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Journal of Imaging Informatics in Medicine

Erschienen in:

24.04.2018

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

verfasst von: Zeinab Naseri, Soghra Sherafat, Hamid Abrishami Moghaddam, Mohammadreza Modaresi, Neda Pak, Fatemeh Zamani

Erschienen in: Journal of Imaging Informatics in Medicine | Ausgabe 5/2018

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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.

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Titel: Semi-automatic Methods for Airway and Adjacent Vessel Measurement in Bronchiectasis Patterns in Lung HRCT Images of Cystic Fibrosis Patients
verfasst von: Zeinab Naseri
Soghra Sherafat
Hamid Abrishami Moghaddam
Mohammadreza Modaresi
Neda Pak
Fatemeh Zamani
Publikationsdatum: 24.04.2018
Verlag: Springer International Publishing
Erschienen in: Journal of Imaging Informatics in Medicine / Ausgabe 5/2018
Print ISSN: 2948-2925
Elektronische ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-018-0076-9

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

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