A novel automated classification technique for diagnosing liver disorders using wavelet and texture features on liver ultrasound images

  • R. Rani KrithigaEmail author
  • C. Lakshmi


A novel automated classification technique for diagnosing liver disorders is contributed in this paper by utilizing the merits of wavelet and texture features of ultrasound images. In this automated classification technique, initially the diseased part of the ultrasound image is isolated based on the application of improved active contour-based segmentation scheme. Improved active contour-based segmentation is mainly for preventing the issue of worse convergence, which is prevalent in the concave boundary regions of ultrasonic images. After segmentation, shift variant bi-orthogonal wavelet transform is applied for decomposing the region of focus into diagonal, vertical and horizontal component images. This shift variant bi-orthogonal wavelet transform is used in this approach for reducing the degree of prediction errors that are most possible in the classical discrete wavelet transform schemas. Finally, an improved random forest classifier (IRFC) is used for classifying the features that are extracted from the wavelet filtered images using gray level run length matrix (GLRLM). The performance of this scheme is evaluated based on sensitivity, specificity and accuracy metrics and shows the comparison of each classifier performance. The results of the proposed scheme infer an overall classification accuracy rate of 97.8% and confirm better results using GLRLM.


Random Forest classifier Shift variant bi-orthogonal wavelet decomposition Gray-level-run-length matrix (GLRLM) Textural features, liver diseases Ultrasound images 



  1. 1.
    Alivar A, Daniali H, Helfroush MS (2014) Classification of liver diseases using ultrasound images based on feature combination. In Computer and Knowledge Engineering (ICCKE), 2014 4th International eConference on IEEE 669–672Google Scholar
  2. 2.
    Alivar A, Danyali H, Helfroush MS (2016) Hierarchical classification of normal, fatty and heterogeneous liver diseases from ultrasound images using serial and parallel feature fusion. Biocybernet Biomed Eng 36(4):697–707CrossRefGoogle Scholar
  3. 3.
    Balaji GN, Subashini TS, Chidambaram N (2016) Detection and diagnosis of dilated cardiomyopathy and hypertrophic cardiomyopathy using image processing techniques. Eng Sci Technol Int J 19(4):1871–1880CrossRefGoogle Scholar
  4. 4.
    Bolon-Canedo V, Sanchez-Marono N, Alonso-Betanzos A (2011) Feature selection and classification in multiple class datasets: an application to KDD cup 99 dataset. Expert Syst Appl 8(5):5947–5957CrossRefGoogle Scholar
  5. 5.
    Hall MA (1999) Correlation-based feature selection for machine learningGoogle Scholar
  6. 6.
    Hwang YN, Lee JH, Kim GY, Jiang YY, Kim SM (2015) Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network. Biomed Mater Eng 26(s1):S1599–S1611Google Scholar
  7. 7.
    Ibrahim HE, Badr SM, Shaheen MA (2012) Adaptive layered approach using machine learning techniques with gain ratio for intrusion detection systems. arXiv preprint arXiv:1210.7650Google Scholar
  8. 8.
    Kalyan K, Jakhia B, Lele RD, Joshi M, Chowdhary A (2014) Artificial neural network application in the diagnosis of disease conditions with liver ultrasound images. Advances in bioinformaticsGoogle Scholar
  9. 9.
    Kitamura T, Takeuchi S, Abe S (2010) Feature selection and fast training of subspace based support vector machines. In Neural Networks (IJCNN), The 2010 International Joint Conference on IEEE 1–6Google Scholar
  10. 10.
    Krishnan KR, Sudhakar R (2013) Automatic classification of liver diseases from ultrasound images using GLRLM texture features. In Soft Computing Applications Springer, Berlin, Heidelberg 611–624Google Scholar
  11. 11.
    Owjimehr M, Danyali H, Helfroush MS, Shakibafard A (2017) Staging of fatty liver diseases based on hierarchical classification and feature fusion for back-scan–converted ultrasound images. Ultrason Imaging 39(2):79–95CrossRefGoogle Scholar
  12. 12.
    Rastghalam R, Pourghassem H (2016) Breast cancer detection using MRF-based probable texture feature and decision-level fusion-based classification using HMM on thermography images. Pattern Recogn 51:176–186CrossRefGoogle Scholar
  13. 13.
    Sabih D, Hussain M (2012) Automated classification of liver disorders using ultrasound images. J Med Syst 36(5):3163–3172CrossRefGoogle Scholar
  14. 14.
    Singh M, Singh S, Gupta S (2014) An information fusion based method for liver classification using texture analysis of ultrasound images. Inform Fusion 19:91–96CrossRefGoogle Scholar
  15. 15.
    Siri SK, Latte MV (2018) Universal Liver Extraction Algorithm: An Improved Chan–Vese Model. Journal of Intelligent SystemsGoogle Scholar
  16. 16.
    Thangaparvathi B, Anandhavalli D, Shalinie SM (2011) A high speed decision tree classifier algorithm for huge dataset. In Recent Trends in Information Technology (ICRTIT), 2011 International Conference on IEEE 695–700Google Scholar
  17. 17.
    Uddin MS, Halder KK, Tahtali M, Lambert AJ, Pickering MR (2015) Speckle reduction and deblurring of ultrasound images using artificial neural network. In Picture Coding Symposium (PCS) 105–108Google Scholar
  18. 18.
    Venkatalakshmi K, MercyShalinie S (2004) Classification of multispectral images using neuro-statistical classifier based on decision fusion and feature fusion. In Intelligent Sensing and Information Processing, 2004. Proceedings of International Conference on. IEEE 283–288Google Scholar
  19. 19.
    Virmani J, Kumar V, Kalra N, Khandelwa N (2013) PCA-SVM based CAD system for focal liver lesions using B-mode ultrasound images. Def Sci J 63(5):478CrossRefGoogle Scholar
  20. 20.
    Zaim A, Yi T, Keck R (2007) Feature-based classification of prostate ultrasound images using multiwavelet and kernel support vector machines. In Neural Networks, 2007. IJCNN 2007. International Joint Conference on IEEE 278–281Google Scholar
  21. 21.
    Zhu W, Zeng N, Wang N (2010) Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. NESUG Proc: Health Care Life Sci Baltimore Maryland 19:67Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Software EngineeringSRM UniversityChennaiIndia

Personalised recommendations