Journal of Digital Imaging

, Volume 32, Issue 5, pp 779–792 | Cite as

Hybrid Airway Segmentation Using Multi-Scale Tubular Structure Filters and Texture Analysis on 3D Chest CT Scans

  • Minho Lee
  • June-Goo LeeEmail author
  • Namkug Kim
  • Joon Beom Seo
  • Sang Min Lee


Airway diseases are frequently related to morphological changes that may influence lung physiology. Accurate airway region segmentation may be useful for quantitative evaluation of disease prognosis and therapy efficacy. The information can also be applied to understand the fundamental mechanisms of various lung diseases. We present a hybrid method to automatically segment the airway regions on 3D volume chest computed tomography (CT) scans. This method uses multi-scale filtering and support vector machine (SVM) classification. The proposed scheme is comprised of two hybrid steps. First, a tubular structure-based multi-scale filter is applied to find the initial candidate airway regions. Second, for identifying candidate airway regions using the fuzzy connectedness technique, the small and disconnected branches of airway regions are detected using SVM classification trained to differentiate between airway and non-airway regions through texture analysis of user-defined landmark points. For development and evaluation of the method, two datasets were incorporated: (1) 55 lung-CT volumes from the Korean Obstructive Lung Disease (KOLD) Cohort Study and (2) 20 cases from the publicly open database (EXACT′09). The average tree-length detection rates of EXACT′09 and KOLD were 56.9 ± 11.0 and 70.5 ± 8.98, respectively. Comparison of the results for the EXACT′09 data set between the presented method and other methods revealed that our approach was a high performer. The method limitations were higher false-positive rates than those of the other methods and risk of leakage. In future studies, application of a convolutional neural network will help overcome these shortcomings.


Airway segmentation Frangi filter Top-hat transform Support vector machine Hybrid filtering Fuzzy connectedness 


Funding Information

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2016R1C1B1011105) and a grant (2014-7006) from the Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.


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Copyright information

© Society for Imaging Informatics in Medicine 2018

Authors and Affiliations

  1. 1.Biomedical Engineering Research Center, Asan Institute for Life SciencesAsan Medical CenterSeoulRepublic of Korea
  2. 2.Department of Convergence Medicine, Asan Medical CenterUniversity of Ulsan College of MedicineSeoulSouth Korea
  3. 3.Department of Radiology and Research Institute of Radiology, Asan Medical CenterUniversity of Ulsan College of MedicineSeoulSouth Korea

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