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Multiscale lung texture signature learning using the Riesz transform.

Adrien Depeursinge1, Antonio Foncubierta-Rodriguez, Dimitri Van de Ville

  • 1University of Applied Sciences Western Switzerland.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|January 5, 2013
PubMed
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This study introduces a new texture analysis method for interstitial lung disease detection using Riesz templates and support vector machines. The approach significantly improves the accuracy of classifying lung tissue from CT scans.

Area of Science:

  • Radiology
  • Medical Imaging
  • Computer-Aided Diagnosis

Background:

  • Interstitial lung diseases (ILDs) pose diagnostic challenges.
  • Accurate image interpretation of high-resolution computed tomography (HRCT) is crucial for ILDs.
  • Current texture analysis methods may lack precision for complex lung pathologies.

Purpose of the Study:

  • To develop and evaluate a novel texture-based computerized analysis for HRCT images of ILDs.
  • To enhance radiologists' ability in distinguishing different lung tissue classes.
  • To improve the classification performance for interstitial lung diseases.

Main Methods:

  • Utilized N-th order Riesz templates at multiple scales to learn lung texture signatures.
  • Employed one-versus-all support vector machines to derive weights for texture descriptors.

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  • Leveraged Riesz wavelets for scale and rotation covariance, with analytical orientation normalization.

    • Main Results:

    • Achieved an average area under the receiver operating characteristic curve of 0.94 for five lung tissue classes.
    • Demonstrated significant improvement in classification performance compared to state-of-the-art texture attributes.
    • The derived lung texture signatures showed optimal class-wise discriminative properties.

    Conclusions:

    • The proposed Riesz template-based texture analysis offers a robust method for ILD interpretation.
    • This approach enhances the diagnostic accuracy of computerized analysis for lung diseases.
    • The method provides precise and discriminative lung texture signatures for improved classification.