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Related Experiment Video

Updated: May 28, 2026

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

Lung texture classification using locally-oriented Riesz components.

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

  • 1University of Applied Sciences Western Switzerland (HES-SO), Switzerland. adrien.depeursinge@hevs.ch

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|October 19, 2011
PubMed
Summary
This summary is machine-generated.

Radiologists can now better interpret interstitial lung diseases (ILD) using high-resolution computed tomography (HRCT) images with a new texture analysis framework. This Riesz transform-based method improves lung tissue classification accuracy, aiding in diagnosis.

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07:53

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Area of Science:

  • Medical Imaging
  • Radiology
  • Image Analysis

Background:

  • Interstitial lung diseases (ILD) present diagnostic challenges in high-resolution computed tomography (HRCT) imaging.
  • Accurate lung tissue characterization is crucial for effective ILD management.
  • Current texture analysis methods may have limitations in scale and orientation invariance.

Purpose of the Study:

  • To develop and evaluate a novel texture analysis framework for HRCT images of ILD.
  • To introduce Riesz transform-based texture descriptors for lung texture analysis.
  • To compare the proposed method's performance against established texture analysis techniques.

Main Methods:

  • Development of a texture analysis framework utilizing the Riesz transform.
  • Extraction of locally-oriented Riesz components as novel texture descriptors.
  • Classification of five lung tissue types using the proposed Riesz features.
  • Comparative analysis with grey-level co-occurrence matrices (GLCM) features.
  • Reconstruction of lung texture templates using principal component analysis.

Main Results:

  • Achieved a global classification accuracy of 78.3% for five lung tissue types.
  • Demonstrated an absolute gain of 6.1% in classification accuracy compared to optimized GLCM features.
  • Showcased the adaptability of Riesz features through texture template reconstruction.
  • Reported balanced performance across various lung textures.

Conclusions:

  • The proposed Riesz transform-based texture analysis framework effectively assists in HRCT interpretation for ILD.
  • The novel Riesz features offer improved classification accuracy and adaptability over traditional methods.
  • This framework has the potential to complement radiologists' expertise in diagnosing ILD.
  • The study opens avenues for further research in computational lung image analysis.