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Related Experiment Video

Updated: May 10, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
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EMPHYSEMA QUANTIFICATION IN A MULTI-SCANNER HRCT COHORT USING LOCAL INTENSITY DISTRIBUTIONS.

C S Mendoza1, G R Washko, J C Ross

  • 1Universidad de Sevilla, Spain.

Proceedings. IEEE International Symposium on Biomedical Imaging
|June 8, 2013
PubMed
Summary

This study shows that local intensity distributions effectively classify emphysema in COPD patients across diverse CT scanners. This simpler method offers a more intuitive approach for multi-scanner analyses.

Keywords:
COPDDensitometryEmphysemaTexture analysisTissue classification

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Area of Science:

  • Medical Imaging
  • Pulmonary Medicine
  • Data Analysis

Background:

  • Chronic Obstructive Pulmonary Disease (COPD) is a major respiratory illness.
  • Accurate emphysema classification is crucial for patient management.
  • Analyzing CT scans from diverse scanners presents challenges.

Purpose of the Study:

  • To evaluate local intensity distributions for classifying emphysema in COPD patients.
  • To address data sparsity in CT scan analysis using kernel density estimation.
  • To compare the proposed method with existing texture-based approaches.

Main Methods:

  • Utilized kernel density estimation for local intensity histograms.
  • Conducted leave-one-subject-out cross-validation and full-lung analyses.
  • Analyzed 342 CT scans from 16 sites, 3 vendors, and 9 scanner models.

Main Results:

  • Local intensity distributions demonstrated efficacy in classifying six emphysema classes.
  • The proposed method showed comparable performance to LBP texture-based methods.
  • Intensity-based analysis proved effective in multi-scanner cohorts.

Conclusions:

  • Local intensity distributions are suitable for emphysema classification in COPD.
  • Kernel density estimation enhances the analysis of sparse CT data.
  • Intensity-based analysis offers a simpler, more intuitive approach for multi-scanner COPD studies.