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Proceedings Paper

Implicit medial representation for vessel segmentation
Author(s): Guillaume Pizaine; Elsa Angelini; Isabelle Bloch; Sherif Makram-Ebeid
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Paper Abstract

In the context of mathematical modeling of complex vessel tree structures with deformable models, we present a novel level set formulation to evolve both the vessel surface and its centerline. The implicit function is computed as the convolution of a geometric primitive, representing the centerline, with localized kernels of continuously-varying scales allowing accurate estimation of the vessel width. The centerline itself is derived as the characteristic function of an underlying signed medialness function, to enforce a tubular shape for the segmented object, and evolves under shape and medialness constraints. Given a set of initial medial loci and radii, this representation first allows for simultaneous recovery of the vessels centerlines and radii, thus enabling surface reconstruction. Secondly, due to the topological adaptivity of the level set segmentation setting, it can handle tree-like structures and bifurcations without additional junction detection schemes nor user inputs. We discuss the shape parameters involved, their tuning and their influence on the control of the segmented shapes, and we present some segmentation results on synthetic images, 2D angiographies, 3D rotational angiographies and 3D-CT scans.

Paper Details

Date Published: 15 March 2011
PDF: 7 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79623Q (15 March 2011); doi: 10.1117/12.878048
Show Author Affiliations
Guillaume Pizaine, Philips Healthcare (France)
Telecom ParisTech, LTCI, CNRS (France)
Elsa Angelini, Telecom ParisTech, LTCI, CNRS (France)
Isabelle Bloch, Telecom ParisTech, LTCI, CNRS (France)
Sherif Makram-Ebeid, Philips Healthcare (France)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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