Abstract
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.
Keywords
- 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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© 2005 Springer-Verlag Berlin Heidelberg
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Yim, Y., Hong, H. (2005). Automatic Segmentation of Pulmonary Structures in Chest CT Images. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_68
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DOI: https://doi.org/10.1007/11578079_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
eBook Packages: Computer ScienceComputer Science (R0)
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