Wang, "Automatic segmentation and diagnosis of breast lesions using morphology method based on ultrasound," in Fuzzy Systems and Knowledge Discovery, L. Wang and Y. Jin, Eds. Berlin, Germany: Springer, 2005, vol. 3614, Lecture Notes in Computer Science, pp. 491-491.
13. A. Madabhushi and D.Metaxas, "Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions," IEEE Trans. Med. Imag., vol. 22, no. 2, pp. 155-169, Feb. 2003.
14. Y.-L. Huang and D.-R. Chen, "Watershed segmentation for breast tumor in 2-d sonography," Ultrasound Med. Biol., vol. 30, no. 5, pp. 625-632, 2004. (Pubitemid 38739368)
15. X. Guofang, M. Brady, J. Noble, and Z. Yongyue, "Segmentation of ultrasound B-mode images with intensity inhomogeneity correction," IEEE Trans. Med. Imag., vol. 21, no. 1, pp. 48-57, Jan. 2002. (Pubitemid 34138822)
16. D. Boukerroui, A. Baskurt, J. Noble, and O. Basset, "Segmentation of ultrasound images-multiresolution 2-D and 3-D algorithm based on global and local statistics," Pattern Recognit. Lett., vol. 24, no. 4-5, pp. 779-790, 2003. (Pubitemid 35337543)
17. J. Dias and J. Leitao, "Wall position and thickness estimation from sequences of echocardiographic images," IEEE Trans. Med. Imag., vol. 15, no. 1, pp. 25-38, Feb. 1996. (Pubitemid 126780296)
18. R. Wagner, S. Smith, J. Sandrik, and H. Lopez, "Statistics of speckle in ultrasound B-scans," IEEE Trans. Sonics Ultrason., vol. 30, no. 3, pp. 156-163, May 1983.
19. E. Brusseau, C. de Korte, F. Mastik, J. Schaar, and A. van der Steen, "Fully automatic luminal contour segmentation in intracoronary ultrasound imaging-a statistical approach," IEEE Trans. Med. Imag., vol. 23, no. 5, pp. 554-566, May 2004.
20. C. Chesnaud, P. Refregier, and V. Boulet, "Statistical region snakebased segmentation adapted to different physical noise models," IEEE Trans. Pattern Anal.Mach. Intell., vol. 21, no. 11, pp. 1145-1157, Nov. 1999. (Pubitemid 32211192)
21. M. H. R. Cardinal, J. Meunier, G. Soulez, R. L. Maurice, E. Therasse, and G. Cloutier, "Intravascular ultrasound image segmentation: A three-dimensional fast-marching method based on gray level distributions," IEEE Trans. Med. Imag., vol. 25, no. 5, pp. 590-601, May 2006.
22. S. Osher and J. A. Sethian, "Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations," J. Comput. Phys., vol. 79, no. 1, pp. 12-49, 1988.
23. L. Saroul, O. Bernard, D. Vray, and D. Friboulet, "Prostate segmentation in echographic images: A variational approach using deformable superellipse and Rayleigh distribution," in Proc. IEEE Int. Symp. Biomed. Imag. (ISBI), Paris, France, May 2008, pp. 129-132.
24. F. Destrempes, J. Meunier, M. F. Giroux, G. Soulez, and G. Cloutier, "Segmentation in ultrasonic B-mode images of carotid arteries using mixture of Nakagami distributions and stochastic optimization," IEEE Trans. Med. Imag., vol. 28, no. 2, pp. 215-229, Feb. 2009.
25. A. Sarti, C. Corsi, E. Mazzini, and C. Lamberti, "Maximum likelihood segmentation of ultrasound images with Rayleigh distribution," IEEE Trans. Ultrason. Ferroelect. Freq. Contr., vol. 52, no. 6, pp. 947-960, Jun. 2005. (Pubitemid 41092627)
26. T. Chan and L. Vese, "Active contours without edges," IEEE Trans. Image Process., vol. 10, no. 2, pp. 266-277, Feb. 2001. (Pubitemid 32254596)
27. F. Lecellier, J. Fadili, S. Jehan-Besson, G. Aubert, M. Revenu, and E. Saloux, "Region-based active contours with exponential family observations," J. Math. Imag. Vis., vol. 36, pp. 28-45, Jan. 2010.
