Paper
13 March 2013 Graph-based bifurcation detection in phase-contrast MR images
Yoo-Jin Jeong, Sebastian Ley, Michael Delles, Rüdiger Dillmann, Roland Unterhinninghofen
Author Affiliations +
Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86691Z (2013) https://doi.org/10.1117/12.2006880
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Dealing with cardiovascular diseases the velocity-encoded magnetic resonance imaging (PC-MRI) is a well-known technique to acquire non-invasive measurements of the blood flow. However, the application of conventional vessel segmentation methods in PC-MR images often leads to problems due to the reduced quality of the morphology image. We proposed a robust centerline extraction method in PC-MR images to overcome those problems. The method yielded satisfying results for the centerline extraction of large vessels but did not consider vessel branches. Therefore, in this paper we present an approach for the detection of bifurcations in PC-MR images. The developed algorithm requires two inputs: the previously computed centerline points of the main vessel and a minimal user input. For each point on the centerline it determines, if there exists a bifurcation in the cross-sectional plane at that position. This is accomplished by an a* path finding algorithm, which computes the path costs for a potential bifurcation point to its corresponding center point. The path costs are determined by the combination of different features derived from the morphology and flow information. By comparison of all cost sums, bifurcations can be detected due to their low amount/value. The algorithm, evaluated on 7 volunteer and 12 patient PC-MRI datasets, yielded satisfying results.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yoo-Jin Jeong, Sebastian Ley, Michael Delles, Rüdiger Dillmann, and Roland Unterhinninghofen "Graph-based bifurcation detection in phase-contrast MR images", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86691Z (13 March 2013); https://doi.org/10.1117/12.2006880
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KEYWORDS
Image segmentation

Magnetic resonance imaging

Ions

Image filtering

Algorithm development

Detection and tracking algorithms

Image quality

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