
Proceedings Paper
Automatic segmentation and diameter measurement of coronary artery vesselsFormat | Member Price | Non-Member Price |
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Paper Abstract
This work presents a hybrid method for 2D artery vessel segmentation and diameter measurement in X-Ray
angiograms. The proposed method is novel in that tracking-based and model-based approaches are combined.
A robust and efficient tracking template, the "annular template", is devised for vessel tracking. It can readily
be applied on X-Ray angiograms without any preprocessing. Starting from an initial tracking point given
by the user the tracking algorithm iteratively repositions the annular template and thereby detects the vessel
boundaries and possible bifurcations. With a user selected end point the tracking process results in a set of
points that describes the contour and topology of an artery vessel segment between the initial and end points.
A "boundary correction and interpolation" operation refines the extracted points which initialize the Snakes
algorithm. Boundary correction adjusts the points to ensure that they lie on the vessel segment of interest.
Boundary interpolation adds more points, so that there are sufficiently many points for the Snakes algorithm
to generate a smooth and accurate vessel segmentation. After the application of Snakes the resulting points are
sequentially connected to represent the vessel contour. Then, the diameters are measured along the extracted
vessel contour. The segmentation and measurement results are compared with manually extracted and measured
vessel segments. The average Precision, Recall and Jaccard Index of 21 vessel samples are 91.5%, 92.1% and
84.9%, respectively. Compared with ground truth measurements of diameters the average relative error is 8.2%,
and the average absolute error is 1.13 pixels.
Paper Details
Date Published: 14 March 2011
PDF: 13 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79623V (14 March 2011); doi: 10.1117/12.877999
Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)
PDF: 13 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79623V (14 March 2011); doi: 10.1117/12.877999
Show Author Affiliations
Josef Pauli, Univ. of Duisburg-Essen (Germany)
Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)
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