Journal of Digital Imaging

, Volume 26, Issue 3, pp 530–543 | Cite as

SVM-Based Characterization of Liver Ultrasound Images Using Wavelet Packet Texture Descriptors

  • Jitendra VirmaniEmail author
  • Vinod Kumar
  • Naveen Kalra
  • Niranjan Khandelwal


A system to characterize normal liver, cirrhotic liver and hepatocellular carcinoma (HCC) evolved on cirrhotic liver is proposed in this paper. The study is performed with 56 real ultrasound images (15 normal, 16 cirrhotic and 25 HCC liver images) taken from 56 subjects. A total of 180 nonoverlapping regions of interest (ROIs), i.e. 60 from each image class, are extracted by an experienced participating radiologist. The multiresolution wavelet packet texture descriptors, i.e. mean, standard deviation and energy features, are computed from all 180 ROIs by using various compact support wavelet filters including Haar, Daubechies (db4 and db6), biorthogonal (bior3.1,bior3.3 and bior4.4), symlets (sym3 and sym5) and coiflets (coif1 and coif2). It is observed that a combined texture descriptor feature vector of length 48 consisting of 16 mean, 16 standard deviation and 16 energy features estimated from all 16 subband feature images (wavelet packets) obtained by second-level decomposition with two-dimensional wavelet packet transform by using Haar wavelet filter gives the best characterization performance of 86.6 %. Feature selection by genetic algorithm-support vector machine method increased the classification accuracy to 88.8 % with sensitivity of 90 % for detecting normal and cirrhotic cases and sensitivity of 86.6 % for HCC cases. Considering limited sensitivity of B-mode ultrasound for detecting HCCs evolved on cirrhotic liver, the sensitivity of 86.6 % for HCC lesions obtained by the proposed computer-aided diagnostic system is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists in lesion interpretation.


Texture analysis Liver ultrasound images Cirrhosis Hepatocellular carcinoma Small hepatocellular carcinoma Large hepatocellular carcinoma Wavelet packet transform Multiresolution analysis Genetic algorithm Support vector machines Computer-aided diagnostic system 



Author Jitendra Virmani would like to acknowledge Ministry of Human Resource Development (MHRD), India for financial support. The authors wish to acknowledge the Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India and Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India for their constant patronage and support in carrying out this research work. The authors would like to thank the anonymous reviewers for their substantive and informed review, which led to significant improvements in the manuscript.


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Copyright information

© Society for Imaging Informatics in Medicine 2012

Authors and Affiliations

  • Jitendra Virmani
    • 1
    Email author
  • Vinod Kumar
    • 1
  • Naveen Kalra
    • 2
  • Niranjan Khandelwal
    • 2
  1. 1.Biomedical Instrumentation Laboratory, Department of Electrical EngineeringIndian Institute of Technology RoorkeeRoorkeeIndia
  2. 2.Department of Radiodiagnosis and ImagingPost Graduate Institute of Medical Education and ResearchChandigarhIndia

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