CoChat
Sign Up
Cited by 23Medical Image Segmentation TechniquesImage Retrieval and Classification TechniquesCleft Lip and Palate Research

Recognition of Fetal Facial Ultrasound Standard Plane Based on Texture Feature Fusion

Open access
Wang, Xiaoli, Liu, Zhonghua, Du, Yongzhao, Diao, Yong, Liu, Peizhong, Lv, Guorong, Zhang, Haojun·2021·Computational and Mathematical Methods in Medicine
DOI 10.1155/2021/6656942PMC PMC8195636
Open this paper in CoChat — free accountOpen PDF

Abstract

In the process of prenatal ultrasound diagnosis, accurate identification of fetal facial ultrasound standard plane (FFUSP) is essential for accurate facial deformity detection and disease screening, such as cleft lip and palate detection and Down syndrome screening check. However, the traditional method of obtaining standard planes is manual screening by doctors. Due to different levels of doctors, this method often leads to large errors in the results. Therefore, in this study, we propose a texture feature fusion method (LH-SVM) for automatic recognition and classification of FFUSP. First, extract image’s texture features, including Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG), then perform feature fusion, and finally adopt Support Vector Machine (SVM) for predictive classification. In our study, we used fetal facial ultrasound images from 20 to 24 weeks of gestation as experimental data for a total of 943 standard plane images (221 ocular axial planes, 298 median sagittal planes, 424 nasolabial coronal planes, and 350 nonstandard planes, OAP, MSP, NCP, N-SP). Based on this data set, we performed five-fold cross-validation. The final test results show that the accuracy rate of the proposed method for FFUSP classification is 94.67%, the average precision rate is 94.27%, the average recall rate is 93.88%, and the average F 1 score is 94.08%. The experimental results indicate that the texture feature fusion method can effectively predict and classify FFUSP, which provides an essential basis for clinical research on the automatic detection method of FFUSP.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1155/2021/6656942

