Open Access

ARTICLE

Automatic Fetal Segmentation Designed on Computer-Aided Detection with Ultrasound Images

Mohana Priya Govindarajan^*, Sangeetha Subramaniam Karuppaiya Bharathi

Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, Tamil Nadu, 600026, India

* Corresponding Author: Mohana Priya Govindarajan. Email:

Computers, Materials & Continua 2024, 81(2), 2967-2986. https://doi.org/10.32604/cmc.2024.055536

Received 30 June 2024; Accepted 07 October 2024; Issue published 18 November 2024

Abstract

In the present research, we describe a computer-aided detection (CAD) method aimed at automatic fetal head circumference (HC) measurement in 2D ultrasonography pictures during all trimesters of pregnancy. The HC might be utilized toward determining gestational age and tracking fetal development. This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited. The CAD system is divided into two steps: to begin, Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull. We identified the HC using dynamic programming, an elliptical fit, and a Hough transform. The computer-aided detection (CAD) program was well-trained on 999 pictures (HC18 challenge data source), and then verified on 335 photos from all trimesters in an independent test set. A skilled sonographer and an expert in medicine personally marked the test set. We used the crown-rump length (CRL) measurement to calculate the reference gestational age (GA). In the first, second, and third trimesters, the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7 ± 2.7, 0.0 ± 4.5, and 2.0 ± 12.0 days, respectively. The regular duration variance between the baseline GA and the health investigator’s GA remained 1.5 ± 3.0, 1.9 ± 5.0, and 4.0 ± 14 a couple of days. The mean variance between the standard GA and the CAD system’s GA remained between 0.5 and 5.0, with an additional variation of 2.9 to 12.5 days. The outcomes reveal that the computer-aided detection (CAD) program outperforms an expert sonographer. When paired with the classifications reported in the literature, the provided system achieves results that are comparable or even better. We have assessed and scheduled this computerized approach for HC evaluation, which includes information from all trimesters of gestation.

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Keywords

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