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J Digit Imaging. 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.

Author information

1
Machine Vision and Medical Image Processing (MVMIP) Lab., Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
2
Machine Vision and Medical Image Processing (MVMIP) Lab., Department of Biomedical Engineering, K.N. Toosi University of Technology, P.O. Box 163151355, Tehran, Iran. moghaddam@kntu.ac.ir.
3
Department of Pediatric Pulmonary and Sleep Medicine, Pediatric Center of Excellence, Children's Medical Center, Tehran, Iran.
4
Pediatric Pulmonary Disease and sleep Medicine Research Center, Pediatric Center of Excellence, Children's Medical Center, Tehran, Iran.
5
Department of Radiology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.

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

PMID:
29691684
PMCID:
PMC6148817
[Available on 2019-10-01]
DOI:
10.1007/s10278-018-0076-9

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