Skip to main content
Log in

Statistical Tests of Anisotropy for Fractional Brownian Textures. Application to Full-field Digital Mammography

  • Published:
Journal of Mathematical Imaging and Vision Aims and scope Submit manuscript

Abstract

In this paper, we propose a new and generic methodology for the analysis of texture anisotropy. The methodology is based on the stochastic modeling of textures by anisotropic fractional Brownian fields. It includes original statistical tests that permit to determine whether a texture is anisotropic or not. These tests are based on the estimation of directional parameters of the fields by generalized quadratic variations. Their construction is founded on a new theoretical result about the convergence of test statistics, which is proved in the paper. The methodology is applied to simulated data and discussed. We show that on a database composed of 116 full-field digital mammograms, about 60 percent of textures can be considered as anisotropic with a high level of confidence. These empirical results strongly suggest that anisotropic fractional Brownian fields are better-suited than the commonly used fractional Brownian fields to the modeling of mammogram textures.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Abry, P., Gonçalves, P., Sellan, F.: Wavelet spectrum analysis and 1/f processes. In: Lecture Notes in Statistics, vol. 103, pp. 15–30. Springer, Berlin (1995)

    Google Scholar 

  2. Adler, R.J.: The Geometry of Random Field. Wiley, New York (1981)

    Google Scholar 

  3. Astley, S., et al. (eds.): Proc. of the 8th International Workshop on Digital Mammography, Manchester, UK, June 2004. LNCS, vol. 4046. Springer, Berlin (2004)

    Google Scholar 

  4. Ayache, A., Bonami, A., Estrade, A.: Identification and series decomposition of anisotropic Gaussian fields. In: Proc. of the Catania ISAAC05 Congress (2005)

  5. Bardet, J.M., Lang, G., Oppenheim, G., et al.: Semi-parametric estimation of the long-range dependence parameter: a survey. In: Theory and Applications of Long-range Dependence, pp. 557–577. Birkhauser, Boston (2003)

    Google Scholar 

  6. Begyn, A.: Asymptotic expansion and central limit theorem for quadratic variations of Gaussian processes. Bernoulli 13(3), 712–753 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  7. Benhamou, C.L., Poupon, S., Lespessailles, E., et al.: Fractal analysis of radiographic trabecular bone texture and bone mineral density. J. Bone Miner. Res. 16(4), 697–703 (2001)

    Article  Google Scholar 

  8. Benson, D., Meerschaert, M.M., Bäumer, B., Scheffler, H.P.: Aquifer operator-scaling and the effect on solute mixing and dispersion. Water Resour. Res. 42, 1–18 (2006)

    Article  Google Scholar 

  9. Beran, J.: Statistics for Long-memory Processes. Chapman Hall, London (1994)

    MATH  Google Scholar 

  10. Biermé, H., Meerschaert, M.M., Scheffler, H.P.: Operator scaling stable random fields. Stoch. Proc. Appl. 117(3), 312–332 (2007)

    Article  MATH  Google Scholar 

  11. Biermé, H., Richard, F.: Estimation of anisotropic Gaussian fields through radon transform. ESAIM: Probab. Stat. 12(1), 30–50 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  12. Bonami, A., Estrade, A.: Anisotropic analysis of some Gaussian models. J. Fourier Anal. Appl. 9, 215–236 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  13. Boyd, N.F., O’Sullivan, B., Campbell, J.E., et al.: Mammographic signs as risk factors for breast cancer. Br. J. Cancer 45, 185–193 (1982)

    Google Scholar 

  14. Brisson, J., Merletti, F., Sadowsky, N.L., et al.: Mammographic features of the breast and breast cancer risk. Am. J. Epidemiol. 115(3), 428–437 (1982)

    Google Scholar 

  15. Brunet-Imbault, B., Lemineur, G., Chappard, C., et al.: A new anisotropy index on trabecular bone radiographic images using the fast Fourier transform. BMC Med. Imaging 5(4), 4 (2005)

    Article  Google Scholar 

  16. Burgess, A., Jacobson, F., Judy, P.: Human observer detection experiments with mammograms and power-law noise. Med. Phys. 28(4), 419–437 (2001)

    Article  Google Scholar 

  17. Byng, J., Boyd, N.N., Fishell, E.: Automated analysis of mammographic densities. Phys. Med. Biol. 41, 909–923 (1996)

    Article  Google Scholar 

  18. Byng, J., Yaffe, M., Lockwood, G., et al.: Automated analysis of mammographic densities and breast carcinoma risk. Cancer 80(1), 66–74 (1997)

    Article  Google Scholar 

  19. Caldwell, C., Stapleton, S., Holdsworth, D., et al.: Characterisation of mammographic parenchymal patterns by fractal dimension. Phys. Med. Biol. 35(2), 235–247 (1990)

    Article  Google Scholar 

  20. Chen, C.-C., Daponte, J., Fox, M.: Fractal feature analysis and classification in medical imaging. IEEE Trans. Pattern. Anal. Mach. Intell. 8(2), 133–142 (1989)

    Google Scholar 

  21. Coeurjolly, J.F.: Inférence statistique pour les mouvements browniens fractionnaires et multifractionnaires. Ph.D. Thesis, University Joseph Fourier (2000)

  22. Cross, G., Jain, A.: Markov random field texture models. IEEE Trans. Pattern. Anal. Mach. Intell. 5(1), 25–39 (1983)

    Article  Google Scholar 

  23. Czörgö, M., Révész, P.: Strong Approximation in Probability and Statistics. Academic Press, San Diego (1981)

    Google Scholar 

  24. Dacunha-Castelle, D., Duflo, M.: Probabilités et Statistiques, vol. 2. Masson, Paris (1983)

    MATH  Google Scholar 

  25. Davies, S., Hall, P.: Fractal analysis of surface roughness by using spatial data. J. R. Stat. Soc. Ser. B 61, 3–37 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  26. Doi, K., et al. (eds.): Proc. of the 3rd International Workshop on Digital Mammography, Chicago, USA, June 1996. Elsevier, Amsterdam (1996)

