The Recognition of Cirrhotic Liver Ultrasonic Images of Multi-feature Fusion Based on BP_Adaboost Neural Network

  • Shourun WangEmail author
  • Zhenkuan Pan
  • Weibo Wei
  • Ximei Zhao
  • Guodong Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 634)


Due to the recognition rate of single character is low, the method of multi-feature fusion was proposed. BP_Adaboost neural network (BP_ANN) was first used to recognize the B-scan ultrasonic image of cirrhotic liver. Gray level co-occurrence matrix (GLCM) and gray level difference statistics (GLDS) were introduced in this paper. In order to improve the objectivity of the experimental results, uniform local binary pattern (U_LBP) was also applied. The texture features were extracted by any combination of these three methods. Then the feature which was extracted by above combination was input to BP_ANN. It was shown that the combination of GLCM and GLDS was better than any others in this experiment, and the recognition rate was 97 %. The design of BP_Adaboost network and the determination of neurons in hidden layer were also discussed.


Neural network Texture feature Pattern recognition Cirrhotic liver Multi-feature fusion 


  1. 1.
    Tromberg, B.J., Shah, N., Lanning, R., Cerussi, A., Espinoza, J., Pham, T., Svaasand, L., Butler, J.: Non-invasive in vivo characterization of breast tumors using photon migration spectroscopy. Neoplasia 2, 26–40 (2000)CrossRefGoogle Scholar
  2. 2.
    Nicholas, D., Nassiri, D., Garbutt, P., Hill, C.: Tissue characterization from ultrasound B-scan data. Ultrasound Med. Biol. 12, 135–143 (1986)CrossRefGoogle Scholar
  3. 3.
    Chunyan, Han: The value of ultrasound in the diagnosis of liver cirrhosis and sonographic findings. J. ChiN. Rural Physician MeD. Specialty 12, 159 (2010)Google Scholar
  4. 4.
    Schwenzer, N.F., Springer, F., Schraml, C., Stefan, N., Machann, J., Schick, F.: Non-invasive assessment and quantification of liver steatosis by ultrasound, computed tomography and magnetic resonance. J. Hepatol. 51, 433–445 (2009)CrossRefGoogle Scholar
  5. 5.
    Kadah, Y.M., Farag, A.A., Zurada, J.M., Badawi, A.M., Youssef, A.-B.M.: Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images. IEEE Trans. Med. Imaging 15, 466–478 (1996)CrossRefGoogle Scholar
  6. 6.
    Gosink, B., Lemon, S., Scheible, W., Leopold, G.: Accuracy of ultrasonography in diagnosis of hepatocellular disease. Am. J. Roentgenol. 133, 19–23 (1979)CrossRefGoogle Scholar
  7. 7.
    İçer, S., Coşkun, A., İkizceli, T.: Quantitative grading using grey relational analysis on ultrasonographic images of a fatty liver. J. Med. Syst. 36, 2521–2528 (2012)CrossRefGoogle Scholar
  8. 8.
    Dong, Y., Zhang, H., Sun, Y.: Study of Motion Blurred Image Restoration Method (2015)Google Scholar
  9. 9.
    Tianjing, W., Baoyu, Z., Zhen, Y.: Compression perception signal acquisition scheme based on filtering. Chin. J. Sci. Instrum. 573–581 (2013)Google Scholar
  10. 10.
    Lizhi, W.: Computer Assistant Diagnosis based on Texture Features of Ultrasonic Liver Images. Central South University, Changsha (2013)Google Scholar
  11. 11.
    Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. SMC Syst. Man Cybern. 3, 610–621 (1973)CrossRefGoogle Scholar
  12. 12.
    Weszka, J.S., Dyer, C.R., Rosenfeld, A.: A comparative study of texture measures for terrain classification. IEEE Trans. Syst. Man Cybern. 6, 269–285 (1976)CrossRefzbMATHGoogle Scholar
  13. 13.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28, 2037–2041 (2006)CrossRefzbMATHGoogle Scholar
  14. 14.
    Madzarov, G., Gjorgjevikj, D.: Multi-class classification using support vector machines in decision tree architecture. In: EUROCON 2009, IEEE, pp. 288–295. IEEE (2009)Google Scholar
  15. 15.
    Soh, L.-K., Tsatsoulis, C.: Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans. Geosci. Remote Sens. 37, 780–795 (1999)CrossRefGoogle Scholar
  16. 16.
    Zhang, H., Huo, Q., Ding, W.: The application of adaBoost-neural network in storedproduct insect classification. In: IEEE International Symposium on IT in Medicine and Education, 2008, ITME 2008, pp. 973–976. IEEE (2008)Google Scholar
  17. 17.
    Mala, K., Sadasivam, V., Alagappan, S.: Neural network based texture analysis of CT images for fatty and cirrhosis liver classification. Appl. Soft Comput. 32, 80–86 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Shourun Wang
    • 1
    Email author
  • Zhenkuan Pan
    • 1
  • Weibo Wei
    • 1
  • Ximei Zhao
    • 1
  • Guodong Wang
    • 1
  1. 1.College of Computer Science and TechnologyQingdao UniversityQingdaoChina

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