30 April 2004 Automated segmentation of pulmonary vascular tree from 3D CT images
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Abstract
This paper describes an algorithm for automated segmentation of pulmonary vessels from thoracic 3D CT images. The lung region is roughly extracted based on thresholding and labeling in order to reduce computational cost in the following filtering step. Vessels are enhanced by application of a line-filter, which is based on a combination of eigen values of a Hessian matrix to provide higher response to vessels compared with the other structures. Initial segmentation is performed by thresholding of the filter output. Since extracted vessels may contain tiny holes and local discontinuities between segments, especially around branchpoints, tracking algorithm is used to fill these gaps. Though the results may still contain not only vessels but also parts of airway walls and noise, such structures can be eliminated by considering the number of branchpoints associated with each structure since vascular trees are characterized as objects with many branchpoints. Therefore, a thinning algorithm is applied to determine the number of branchpoints and the final segmentation is obtained by thresholding with regard to the number of branchpoints. We applied the algorithm to five healthy human scans and obtained visually promising results. In order to evaluate our segmentation results quantitatively, approximately 2,000 manually identified points inside the vascular tree were selected in each case to check how many were correctly included in the segmentation result. On average, 98% of the manually identified vessel points were properly marked as vessels. This result demonstrates the promising performance of our algorithm and its utility for further analyses.
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Hidenori Shikata, Hidenori Shikata, Eric A. Hoffman, Eric A. Hoffman, Milan Sonka, Milan Sonka, "Automated segmentation of pulmonary vascular tree from 3D CT images", Proc. SPIE 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, (30 April 2004); doi: 10.1117/12.537032; https://doi.org/10.1117/12.537032
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