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Critical Reviews™ in Biomedical Engineering

ISSN Imprimir: 0278-940X
ISSN En Línea: 1943-619X

Critical Reviews™ in Biomedical Engineering

DOI: 10.1615/CritRevBiomedEng.2014012131
pages 351-367

A Multi-Step Algorithm for Measuring Airway Luminal Diameter and Wall Thickness in Lung CT Images

Mohammadreza Heydarian
Department of Computing and Software, McMaster University, Hamilton Ontario, Canada
Michael D. Noseworthy
Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada; McMaster School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada; Imaging Research Centre, St. Joseph's Healthcare, Hamilton, Ontario, Canada; Department of Radiology, McMaster University, Hamilton, Ontario, Canada
Markad V. Kamath
Department of Medicine, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8N 3Z5 Canada
Colm Boylan
Department of Radiology, McMaster University and St. Joseph's Health Care Hamilton
W. F. S. Poehlman
Department of Computing and Software, McMaster University, Hamilton Ontario, Canada

SINOPSIS

Accurate measurements of airway diameter and wall thickness are important parameters in understanding numerous pulmonary diseases. Here, we describe an automated method of measuring small airway luminal diameter and wall thickness over numerous contiguous computed tomography (CT) images. Using CT lung images from 22 patients and an airway phantom, a seeded region-growing algorithm was first applied to identify the lumen of the airway. The result was applied as an initial region for boundary determination using the level set method. Once found, subsequent algorithmic expansion of the luminal border was used to calculate airway wall thickness. This algorithm automatically evaluates neighboring slices of the airway and measures the airway luminal diameter and wall thickness. This approach also detects airway bifurcations. Our new procedure provides rapid, automated, accurate, and clinically important lung airway measurements that would be useful to radiologists who use CT images for pulmonary disease assessment.