Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector
Open accessAbstract
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.
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FV is able to obtain a high discriminative subspace with complementary feature subspace exploration.
“Compared with the conventional methods, FV is able to obtain high discriminative subspace with the complementary feature subspace exploration.”
From Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector (2015) · cites this paper as [40]
The Methodology > Fisher Vector section says FV encodes higher-order statistics to discriminate differences between image distributions, and Experimental Results > Recognition Results reports better recognition performance than conventional methods. Neither passage says FV obtains a discriminative subspace through complementary feature-subspace exploration. Page numbers are unavailable in our copy. (LLM, conf=0.82)
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- Authors
- Lei, B., Tan, E.-L., Chen, S., Zhuo, L., Li, S., Ni, D., & Wang, T.
- Affiliations
- Shenzhen University (CN)
- Nanyang Technological University (SG)
- Shenzhen Maternity and Child Healthcare Hospital (CN)
- Corresponding author
- Ni, Dong
- Wang, Tianfu
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-05-01
- Volume
- 10
- Issue
- 5
- Pages
- e0121838
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4416891
- OpenAlex
- W2145118053
- Cited by
- 43
- References
- 39
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 10 · 2022: 3 · 2021: 7 · 2020: 4 · 2018: 2 · 2017: 3 · 2016: 6 · 2015: 2
- FWCI
- 3.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (81270707, 61402296, 61101026, 61427806, 61372006)
- China Postdoctoral Science Foundation (2014T70824, 2013M540663)
- Natural Science Foundation of Guangdong Province (S2013040014448)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- prenatal ultrasound, Fisher vectors, spatial feature extraction, support vector machine
- MeSH terms
- Algorithms, Face, Humans, Pattern Recognition, Automated, ROC Curve, Ultrasonics, Ultrasonography, Prenatal, Principal Component Analysis
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