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

Filippo Molinari; Guang Zeng; Jasjit S. Suri

doi:10.1117/12.843979

12 March 2010 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

Filippo Molinari, Guang Zeng, Jasjit S. Suri

Author Affiliations +

Proceedings Volume 7623, Medical Imaging 2010: Image Processing; 76233W (2010) https://doi.org/10.1117/12.843979
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

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.

Citation Download Citation

Filippo Molinari, Guang Zeng, and Jasjit S. Suri "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", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76233W (12 March 2010); https://doi.org/10.1117/12.843979

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available