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Semi-Automated Lung Segmentation Based on Region-Growing Methods in Interstitial Lung Disease.

Mădălin-Cristian Moraru1,2, Cristiana-Iulia Dumitrescu3, Suzana Măceș2,4,5

  • 1Doctoral School, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania.

Journal of Clinical Medicine
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a semi-automated lung segmentation method using region-growing algorithms for computed tomography (CT) scans. The technique accurately delineates lung boundaries, balancing automation with user control for pulmonary disorder analysis.

Keywords:
high-resolution CThistogram analysisinterstitial lung diseaselung segmentationplanning softwarepulmonary fibrosisquantitative imagingregion growingsemi-automated segmentation

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Area of Science:

  • Medical Imaging
  • Radiology
  • Pulmonary Medicine

Background:

  • Computed tomography (CT) is essential for diagnosing pulmonary disorders.
  • Quantitative CT analysis, crucial for conditions like fibrosis, necessitates accurate lung segmentation.
  • Manual segmentation is laborious and subjective; automated methods can be unreliable.

Purpose of the Study:

  • To develop and evaluate a semi-automated lung segmentation method for CT images.
  • To address the limitations of manual and fully automated segmentation techniques.
  • To improve the efficiency and accuracy of lung segmentation in computer-aided diagnosis (CAD) systems.

Main Methods:

  • Implementation of a region-growing algorithm for lung segmentation.
  • Development of a semi-automated approach balancing automation and user interaction.
  • Testing and validation of the segmentation method on a software platform.

Main Results:

  • The proposed semi-automated method effectively delineates lung boundaries in CT scans.
  • The region-growing approach balances automation with necessary user control.
  • The technique minimizes both computational complexity and manual effort required for segmentation.

Conclusions:

  • The developed semi-automated lung segmentation technique provides accurate delineation of lung boundaries.
  • This method offers an efficient alternative to manual segmentation for quantitative CT analysis.
  • The approach is suitable for CAD systems in diagnosing pulmonary diseases like COPD, pneumonia, and lung cancer.