Zhang, L., Ye, X., Lambrou, T. et al. (3 more authors) (2016) A supervised texton based approach for automatic segmentation and measurement of the fetal head and femur in 2D ultrasound images. Physics in Medicine and Biology, 61 (3). pp. 1095-1115. ISSN 0031-9155
Abstract
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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2018 IOP Publishing. This is an author produced version of a paper subsequently published in Physics in Medicine and Biology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Algorithms; Female; Femur; Head; Humans; Image Processing, Computer-Assisted; Pregnancy; Signal-To-Noise Ratio; Ultrasonography, Prenatal |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 28 Feb 2018 13:53 |
Last Modified: | 28 Feb 2018 20:52 |
Published Version: | https://doi.org/10.1088/0031-9155/61/3/1095 |
Status: | Published |
Publisher: | IOP Publishing |
Refereed: | Yes |
Identification Number: | 10.1088/0031-9155/61/3/1095 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127444 |