Paper
3 March 2009 Bronchial segment matching in low-dose lung CT scan pairs
Jaesung Lee, Anthony P. Reeves, David F. Yankelevitz, Claudia I. Henschke
Author Affiliations +
Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72600A (2009) https://doi.org/10.1117/12.812024
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Documenting any change in airway dimensions over time may be relevant for monitoring the progression of pulmonary diseases. In order to correctly measure the change in segmental dimensions of airways, it is necessary to locate the identical airway segments across two scans. In this paper, we present an automated method to match individual bronchial segments from a pair of low-dose CT scans. Our method uses the intensity information in addition to the graph structure as evidences for matching the individual segments. 3D image correlation matching technique is employed to match the region of interest around the branch points in two scans and therefore locate the matching bronchial segments. The matching process was designed to address the differences in airway tree structures from two scans due to the variation in tree segmentations. The algorithm was evaluated using 114 pairs of low-dose CT scans (120 kV, 40 mAs). The total number of segments matched was 3591, of which 99.7% were correctly matched. When the matching was limited to the bronchial segments of the fourth generation or less, the algorithm correctly identified all of 1553 matched segments.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jaesung Lee, Anthony P. Reeves, David F. Yankelevitz, and Claudia I. Henschke "Bronchial segment matching in low-dose lung CT scan pairs", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72600A (3 March 2009); https://doi.org/10.1117/12.812024
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Cited by 3 scholarly publications and 1 patent.
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KEYWORDS
Image segmentation

Computed tomography

Image processing algorithms and systems

3D image processing

Spherical lenses

Image registration

Lung

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