Skip to main content
Journal cover image

Adaptive border marching algorithm: automatic lung segmentation on chest CT images.

Publication ,  Journal Article
Pu, J; Roos, J; Yi, CA; Napel, S; Rubin, GD; Paik, DS
Published in: Comput Med Imaging Graph
September 2008

Segmentation of the lungs in chest-computed tomography (CT) is often performed as a preprocessing step in lung imaging. This task is complicated especially in presence of disease. This paper presents a lung segmentation algorithm called adaptive border marching (ABM). Its novelty lies in the fact that it smoothes the lung border in a geometric way and can be used to reliably include juxtapleural nodules while minimizing oversegmentation of adjacent regions such as the abdomen and mediastinum. Our experiments using 20 datasets demonstrate that this computational geometry algorithm can re-include all juxtapleural nodules and achieve an average oversegmentation ratio of 0.43% and an average under-segmentation ratio of 1.63% relative to an expert determined reference standard. The segmentation time of a typical case is under 1min on a typical PC. As compared to other available methods, ABM is more robust, more efficient and more straightforward to implement, and once the chest CT images are input, there is no further interaction needed from users. The clinical impact of this method is in potentially avoiding false negative CAD findings due to juxtapleural nodules and improving volumetry and doubling time accuracy.

Duke Scholars

Published In

Comput Med Imaging Graph

DOI

ISSN

0895-6111

Publication Date

September 2008

Volume

32

Issue

6

Start / End Page

452 / 462

Location

United States

Related Subject Headings

  • Young Adult
  • Tomography, X-Ray Computed
  • Solitary Pulmonary Nodule
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Radiography, Thoracic
  • Radiographic Image Interpretation, Computer-Assisted
  • Radiographic Image Enhancement
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Pu, J., Roos, J., Yi, C. A., Napel, S., Rubin, G. D., & Paik, D. S. (2008). Adaptive border marching algorithm: automatic lung segmentation on chest CT images. Comput Med Imaging Graph, 32(6), 452–462. https://doi.org/10.1016/j.compmedimag.2008.04.005
Pu, Jiantao, Justus Roos, Chin A. Yi, Sandy Napel, Geoffrey D. Rubin, and David S. Paik. “Adaptive border marching algorithm: automatic lung segmentation on chest CT images.Comput Med Imaging Graph 32, no. 6 (September 2008): 452–62. https://doi.org/10.1016/j.compmedimag.2008.04.005.
Pu J, Roos J, Yi CA, Napel S, Rubin GD, Paik DS. Adaptive border marching algorithm: automatic lung segmentation on chest CT images. Comput Med Imaging Graph. 2008 Sep;32(6):452–62.
Pu, Jiantao, et al. “Adaptive border marching algorithm: automatic lung segmentation on chest CT images.Comput Med Imaging Graph, vol. 32, no. 6, Sept. 2008, pp. 452–62. Pubmed, doi:10.1016/j.compmedimag.2008.04.005.
Pu J, Roos J, Yi CA, Napel S, Rubin GD, Paik DS. Adaptive border marching algorithm: automatic lung segmentation on chest CT images. Comput Med Imaging Graph. 2008 Sep;32(6):452–462.
Journal cover image

Published In

Comput Med Imaging Graph

DOI

ISSN

0895-6111

Publication Date

September 2008

Volume

32

Issue

6

Start / End Page

452 / 462

Location

United States

Related Subject Headings

  • Young Adult
  • Tomography, X-Ray Computed
  • Solitary Pulmonary Nodule
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Radiography, Thoracic
  • Radiographic Image Interpretation, Computer-Assisted
  • Radiographic Image Enhancement
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging