Automatic Segmentation of Pulmonary Structures in Chest CT Images

  • Yeny Yim
  • Helen Hong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


We propose an automatic segmentation method for accurately identifying lung surfaces, airways, and pulmonary vessels in chest CT images. Our method consists of four steps. First, lungs and airways are extracted by inverse seeded region growing and connected component labeling. Second, pulmonary vessels are extracted from the result of first step by gray-level thresholding. Third, trachea and large airways are delineated from the lungs by three-dimensional region growing based on partitioning. Finally, accurate lung regions are obtained by subtracting the result of third step from the result of first step. The proposed method has been applied to 10 patient datasets with lung cancer or pulmonary embolism. Experimental results show that our segmentation method extracts lung surfaces, airways, and pulmonary vessels automatically and accurately.


Automatic Segmentation Pulmonary Vessel Large Airway Connected Component Label Chest Compute Tomography Scan 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yeny Yim
    • 1
  • Helen Hong
    • 2
  1. 1.School of Electrical Engineering and Computer ScienceSeoul National University 
  2. 2.School of Computer Science and Engineering, BK21: Information TechnologySeoul National UniversitySeoulKorea

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