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


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.

SUBMITTER: Lei B 

PROVIDER: S-EPMC4416891 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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

Lei Baiying B   Tan Ee-Leng EL   Chen Siping S   Zhuo Liu L   Li Shengli S   Ni Dong D   Wang Tianfu T  

PloS one 20150501 5


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 develo  ...[more]

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