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Proceedings Paper

Robust centerline extraction from tubular structures in medical images
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Paper Abstract

Extraction of centerlines is useful to analyzing objects in medical images, such as lung, bronchia, blood vessels, and colon. Given the noise and other imaging artifacts that are present in medical images, it is crucial to use robust algorithms that are (1) noise tolerant, (2) computationally efficient, (3) accurate and (4) preferably, do not require an accurate segmentation and can directly operate on grayscale data. We propose a new centerline extraction method that employs a Gaussian type probability model to build a more robust distance field. The model is computed using an integration of the image gradient field, in order to estimate boundaries of interest. Probabilities assigned to boundary voxels are then used to compute a modified distance field. Standard distance field algorithms are then applied to extract the centerline. We illustrate the accuracy and robustness of our algorithm on a synthetically generated example volume and a radiologist supervised segmented head MRT angiography dataset with significant amounts of Gaussian noise, as well as on three publicly available medical volume datasets. Comparison to traditional distance field algorithms is also presented.

Paper Details

Date Published: 22 March 2007
PDF: 12 pages
Proc. SPIE 6509, Medical Imaging 2007: Visualization and Image-Guided Procedures, 65092V (22 March 2007); doi: 10.1117/12.706951
Show Author Affiliations
Jianfei Liu, The Univ. of North Carolina at Charlotte (United States)
Kalpathi Subramanian, The Univ. of North Carolina at Charlotte (United States)

Published in SPIE Proceedings Vol. 6509:
Medical Imaging 2007: Visualization and Image-Guided Procedures
Kevin R. Cleary; Michael I. Miga, Editor(s)

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