Hermawati, F.A.; Ronando, E.; Sulistyawati, D.H.; Tjandrasa, H.; Suciati, N. Fully Automatic Segmentation Framework for FL Biometric Measurement in Foetal Ultrasound Images. Preprints2023, 2023121063. https://doi.org/10.20944/preprints202312.1063.v1
APA Style
Hermawati, F.A., Ronando, E., Sulistyawati, D.H., Tjandrasa, H., & Suciati, N. (2023). Fully Automatic Segmentation Framework for FL Biometric Measurement in Foetal Ultrasound Images. Preprints. https://doi.org/10.20944/preprints202312.1063.v1
Chicago/Turabian Style
Hermawati, F.A., Handayani Tjandrasa and Nanik Suciati. 2023 "Fully Automatic Segmentation Framework for FL Biometric Measurement in Foetal Ultrasound Images" Preprints. https://doi.org/10.20944/preprints202312.1063.v1
Abstract
Femur length (FL) is a biometric measurement of ultrasound images used to identify the growth patterns and abnormalities of the foetus. Automatic segmentation of FL can improve the efficiency and convenience of inexperienced users. The femur region in a low-cost ultrasound image is a small area with low contrast intensity compared with the non-femur region. This study proposed a fully automated framework for segmenting and measuring the FL in a portable ultrasound image. We utilized statistical modeling, employing Gaussian mixture functions for thresholding to extract the femur area from the background. We also used the localizing region-based active contour method to segment the femur region accurately. We also proposed a refinement step in this framework to obtain a more accurate femur area in the ultrasound image. Because the length and position of the femur are the main factors affecting foetal biometric measurement accuracy, we not only evaluated segmentation results using region-based methods, such as sensitivity, specificity, and dice similarity. We also applied the FL measurement approach to evaluate the angle and length accuracy relative to the ground truth. In terms of region-based segmentation performance, the proposed method provided the best average performance with a sensitivity of 0.78, specificity of 0.87, and dice similarity of 0.81. For FL measurement performance, the proposed framework achieved an average angle accuracy of 0.99, distance accuracy of 0.88, and length accuracy of 0.93.
Keywords
image segmentation; thresholding; ultrasound image; FL; Gaussian mixture function
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
