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Quantitative processed images acquired by histogram-SNR imaging used to evaluate parenchymal heterogeneity in the liver

Abstract

Purpose: To evaluate a new system for displaying processed images of liver parenchyma based on quantitative estimation of heterogeneity by texture analysis.Methods: We measured the signal to noise ratio, one of the first-order statistics in the histogram of enveloped amplitude of radio-frequency backscattered echoes, using a 3.75-MHz transducer with texture analysis in conjunction with a new method in which the small ROI (region of interest) is segmented into multiple layers to minimize the influence of tissue attenuation and beam diffraction. In our computerized system, gray-display and color-display images, two types of processed images, were produced from the visual intensity of each small ROI, which was based on its signal to noise ratio. We studied 10 cases of normal liver, 10 cases of fatty liver, and 10 cases of cirrhotic liver. The processed images obtained from these livers were reviewed to observe their features and to compare their usefulness in estimating the heterogeneity of the liver parenchyma with that of conventional B-mode images.Results: Gray-display images of cirrhotic livers appeared much blacker than the images produced from other disorders, and color-display images of cirrhotic liver appeared much bluer or greener than the others. Rate of correct diagnosis from B-mode images was 68.3 ±6.8%; from gray-display images, 85.8±7.4%; and from color-display images, 91.7±8.2%. Rate of correct assessment from B-mode images and gray-display images was significantly correlated (p=0.0015), as was rate of correct assessment from the B-mode images and the color-display images (p=0.0060).Conclusion: The processed images obtained using this computerized system contributed to the correct and objective interpretation of the heterogeneity of the liver parenchyma.

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References

  1. Bamber JC, Hill CR, King JA: Acoustic properties of normal and cancerous human liver, II: dependence on tissue structure. Ultrasound Med Biol 1981;7: 135–144.

    PubMed  CAS  Article  Google Scholar 

  2. Garra BS, Insana MF, Shawker TH, et al: Quantitative estimation of liver attenuation and echogenicity: normal state versus diffuse liver disease. Radiology 1987;162: 61–67.

    PubMed  CAS  Google Scholar 

  3. Itoh K, Yasuda Y, Suzuki O, et al: Studies on frequency-dependent attenuation in the normal liver and spleen and in liver diseases, using the spectral-shift zero-crossing method. J Clin Ultrasound 1988;16: 553–562.

    PubMed  CAS  Google Scholar 

  4. Maklad NF, Ophir J, Balsara V: Attenuation of ultrasound in normal liver and diffuse liver disease in vivo. Ultrason Imaging 1984;6: 117–125.

    PubMed  CAS  Article  Google Scholar 

  5. Taniguchi N, Fujii Y, Wang Y, et al: Estimation method of the tissue internal echo using RF signal histogram. Ultrasound Med Biol 2000;26 (suppl 2): A69. (Abstract)

    Google Scholar 

  6. Fujii Y, Taniguchi N, Wang Y, et al: Clinical application of the new method that segments the region of interest into multiple layers for amplitude histogram analysis using RF signal in the cirrhotic liver [in Japanese]. Journal of Medical Ultrasonics 2001;28: J25-J33.

    Google Scholar 

  7. Burckhardt CB: Speckle in ultrasound B-mode scans. IEEE Trans Sonics Ultrason 1978:25 (Suppl): 1–6.

    Google Scholar 

  8. Wagner RF, Smith SW, Sandrik JM, et al: Statistics of speckle in ultrasound B-scans. IEEE Trans Sonics Ultrason 1983;30 (Suppl): 156–163.

    Google Scholar 

  9. Fujii Y, Taniguchi N, Takano R, et al: Texture analysis with the new method that segments the region of interest into multiple layers for amplitude histogram analysis using RF signal in the fibrous liver of rats [in Japanese]. Journal of Medical Ultrasonics 2001;28: J681-J691.

    Google Scholar 

  10. Middleton D: Statistical Communication Theory. New York, NY: McGra-Hill; 1960.

    Google Scholar 

  11. Oosterveld BJ, Thijssen JM, Verhoef WA: Texture of B-mode echograms: 3-D simulations and experiments of the effects of diffraction and scatterer density. Ultrason Imaging 1985;7: 142–160.

    PubMed  CAS  Article  Google Scholar 

  12. Thijssen JM, Oosterveld BJ: Texture in tissue echograms: speckle or information? J Ultrasound Med 1990;9: 215–229.

    PubMed  CAS  Google Scholar 

  13. Tsao TW, Itoh T, Konishi T: A new method for amplitude histogram analysis in B-mode ultrasonography. 51th proceedings of the Japan Society of Ultrasonics in Medicine 1987; 171–172. (Abstract in Japanese)

  14. Tsao TW, Itoh T, Konishi T: Formulation and statistical evaluation for parametric histogram variations in B-mode ultrasonography [in Japanese]. Jpn J Med Ultrasonics 1989;16: 1–12.

    Google Scholar 

  15. Dutt V, Greenleaf JF: Speckle analysis using signal to noise ratios based on fractional order moments. Ultrason Imaging 1995;17: 251–268.

    PubMed  CAS  Article  Google Scholar 

  16. Tuthill TA, Sperry RH, Parker KJ: Deviations from Rayleigh statistics in ultrasound speckle. Ultrason Imaging 1988;10: 81–89.

    PubMed  CAS  Article  Google Scholar 

  17. Clifford L, Fitzgerald P, James D: Non-Rayleigh first-order statistics of ultrasounic backscatter from normal myocardium. Ultrasound Med Biol 1993;19: 487–495.

    PubMed  CAS  Article  Google Scholar 

  18. Shankear PM: A general statistical model for ultrasonic backscattering from tissue. IEEE UFFC 2000;47: 727–736.

    Google Scholar 

  19. Huisman HJ, Thijssen JM, Wagener DJ, et al: Quantitative ultrasonic analysis of liver metastases. Ultrasound Med Biol 1998;24: 67–77.

    PubMed  CAS  Article  Google Scholar 

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Fujii, Y., Taniguchi, N., Itoh, K. et al. Quantitative processed images acquired by histogram-SNR imaging used to evaluate parenchymal heterogeneity in the liver. J Med Ultrasonics 30, 13–19 (2003). https://doi.org/10.1007/BF02485165

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  • DOI: https://doi.org/10.1007/BF02485165

Keywords

  • liver
  • signal to noise ratio
  • texture analysis
  • tissue characterization
  • ultrasound