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 35

  1. [1]American Institute of Ultrasound in Medicine AIUM practice guideline for the performance of obstetric ultrasound examinations Journal of Ultrasound in Medicine 2010 29 1 157 166 10.7863/jum.2010.29.1.157 2-s2.0-75149128750 20040792 · DOI
  2. [6]Rahmatullah B. Papageorghiou A. Noble J. A. Suzuki K. Wang F. Shen D. Yan P. Automated selection of standardized planes from ultrasound volume Machine Learning in Medical Imaging 2011 7009 Berlin, Heidelberg Springer 35 42 Lecture Notes in Computer Science 10.1007/978-3-642-24319-6_5 2-s2.0-80053988779 · DOI
  3. [8]Li S. Wen H. Fetal anatomic ultrasound sections and their values in the second trimester of pregnancy Chinese Journal of Medical Ultrasound (Electronic Edition) 2010 7 3 366 381
  4. [9]Shengli L. I. Huaxuan W. E. N. Fetal anatomic ultrasound sections and their values in the second trimester of pregnancy (continued)[J] Chinese Journal of Medical Ultrasound (Electronic Edition) 2010 7 4 617 643
  5. [10]Deter R. Li J. Lee W. Liu S. Romero R. Quantitative assessment of gestational sac shape: the gestational sac shape score Ultrasound in Obstetrics and Gynecology 2007 29 5 574 582 10.1002/uog.3994 2-s2.0-34249717731 17444551 PMC3516405 · DOI
  6. [11]Zhang L. Chen S. Chin C. T. Wang T. Li S. Intelligent scanning: automated standard plane selection and biometric measurement of early gestational sac in routine ultrasound examination Medical Physics 2012 39 8 5015 5027 10.1118/1.4736415 2-s2.0-84864666518 22894427 · DOI
  7. [12]Salomon L. Alfirevic Z. Berghella V. Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan Ultrasound in Obstetrics & Gynecology 2011 37 1 116 126 10.1002/uog.8831 2-s2.0-78650647051 20842655 · DOI · Paper page
  8. [13]American Institute of Ultrasound in Medicine AIUM practice guideline for the performance of obstetric ultrasound examinations Journal of Ultrasound in Medicine 2013 32 6 1083 1101 23716532
  9. [14]Yaqub M. Kelly B. Papageorghiou A. T. Navab N. Hornegger J. Wells W. Frangi A. Guided random forests for identification of key fetal anatomy and image categorization in ultrasound scans Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015 2015 9351 Cham Springer 687 694 Lecture Notes in Computer Science 10.1007/978-3-319-24574-4_82 2-s2.0-84951821800 · DOI · Paper page
  10. [15]Baumgartner C. F. Kamnitsas K. Matthew J. Smith S. Kainz B. Rueckert D. Ourselin S. Joskowicz L. Sabuncu M. Unal G. Wells W. Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. MICCAI 2016 2016 9901 Cham Springer 203 211 Lecture Notes in Computer Science 10.1007/978-3-319-46723-8_24 2-s2.0-84996563600 · DOI
  11. [16]Rahmatullah B. Papageorghiou A. T. Noble J. A. Integration of local and global features for anatomical object detection in ultrasound Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012 2012 7512 Berlin, Heidelberg Springer 402 409 Lecture Notes in Computer Science 10.1007/978-3-642-33454-2_50 23286156 · DOI
  12. [17]Maraci M. Napolitano R. Papageorghiou A. T. Noble J. A. P22.03: searching for structures of interest in an ultrasound video sequence with an application for detection of breech Ultrasound in Obstetrics & Gynecology 2014 44 S1 315 315 10.1002/uog.14429 · DOI
  13. [18]Torrents-Barrena J. Piella G. Masoller N. Segmentation and classification in MRI and US fetal imaging: recent trends and future prospects Medical Image Analysis 2019 51 61 88 10.1016/j.media.2018.10.003 2-s2.0-85055750059 30390513 · DOI · Paper page
  14. [19]Zhu X. Suk H. I. Wang L. Lee S. W. Shen D. Alzheimer’s Disease Neuroimaging Initiative A novel relational regularization feature selection method for joint regression and classification in AD diagnosis Medical Image Analysis 2017 38 205 214 10.1016/j.media.2015.10.008 2-s2.0-84949058046 26674971 PMC4862945 · DOI · Paper page
  15. [20]Chatfield K. Lempitsky V. Vedaldi A. Zisserman A. The devil is in the details: an evaluation of recent feature encoding methods Procedings of the British Machine Vision Conference 2011 2011 Dundee, UK 76.1 76.12 10.5244/c.25.76 · DOI · Paper page
  16. [21]Maji S. Berg A. C. Malik J. Classification using intersection kernel support vector machines is efficient 2008 IEEE Conference on Computer Vision and Pattern Recognition 2008 Anchorage, AK, USA 1 8 10.1109/CVPR.2008.4587630 2-s2.0-51949098112 · DOI
  17. [22]Fekri-Ershad S. Texture image analysis and texture classification methods-a review International Online Journal of Image Processing and Pattern Recognition 2019 2 1 1 29
  18. [23]Liu X. Annangi P. Gupta M. Learning-based scan plane identification from fetal head ultrasound images Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy 2012 San Diego, CA, USA 10.1117/12.911516 2-s2.0-84860748239 · DOI
  19. [25]Lei B. Zhuo L. Chen S. Li S. Ni D. Wang T. Automatic recognition of fetal standard plane in ultrasound image 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) 2014 Beijing, China 85 88 10.1109/isbi.2014.6867815 · DOI
  20. [27]Liu S. Zhuo L. Dong N. Automatic alignment of the reference standard planes of fetal face from three-dimensional ultrasound image Journal of Biomedical Engineering Research 2016 35 4 229 233
  21. [28]Bridge C. Automated annotation and quantitative description of ultrasound videos of the fetal heart Medical Image Analysis 2017 36 147 161 10.1016/j.media.2016.11.006 2-s2.0-84999040058 27907850 · DOI
  22. [29]Fekri-Ershad S. Tajeripour F. Impulse-noise resistant color-texture classification approach using hybrid color local binary patterns and Kullback–Leibler divergence The Computer Journal 2017 60 11 1633 1648 10.1093/comjnl/bxx033 · DOI
  23. [30]Fekri-Ershad S. Bark texture classification using improved local ternary patterns and multilayer neural network Expert Systems with Applications 2020 158, article 113509 10.1016/j.eswa.2020.113509 · DOI
  24. [31]Chen H. Ni D. Yang X. Li S. Heng P. A. Wu G. Zhang D. Zhou L. Fetal abdominal standard plane localization through representation learning with knowledge transfer Machine Learning in Medical Imaging. MLMI 2014 2014 8679 125 132 Lecture Notes in Computer Science 10.1007/978-3-319-10581-9_16 · DOI
  25. [33]Chen H. Ni D. Qin J. Standard plane localization in fetal ultrasound via domain transferred deep neural networks IEEE Journal of Biomedical and Health Informatics 2015 19 5 1627 1636 10.1109/JBHI.2015.2425041 2-s2.0-84940970126 25910262 · DOI · Paper page
  26. [34]Chen H. Wu L. Dou Q. Ultrasound standard plane detection using a composite neural network framework IEEE Transactions on Cybernetics 2017 47 6 1576 1586 10.1109/TCYB.2017.2685080 2-s2.0-85017139145 28371793 · DOI · Paper page
  27. [36]Kim H. P. Lee S. M. Kwon J.-Y. Park Y. Kim K. C. Seo J. K. Automatic evaluation of fetal head biometry from ultrasound images using machine learning Physiological Measurement 2019 40 6, article 065009 10.1088/1361-6579/ab21ac 2-s2.0-85069266515 31091515 · DOI
  28. [38]Sridar P. Kumar A. Li C. Automatic measurement of thalamic diameter in 2-D fetal ultrasound brain images using shape prior constrained regularized level sets IEEE Journal of Biomedical and Health Informatics 2017 21 4 1069 1078 10.1109/JBHI.2016.2582175 2-s2.0-85023635426 27333614 · DOI
  29. [39]Lin Z. Li S. Ni D. Multi-task learning for quality assessment of fetal head ultrasound images Medical Image Analysis 2019 58, article 101548 10.1016/j.media.2019.101548 2-s2.0-85072224847 31525671 · DOI · Paper page
  30. [40]Xing Y. Y. Yang F. Tang Y. J. Zhang L. Y. Ultrasound fetal head edge detection using fusion UNet ++ Journal of Image and Graphics 2020 25 2 366 377
  31. [41]Ojala T. Pietikainen M. Harwood D. A comparative study of texture measures with classification based on featured distributions Pattern Recoonition 1996 29 1 51 59 10.1016/0031-3203(95)00067-4 2-s2.0-0029669420 · DOI · Paper page
  32. [43]Chang C.-C. Lin C.-J. LIBSVM ACM Transactions on Intelligent Systems and Technology 2011 2 3 1 27 10.1145/1961189.1961199 2-s2.0-79955702502 · DOI · Paper page
  33. [44]OJALA T. PIETIKAINEN M. MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns IEEE Transactions on Pattern Analysis and Machine Intelligence 2002 24 7 971 987 10.1109/TPAMI.2002.1017623 2-s2.0-0036647193 · DOI · Paper page
  34. [45]Min R. Stanley D. A. Yuan Z. Bonner A. Zhang Z. A deep non-linear feature mapping for large-margin kNN classification 2009 Ninth IEEE International Conference on Data Mining 2009 Miami Beach, FL, USA 357 366 10.1109/icdm.2009.27 2-s2.0-77951179654 · DOI
  35. [46]Rish I. An empirical study of the naive Bayes classifier [J] Journal of Universal Computer Science 2001 1 2 p. 127

