Advertisement

Journal of Medical Ultrasonics

, Volume 38, Issue 3, pp 105–117 | Cite as

Usefulness of textural analysis as a tool for noninvasive liver fibrosis staging

  • Cristian Vicas
  • Monica Lupsor
  • Radu Badea
  • Sergiu Nedevschi
Original Article

Abstract

Purpose

Noninvasive diagnosis of liver fibrosis is a popular topic in the medical literature. Textural analysis on B-mode ultrasound is viewed as a noninvasive tool for fibrosis staging. A liver tissue model is proposed and used to simulate ultrasound images.

Methods

One hundred and twenty-five patients with chronic hepatitis C were included in this study. Patients were investigated using B-mode ultrasound and liver biopsy (Metavir scoring). A texture analysis tool consisting of 12 algorithms and a logistic regression classifier was implemented and validated. Tissue model parameters were varied and ultrasound images were generated.

Results

Texture analysis can discriminate between stages F0 and F4 using actual patient data (accuracy 69.5%) and synthetic images (accuracy 76.6%). A human expert is less sensitive than texture analysis in discriminating subtle changes in ultrasound images. High fibrosis detection accuracies are correlated with larger differences in portal space density (r 2 = 0.5). Accuracies measured when we varied only the fibrosis stage and kept the rest of the tissue parameters constant showed high detection rates only in a narrow parameter interval.

Conclusion

The texture analysis system shows limited performance in staging fibrosis and it cannot be used for accurate monitoring of fibrosis evolution over time.

Keywords

Tissue model Fibrosis staging Noninvasive diagnosis Texture analysis 

Notes

Acknowledgments

Part of this work was funded by the National Council for Scientific Research in Higher Education Grant No. 41-071/2007: SONOFIBROCAST.

