{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T06:49:43Z","timestamp":1765176583900,"version":"3.37.3"},"reference-count":46,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T00:00:00Z","timestamp":1622678400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003815","name":"Huaqiao University","doi-asserted-by":"publisher","award":["ZQN-PY518","2020HZ02014","2018N042S","2019C029R"],"award-info":[{"award-number":["ZQN-PY518","2020HZ02014","2018N042S","2019C029R"]}],"id":[{"id":"10.13039\/501100003815","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fujian Provincial Science and Technology Major Project","award":["ZQN-PY518","2020HZ02014","2018N042S","2019C029R"],"award-info":[{"award-number":["ZQN-PY518","2020HZ02014","2018N042S","2019C029R"]}]},{"name":"Quanzhou scientific and technological planning projects","award":["ZQN-PY518","2020HZ02014","2018N042S","2019C029R"],"award-info":[{"award-number":["ZQN-PY518","2020HZ02014","2018N042S","2019C029R"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2021,6,3]]},"abstract":"<jats:p>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\u2019s 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 <jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>F<\/a:mi>\n                        <a:mn>1<\/a:mn>\n                     <\/a:math>\n                  <\/jats:inline-formula> 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.<\/jats:p>","DOI":"10.1155\/2021\/6656942","type":"journal-article","created":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T19:35:40Z","timestamp":1622835340000},"page":"1-12","source":"Crossref","is-referenced-by-count":14,"title":["Recognition of Fetal Facial Ultrasound Standard Plane Based on Texture Feature Fusion"],"prefix":"10.1155","volume":"2021","author":[{"given":"Xiaoli","family":"Wang","sequence":"first","affiliation":[{"name":"College of Medicine, Huaqiao University, Quanzhou 362021, China"}]},{"given":"Zhonghua","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362021, China"},{"name":"Collaborative Innovation Center for Maternal and Infant Health Service Application Technology, Quanzhou Medical College, Quanzhou 362021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4261-9648","authenticated-orcid":true,"given":"Yongzhao","family":"Du","sequence":"additional","affiliation":[{"name":"College of Medicine, Huaqiao University, Quanzhou 362021, China"},{"name":"Collaborative Innovation Center for Maternal and Infant Health Service Application Technology, Quanzhou Medical College, Quanzhou 362021, China"},{"name":"College of Engineering, Huaqiao University, Quanzhou 362021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0633-1906","authenticated-orcid":true,"given":"Yong","family":"Diao","sequence":"additional","affiliation":[{"name":"College of Medicine, Huaqiao University, Quanzhou 362021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0809-6364","authenticated-orcid":true,"given":"Peizhong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Medicine, Huaqiao University, Quanzhou 362021, China"},{"name":"Collaborative Innovation Center for Maternal and Infant Health Service Application Technology, Quanzhou Medical College, Quanzhou 362021, China"},{"name":"College of Engineering, Huaqiao University, Quanzhou 362021, China"}]},{"given":"Guorong","family":"Lv","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center for Maternal and Infant Health Service Application Technology, Quanzhou Medical College, Quanzhou 362021, China"},{"name":"Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362021, China"}]},{"given":"Haojun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Biomedical Ultrasound Laboratory, The University of Southern California (USC), Los Angeles, USA"}]}],"member":"311","reference":[{"doi-asserted-by":"publisher","key":"1","DOI":"10.7863\/jum.2010.29.1.157"},{"doi-asserted-by":"publisher","key":"2","DOI":"10.1109\/TMI.2017.2712367"},{"doi-asserted-by":"publisher","key":"3","DOI":"10.1109\/ACCESS.2020.2991845"},{"issue":"70","key":"4","first-page":"33","article-title":"Standardized analysis of ultrasound screening section in mid pregnancy fetus","volume":"18","author":"H. 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