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Proceedings Paper

Robust RANSAC-based blood vessel segmentation
Author(s): Ahmed Yureidini; Erwan Kerrien; Stéphane Cotin
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Paper Abstract

Many vascular clinical applications require a vessel segmentation process that is able to extract both the centerline and the surface of the blood vessels. However, noise and topology issues (such as kissing vessels) prevent existing algorithm from being able to easily retrieve such a complex system as the brain vasculature. We propose here a new blood vessel tracking algorithm that 1) detects the vessel centerline; 2) provides a local radius estimate; and 3) extracts a dense set of points at the blood vessel surface. This algorithm is based on a RANSAC-based robust fitting of successive cylinders along the vessel. Our method was validated against the Multiple Hypothesis Tracking (MHT) algorithm on 10 3DRA patient data of the brain vasculature. Over 744 blood vessels of various sizes were considered for each patient. Our results demonstrated a greater ability of our algorithm to track small, tortuous and touching vessels (96% success rate), compared to MHT (65% success rate). The computed centerline precision was below 1 voxel when compared to MHT. Moreover, our results were obtained with the same set of parameters for all patients and all blood vessels, except for the seed point for each vessel, also necessary for MHT. The proposed algorithm is thereafter able to extract the full intracranial vasculature with little user interaction.

Paper Details

Date Published: 14 February 2012
PDF: 8 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83141M (14 February 2012); doi: 10.1117/12.911670
Show Author Affiliations
Ahmed Yureidini, Inria Nancy Grand-Est (France)
Inria Lille - Nord Europe (France)
Erwan Kerrien, Inria Nancy Grand-Est (France)
Stéphane Cotin, Inria Lille - Nord Europe (France)


Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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