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Automatic staging of placental maturity based on dense descriptor

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 3rd International Conference on Biomedical Engineering and Biotechnology, 25–28 September 2014, Beijing, China

Article type: Research Article

Authors: Li, Xinyao | Yao, Yuan | Ni, Dong | Chen, Siping | Li, Shengli | Lei, Baiying; | Wang, Tianfu;

Affiliations: Department of Biomedical Engineering, School of Medicine, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China | Department of Ultrasound, Affiliated Shenzhen Maternal and Child Healthcare, Hospital of Nanfang Medical University, Shenzhen, China

Note: [] Corresponding authors: Baiying Lei, Department of Biomedical Engineering, School of Medicine, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China. Tel.: +86-755-26534314; Fax: +86-755-26534940; E-mail: [email protected].

Note: [] Tianfu Wang, Department of Biomedical Engineering, School of Medicine, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China. Tel.: +86-755-26534314; Fax: +86-755-26534940; E-mail: [email protected].

Keywords: Placental staging, daisy descriptor, dense sampling, vector locally aggregated descriptor

DOI: 10.3233/BME-141100

Journal: Bio-Medical Materials and Engineering, vol. 24, no. 6, pp. 2821-2829, 2014

Published: 2014
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Abstract

Currently, placental maturity staging is mainly based on subjective observation of the physician. To address this issue, a new method is proposed for automatic staging of placental maturity based on B-mode ultrasound images. Due to small variations in the placental images, dense descriptor is utilized in place of the sparse descriptor to boost performance. Dense sampled DAISY descriptor is investigated for the demonstrated scale and translation invariant properties. Moreover, the extracted dense features are encoded by vector locally aggregated descriptor (VLAD) for performance boosting. The experimental results demonstrate an accuracy of 0.874, a sensitivity of 0.996 and a specificity of 0.874 for placental maturity staging. The experimental results also show that the dense features outperform the sparse features.

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