    Google Scholar 

  27. Falconer, K.J.: Fractal Geometry. Wiley, New York (1990)

    MATH  Google Scholar 

  28. Gale, A.G., et al. (eds.): Proc. of the 2nd International Workshop on Digital Mammography, York, England, July 1994. Elsevier, Amsterdam (1994)

    Google Scholar 

  29. Grosjean, B., Moisan, L.: A-contrario detectability of spots in textured backgrounds. J. Math. Imaging Vis. 33(3), 313–337 (2009)

    Article  MathSciNet  Google Scholar 

  30. Heine, J., Deine, S., Velthuizen, R., et al.: On the statistical nature of mammograms. Med. Phys. 26(11), 2254–2269 (1999)

    Article  Google Scholar 

  31. Heine, J., Malhorta, P.: Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography: serial breast tissue change and related temporal influences. Acad. Radiol. 9, 317–335 (2002)

    Article  Google Scholar 

  32. Heine, J., Malhorta, P.: Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography: tissue and related risk factors. Acad. Radiol. 9, 298–316 (2002)

    Article  Google Scholar 

  33. Heine, J., Velthuizen, R.: Spectral analysis of full field digital mammography data. Med. Phys. 29(5), 647–661 (2002)

    Article  Google Scholar 

  34. Istas, J.: Identifying the anisotropical function of a d-dimensional Gaussian self-similar process with stationary increments. Stat. Inference Stoch. Process. 10(1), 97–106 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  35. Istas, J., Lang, G.: Quadratic variations and estimation of the local Holder index of a Gaussian process. Ann. Inst. Henri Poincaré, Probab. Stat. 33(4), 407–436 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  36. Jennane, R., Harba, R., Lemineur, G., et al.: Estimation of the 3D self-similarity parameter of trabecular bone from its projection. Med. Image Anal. 11, 91–98 (2007)

    Article  Google Scholar 

  37. Kamont, A.: On the fractional anisotropic Wiener field. Probab. Math. Stat. 16, 85–98 (1996)

    MATH  MathSciNet  Google Scholar 

  38. Karssemeijer, N., et al. (eds.): 4th International Workshop on Digital Mammography, Nijmegen, The Netherlands, June 1998. Kluwer Academic, Dordrecht (1998)

    Google Scholar 

  39. Kent, J.T., Wood, A.T.A.: Estimating the fractal dimension of a locally self-similar Gaussian process by using increments. J. R. Stat. Soc. Ser. B 59(3), 679–699 (1997)

    MATH  MathSciNet  Google Scholar 

  40. Kestener, P., Lina, J.-M., Saint-Jean, P., et al.: Wavelet-based multifractal formalism to assist in diagnosis in digitized mammograms. Image Anal. Stereol. 20, 169–174 (2001)

    MATH  Google Scholar 

  41. Kolmogorov, A.N.: Wienersche Spiralen und einige andere interessante Kurven in Hilbertsche Raum. C. R. (Dokl.) Acad. Sci. URSS 26, 115–118 (1940)

    Google Scholar 

  42. Leger, S.: Analyse stochastique de signaux multi-fractaux et estimations de paramètres. Ph.D. Thesis, Université d’Orléans (2000)

  43. Lundahl, T., Ohley, W.J., Kay, S.M., Siffe, R.: Fractional Brownian motion: a maximum likelihood estimator and its application to image texture. IEEE Trans. Med. Imaging 5(3), 152–161 (1986)

    Article  Google Scholar 

  44. Mandelbrot, B.B., Van Ness, J.: Fractional Brownian motion, fractionnal noises and applications. SIAM Rev. 10, 422–437 (1968)

    Article  MATH  MathSciNet  Google Scholar 

  45. Peitgen, H.-O. (ed.): 6th International Workshop on Digital Mammography, Bremen, Germany, June 2002. Springer, Berlin (2002)

    Google Scholar 

  46. Pentland, A.: Fractal-based description of natural scenes. IEEE Trans. Pattern. Anal. Mach. Intell. 6, 661–674 (1984)

    Article  Google Scholar 

  47. Stein, M.L.: Fast and exact simulation of fractional Brownian surfaces. J. Comput. Graph. Stat. 11(3), 587–599 (2002)

    Article  Google Scholar 

  48. Wolfe, J.N.: Ducts as a sole indicator of breast carcinoma. Radiology 89, 206–210 (1967)

    Google Scholar 

  49. Wolfe, J.N.: A study of breast parenchyma by mammography in the normal woman and those with benign and malignant disease. Radiology 89, 201–205 (1967)

    Google Scholar 

  50. Xiao, Y.: Sample path properties of anisotropic Gaussian random fields. In: Khoshnevisan, D., Rassoul-Agha, F. (eds.): A Minicourse on Stochastic Partial Differential Equations. Lecture Notes in Math., vol. 1962, pp. 145–212. Springer, New York (2009)

    Chapter  Google Scholar 

  51. Yaffe, M., et al. (eds.): Proc. of the 5th International Workshop on Digital Mammography, Toronto, Canada, June 2000. Medical Physics Publishing, Toronto (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Frédéric Richard.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Richard, F., Bierme, H. Statistical Tests of Anisotropy for Fractional Brownian Textures. Application to Full-field Digital Mammography. J Math Imaging Vis 36, 227–240 (2010). https://doi.org/10.1007/s10851-009-0181-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10851-009-0181-y

Navigation