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Proceedings Paper

Automatic segmentation of the lumen of the carotid artery in ultrasound B-mode images
Author(s): André M. F. Santos; Jão Manuel R. S. Tavares; Luísa Sousa; Rosa Santos; Pedro Castro; Elsa Azevedo
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Paper Abstract

A new algorithm is proposed for the segmentation of the lumen and bifurcation boundaries of the carotid artery in B-mode ultrasound images. It uses the hipoechogenic characteristics of the lumen for the identification of the carotid boundaries and the echogenic characteristics for the identification of the bifurcation boundaries. The image to be segmented is processed with the application of an anisotropic diffusion filter for speckle removal and morphologic operators are employed in the detection of the artery. The obtained information is then used in the definition of two initial contours, one corresponding to the lumen and the other to the bifurcation boundaries, for the posterior application of the Chan-vese level set segmentation model. A set of longitudinal B-mode images of the common carotid artery (CCA) was acquired with a GE Healthcare Vivid-e ultrasound system (GE Healthcare, United Kingdom). All the acquired images include a part of the CCA and of the bifurcation that separates the CCA into the internal and external carotid arteries. In order to achieve the uppermost robustness in the imaging acquisition process, i.e., images with high contrast and low speckle noise, the scanner was adjusted differently for each acquisition and according to the medical exam. The obtained results prove that we were able to successfully apply a carotid segmentation technique based on cervical ultrasonography. The main advantage of the new segmentation method relies on the automatic identification of the carotid lumen, overcoming the limitations of the traditional methods.

Paper Details

Date Published: 28 February 2013
PDF: 16 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86703I (28 February 2013); doi: 10.1117/12.2007259
Show Author Affiliations
André M. F. Santos, Univ. do Porto (Portugal)
Jão Manuel R. S. Tavares, Univ. do Porto (Portugal)
Luísa Sousa, Univ. do Porto (Portugal)
Rosa Santos, Univ. do Porto (Portugal)
Pedro Castro, Univ. do Porto (Portugal)
Elsa Azevedo, Univ. do Porto (Portugal)

Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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