Automatic Detection of Fetal Head using Haar Cascade and Fit Ellipse

Nadiyah, Putri; Rofiqah, Noor; Firdaus, Qurina; Sigit, Riyanto; Yuniarti, Heny

doi:10.1109/isitia.2019.8937172

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…In the past research, many methods based on machine learning have been used to extract skull edge features, such as Haar-like features combined with different classifiers ( 4 – 9 ). There are also some methods based on gradient ( 10 ), threshold ( 11 ), active boundary model ( 12 ), contour fragment model ( 13 ), multi-groupfilters mixing ( 14 ) to extract features of skull region or boundary.…”

Section: Related Work and Motivationmentioning

confidence: 99%

RDHCformer: Fusing ResDCN and Transformers for Fetal Head Circumference Automatic Measurement in 2D Ultrasound Images

et al. 2022

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Fetal head circumference (HC) is an important biological parameter to monitor the healthy development of the fetus. Since there are some HC measurement errors that affected by the skill and experience of the sonographers, a rapid, accurate and automatic measurement for fetal HC in prenatal ultrasound is of great significance. We proposed a new one-stage network for rotating elliptic object detection based on anchor-free method, which is also an end-to-end network for fetal HC auto-measurement that no need for any post-processing. The network structure used simple transformer structure combined with convolutional neural network (CNN) for a lightweight design, meanwhile, made full use of powerful global feature extraction ability of transformer and local feature extraction ability of CNN to extract continuous and complete skull edge information. The two complement each other for promoting detection precision of fetal HC without significantly increasing the amount of computation. In order to reduce the large variation of intersection over union (IOU) in rotating elliptic object detection caused by slight angle deviation, we used soft stage-wise regression (SSR) strategy for angle regression and added KLD that is approximate to IOU loss into total loss function. The proposed method achieved good results on the HC18 dataset to prove its effectiveness. This study is expected to help less experienced sonographers, provide help for precision medicine, and relieve the shortage of sonographers for prenatal ultrasound in worldwide.

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