Classification and Retrieval of Focal and Diffuse Liver from Ultrasound Images Using Machine Learning Techniques

  • Ramamoorthy Suganya
  • R. Kirubakaran
  • S. Rajaram
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 264)


Medical Diagnosis has been gaining importance in everyday life. The diseases and their symptoms are highly varying and there is always a need for a continuous update of knowledge needed for the doctors. This forces lots of challenges as the diagnostic tools need to visualize organs and soft tissues and further classify them for diagnosis. One such application of diagnostic ultrasound is liver imaging. The existing approaches for classification & retrieval system have the following issues: speckle noise, semantic gap, computational time, dimensionality reduction and accuracy of retrieved images from large dataset. This paper proposes a new method for the classification & retrieval of liver diseases from ultrasound image dataset. The proposed work concentrates on diagnosing both focal and diffuse liver diseases from ultrasound images. The contribution of this paper relies on the following areas. Speckle reduction by Modified Laplacian Pyramid Nonlinear Diffusion (MLPND), Mutual Information (MI) based image registration, Image texture analysis by Haralick’s features, Image Classification & retrieval by machine learning algorithms. The dataset used in each phase of the work are authenticated dataset provided by doctors. The results at each phase have been evaluated with doctors in the relevant field.

The CNR value for MLPND has improved 95% compared to existing speckle reduction methods. The MI based registration with optimization techniques to reduce the computation time & monitor the growth of the liver diseases. The results retrieved from different machine learning techniques indicate that the proposed methods improve the image quality and overcome the fuzzy nature of dataset.


Speckle reduction Mutual information Haralick’s features machine learning algorithms ultrasound liver 


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  1. 1.
    Abd-Elmoniem, K.Z., Youssef, A.M., Kadah, Y.M.: Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion. IEEE Transaction on Biomedical Engineering 49(9), 997–1014 (2002)CrossRefGoogle Scholar
  2. 2.
    Zhang, F., Yoo, Y.M., Koh, L.M., Kim, Y.: Nonlinear diffusion in laplacian pyramid domain for ultrasonic speckle reduction. IEEE Transaction on Medical Imaging 26(2) (2007)Google Scholar
  3. 3.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transaction on Systems, Man and Cybernatics 3(6), 610–621 (1973)CrossRefGoogle Scholar
  4. 4.
    Medina, J.M., Castillo, S.J., Barranco, C.D., Campana, J.R.: On the use of a fuzzy object relational database for flexible retrieval of medical images. IEEE Transaction on Fuzzy Systems 20(4), 786–803 (2012)CrossRefGoogle Scholar
  5. 5.
    Lee, W.L., Chen, Y.C., Hsieh, K.S.: Ultrasonic liver tissues classification by fractal feature vector based on M-band wavelet transform. IEEE Transaction on Medical Imaging 22(3), 382–392 (2003)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Assy, N., Nasser, G., Djibre, A., Beniashvili, Z., Elias, S., Zidan, J.: Characteristics of common solid liver lesions and recommendations for diagnostic workup. World Journal of Gastroenterology 15(26), 3217–3227 (2009)CrossRefGoogle Scholar
  7. 7.
    Wen, P.: Medical image registration based-on points, contour and curves. In: International Conference on Biomedical Engineering and Informatics, pp. 132–136 (2008)Google Scholar
  8. 8.
    Yu, Y.J., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Transaction on Image Processing 11(11), 1260–1270 (2002)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ramamoorthy Suganya
    • 1
  • R. Kirubakaran
    • 1
  • S. Rajaram
    • 2
  1. 1.Dept. of Computer Science and EngineeringThiagarajar College of EnggMaduraiIndia
  2. 2.Dept of Electronics and Communication EngineeringThiagarajar College of EnggMaduraiIndia

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