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Automatic segmentation of the lungs using robust level sets.

Margarida Silveira1, Jacinto Nascimento, Jorge Marques

  • 1Instituto Superior Técnico - Instituto de Sistemas e Robótica, Av. Rovisco Pais, 1049-001, Lisboa, Portugal.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
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This study introduces an automatic lung segmentation method for CT images using a geometric active contour. The technique effectively isolates lung regions by refining edge data and assigning confidence weights during curve evolution.

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis

Background:

  • Accurate lung segmentation in computed tomography (CT) is crucial for diagnosing and monitoring respiratory diseases.
  • Existing segmentation methods may struggle with image noise and complex anatomical structures.

Purpose of the Study:

  • To develop and validate a novel, fully automatic method for lung segmentation in CT images.
  • To enhance the robustness of segmentation through an active contour model with adaptive weighting.

Main Methods:

  • A robust geometric active contour model is employed for lung segmentation.
  • The method incorporates grey-level thresholding, edge detection, and stroke classification with confidence weighting.
  • Curve evolution includes automatic initialization, splitting, and outlier rejection based on stroke properties.

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Main Results:

  • The proposed technique demonstrates effective automatic lung segmentation in CT images.
  • The active contour model successfully adapts to image features and refines segmentation boundaries.
  • Experimental validation confirms the method's efficacy and robustness.

Conclusions:

  • The developed geometric active contour method provides an effective solution for automatic lung segmentation in CT.
  • This approach offers potential for improved computer-aided diagnosis and analysis of lung conditions.