28. M. A. Pereyra and H. Batatia, "Modeling ultrasound echoes in skin tissues using symmetric-stable processes," IEEE Trans. Ultrason. Ferroelect. Freq. Contr., vol. 59, no. 1, pp. 60-72, Jan. 2012.
29. F. Y. Wu, "The Potts model," Rev. Mod. Phys., vol. 54, no. 1, pp. 235-268, Jan. 1982.
30. O. Eches, N. Dobigeon, and J.-Y. Tourneret, "Enhancing hyperspectral image unmixing with spatial correlations," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 11, pp. 4239-4247, Nov. 2011.
31. D. Van de Sompel and M. Brady, "Simultaneous reconstruction and segmentation algorithm for positron emission tomography and transmission tomography," in Proc. 5th IEEE Int. Symp. Biomed. Imag.: From Nano to Macro, Paris, France, May 2008, pp. 1035-1038.
32. B. Scherrer, F. Forbes, C. Garbay, and M. Dojat, "Distributed local MRF models for tissue and structure brain segmentation," IEEE Trans. Med. Imag., vol. 28, no. 8, pp. 1278-1295, Aug. 2009.
33. L. Risser, J. Idier, P. Ciuciu, and T. Vincent, "Fast bilinear extrapolation of 3-D ising field partition function. Application to fMRI image analysis," in Proc. ICIP, Cairo, Egypt, 2009, pp. 833-836.
34. J. Marroquin, E. Santana, and S. Botello, "Hidden Markov measure field models for image segmentation," IEEE Trans. Patt. Anal. Mach. Intell., vol. 25, no. 11, pp. 1380-1387, Nov. 2003.
35. M. Woolrich, T. Behrens, C. Beckmann, and S. Smith, "Mixture models with adaptive spatial regularization for segmentation with an application to fMRI data," IEEE Trans. Med. Imag., vol. 24, no. 1, pp. 1-11, Jan. 2005. (Pubitemid 40088551)
36. P. Morse and K. Ingard, Theoretical Acoustics. Princeton, NJ: Princeton Univ. Press, 1987.
37. D. Salas-Gonzalez, E. E. Kuruoglu, and D. P. Ruiz, "Finite mixture of-stable distributions," Digit. Signal Process., vol. 19, pp. 250-264, Mar. 2009.
38. E. E. Kuruoglu and J. Zerubia, "Modeling SAR images with a generalization of the Rayleigh distribution," IEEE Trans. Image Process., vol. 13, no. 4, pp. 527-533, Apr. 2004.
39. A. Achim, E. E. Kuruoglu, and J. Zerubia, "SAR image filtering based on the heavy-tailed Rayleigh model," IEEE Trans. Image Process., vol. 15, no. 9, pp. 2686-2693, Sep. 2006. (Pubitemid 44270209)
40. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images," IEEE Trans. Patt. Anal.Mach. Intell., vol. 6, no. 6, pp. 721-741, Nov. 1984. (Pubitemid 15453722)
41. J. Besag, "Spatial interaction and the statistical analysis of lattice systems," J. Roy. Stat. Soc., vol. 36, no. 2, pp. 192-236, 1974.
42. R. Kindermann and J. L. Snell, Markov random fields and their applications. Providence, RI: Amer. Math. Soc, 1980.
43. Z. Zhou, R. Leahy, and J. Qi, "Approximate maximum likelihood hyperparameter estimation for Gibbs prior," IEEE Trans. Image Process., vol. 6, no. 6, pp. 844-861, Jun. 1997. (Pubitemid 127768398)
44. X. Descombes, R. Morris, J. Zerubia, and M. Berthod, "Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood," IEEE Trans. Image Process., vol. 8, no. 7, pp. 945-963, Jun. 1999.
