A supervised texton based approach for automatic segmentation and measurement of the fetal head and femur in 2D ultrasound images
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This paper presents a supervised texton based approach for the accurate segmentation and measurement of ultrasound fetal head (BPD, OFD, HC) and femur (FL). The method consists of several steps. First, a non-linear diffusion technique is utilized to reduce the speckle noise. Then, based on the assumption that cross sectional intensity profiles of skull and femur can be approximated by Gaussian-like curves, a multi-scale and multi-orientation filter bank is designed to extract texton features specific to ultrasound fetal anatomic structure. The extracted texton cues, together with multi-scale local brightness, are then built into a unified framework for boundary detection of ultrasound fetal head and femur. Finally, for fetal head, a direct least square ellipse fitting method is used to construct a closed head contour, whilst, for fetal femur a closed contour is produced by connecting the detected femur boundaries. The presented method is demonstrated to be promising for clinical applications. Overall the evaluation results of fetal head segmentation and measurement from our method are comparable with the inter-observer difference of experts, with the best average precision of 96.85%, the maximum symmetric contour distance (MSD) of 1.46 mm, average symmetric contour distance (ASD) of 0.53 mm; while for fetal femur, the overall performance of our method is better than the inter-observer difference of experts, with the average precision of 84.37%, MSD of 2.72 mm and ASD of 0.31 mm.
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Ten studies assessed automated measurement of fetal abdominal circumference, femur length, head circumference and biparietal diameter.
“Ten studies assessed automated measurement of the fetal abdominal circumference (AC), femur length (FL), head circumference and biparietal diameter.8,[32][33][34][35][36][37][38][39][40]”
From Artificial intelligence in obstetric ultrasound: A scoping review. (2023) · cites this paper as [8]
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- Authors
- Zhang, L., Ye, X., Lambrou, T., Duan, W., Allinson, N., & Dudley, N. J.
- Affiliations
- University of Lincoln (GB)
- Lincoln County Hospital (GB)
- Corresponding author
- Ye, Xujiong
- Venue
- Physics in Medicine & Biology
- Publisher
- IOP Publishing
- Published
- 2016-01-13
- Volume
- 61
- Issue
- 3
- Pages
- 1095-1115
- ISSN
- 0031-9155 (ISSN-L)
- 1361-6560
- OpenAlex
- W2211303824
- Cited by
- 34
- References
- 32
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 4 · 2022: 5 · 2021: 5 · 2020: 6 · 2019: 3 · 2018: 1 · 2017: 3
- FWCI
- 1.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Physics in Medicine and Biology (publishedVersion)
- PubMed (publishedVersion)
- Lincoln Repository (University of Lincoln) (acceptedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- The University Research Investment Funding (RIF2014-21 A16988)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Keywords
- fetal head segmentation, femur segmentation, ultrasound fetal biometry, texton features, speckle reduction, boundary detection, ellipse fitting, fetal head circumference, femur length
- MeSH terms
- Algorithms, Female, Femur, Head, Humans, Image Processing, Computer-Assisted, Pregnancy, Ultrasonography, Prenatal, Signal-To-Noise Ratio
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