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Two-pass region growing algorithm for segmenting airway tree from MDCT chest scans.

Anna Fabijańska¹

¹Computer Engineering Department, Technical University of Lodz, 90-924 Lodz, Poland. an fab@kis.p.lodz.pl

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|May 29, 2009

Summary

This study introduces an automated algorithm for segmenting pulmonary airways from CT scans. The method enhances airway tree detection and reduces errors compared to traditional techniques.

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Area of Science:

Medical imaging
Pulmonary diagnostics
Computational anatomy

Background:

Pulmonary airway investigation is crucial for diagnosing lung diseases.
High-resolution multidetector computed tomography (MDCT) provides detailed chest scan data.
Accurate segmentation of the airway tree is essential for quantitative analysis.

Purpose of the Study:

To present a novel, fully automated algorithm for pulmonary airway tree segmentation.
To improve the accuracy and efficiency of airway analysis from MDCT scans.
To compare the proposed method with existing region growing techniques.

Main Methods:

The algorithm employs a two-pass 3D seeded region growing approach.
The first pass generates a preliminary airway tree segmentation.

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The second pass refines the segmentation using a morphological gradient.

Main Results:

The automated algorithm successfully segmented up to 10 generations of bronchi.
The method significantly diminished leakages into the lung parenchyma.
Comparison with manual thresholding showed superior performance.

Conclusions:

The proposed two-pass region growing algorithm offers an effective solution for automated airway tree segmentation.
This technique enhances the reliability of pulmonary airway investigation using MDCT.
The algorithm's ability to reduce errors makes it a valuable tool in clinical practice.