Abstract
We propose a fully automatic method for detecting the carotid artery from volumetric ultrasound images as a preprocessing stage for building three-dimensional images of the structure of the carotid artery. The proposed detector utilizes support vector machine classifiers to discriminate between carotid artery images and non-carotid artery images using two kinds of LBP-based features. The detector switches between these features depending on the anatomical position along the carotid artery. The detector narrows the search area for detection in consideration of the three-dimensional continuity of the carotid artery to suppress false positives and improve processing speed. We evaluate our proposed method using actual clinical cases. Accuracies of detection are 100 %, 87.5 % and 68.8 % for the common carotid artery, internal carotid artery, and external carotid artery sections, respectively. We also confirm that detection can be performed in real time using a personal computer.
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Kawai, F., Hayata, K., Ohmiya, J., Kondo, S., Ishikawa, K., Yamamoto, M. (2013). Fully Automatic Detection of the Carotid Artery from Volumetric Ultrasound Images Using Anatomical Position-Dependent LBP Features. In: Wu, G., Zhang, D., Shen, D., Yan, P., Suzuki, K., Wang, F. (eds) Machine Learning in Medical Imaging. MLMI 2013. Lecture Notes in Computer Science, vol 8184. Springer, Cham. https://doi.org/10.1007/978-3-319-02267-3_6
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DOI: https://doi.org/10.1007/978-3-319-02267-3_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02266-6
Online ISBN: 978-3-319-02267-3
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