Share Email Print

Proceedings Paper

Automatic recognition and validation of the common carotid artery wall segmentation in 100 longitudinal ultrasound images: an integrated approach using feature selection, fitting and classification
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Most of the algorithms for the common carotid artery (CCA) segmentation require human interaction. The aim of this study is to show a novel accurate algorithm for the computer-based automated tracing of CCA in longitudinal B-Mode ultrasound images. One hundred ultrasound B-Mode longitudinal images of the CCA were processed to delineate the region of interest containing the artery. The algorithm is based on geometric feature extraction, line fitting, and classification. Output of the algorithm is the tracings of the near and far adventitia layers. Performance of the algorithm was validated against human tracings (ground truth) and benchmarked with a previously developed automated technique. Ninety-eight images were correctly processed, resulting in an overall system error (with respect to ground truth) equal to 0.18 ± 0.17 mm (near adventitia) and 0.17 ± 0.24 mm (far adventitia). In far adventitia detection, our novel technique outperformed the current standard method, which showed overall system errors equal to 0.07 ± 0.07 mm and 0.49 ± 0.27 mm for near and far adventitia, respectively. We also showed that our new technique is quite insensitive to noise and has performance independent on the subset of images used for training the classifiers. Superior architecture of this methodology could constitute a general basis for the development of completely automatic CCA segmentation strategies.

Paper Details

Date Published: 12 March 2010
PDF: 10 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76233W (12 March 2010); doi: 10.1117/12.843979
Show Author Affiliations
Filippo Molinari, Politecnico di Torino (Italy)
Guang Zeng, Clemson Univ. (United States)
Jasjit S. Suri, Biomedical Technologies (United States)
Univ. of Idaho (United States)

Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?