Pulmonary airways tree segmentation from CT examinations using adaptive volume of interest

Sang Cheol Park, Won Pil Kim, Bin Zheng, Joseph K. Leader, Jiantao Pu, Jun Tan, David Gur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Airways tree segmentation is an important step in quantitatively assessing the severity of and changes in several lung diseases such as chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis. It can also be used in guiding bronchoscopy. The purpose of this study is to develop an automated scheme for segmenting the airways tree structure depicted on chest CT examinations. After lung volume segmentation, the scheme defines the first cylinder-like volume of interest (VOI) using a series of images depicting the trachea. The scheme then iteratively defines and adds subsequent VOIs using a region growing algorithm combined with adaptively determined thresholds in order to trace possible sections of airways located inside the combined VOI in question. The airway tree segmentation process is automatically terminated after the scheme assesses all defined VOIs in the iteratively assembled VOI list. In this preliminary study, ten CT examinations with 1.25mm section thickness and two different CT image reconstruction kernels ("bone" and "standard") were selected and used to test the proposed airways tree segmentation scheme. The experiment results showed that (1) adopting this approach affectively prevented the scheme from infiltrating into the parenchyma, (2) the proposed method reasonably accurately segmented the airways trees with lower false positive identification rate as compared with other previously reported schemes that are based on 2-D image segmentation and data analyses, and (3) the proposed adaptive, iterative threshold selection method for the region growing step in each identified VOI enables the scheme to segment the airways trees reliably to the 4th generation in this limited dataset with successful segmentation up to the 5th generation in a fraction of the airways tree branches.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2009 - Image Processing
DOIs
StatePublished - Dec 15 2009
EventMedical Imaging 2009 - Image Processing - Lake Buena Vista, FL, United States
Duration: Feb 8 2009Feb 10 2009

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7259
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2009 - Image Processing
CountryUnited States
CityLake Buena Vista, FL
Period2/8/092/10/09

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Keywords

  • 3-D region growing
  • Computed tomography
  • Pulmonary airways tree segmentation
  • Threshold selection
  • Volume of interest

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Cite this

Park, S. C., Kim, W. P., Zheng, B., Leader, J. K., Pu, J., Tan, J., & Gur, D. (2009). Pulmonary airways tree segmentation from CT examinations using adaptive volume of interest. In Medical Imaging 2009 - Image Processing [72593U] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 7259). https://doi.org/10.1117/12.810947