Cited by

Sorted by citations

Authors
Wang, X., Liu, Z., Du, Y., Diao, Y., Liu, P., Lv, G., & Zhang, H.
Affiliations
  • Huaqiao University (CN)
  • Fujian Medical University (CN)
  • Second Affiliated Hospital of Fujian Medical University (CN)
  • University of Southern California (US)
Corresponding author
  • Diao, Yong
  • Liu, Peizhong
ORCID
Venue
Computational and Mathematical Methods in Medicine
Publisher
Wiley
Published
2021-06-03
Volume
2021
Pages
1-12
ISSN
  • 1748-670X (ISSN-L)
  • 1748-6718
DOI
10.1155/2021/6656942
PMC
PMC8195636
OpenAlex
W3168697795
Cited by
23
References
10
Citations by year
2026: 1 · 2025: 5 · 2024: 5 · 2023: 8 · 2022: 4
FWCI
1.53
Type
journal article
Language
en
Open access
Licence
Locations
  • Computational and Mathematical Methods in Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Huaqiao University (ZQN-PY518, 2020HZ02014, 2018N042S, 2019C029R)
  • Fujian Provincial Science and Technology Major Project (ZQN-PY518, 2020HZ02014, 2018N042S, 2019C029R)
  • Quanzhou scientific and technological planning projects (ZQN-PY518, 2020HZ02014, 2018N042S, 2019C029R)
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
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Cleft Lip and Palate Research
Keywords
texture feature fusion, local binary pattern, histogram of oriented gradients, support vector machine, prenatal ultrasound diagnosis, Down syndrome screening
MeSH terms
Cleft Lip, Cleft Palate, Face, Female, Humans, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Pregnancy, Ultrasonography, Prenatal, Computational Biology, Support Vector Machine

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review