References

  1. 1.
    Oosterveld B, Thijssen M, Romijnt R. Ultrasound attenuation and texture analysis of diffuse liver disease: methods and preliminary results. Phys Med. 1991;36:1034–64.Google Scholar
  2. 2.
    Fujii Y, Taniguchi N, Wang Y, et al. Clinical application of a new method that segments the region of interest into multiple layers for RF amplitude histogram analysis in the cirrhotic liver. J Med Ultrason. 2004;31:91–8.Google Scholar
  3. 3.
    Saverymuttu SH, Joseph AE, Maxwell JD. Ultrasound scanning in the detection of hepatic fibrosis and steatosis. Br Med J (Clin Res Ed). 1986;292:13–5.CrossRefGoogle Scholar
  4. 4.
    Colli A, Fraquelli M, Andreoletti M, et al. Severe liver fibrosis or cirrhosis: accuracy of US for detection—analysis of 300 cases. Radiology. 2003;227:89–94.PubMedCrossRefGoogle Scholar
  5. 5.
    Li GD, Min LQ, Zang HY, et al. Some information of B-scan image that detected by CNN. In: Proceedings of the 2006 IEEE international conference on information acquisition, vol 1 and 2; 2006. p. 819–23.Google Scholar
  6. 6.
    Abe C, Kahn CE Jr, Doi K, et al. Computer-aided detection of diffuse liver disease in ultrasound images. Invest Radiol. 1992;27:71–7.PubMedCrossRefGoogle Scholar
  7. 7.
    Wu CM, Chen YC, Hsieh KS. Texture features for classification of ultrasonic liver images. IEEE Trans Med Imaging. 1992;11:141–52.PubMedCrossRefGoogle Scholar
  8. 8.
    Cao GT, Shi PF, Hu B. Liver fibrosis identification based on ultrasound images captured under varied imaging protocols. J Zhejiang Univ Sci B. 2005;6:1107–14.PubMedCrossRefGoogle Scholar
  9. 9.
    Yamada H, Ebara M, Yamaguchi T, et al. A pilot approach for quantitative assessment of liver fibrosis using ultrasound: preliminary results in 79 cases. J Hepatol. 2006;44:68–75.PubMedCrossRefGoogle Scholar
  10. 10.
    Jeong JW, Lee S, Lee JW, et al. The echotextural characteristics for the diagnosis of the liver cirrhosis using the sonographic images. Proc Annu Int IEEE Eng Med Biol Soc. 2007;2007:1343–5.Google Scholar
  11. 11.
    Mojsilovic A, Popovic M, Markovic S, et al. Characterization of visually similar diffuse diseases from B-scan liver images using nonseparable wavelet transform. IEEE Trans Med Imaging. 1998;17:541–9.PubMedCrossRefGoogle Scholar
  12. 12.
    Ahmadian A, Mostafa A, Abolhassani MD, et al. A texture classification method for diffused liver diseases using Gabor wavelets. Proc Annu Int IEEE Eng Med Biol Soc. 2005;2005:1567–70.Google Scholar
  13. 13.
    Badawi AM, Derbala AS, Youssef AM. Fuzzy logic algorithm for quantitative tissue characterization of diffuse liver diseases from ultrasound images. Int J Med Inform. 1999;55:135–47.PubMedCrossRefGoogle Scholar
  14. 14.
    Horng M-H, Sun YN, Lin XZ. Texture feature coding method for classification of liver sonography. Comput Med Imaging Graph. 2002;26:33–42.PubMedCrossRefGoogle Scholar
  15. 15.
    Bonekamp S, Kamel I, Solga S, et al. Can imaging modalities diagnose and stage hepatic fibrosis and cirrhosis accurately? J Hepatol. 2009;50:17–35.PubMedCrossRefGoogle Scholar
  16. 16.
    Crawford AR, Lin XZ, Crawford JM. The normal adult human liver biopsy: a quantitative reference standard. Hepatology. 1998;28:323–31.PubMedCrossRefGoogle Scholar
  17. 17.
    Bedossa P, Poynard T. An algorithm for the grading of activity in chronic hepatitis C. The METAVIR Cooperative Study Group. Hepatology. 1996;24:289–93.PubMedCrossRefGoogle Scholar
  18. 18.
    Jensen J, Munk P. Computer phantoms for simulating ultrasound B-mode and cfm images. Acoust Imaging. 1997;23:75–80.Google Scholar
  19. 19.
    Jensen J. Field: a program for simulating ultrasound systems. Med Biol Eng Comput. 1996;34:351–2.CrossRefGoogle Scholar
  20. 20.
    Jensen J, Svendsen N. Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers. IEEE Trans Ultrason Ferroelectr Freq Control. 1992;39:262–7.PubMedCrossRefGoogle Scholar
  21. 21.
    Wagner R, Smith S, Sandrik J, et al. Statistics of speckle in ultrasound B-scans. IEEE Trans Sonics Ultrason. 1983;30:156–63.CrossRefGoogle Scholar
  22. 22.
    Randen T, Husoy JH. Filtering for texture classification: a comparative study. IEEE Trans Pattern Anal. 1999;21:291–310.CrossRefGoogle Scholar
  23. 23.
    [No authors listed]. Intraobserver and interobserver variations in liver biopsy interpretation in patients with chronic hepatitis C. The French METAVIR Cooperative Study Group. Hepatology. 1994;20:15–20.Google Scholar
  24. 24.
    Cao G, Shi P, Hu B. Ultrasonic liver characterization using phase congruency. Proc Annu Int IEEE Eng Med Biol Soc. 2005;2005:6356–9.Google Scholar
  25. 25.
    Cao GT, Shi PF, Hu B. Liver fibrosis identification based on ultrasound images. Proc Annu Int IEEE Eng Med Biol Soc. 2005;2005:6317–20.Google Scholar
  26. 26.
    Gaitini D, Baruch Y, Ghersin E, et al. Feasibility study of ultrasonic fatty liver biopsy: texture vs. attenuation and backscatter. Ultrasound Med Biol. 2004;30:1321–7.PubMedCrossRefGoogle Scholar
  27. 27.
