Name
Abstract | ||
|---|---|---|
Ultrasound images are the most used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. In particular, the obstetrician uses fetus head images to monitor the growth state and identify essential features such as Gestational age (GA), estimated fetus weight (EFW), and brain anatomical structures. However, this work requires an expert obstetrician, and it is time-consuming and costly. Therefore, we proposed an automatic framework by adopting a hybrid approach that combines three components i) automatic segmentation to segment the region of interest (ROI) in the fetus head, ii) measurement extraction to measure the segmented ROI, and iii) anomaly and features detection to predict fetus GA, EFW, and abnormality status. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3233/SHTI220793 | International Conference on Informatics, Management and Technology in Healthcare (ICIMTH) |
Keywords | DocType | Volume |
Estimated fetus weight,Gestational age,Image segmentation,Ultrasound,fetus abnormality,fetus head | Conference | 295 |
ISSN | Citations | PageRank |
1879-8365 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahmood Alzubaidi | 1 | 0 | 0.34 |
| Marco Agus | 2 | 0 | 0.34 |
| Uzair Shah | 3 | 0 | 0.34 |
| Michel Makhlouf | 4 | 0 | 0.34 |
| Khalid Alyafei | 5 | 0 | 0.34 |
| Ali Zaidi | 6 | 0 | 0.34 |
| Mowafa Househ | 7 | 0 | 3.04 |