A supervised texton based approach for automatic segmentation and measurement of the fetal head and femur in 2D ultrasound images

Zhang, Lei and Ye, Xujiong and Lambrou, Tryphon and Duan, Wenting and Allinson, Nigel and Dudley, Nicholas (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). p. 1095. ISSN 0031-9155

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Item Type:Article
Item Status:Live Archive

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 0.31mm.

Keywords:Ultrasound image segmentation, fetal head, fetal femur, textons, automatic fetal biometric measurements, NotOAChecked
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:19715
Deposited On:16 Dec 2015 13:29

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