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Review
. 2018 Oct;31(5):727-737.
doi: 10.1007/s10278-018-0076-9.

Semi-automatic Methods for Airway and Adjacent Vessel Measurement in Bronchiectasis Patterns in Lung HRCT Images of Cystic Fibrosis Patients

Zeinab Naseri  1 , Soghra Sherafat  1 , Hamid Abrishami Moghaddam  2 , Mohammadreza Modaresi  3   4 , Neda Pak  5 , Fatemeh Zamani  5
Affiliations
Review

Semi-automatic Methods for Airway and Adjacent Vessel Measurement in Bronchiectasis Patterns in Lung HRCT Images of Cystic Fibrosis Patients

Zeinab Naseri et al. J Digit Imaging. 2018 Oct.

Abstract

Airway and vessel characterization of bronchiectasis patterns in lung high-resolution computed tomography (HRCT) images of cystic fibrosis (CF) patients is very important to compute the score of disease severity. We propose a hybrid and evolutionary optimized threshold and model-based method for characterization of airway and vessel in lung HRCT images of CF patients. First, the initial model of airway and vessel is obtained using the enhanced threshold-based method. Then, the model is fitted to the actual image by optimizing its parameters using particle swarm optimization (PSO) evolutionary algorithm. The experimental results demonstrated the outperformance of the proposed method over its counterpart in R-squared, mean and variance of error, and run time. Moreover, the proposed method outperformed its counterpart for airway inner diameter/vessel diameter (AID/VD) and airway wall thickness/vessel diameter (AWT/VD) biomarkers in R-squared and slope of regression analysis.

Keywords: Airway and adjacent vessel measurement; Cystic fibrosis; Lung high-resolution computed tomography; Particle swarm optimization.

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Conflict of interest statement

The ethical permission for retrospective use of HRCT lung images was provided by the local ethics committee of Tehran University of Medical Sciences, Tehran, Iran (approval number TR.TUMS.MEDICINE.REC.1396.4599).

Figures

Fig. 1
Fig. 1
The flowchart of the proposed hybrid algorithm for airway and vessel characterization in bronchiectasis patterns. LoG, Laplacian of Gaussian, PSO, particle swarm optimization
Fig. 2
Fig. 2
Radiologist measurements on a magnified patch. a Inner airway boundary. b Outer airway boundary. c Vessel boundary
Fig. 3
Fig. 3
The procedure for extracting the lumen region. a Input image. b Enhancement of edges by using Frangi filter. c Connected components labeled of (b). d Lumen region. e Inner airway boundary
Fig. 4
Fig. 4
a Approximated external boundary of airway and adjacent vessel region. b Intersection of radial intensity profiles from the center of lumen and the boundary obtained in (a). c Boundary pixels of airway. d Inner and outer boundaries of airway
Fig. 5
Fig. 5
a Boundary pixels of vessel. b Boundary of vessel
Fig. 6
Fig. 6
Model of airway and its adjacent vessel
Fig. 7.
Fig. 7.
Contribution of pixel (i, j) in the cost function
Fig. 8
Fig. 8
Regression analysis of AID, AOD, and VD measurements using threshold-based (up) and model-based (down) methods versus the average of the two radiologists’ measurements
Fig. 9
Fig. 9
Regression analysis of AID, AOD, and VD measurements performed by radiologist 2 versus radiologist 1
Fig. 10
Fig. 10
Bland–Altman analysis of AID/VD (top) and AWT/VD (bottom) ratios for the measurements performed by the radiologists and our threshold-based and our model-based methods
See this image and copyright information in PMC

References

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