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Medical Physics / Volume 39 / Issue 1 / ULTRASOUND PHYSICS

Med. Phys. 39, 378 (2012); http://dx.doi.org/10.1118/1.3670373 (14 pages)

Carotid artery recognition system: A comparison of three automated paradigms for ultrasound images

Filippo Molinari and Kristen M. Meiburger

Biolab, Department of Electronics, Politecnico di Torino, Corso Duca degli Abruzzi, 24 10129 Torino, Italy

Guang Zeng

MBF Bioscience, Williston, Vermont 05495

U. Rajendra Acharya

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore

William Liboni

Neurology Division, Gradenigo Hospital, Torino, Italy

Andrew Nicolaides

Vascular Screening and Diagnostic Centre, London and Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus

Jasjit S. Suri

Global Biomedical Technologies, Inc., Roseville, California 95661 and (Aff) Department of Biomedical Engineering, Idaho State University, Pocatello, Idaho 83209

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(Received 27 June 2011; accepted 23 November 2011; revised 30 September 2011; published online 22 December 2011)

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Purpose: The development of completely automated techniques for arterial wall segmentation and intima-media thickness measurement requires the recognition of the artery in the image frame. Conceptually, automated techniques can be thought of as the combination of two cascaded stages: artery recognition and wall segmentation. In this paper, the authors show three carotid artery recognition systems (CARS) that are fully automated.
Methods: The first technique is based on a first-order derivative Gaussian edge analysis (CARSgd). The second method is based on an integrated approach (CARSia) that combines image feature extraction, fitting, and classification. The third strategy is based on signal analysis (CARSsa). The output of all the three paradigms provide tracing of the far adventitial (ADF). The authors validated CARSgd, CARSia, and CARSsa on a dataset of 365 longitudinal B-Mode carotid images, acquired by different sonographers. Performance evaluation of the carotid recognition process was done in three ways: (1) visual inspection by experts; (2) by measuring the Hausdorff distance (HD) between the automatic far adventitial (ADF) and the manually traced ADF, and (3) by measuring the HD between ADF and the lumen-intima (GTLI) and media-adventitia (GTMA) borders of the arterial walls.
Results: The average HD between ADF and the manual ADF was 1.53 ± 1.51 mm for CARSgd, 1.82 ± 3.08 mm for CARSia, and 2.56 ± 2.89 mm for CARSsa. The average HD between GTLI and ADF for CARSgd, CARSia, and CARSsa were 2.16 ± 1.16 mm, 2.71 ± 2.89 mm, and 2.66 ± 1.52 mm, respectively. The average HD between ADF and GTMA for CARSgd, CARSia, and CARSsa were 1.54 ± 1.19 mm, 1.86 ± 2.66 mm, and 1.95 ± 1.64 mm, respectively. Considering a maximum distance of 50 pixels (about 3 mm), CARSgd showed an identification accuracy of 100%, CARSia of 92%, and CARSsa of 96%. These identification accuracies were confirmed by visual inspection. All the three systems work on MATLAB, Windows OS, and on a PC based cross platform medical application written in Java called ATHEROEDGE™ with 1 s per image.
Conclusions: CARSgd showed very accurate ADF profiles coupled with a low computational burden and without the need for specific tuning. It can be thought of as a reference technique for carotid localization, to be used in automated intima-media thickness measurement strategies.

© 2012 American Association of Physicists in Medicine

Article Outline

  1. INTRODUCTION
  2. MATERIALS AND METHODS
    1. Image database and preprocessing steps
    2. CARSgd: Far adventitia border detection based on first-order derivative Gaussian edge analysis
    3. CARSia: Far adventitia border detection using feature extraction and fitting
    4. CARSsa: Local statistics approach
    5. Hausdorff distance metric: How good is the carotid artery recognition?
    6. IMT measurement by first-order absolute moment (FOAM) operator
  3. RESULTS
  4. DISCUSSION AND CONCLUSIONS
    1. Rationale for using the Hausdorff distance
    2. Calibration factor
    3. Possible inaccuracy sources and developed strategic solutions
    4. Robustness to noise
    5. Comparison with other methods
Digital Object Identifier
http://dx.doi.org/10.1118/1.3670373

KEYWORDS and PACS

Keywords

blood vessels, image recognition, image segmentation, medical image processing, thickness measurement, carotid artery, ultrasound, scale, feature extraction, higher order derivative, local statistics, localization, intima-media thickness, Hausdorff distance

PACS

  • 87.57.-s

    Medical imaging

  • 87.19.U-

    Hemodynamics

  • 06.30.Bp

    Spatial dimensions (e.g., position, lengths, volume, angles, and displacements)

PUBLICATION DATA

ISSN

0094-2405 (print)  

Publisher

$abstract.society.value is a Member of CrossRef American Association of Physicists in Medicine

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Medical Physics is an official science journal of the AAPM and of the COMP/CCPM/IOMP
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