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Texture feature coding method for classification of liver sonography.

Ming-Huwi Horng1, Yung-Nien Sun, Xi-Zhang Lin

  • 1Department of Information Management, Nan Hua University, No. 32, Chung Keng Li, Dalin Chiayi, Taiwan, ROC. mh.horng@msa.hinet.net

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|December 6, 2001
PubMed
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A novel texture feature coding method (TFCM) enhances ultrasonic liver image classification. TFCM-based texture features significantly improve diagnostic accuracy for liver diseases compared to conventional methods.

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Accurate classification of liver diseases from ultrasonic images is crucial for diagnosis.
  • Existing texture analysis methods have limitations in capturing complex liver tissue characteristics.

Purpose of the Study:

  • To introduce and evaluate a new texture analysis method, the texture feature coding method (TFCM), for ultrasonic liver image classification.
  • To compare the performance of TFCM against conventional texture analysis techniques.

Main Methods:

  • TFCM transforms gray-level images into feature images using texture feature numbers (TFNs).
  • TFNs generate histograms and co-occurrence matrices for texture feature descriptors.
  • Supervised maximum likelihood (ML) classifiers were trained and tested on biopsy-proven liver images (30 train, 90 test).

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  • Conventional methods included gray-level CM, texture spectrum, statistical feature matrix, and fractal dimension.

    • Main Results:

    • The ML classifier combined with TFCM texture features demonstrated superior classification accuracy.
    • TFCM outperformed four conventional texture analysis methods in discriminating between normal liver, hepatitis, and cirrhosis.

    Conclusions:

    • TFCM is an effective method for texture analysis in ultrasonic liver images.
    • This novel approach offers improved diagnostic performance for liver disease classification.