Semi-automatic Methods for Airway and Adjacent Vessel Measurement in Bronchiectasis Patterns in Lung HRCT Images of Cystic Fibrosis Patients.
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 cha...
| Publicado en: | Journal of Digital Imaging Vol. 31; no. 5; pp. 727 - 738 |
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| Autores principales: | , , , , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131880812&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131880812 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2018 vid: 31 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131880812 131880812 131880812 10.1007/s10278-018-0076-9 131880812 ppf: 727 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Semi-automatic Methods for Airway and Adjacent Vessel Measurement in Bronchiectasis Patterns in Lung HRCT Images of Cystic Fibrosis Patients. aug: au: Naseri, Zeinab Sherafat, Soghra Abrishami Moghaddam, Hamid Modaresi, Mohammadreza Pak, Neda Zamani, Fatemeh affil: Machine Vision and Medical Image Processing (MVMIP) Lab., Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran sug: subj: Bronchiectasis Radiography Tomography, X-Ray Computed Methods Cystic Fibrosis Radiography Automation Methods Radiography, Thoracic Human Algorithms Biological Markers Regression ab: 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. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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