    Horng M-H. An ultrasonic image evaluation system for assessing the severity of chronic liver disease. Comput Med Imaging Graph. 2007;31:485–91. doi: 10.1016/j.compmedimag.2007.05.001.PubMedCrossRefGoogle Scholar
  28. 28.
    Bedossa P, Dargere D, Paradis V. Sampling variability of liver fibrosis in chronic hepatitis C. Hepatology. 2003;38:1449–57.PubMedGoogle Scholar
  29. 29.
    Jan J. Medical image processing, reconstruction, and restoration: concepts and methods. Boca Raton: CRC; 2006.Google Scholar
  30. 30.
    De Berg M, Cheong O, Van Kreveld M, et al. Delaunay triangulations. Computational geometry: algorithms and applications. New York: Springer; 2008.Google Scholar
  31. 31.
    Dantas RG, Costa ET, Leeman S. Ultrasound speckle and equivalent scatterers. Ultrasonics. 2005;43:405–20.PubMedCrossRefGoogle Scholar
  32. 32.
    Gao H, Choi H, Claus P, et al. A fast convolution-based methodology to simulate 2D/3D cardiac ultrasound images. IEEE Trans Ultrason Ferroelectr Freq Control. 2009;56:404–9.PubMedCrossRefGoogle Scholar
  33. 33.
    Materka A, Strzelecki M. Texture analysis methods: a review. Lodz: Technical University of Lodz, Institute of Electronics; 1998.Google Scholar
  34. 34.
    Kadah YM, Farag AA, Zurada JM, et al. Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images. IEEE Trans Med Imaging. 1996;15:466–78.PubMedCrossRefGoogle Scholar
  35. 35.
    Yeh WC, Huang SW, Li PC. Liver fibrosis grade classification with B-mode ultrasound. Ultrasound Med Biol. 2003;29:1229–35.PubMedCrossRefGoogle Scholar
  36. 36.
    Lee W, Hsien K, Chen Y. A study of ultrasonic liver images classification with artificial neural networks based on fractal geometry and multiresolution analysis. Biomed Eng Appl Basis Commun. 2004;16:17–25.Google Scholar
  37. 37.
    Xia Y, Feng DG, Zhao RC. Morphology-based multifractal estimation for texture segmentation. IEEE Trans Image Process. 2006;15:614–23.PubMedCrossRefGoogle Scholar
  38. 38.
    Friedman J, Hastie T, Tibshirani R. The elements of statistical learning: data mining, inference, and prediction. Berlin: Springer; 2001.Google Scholar
  39. 39.
    Landwehr N, Hall M, Frank E. Logistic model trees. Mach Learn. 2005;59:161–205.CrossRefGoogle Scholar
  40. 40.
    Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting. Ann Stat. 2000;28:337–74.CrossRefGoogle Scholar
  41. 41.
    Grandvalet Y, Bengio Y. Hypothesis testing for cross-validation. Technical report 1285. Montreal: Universite de Montreal; 2006.Google Scholar
  42. 42.
    Bengio Y, Grandvalet Y. No unbiased estimator of the variance of k-fold cross-validation. J Mach Learn Res. 2004;5:1089–105.Google Scholar
  43. 43.
    Brodatz P. Textures: a photographic album for artists and designers. New York: Dover; 1966.Google Scholar
  44. 44.
    Witten I, Frank E. Data mining: practical machine learning tools and techniques. Menlo Park: Morgan Kaufmann; 2005.Google Scholar
  45. 45.
    Reichl T, Passenger J, Acosta O, et al. Ultrasound goes GPU: real-time simulation using CUDA. Proc SPIE. 2009;7261;726116. doi: 10.1117/12.812486
  46. 46.
    Kutter O, Shams R, Navab N. Visualization and GPU-accelerated simulation of medical ultrasound from CT images. Comput Methods Programs Biomed. 2009;94:250–66.PubMedCrossRefGoogle Scholar
  47. 47.
    Noble JA, Boukerroui D. Ultrasound image segmentation: a survey. IEEE Trans Med Imaging. 2006;25:987–1010.PubMedCrossRefGoogle Scholar
  48. 48.
    Zatari D, Botros N, Dunn F. A simulation algorithm for ultrasound liver backscattered signals. Ultrasonics. 1995;33:469–74.PubMedCrossRefGoogle Scholar
  49. 49.
    Yamaguchi T, Hachiya H. Modeling of the cirrhotic liver considering the liver lobule structure. Jpn J Appl Phys. 1999;38:3388–92.CrossRefGoogle Scholar
  50. 50.
    Yamaguchi T, Hachiya H. Proposal of a parametric imaging method for quantitative diagnosis of liver fibrosis. J Med Ultrason. 2010;37:155–66. doi: 10.1007/s10396-010-0270-y.Google Scholar
  51. 51.
    Szebeni A, Tolvaj G, Zalatnai A. Correlation of ultrasound attenuation and histopathological parameters of the liver in chronic diffuse liver diseases. Eur J Gastroenterol Hepat. 2006;18:37–42.CrossRefGoogle Scholar
  52. 52.
    Machado CB, Pereira WCD, Meziri M, et al. Characterization of in vitro healthy and pathological human liver tissue periodicity using backscattered ultrasound signals. Ultrasound Med Biol. 2006;32:649–57.PubMedCrossRefGoogle Scholar
  53. 53.
    Nakajima S, Shibuya K, Kamiyama N, et al. Comparison of ultrasound colored image views produced by application of statistical analysis of radio-frequency signals and histological findings in patients with chronic hepatitis C. J Med Ultrason. 2010;37:51–8.CrossRefGoogle Scholar

Copyright information

© The Japan Society of Ultrasonics in Medicine 2011

Authors and Affiliations

  • Cristian Vicas
    • 1
  • Monica Lupsor
    • 2
  • Radu Badea
    • 2
  • Sergiu Nedevschi
    • 1
  1. 1.Automation and Computer ScienceTechnical University of Cluj-NapocaCluj-NapocaRomania
  2. 2.Department of Ultrasonography3rd Medical ClinicCluj-NapocaRomania

Personalised recommendations