| Autor: |
Zaafouri, Ahmed, Sahli, Hanene, Rachdi, Radhouane, Sayadi, Mounir |
| Zdroj: |
International Journal of Biomedical Engineering and Technology; 2024, Vol. 45 Issue: 4 p354-367, 14p |
| Abstrakt: |
This paper introduces a new approach for foetal head segmentation and biometry measurement based on Gabor fully convolutional networks (G-FCN) along with the ellipse fitting technique. A fully convolutional network (FCN) training process based on Gabor features is presented. The new approach tends to accelerate the training stage and gives successful results. The Gabor wavelets with their steerable properties (i.e., their scales and orientations) are able to reinforce the robustness of G-FCN and reduce the training complexity. The proposed model is applied for foetal US image segmentation and foetal head circumference (HC) measurement using the elliptical fit technique. Our datasets are provided from a radiographic sequence of the foetus during different periods of pregnancy. An experimental study is conducted to prove the usefulness of the proposed algorithm for foetal biometric purposes. In addition, the automated approach makes it easier for doctors to diagnose US images. |
| Databáze: |
Supplemental Index |
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