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Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector

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  • Baiying Lei
  • Ee-Leng Tan
  • Siping Chen
  • Liu Zhuo
  • Shengli Li
  • Dong Ni
  • Tianfu Wang

Abstract

Acquisition of the standard plane is the prerequisite of biometric measurement and diagnosis during the ultrasound (US) examination. In this paper, a new algorithm is developed for the automatic recognition of the fetal facial standard planes (FFSPs) such as the axial, coronal, and sagittal planes. Specifically, densely sampled root scale invariant feature transform (RootSIFT) features are extracted and then encoded by Fisher vector (FV). The Fisher network with multi-layer design is also developed to extract spatial information to boost the classification performance. Finally, automatic recognition of the FFSPs is implemented by support vector machine (SVM) classifier based on the stochastic dual coordinate ascent (SDCA) algorithm. Experimental results using our dataset demonstrate that the proposed method achieves an accuracy of 93.27% and a mean average precision (mAP) of 99.19% in recognizing different FFSPs. Furthermore, the comparative analyses reveal the superiority of the proposed method based on FV over the traditional methods.

Suggested Citation

  • Baiying Lei & Ee-Leng Tan & Siping Chen & Liu Zhuo & Shengli Li & Dong Ni & Tianfu Wang, 2015. "Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-20, May.
    • Handle: RePEc:plo:pone00:0121838
    DOI: 10.1371/journal.pone.0121838
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    Cited by:

    1. Yuzhou Wu & Cheng Peng & Xuechen Chen & Xin Yao & Zhigang Chen, 2022. "A Data-Driven System Based on Deep Learning for Diagnosis Fetal Cavum Septum Pellucidum in Ultrasound Images," Mathematics, MDPI, vol. 10(23), pages 1-18, December.

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