The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Supervised classification approach of biometric measures for automatic fetal defect screening in head ultrasound images
Abstract
This paper presents an advanced approach for foetal brain abnormalities diagnostic by integrating significant biometric features in the identification process. In foetal anomaly diagnosis, manual evaluation of foetal behaviour in ultrasound images is a subjective, slow and error-prone task, especially in the preliminary treatment phases. The effectiveness of this appearance is strictly subject to the attention and the experience of gynaecologists. In this case, automatic methods of image analysis offer the possibility of obtaining a homogeneous, objective and above all fast diagnosis of the foetal head in order to identify pregnancy behaviour. Indeed, we propose a computerised diagnostic method based on morphological characteristics and a supervised classification method to categorise subjects into two groups: normal and affected cases. The presented method is validated on a real integrated microcephaly and dolichocephaly cases. The studied database contains the same gestational age of both normal and abnormal foetuses. The results show that the use of a support vector machine (SVM) classifier is an effective way to enhance recognition and detection for rapid and accurate foetal head diagnostic.
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In the citing paper
“Researchers Xie et al. [6,93] and Sahli et al. [94] reported a method for classifying US images into a binary system of ‘normal’ and ‘abnormal’ cases”
In the citing paper
“To address the heterogeneity in brain images from different GA, studies described the need for matching GA of US images in algorithms [82,94].”
Citations
Cites
Advances in kernel methods: support vector learning
Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809
Krystkowiak, P. C., Gaura-Schmidt, V., Labalette, M., Rialland, A., Rémy, P., Peschanski, M., & Bachoud‐Lévi, A. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000166 · Cited by 124
Mouelhi, A., Sayadi, M., Fnaiech, F., Mrad, K., & Romdhane, K. B. (2013) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2013.04.003 · Cited by 94
Self-citationJeleń, Ł., Fevens, T., & Krzyżak, A. (2008) · International Journal of Applied Mathematics and Computer Science · doi:10.2478/v10006-008-0007-x · Cited by 93
Circular shortest path in images
Sun, C., & Pallottino, S. (2002) · Pattern Recognition · doi:10.1016/s0031-3203(02)00085-7 · Cited by 84
Pathak, S. D., Chalana, V., & Kim, Y. (1997) · Ultrasound in Medicine & Biology · doi:10.1016/s0301-5629(97)00009-4 · Cited by 53
Improved segmentation of ultrasound images for fetal biometry, using morphological operators
Shrimali, V., Anand, R. S., & Kumar, V. (2009) · doi:10.1109/iembs.2009.5334470 · Cited by 39
An advanced intelligent ELISA test for bovine tuberculosis diagnosis
Sahli, H., Mouelhi, A., Diouani, M. F., et al. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.05.031 · Cited by 36
Self-citationSlama, A. B., Mouelhi, A., Sahli, H., Manoubi, S., Mbarek, C. C., Trabelsi, H., Fnaiech, F., & Sayadi, M. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.07.005 · Cited by 32
Self-citationChaabouni, H., Châabouni, M., Mâazoul, F., et al. (2001) · Annales de Génétique · doi:10.1016/s0003-3995(01)01046-2 · Cited by 27
Research works printed but not linked: 6
- Sahli H, 2018, Iran J Sci Technol Trans Sci, 43, 1
- Foi A, Maggioni M, Pepe A, et al. Head contour extraction from the fetal ultrasound images by difference of Gaussians revolved along elliptical paths. Proceedings of challenge US: biometric measurements from fetal ultrasound images ISBI 2012; 2012. p. 1–3.
- Ciurte A, Bresson X, Cuadra MB. A semi-supervised patchbased approach for segmentation of fetal ultrasound imaging. Proceedings of challenge US: biometric measurements from fetal ultrasound images, ISBI 2012; 2012. p. 5–7.
- Ponomarev GV, Gelfand MS, Kazanov MD. A multilevel thresholding combined with edge detection and shape-based recognition for segmentation of fetal ultrasound images. Proceedings of challenge US: biometric measurements from fetal ultrasound images, ISBI 2012; 2012 May; 2012. p. 17–19.
- Sun C. Automatic fetal head measurements from ultrasound images using circular shortest paths. Proceedings of challenge US: biometric measurements from fetal ultrasound images, ISBI 2012; 2012 May; 2012. p. 13–15.
- Tanaka R, 2019, J Med Eng Technol, 1
Cited by
Artificial intelligence in obstetric ultrasound: A scoping review.
Horgan, R., Nehme, L., & Abuhamad, A. (2023) · Prenatal Diagnosis · doi:10.1002/pd.6411 · Cited by 72
“102 The remaining studies evaluated segmentation of the fetal cranium 103 and detection and localization of abnormal lesions in the axial plane to classify the image as normal or abnormal.”
BackgroundArtificial Intelligence in Prenatal Ultrasound Diagnosis
He, F., Wang, Y., Xiu, Y., Zhang, Y., & Chen, L. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.729978 · Cited by 62
Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44
Metadata
- Authors
- Sahli, H., Mouelhi, A., Ben Slama, A., Sayadi, M., & Rachdi, R.
- Affiliations
- Tunis University (TN)
- University of Tunis El-Manar, ISTMT, LR13ES07, Laboratory of Biophysics and Medical Technologies, Tunis, Tunisia;
- Military Hospital of Tunis (TN)
- Corresponding author
- Sahli, Hanene
- Venue
- Journal of Medical Engineering & Technology
- Publisher
- Informa UK Limited
- Published
- 2019-07-04
- Volume
- 43
- Issue
- 5
- Pages
- 279-286
- ISSN
- 0309-1902 (ISSN-L)
- 1464-522X
- OpenAlex
- W2973096080
- Cited by
- 21
- References
- 16
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 4 · 2023: 4 · 2022: 3 · 2021: 3 · 2020: 1
- FWCI
- 1.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Medical Engineering & Technology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Keywords
- fetal brain anomalies, biometric traits, microcephaly, dolichocephaly, support vector machine, supervised classification
- MeSH terms
- Biometry, Brain, Fetus, Gestational Age, Head, Humans, Image Processing, Computer-Assisted, Prenatal Diagnosis, Ultrasonography, Databases, Factual, Support Vector Machine
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