45. J. Moller, A. N. Pettitt, R. Reeves, and K. K. Berthelsen, "An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants," Biometrika, vol. 93, no. 2, pp. 451-458, Jun. 2006. (Pubitemid 43992204)
46. C. McGrory, D. Titterington, R. Reeves, and A. Pettitt, "Variational Bayes for estimating the parameters of a hidden Potts model," Stat. Comput., vol. 19, pp. 329-340, 2009.
47. C. P. Robert and G. Casella, Monte Carlo Statistical Methods. New York: Springer-Verlag, 1999.
48. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Stat. Soc., vol. 39, no. 1, pp. 1-38, 1977.
49. J. Diebolt and E. H. S. Ip, "Stochastic EM: Method and application," in Markov Chain Monte Carlo in Practice,W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, Eds. London, U.K.: Chapman & Hall, 1996.
50. N. Dobigeon and J.-Y. Tourneret, "Bayesian orthogonal component analysis for sparse representation," IEEE Trans. Signal Process., vol. 58, no. 5, pp. 2675-2685, May 2010.
51. N. Dobigeon, A. Hero, and J.-Y. Tourneret, "Hierarchical Bayesian sparse image reconstruction with application to MRFM," IEEE Trans. Image Process., vol. 18, no. 9, pp. 2059-2070, Sep. 2009.
52. T. Vincent, L. Risser, and P. Ciuciu, "Spatially adaptive mixture modeling for analysis of fMRI time series," IEEE Trans. Med. Imag., vol. 29, no. 4, pp. 1059-1074, Apr. 2010.
53. K. Kayabol, E. Kuruoglu, and B. Sankur, "Bayesian separation of imagesmodeled with MRFs usingMCMC," IEEE Trans. Image Process., vol. 18, no. 5, pp. 982-994, May 2009.
54. M. Mignotte, "Image denoising by averaging of piecewise constant simulations of image partitions," IEEE Trans. Image Process., vol. 16, no. 2, pp. 523-533, Feb. 2007. (Pubitemid 46421692)
55. M. Pereyra, N. Dobigeon, H. Batatia, and J.-Y. Tourneret, Segmentation of skin lesions in 2-D and 3-D ultrasound images using a spatially coherent generalized Rayleigh mixture model Univ. Toulouse, France, IRIT/INP-ENSEEIHT, 2011 Online. Available: http://pereyra.perso. enseeiht.fr/pdf/Pereyra-TMI-TR- 2011.pdf
56. J. Gonzalez, Y. Low, A. Gretton, and C. Guestrin, "Parallel Gibbs sampling: From colored fields to thin junction trees," in Proc. Artif. Intell. Stat. (AISTATS), Ft. Lauderdale, FL, May 2011.
57. G. O. Roberts, "Markov chain concepts related to sampling algorithms," in Markov Chain Monte Carlo in Practice, W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, Eds. London, U.K.: Chapman Hall, 1996, pp. 259-273.
58. Z. Sun and C. Han, "Heavy-tailed Rayleigh distribution: A new tool for the modeling of SAR amplitude images," in Proc. IEEE Int. Geosci. Remote Sens. Symp., Boston, MA, Jul. 2008, vol. 4, pp. 1253-1256.
59. C. L. Nikias and M. Shao, Signal Processing with Alpha-Stable Distribution and Applications. New York: Wiley, 1995.
60. A. Gelman and D. Rubin, "Inference from iterative simulation using multiple sequences," Stat. Sci., vol. 7, no. 4, pp. 457-511, 1992.
61. C. P. Robert and S. Richardson, "Markov chain Monte Carlomethods," in Discretization and MCMC Convergence Assessment, C. P. Robert, Ed. New York: Springer Verlag, 1998, pp. 1-25.
62. A.Gelman, J. B. Carlin, H. P. Robert, andD. B. Rubin, Bayesian Data Analysis. London, U.K.: Chapman Hall, 1995.
63. T. Hergum, S. Langeland, E. Remme, and H. Torp, "Fast ultrasound imaging simulation in k-space," IEEE Trans. Ultrason. Ferroelect. Freq. Contr., vol. 56, no. 6, pp. 1159-1167, Jun. 2009.
64. T. Szabo, Diagnostic Ultrasound Imaging. Boston, MA: Academic, 2004.